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Earth and Planetary Sciences â€ș Atmospheric Science

Precipitation Measurement and Analysis

Works Count: 37503

Description

This cluster of papers focuses on the estimation, validation, and improvement of satellite-based precipitation data using a variety of techniques including gauge and radar measurements, hydrological modeling, and spatial interpolation. It covers topics such as global measurement, rainfall estimation from satellite imagery, and the challenges of satellite rainfall estimation over complex terrain.

Keywords

Satellite; Precipitation; Estimation; Validation; Rainfall; Gauge; Radar; Hydrological Modeling; Global Measurement; Spatial Interpolation

Microphysics of Clouds and Precipitation

1978-01-01
H. R. Pruppacher , James D. Klett

Microwave Remote Sensing

1999-12-27
R. K. Moore
Abstract The sections in this article are Radiometers Radar Scattering Radar Scatterometers Radar Altimeters Ground‐Penetrating Radars Imaging Radars Real‐Aperture Radars Synthetic‐Aperture Radars Abstract The sections in this article are Radiometers Radar Scattering Radar Scatterometers Radar Altimeters Ground‐Penetrating Radars Imaging Radars Real‐Aperture Radars Synthetic‐Aperture Radars

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

2009-09-30
George J. Huffman , Robert F. Adler , David T. Bolvin +1 more

A well‐calibrated ocean algorithm for special sensor microwave / imager

1997-04-15
F. J. Wentz
I describe an algorithm for retrieving geophysical parameters over the ocean from special sensor microwave / imager (SSM/I) observations. This algorithm is based on a model for the brightness temperature 
 I describe an algorithm for retrieving geophysical parameters over the ocean from special sensor microwave / imager (SSM/I) observations. This algorithm is based on a model for the brightness temperature T B of the ocean and intervening atmosphere. The retrieved parameters are the near‐surface wind speed W , the columnar water vapor V , the columnar cloud liquid water L , and the line‐of‐sight wind W LS . I restrict my analysis to ocean scenes free of rain, and when the algorithm detects rain, the retrievals are discarded. The model and algorithm are precisely calibrated using a very large in situ database containing 37,650 SSM/I overpasses of buoys and 35,108 overpasses of radiosonde sites. A detailed error analysis indicates that the T B model rms accuracy is between 0.5 and 1 K and that the rms retrieval accuracies for wind, vapor, and cloud are 0.9 ms −1 , 1.2 mm, and 0.025 mm, respectively. The error in specifying the cloud temperature will introduce an additional 10% error in the cloud water retrieval. The spatial resolution for these accuracies is 50 km. The systematic errors in the retrievals are smaller than the rms errors, being about 0.3 ms −1 , 0.6 mm, and 0.005 mm for W , V , and L , respectively. The one exception is the systematic error in wind speed of −1.0 ms −1 that occurs for observations within ±20° of upwind. The inclusion of the line‐of‐sight wind W LS in the retrieval significantly reduces the error in wind speed due to wind direction variations. The wind error for upwind observations is reduced from −3.0 to −1.0 ms −1 . Finally, I find a small signal in the 19‐GHz, horizontal polarization (h pol ) T B residual Δ T BH that is related to the effective air pressure of the water vapor profile. This information may be of some use in specifying the vertical distribution of water vapor.
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Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data

1995-09-01
Matthias Steiner , Robert A. Houze , Sandra E. Yuter
Three algorithms extract information on precipitation type, structure, and amount from operational radar and rain gauge data. Tests on one month of data from one site show that the algorithms 
 Three algorithms extract information on precipitation type, structure, and amount from operational radar and rain gauge data. Tests on one month of data from one site show that the algorithms perform accurately and provide products that characterize the essential features of the precipitation climatology. Input to the algorithms are the operationally executed volume scans of a radar and the data from a surrounding rain gauge network. The algorithms separate the radar echoes into convective and stratiform regions, statistically summarize the vertical structure of the radar echoes, and determine precipitation rates and amounts on high spatial resolution. The convective and stratiform regions are separated on the basis of the intensity and sharpness of the peaks of echo intensity. The peaks indicate the centers of the convective region. Precipitation not identified as convective is stratiform. This method avoids the problem of underestimating the stratiform precipitation. The separation criteria are applied in exactly the same way throughout the observational domain and the product generated by the algorithm can be compared directly to model output. An independent test of the algorithm on data for which high-resolution dual-Doppler observations are available shows that the convective stratiform separation algorithm is consistent with the physical definitions of convective and stratiform precipitation. The vertical structure algorithm presents the frequency distribution of radar reflectivity as a function of height and thus summarizes in a single plot the vertical structure of all the radar echoes observed during a month (or any other time period). Separate plots reveal the essential differences in structure between the convective and stratiform echoes. Tests yield similar results (within less than 10%) for monthly rain statistics regardless of the technique used for estimating the precipitation, as long as the radar reflectivity values are adjusted to agree with monthly rain gauge data. It makes little difference whether the adjustment is by monthly mean rates or percentiles. Further tests show that 1-h sampling is sufficient to obtain an accurate estimate of monthly rain statistics.
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Drag coefficient and terminal velocity of spherical and nonspherical particles

1989-05-01
Ali Shahbaz Haider , Octave Levenspiel

Fall speeds and masses of solid precipitation particles

1974-05-20
John D. Locatelli , Peter V. Hobbs
Measurements have been made of the fall speeds and masses of a large number of different types of solid precipitation particles. Particular attention is paid to the effects of riming 
 Measurements have been made of the fall speeds and masses of a large number of different types of solid precipitation particles. Particular attention is paid to the effects of riming and aggregation on the fall speeds and masses. Empirical expressions are given for the relationships between fall speeds and maximum dimensions and between masses and maximum dimensions for the particles studied. The results are compared with other experimental observations when they exist.

Natural Variations in the Analytical Form of the Raindrop Size Distribution

1983-10-01
Carlton W. Ulbrich
Empirical analyses are shown to imply variation in the shape or analytical form of the raindrop size distribution consistent with that observed experimentally and predicted theoretically. These natural variations in 
 Empirical analyses are shown to imply variation in the shape or analytical form of the raindrop size distribution consistent with that observed experimentally and predicted theoretically. These natural variations in distribution shape are demonstrated by deriving relationships between pairs of integral rainfall parameters using a three parameter gamma drop size distribution and comparing the expressions with empirical. There comparisons produce values for the size distribution parameters which display a systematic dependence of one of the parameters on another between different rainfall types as well as from moment to moment within a given rainfall type. The implications of this finding are explored in terms of the use of a three-parameter gamma distribution in dual-measurement techniques to determine rainfall rate.
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The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales

2007-02-01
George J. Huffman , David T. Bolvin , Eric Nelkin +6 more
Abstract The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, 
 Abstract The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the surface observation–based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other finescale estimators. Examples are provided of a flood event and diurnal cycle determination.

Topography, relief, and TRMM‐derived rainfall variations along the Himalaya

2006-04-01
Bodo Bookhagen , Douglas W. Burbank
Along the southern Himalayan topographic front, the Indian summer monsoon modulates erosive processes and rates. To investigate the influence of topography and relief on rainfall generation and resultant erosion, we 
 Along the southern Himalayan topographic front, the Indian summer monsoon modulates erosive processes and rates. To investigate the influence of topography and relief on rainfall generation and resultant erosion, we processed satellite rainfall amounts for the last 8 years (1998–2005) from the Tropical Rainfall Measurement Mission (TRMM). Based upon a spatial resolution of ∌5 × 5 km for the Himalaya, we identify (1) the spatial distribution of rainfall and (2) the large‐scale relationships between topography, relief, and rainfall locations. Our results show two distinct rainfall maxima along strike in the Himalaya. The first, outer rainfall peak occurs along the southern margin of the Lesser Himalaya within a narrow band of mean elevation (0.9 ± 0.4 km) and mean relief (1.2 ± 0.2 km). The second, discontinuous, inner band typically occurs along the southern flank of the Greater Himalaya (elevation and relief: both 2.1 ± 0.3 km).
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The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present)

2003-12-01
R. F. Adler , George J. Huffman , A. T. C. Chang +7 more
The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5° latitude × 2.5° longitude resolution is available from 
 The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5° latitude × 2.5° longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
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Evidence from Tropical Raindrop Spectra of the Origin of Rain from Stratiform versus Convective Clouds

1996-03-01
Ali Tokay , David Short
An analysis of temporal variations in gamma parameters of raindrop spectra is presented utilizing surface-based observations from the Tropical Ocean Global Atmosphere Couple Ocean-Atmosphere Experiment. An observed dramatic change in 
 An analysis of temporal variations in gamma parameters of raindrop spectra is presented utilizing surface-based observations from the Tropical Ocean Global Atmosphere Couple Ocean-Atmosphere Experiment. An observed dramatic change in the N0 parameter, found to occur during rainfall events with little change in rainfall rate, is suggestive of a transition from rain of convective origin to rain originating from the stratiform portion of tropical systems. An empirical stratiform-convective classification method based on N0 and R (rainfall rate) is presented. Properties of the drop size spectra from the stratiform classification are consistent with micro-physical processes occurring within an aggregation/melting layer aloft, which produces more large raindrops and fewer small to medium size raindrops than rain from the convective classification, at the same rainfall rate. The occurrence of precipitation was found to be 74% (stratiform) and 26% (convective), but total rainfall, on the other hand, was 32% and 68%, respectively. Case studies of the tropical systems studied here indicate that heavy convective showers are generally followed by longer intervals of lighter rain from the stratiform portion of the cloud systems. Differences in the shapes of the frequency distributions of the integral rainfall parameters (i.e., liquid water content, rainfall rate, and radar reflectivity) suggest that the lognormal distribution applies to some, but not all cases. The analysis shows that almost all the precipitation with a radar reflectivity above 40 dBZ falls within the convective classification. Regarding radar reflectivity versus rainfall rate relationships, the exponent is lower and the intercept is higher in the tropical stratiform classification than in the tropical convective classification. Collision and evaporation rates, which are important for cloud-modeling studies, indicate substantial variation at different rainfall rates and between the two types.
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Generating surfaces of daily meteorological variables over large regions of complex terrain

1997-03-01
Peter Thornton , Steven W. Running , Michael A. White
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Doppler radar characteristics of precipitation at vertical incidence

1973-02-01
David Atlas , R. C. Srivastava , R. S. Sekhon
A comprehensive review and extension of the theoretical bases for the measurement of the characteristics of rain and snow with vertically pointing Doppler radar are presented. The drop size distribution 
 A comprehensive review and extension of the theoretical bases for the measurement of the characteristics of rain and snow with vertically pointing Doppler radar are presented. The drop size distribution in rain can be computed from the Doppler spectrum, provided that the updraft can be estimated, but difficulties are involved in the case of snow. Doppler spectra and their moments are computed for rain by using various power law relations of fall speed υ versus particle diameter D and an exponential fit to the actual fall speed data. In the former case, there is no sharp upper bound to the spectra and all the spectral moments increase with rainfall rate R without limit; in the latter case, there is a sharp upper bound of the spectra corresponding to the limiting terminal velocity of raindrops, and the spectral moments approach an asymptote. Accordingly, the power laws are useful approximations over only limited ranges of precipitation rate. A comparison of theoretical and experimental mean Doppler velocity ă€ˆÏ…ă€‰ as a function of radar reflectivity factor Z shows that the empirical relation ă€ˆÏ…ă€‰ = 2.6 Z 0.107 of J. Joss and A. Waldvogel seems to be the only practical relation; even so, the scatter in ă€ˆÏ…ă€‰ is about ±1 m sec −1 . This is also the kind of error to be expected in measuring updraft speeds by present methods. Such updraft errors result in unacceptably large errors in the drop number concentration estimated from Doppler spectra. In the absence of updrafts the mean Doppler velocity ă€ˆÏ…ă€‰ is uniquely related to Λ, the slope of the exponential drop size distribution. Simultaneous measurements of Z and ă€ˆÏ…ă€‰ can then be used to estimate N 0 , Λ, D 0 , M , and R , where N 0 is the intercept of the exponential drop size distribution at D = 0, D 0 is the median volume diameter, and M is the liquid‐water content.

A Proposed Tropical Rainfall Measuring Mission (TRMM) Satellite

1988-03-01
Joanne Simpson , Robert F. Adler , Gerald R. North
The Tropical Rainfall Measuring Mission (TRMM) satellite is planned for an operational duration of at least three years, beginning in the mid-1990's. The main scientific goals for it are to 
 The Tropical Rainfall Measuring Mission (TRMM) satellite is planned for an operational duration of at least three years, beginning in the mid-1990's. The main scientific goals for it are to determine the distribution and variability of precipitation and latent-heat release on a monthly average over areas of about 105 km2, for use in improving short-term climate models, global circulation models and in understanding the hydrological cycle, particularly as it is affected by tropical oceanic rainfall and its variability. The TRMM satellite's instrumentation will consist of the first quantitative spaceborne weather radar, a multichannel passive microwave radiometer and an AVHRR (Advanced Very High Resolution Radiometer). The satellite's orbit will be low altitude (about 320 km) for high resolution and low inclination (30° to 35°) in order to visit each sampling area in the tropics about twice daily at a different hour of the day. A strong validation effort is planned with several key ground sites to be instrumented with calibrated multiparameter rain radars. Mission goals and science issues are summarized. Research progress on rain retrieval algorithms is described. Radar and passive microwave algorithms are discussed and the use of radiative models in conjunction with cloud dynamical-microphysical models is emphasized especially. Algorithms are being and will continue to be tested and improved using microwave instruments on high-altitude aircraft overflying precipitating convective systems, located in the range of well-calibrated radars.
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MPM?An atmospheric millimeter-wave propagation model

1989-06-01
Hans J. Liebe

The Determination of Kinematic Properties of a Wind Field Using Doppler Radar

1968-02-01
K. A. Browning , Raymond Wexler
A technique is proposed for the measurement of kinematic properties of a wind field in situations of widespread precipitation, using a single Doppler radar to sense the motion of the 
 A technique is proposed for the measurement of kinematic properties of a wind field in situations of widespread precipitation, using a single Doppler radar to sense the motion of the precipitation particles. The technique is an extension of ideas put forward by Probert-Jones, Lhermitte, Atlas, Caton and Harrold, and is based upon the Velocity-Azimuth Display (VAD) obtained by scanning the radar beam about a vertical axis at a fixed elevation angle. Harmonic analysis of the VAD permits divergence to be obtained from the magnitude of the “zeroth” harmonic, wind speed and direction to be obtained from the amplitude and phase of the first harmonic, and resultant deformation and the axis of dilatation to be obtained from the amplitude and phase of the second harmonic. Although limitations to the accuracy of this technique are imposed by inhomogeneities in the horizontal distribution of precipitation fall speed and, in the presence of strong vertical wind shear, by elevation angle errors and reflectivity inhomogeneities, the errors resulting from these effects can be made acceptably small by scanning at appropriate elevation angles and ranges. An optimum scanning procedure is suggested. A short case study is also presented to support the view that meaningful estimates of mesoscale divergence and deformation can be obtained using this technique.
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Assessing objective techniques for gauge‐based analyses of global daily precipitation

2008-02-27
Mingyue Chen , Wei Shi , Pingping Xie +4 more
Three objective techniques used to obtain gauge‐based daily precipitation analyses over global land areas are assessed. The objective techniques include the inverse‐distance weighting algorithms of Cressman (1959) and Shepard (1968), 
 Three objective techniques used to obtain gauge‐based daily precipitation analyses over global land areas are assessed. The objective techniques include the inverse‐distance weighting algorithms of Cressman (1959) and Shepard (1968), and the optimal interpolation (OI) method of Gandin (1965). Intercomparisons and cross‐validation tests are conducted to examine their performance over various parts of the globe where station network densities are different. The gauge data used in the examinations are quality controlled daily precipitation reports from roughly 16,000 stations over the global land areas that have been collected by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). Data sources include daily summary files from the Global Telecommunication System (GTS), and the CPC unified daily gauge data sets over the contiguous United States (CONUS), Mexico, and South America. All three objective techniques are capable of generating useful daily precipitation analyses with biases of generally less than 1% over most parts of the global land areas. The OI method consistently performs the best among the three techniques for almost all situations (regions, seasons, and network densities). The Shepard scheme compares reasonably well with the OI, while the Cressman method tends to generate smooth precipitation fields with wider raining areas relative to the station observations. The quality of the gauge‐based analyses degrades as the network of station observations becomes sparser, although the OI technique exhibits relatively stable performance statistics over regions covered by fewer gauges.
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The Global Precipitation Measurement Mission

2013-09-09
Arthur Y. Hou , Ramesh K. Kakar , Steven P. Neeck +6 more
Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and 
 Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting.
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Interpolating mean rainfall using thin plate smoothing splines

1995-07-01
Michael F. Hutchinson
Abstract Thin plate smoothing splines provide accurate, operationally straightforward and computationally efficient solutions to the problem of the spatial interpolation of annual mean rainfall for a standard period from point 
 Abstract Thin plate smoothing splines provide accurate, operationally straightforward and computationally efficient solutions to the problem of the spatial interpolation of annual mean rainfall for a standard period from point data which contains many short period rainfall means. The analyses depend on developing a statistical model of the spatial variation of the observed rainfall means, considered as noisy estimates of standard period means. The error structure of this model has two components which allow separately for strong spatially correlated departures of observed short term means from standard period means and for uncorrelated deficiencies in the representation of standard period mean rainfall by a smooth function of position and elevation. Thin plate splines, with the degree of smoothing determining by minimising generalised cross validation, can estimate this smooth function in two ways. First, the spatially correlated error structure of the data can be accommodated directly by estimating the corresponding non-diagonal error covariance matrix. Secondly, spatial correlation in the data error structure can be removed by standardising the observed short term means to standard period mean estimates using linear regression. When applied to data both methods give similar interpolation accuracy, and error estimates of the fitted surfaces are in good agreement with residuals from withheld data. Simplified versions of the data error model, which require only minimal summary data at each location, are also presented. The interpolation accuracy obtained with these models is only slightly inferior to that obtained with more complete statistical models. It is shown that the incorporation of a continuous, spatially varying, dependence on appropriately scaled elevation makes a dominant contribution to surface accuracy. Incorporating dependence on aspect, as determined from a digital elevation model, makes only a marginal further improvement.

Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations

2001-02-01
George J. Huffman , R. F. Adler , Mark M. Morrissey +5 more
The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N–40°S), 
 The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N–40°S), the Threshold-Matched Precipitation Index (TMPI) provides precipitation estimates in which the 3-hourly infrared brightness temperatures (IR Tb) are compared with a threshold and all “cold” pixels are given a single precipitation rate. This approach is an adaptation of the Geostationary Operational Environmental Satellite Precipitation Index, but for the TMPI the IR Tb threshold and conditional rain rate are set locally by month from Special Sensor Microwave Imager–based precipitation frequency and the Global Precipitation Climatology Project (GPCP) satellite–gauge (SG) combined monthly precipitation estimate, respectively. At higher latitudes the 1DD features a rescaled daily Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) precipitation. The frequency of rain days in the TOVS is scaled down to match that in the TMPI at the data boundaries, and the resulting nonzero TOVS values are scaled locally to sum to the SG (which is a globally complete monthly product). The GPCP has approved the 1DD as an official product, and data have been produced for 1997 through 1999, with production continuing a few months behind real time (to allow access to monthly input data). The time series of the daily 1DD global images shows good continuity in time and across the data boundaries. Various examples are shown to illustrate uses. Validation for individual gridbox values shows a very high mean absolute error, but it improves quickly when users perform time/space averaging according to their own requirements.
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Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models

2007-01-01
Elizabeth E. Ebert , John E. Janowiak , Chris Kidd
An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as 
 An increasing number of satellite-based rainfall products are now available in near–real time over the Internet to help meet the needs of weather forecasters and climate scientists, as well as a wide range of decision makers, including hydrologists, agriculturalists, emergency managers, and industrialists. Many of these satellite products are so newly developed that a comprehensive evaluation has not yet been undertaken. This article provides potential users of short-interval satellite rainfall estimates with information on the accuracy of such estimates. Since late 2002 the authors have been performing daily validation and intercomparisons of several operational satellite rainfall retrieval algorithms over Australia, the United States, and northwestern Europe. Short-range quantitative precipitation forecasts from four numerical weather prediction (NWP) models are also included for comparison. Synthesis of four years of daily rainfall validation results shows that the satellite-derived estimates of precipitation occurrence, amount, and intensity are most accurate during the warm season and at lower latitudes, where the rainfall is primarily convective in nature. In contrast, the NWP models perform better than the satellite estimates during the cool season when non-convective precipitation is dominant. An optimal rain-monitoring strategy for remote regions might therefore judiciously combine information from both satellite and NWP models.
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CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution

2004-06-01
R. Joyce , John E. Janowiak , Phillip A. Arkin +1 more
A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate 
 A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate Prediction Center morphing method (CMORPH) uses motion vectors derived from half-hourly interval geostationary satellite IR imagery to propagate the relatively high quality precipitation estimates derived from passive microwave data. In addition, the shape and intensity of the precipitation features are modified (morphed) during the time between microwave sensor scans by performing a time-weighted linear interpolation. This process yields spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field. CMORPH showed substantial improvements over both simple averaging of the microwave estimates and over techniques that blend microwave and infrared information but that derive estimates of precipitation from infrared data when passive microwave information is unavailable. In particular, CMORPH outperforms these blended techniques in terms of daily spatial correlation with a validating rain gauge analysis over Australia by an average of 0.14, 0.27, 0.26, 0.22, and 0.20 for April, May, June–August, September, and October 2003, respectively. CMORPH also yields higher equitable threat scores over Australia for the same periods by an average of 0.11, 0.14, 0.13, 0.14, and 0.13. Over the United States for June–August, September, and October 2003, spatial correlation was higher for CMORPH relative to the average of the same techniques by an average of 0.10, 0.13, and 0.13, respectively, and equitable threat scores were higher by an average of 0.06, 0.09, and 0.10, respectively.

APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

2012-02-20
Akiyo Yatagai , Kenji Kamiguchi , Osamu Arakawa +3 more
A daily gridded precipitation dataset covering a period of more than 57 yr was created by collecting and analyzing rain gauge observation data across Asia through the activities of the 
 A daily gridded precipitation dataset covering a period of more than 57 yr was created by collecting and analyzing rain gauge observation data across Asia through the activities of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project. APHRODITE's daily gridded precipitation is presently the only long-term, continental-scale, high-resolution daily product. The product is based on data collected at 5,000–12,000 stations, which represent 2.3–4.5 times the data made available through the Global Telecommunication System network and is used for most daily gridded precipitation products. Hence, the APHRODITE project has substantially improved the depiction of the areal distribution and variability of precipitation around the Himalayas, Southeast Asia, and mountainous regions of the Middle East. The APHRODITE project now contributes to studies such as the determination of Asian monsoon precipitation change, evaluation of water resources, verification of high-resolution model simulations and satellite precipitation estimates, and improvement of precipitation forecasts. The APHRODITE project carries out outreach activities with Asian countries, and communicates with national institutions and world data centers. We have released open-access APHRO_V1101 datasets for monsoon Asia, the Middle East, and northern Eurasia (at 0.5° × 0.5° and 0.25° × 0.25° resolution) and the APHRO_JP_V1005 dataset for Japan (at 0.05° × 0.05° resolution; see www.chikyu.ac.jp/precip/ and http://aphrodite.suiri.tsukuba.ac.jp/). We welcome cooperation and feedback from users.

A precipitation climatology of the Alps from high-resolution rain-gauge observations

1998-06-30
Christoph Frei , Christoph SchÀr
A new precipitation climatology covering the European Alps is presented. The analysis covers the entire mountain range including adjacent foreland areas and exhibits a resolution of about 25 km. It 
 A new precipitation climatology covering the European Alps is presented. The analysis covers the entire mountain range including adjacent foreland areas and exhibits a resolution of about 25 km. It is based on observations at one of the densest rain-gauge networks over complex topography world-wide, embracing more than 6600 stations from the high-resolution networks of the Alpine countries. The climatology is determined from daily analyses of bias-uncorrected, quality controlled data for the 20 year period 1971–1990. The daily precipitation fields were produced with an advanced distance-weighting scheme commonly adopted for the analysis of precipitation on a global scale. The paper describes the baseline seasonal means derived from the daily analysis fields. The results depict the mesoscale distribution of the Alpine precipitation climate, its relations to the topography, and its seasonal cycle. Gridded analysis results are also provided in digital form. The most prominent Alpine effects include the enhancement of precipitation along the Alpine foothills, and the shielding of the inner-Alpine valleys. A detailed analysis along a section across the Alps also demonstrates that a simple precipitation–height relationship does not exist on the Alpine scale, because much of the topographic signal is associated with slope and shielding rather than height effects. Although systematic biases associated with the rain-gauge measurement and the topographic clustering of the stations are not corrected for, a qualitative validation of the results, using existing national climatologies shows good agreement on the mesoscale. Furthermore a comparison is made between the present climatology and the Alpine sections of the global climatology of Legates and Willmott and the Greater European climatology from the Climate Research Unit (University of East Anglia). Results indicate that the pattern and magnitude of analysed Alpine precipitation critically depend upon the density of available observations and the analysis procedure adopted. © 1998 Royal Meteorological Society

Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

2004-12-01
Yang Hong , Kuolin Hsu , Soroosh Sorooshian +1 more
Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud 
 Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 ÎŒm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.
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Rainfall energy and its relationship to soil loss

1958-04-01
W. H. Wischmeier , Dwight D. Smith
A relatively simple procedure is presented for computation of kinetic energy of a rainstorm from information on a recording‐raingage chart. An equation is developed describing rainfall energy as a function 
 A relatively simple procedure is presented for computation of kinetic energy of a rainstorm from information on a recording‐raingage chart. An equation is developed describing rainfall energy as a function of rainfall intensity. The effects of rainfall energy and its interaction with other variables are evaluated in multiple regression analyses based on data representing four soil types. Application of this information to separate the effects of rainfall from those of physical and management characteristics in plot data is discussed briefly.

The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit

2000-12-01
Christian D. Kummerow , Joanne Simpson , O. W. Thiele +7 more
The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, 
 The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dBZ) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).
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The WSR-88D Rainfall Algorithm

1998-06-01
Richard Fulton , Jay P. Breidenbach , Dong-Jun Seo +2 more
A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support 
 A detailed description of the operational WSR-88D rainfall estimation algorithm is presented. This algorithm, called the Precipitation Processing System, produces radar-derived rainfall products in real time for forecasters in support of the National Weather Service’s warning and forecast missions. It transforms reflectivity factor measurements into rainfall accumulations and incorporates rain gauge data to improve the radar estimates. The products are used as guidance to issue flood watches and warnings to the public and as input into numerical hydrologic and atmospheric models. The processing steps to quality control and compute the rainfall estimates are described, and the current deficiencies and future plans for improvement are discussed.

A Gauge-Based Analysis of Daily Precipitation over East Asia

2007-06-01
Pingping Xie , Mingyue Chen , Song Yang +4 more
Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude–longitude grid over East Asia (5°–60°N, 65°–155°E) for a 26-yr period from 1978 to 2003 using 
 Abstract A new gauge-based analysis of daily precipitation has been constructed on a 0.5° latitude–longitude grid over East Asia (5°–60°N, 65°–155°E) for a 26-yr period from 1978 to 2003 using gauge observations at over 2200 stations collected from several individual sources. First, analyzed fields of daily climatology are computed by interpolating station climatology defined as the summation of the first six harmonics of the 365-calendar-day time series of the mean daily values averaged over a 20-yr period from 1978 to 1997. These fields of daily climatology are then adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation climatology to correct the bias caused by orographic effects. Gridded fields of the ratio of daily precipitation to the daily climatology are created by interpolating the corresponding station values using the optimal interpolation method. Analyses of total daily precipitation are finally calculated by multiplying the daily climatology by the daily ratio. Cross-validation tests indicated that this gauge-based analysis has high quantitative quality with a negligible bias and a correlation coefficient of ∌0.6 for comparisons between withdrawn station data and the analysis at a 0.05° latitude–longitude grid box. The quality of the analysis increases with the gauge network density. The mean distribution and annual cycle of this new gauge analysis present similar patterns but with more detailed structures and slightly larger magnitude compared to other published monthly gauge analyses over the region. The East Asia gauge analysis is applied to verify the performance of five satellite-based precipitation estimates. This examination reveals the regionally and seasonally dependent performance of the satellite products with the best statistics observed for relatively wet regions. Further improvements of the daily gauge analysis are underway to increase the gauge network density and to refine the algorithm to better deal with the orographic effects especially over South and Southeast Asia.
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Rain-Profiling Algorithm for the TRMM Precipitation Radar

2000-12-01
Toshio Iguchi , Toshiaki Kozu , R. Meneghini +2 more
This paper describes the Tropical Rainfall Measuring Mission (TRMM) standard algorithm that estimates the vertical profiles of attenuation-corrected radar reflectivity factor and rainfall rate. In particular, this paper focuses on 
 This paper describes the Tropical Rainfall Measuring Mission (TRMM) standard algorithm that estimates the vertical profiles of attenuation-corrected radar reflectivity factor and rainfall rate. In particular, this paper focuses on the critical steps in the algorithm. These steps are attenuation correction, selection of the default drop size distribution model including vertical variations, and correction for the nonuniform beam-filling effect. The attenuation correction is based on a hybrid of the Hitschfeld–Bordan method and a surface reference method. A new algorithm to obtain an optimum weighting function is described. The nonuniform beam-filling problem is analyzed as a two-dimensional problem. The default drop size distribution model is selected according to the criterion that the attenuation estimates derived from the model and the independent estimates from the surface reference with the nonuniform beam-filling correction are consistent for rain over ocean. It is found that the drop size distribution models that are consistent for convective rain over ocean are not consistent over land, indicating a change in the size distributions associated with convective rain over land and ocean, respectively.
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Global Precipitation Map Using Satellite-Borne Microwave Radiometers by the GSMaP Project: Production and Validation

2007-06-28
Takuji Kubota , Shoichi Shige , Hiroshi Hashizume +7 more
This paper documents the production and validation of retrieved rainfall data obtained from satellite-borne microwave radiometers by the Global Satellite Mapping of Precipitation (GSMaP) Project. Using various attributes of precipitation 
 This paper documents the production and validation of retrieved rainfall data obtained from satellite-borne microwave radiometers by the Global Satellite Mapping of Precipitation (GSMaP) Project. Using various attributes of precipitation derived from Tropical Rainfall Measuring Mission (TRMM) satellite data, the GSMaP has implemented hydrometeor profiles derived from Precipitation Radar (PR), statistical rain/no-rain classification, and scattering algorithms using polarization-corrected temperatures (PCTs) at 85.5 and 37 GHz. Combined scattering-based surface rainfalls are computed depending on rainfall intensities. PCT85 is not used for stronger rainfalls, because strong depressions of PCT85 are related to tall precipitation-top heights. Therefore, for stronger rainfalls, PCT37 is used, with PCT85 used for weaker rainfalls. With the suspiciously strong rainfalls retrieved from PCT85 deleted, the combined rainfalls correspond well to the PR rain rates over land. The GSMaP algorithm for the TRMM Microwave Imager (TMI) is validated using the TRMM PR, ground radar [Kwajalein (KWAJ) radar and COBRA], and Radar Automated Meteorological Data Acquisition System (AMeDAS) precipitation analysis (RA). Monthly surface rainfalls retrieved from six microwave radiometers (GSMaP_MWR) are compared with the gauge-based dataset. Rain rates retrieved from the TMI (GSMaP_TMI) are in better agreement with the PR estimates over land everywhere except over tropical Africa in the boreal summer. Validation results of the KWAJ radar and COBRA show a good linear relationship for instantaneous rainfall rates, while validation around Japan using the RA shows a good relationship in the warm season. Poor results, connected to weak-precipitation cases, are found in the cold season around Japan.

The aR<sup>b</sup>relation in the calculation of rain attenuation

1978-03-01
Rasmus LĂžvenstein Olsen , David Rogers , D. B. Hodge
Because of its simplicity, the empirical relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A = aR^{b}</tex> between the specific attenuation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</tex> and the rainrate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</tex> is often used in the 
 Because of its simplicity, the empirical relation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A = aR^{b}</tex> between the specific attenuation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</tex> and the rainrate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</tex> is often used in the calculation of rain attenuation statistics. Values for the frequency-dependent parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</tex> are available, however, for only a limited number of frequencies. Some of these values, furthermore, were obtained experimentally, and may contain errors due to limitations in the experimental techniques employed. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aR^{b}</tex> relation is shown to be an approximation to a more general relation, except in the low-frequency and optical limits. Because the approximation is a good one, however, a comprehensive and self-consistent set of values for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</tex> is presented in both tabular and graphical form for the frequency range <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f = 1-1000</tex> GHz. These values were computed by applying logarithmic regression to Mie scattering calculations. The dropsize distributions of Laws and Parsons, Marshall and Palmer, and Joss et al., were employed to provide calculations applicable to "widespread" and "convective" rain. Empirical equations for some of the curves of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a(f)</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b(f)</tex> are presented for use in systems studies requiring calculations at many frequencies. Some comparison is also made with experimental results, and suggestions are given regarding application of the various calculations.

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

2014-06-27
Hamed Ashouri , Kuolin Hsu , Soroosh Sorooshian +5 more
Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data 
 Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.
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Evaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall

2000-09-01
Soroosh Sorooshian , Kuolin Hsu , Xiaogang Gao +3 more
PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) 
 PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.25° every half-hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain-rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES-IR-TRMM TMI based product, named PERSIANN-GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain-rate estimates agree well with the National Climatic Data Center radar-gauge composite data over Florida and Texas (correlation coefficient p > 0.7). The product also compares well (p ~ 0.77–0.90) with the monthly World Meteorological Organization gauge measurements for 5° × 5° grid locations having high gauge densities. The PERSIANN-GT product was evaluated further by comparing it with current TRMM products (3A11, 3B31, 3B42, 3B43) over the entire study region. The estimates compare well with the TRMM 3B43 1° × 5 1° monthly product, but the PERSIANN-GT products indicate higher rainfall over the western Pacific Ocean when compared to the adjusted geosynchronous precipitation index–based TRMM 3B42 product.
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THE DISTRIBUTION OF RAINDROPS WITH SIZE

1948-08-01
J. S. Marshall , W. Mc K. Palmer
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The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

2001-11-01
Christian D. Kummerow , Yang Hong , William S. Olson +7 more
This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are 
 This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5° grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5° grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI- and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low from both TMI and PR. The consistent bias between these two sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.
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Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs

1997-11-01
Pingping Xie , Phillip A. Arkin
Gridded fields (analyses) of global monthly precipitation have been constructed on a 2.5° latitude–longitude grid for the 17-yr period from 1979 to 1995 by merging several kinds of information sources 
 Gridded fields (analyses) of global monthly precipitation have been constructed on a 2.5° latitude–longitude grid for the 17-yr period from 1979 to 1995 by merging several kinds of information sources with different characteristics, including gauge observations, estimates inferred from a variety of satellite observations, and the NCEP–NCAR reanalysis. This new dataset, which the authors have named the CPC Merged Analysis of Precipitation (CMAP), contains precipitation distributions with full global coverage and improved quality compared to the individual data sources. Examinations showed no discontinuity during the 17-yr period, despite the different data sources used for the different subperiods. Comparisons of the CMAP with the merged analysis of Huffman et al. revealed remarkable agreements over the global land areas and over tropical and subtropical oceanic areas, with differences observed over extratropical oceanic areas. The 17-yr CMAP dataset is used to investigate the annual and interannual variability in large-scale precipitation. The mean distribution and the annual cycle in the 17-yr dataset exhibit reasonable agreement with existing long-term means except over the eastern tropical Pacific. The interannual variability associated with the El Niño-Southern Oscillation phenomenon resembles that found in previous studies, but with substantial additional details, particularly over the oceans. With complete global coverage, extended period and improved quality, the 17-yr dataset of the CMAP provides very useful information for climate analysis, numerical model validation, hydrological research, and many other applications. Further work is under way to improve the quality, extend the temporal coverage, and to refine the resolution of the merged analysis.
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Kinetics of Precipitation.

1965-03-01
Silvester Liotta
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTKinetics of Precipitation.Silvester LiottaCite this: J. Am. Chem. Soc. 1965, 87, 6, 1414Publication Date (Print):March 1, 1965Publication History Published online1 May 2002Published inissue 1 March 1965https://pubs.acs.org/doi/10.1021/ja01084a069https://doi.org/10.1021/ja01084a069research-articleACS PublicationsRequest 
 ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTKinetics of Precipitation.Silvester LiottaCite this: J. Am. Chem. Soc. 1965, 87, 6, 1414Publication Date (Print):March 1, 1965Publication History Published online1 May 2002Published inissue 1 March 1965https://pubs.acs.org/doi/10.1021/ja01084a069https://doi.org/10.1021/ja01084a069research-articleACS PublicationsRequest reuse permissionsArticle Views207Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose Get e-Alerts

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

2017-01-30
Hylke E. Beck , Albert I. J. M. van Dijk , Vincenzo Levizzani +4 more
Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a 
 Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (&lt; 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska ByrĂ„ns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.
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The Global Precipitation Measurement (GPM) Mission for Science and Society

2016-12-09
Gail Skofronick‐Jackson , Walter A. Petersen , Wesley Berg +7 more
Abstract Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. 
 Abstract Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.
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Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

2017-12-08
Hylke E. Beck , Noemi Vergopolan , Ming Pan +7 more
Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 
 Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( &lt; 50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.
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A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

2017-12-13
Qiaohong Sun , Chiyuan Miao , Qingyun Duan +3 more
Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data 
 Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.
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MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment

2018-09-11
Hylke E. Beck , Eric F. Wood , Ming Pan +5 more
Abstract We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land 
 Abstract We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr −1 , respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P , improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.
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Microphysics of Clouds and Precipitation

1998-01-01
H. R. Pruppacher , James D. Klett , Pao K. Wang
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Doppler Radar and Weather Observations

1993-01-01

Microphysics of Clouds and Precipitation

2010-01-01
H.R. Pruppacher , J.D. Klett

Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia

2025-06-24
Ibrahim H. Elsebaie , Atef Q. Kawara , Raied Saad Alharbi +1 more | Atmosphere
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. 
 Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments.

Quantifying the Added Values of a Merged Precipitation Product in Streamflow Prediction over the Central Himalayas

2025-06-24
Shrija Guragain , Suraj Shah , Raffaele Albano +3 more | Remote Sensing
Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, 
 Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting.

Analysis of Precipitation Totals Based on Radar and Rain Gauge Data

2025-06-23
Karol Dzwonkowski , Ireneusz Winnicki , S. Pietrek +1 more | Remote Sensing
The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships 
 The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products.

Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Clustering

2025-06-20
Ujjwal Singh , Sadaf Nasreen , Gaurav Tripathi +7 more | Journal Of Big Data
Abstract Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric 
 Abstract Accurate precipitation estimation at high spatial and temporal resolutions is essential for hydrological and meteorological applications, especially in regions experiencing water resource degradation. This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. Eight clusters were delineated based on Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation and Shuttle Radar Topography Mission (SRTM) elevation data, with one representative station per cluster used for training and validation, and an additional 19 stations employed solely for independent validation. We downscaled 255 months (June 2000–September 2021) of IMERG precipitation data from 11 to 1 km spatial resolution across the Czech Republic. The disaggregated precipitation demonstrated marked accuracy improvements when evaluated against observed station data, with $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> values ranging from 0.63 to 0.85, RMSE between 17.43 mm and 32.41 mm, NSE from 0.39 to 0.82, and KGE spanning 0.67 to 0.86-indicating a significant reduction in the bias inherent in the original IMERG data. The proposed methodology achieved (1) enhanced agreement between disaggregated and observed monthly precipitation, (2) significant improvement in IMERG data accuracy at finer scales, and (3) demonstrated operational potential in regions with sparse ground-based observations. This approach offers a promising solution for generating reliable, high-resolution precipitation datasets in data-scarce environments, with broad applicability in global hydrological and meteorological modelling.

Near real-time integration of multi-source precipitation products using a multiscale convolutional neural network

2025-06-19
Duong Du Bui , Truong Xuan Ngo , Thi Nhat Thanh Nguyen +6 more | Journal of Hydrometeorology
Abstract Merging multi-source precipitation data based on deep learning models to create an accurate rainfall dataset has received significant interest in recent years. This article proposes a deep learning model 
 Abstract Merging multi-source precipitation data based on deep learning models to create an accurate rainfall dataset has received significant interest in recent years. This article proposes a deep learning model to produce a high-accuracy, near real-time precipitation product for the North Central region of Vietnam during the period 2019–2023, with a spatial resolution of 0.04 0 and a temporal resolution of 1 hour. The input multi-source data including near real-time satellite-derived precipitation products (PERSIANN-CCS, GSMaP-NRT, and IMERG-Early Run), radar precipitation, and gauge observations, and spatial features NE and POP are merged by a multiscale CNN based model with focal loss function and mean square error loss function for classification and regression tasks, respectively. Extensive experiments demonstrate that the proposed precipitation product outperforms all the input precipitation products and the post-real-time global precipitation products including GSMaP-MVK-Gauge and IMERG-Final Run. It achieves classification metrics with a CSI of 0.65 and a BIAS of 1.03, with improvements from 31.58% to 54.8% in CSI and from 17.47% to 105.82% in BIAS, compared to radar, GSMaP-MVK-Gauge, and IMERG-Final Run products. For regression metrics, it achieves an RMSE of 3.34 mm/h, and an mKGE of 0.70, with improvements from 10.18% to 100% in RMSE and from 15.71% to 71.43% in mKGE over the same reference products. These results indicate that the merged product has a greater capability to detect rainfall events and significantly better overall performance, with lower systematic and random errors compared to the same reference products. Moreover, the proposed method outperforms the other methods, including Random Forest, Long Short-Term Memory, and the original multiscale CNN.

Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data

2025-06-18
Wonbae Bang , Jacob T. Carlin , Kwonil Kim +3 more | Geoscientific model development
Abstract. Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the 
 Abstract. Winter precipitation types (WPTs) are controlled by many factors, including thermodynamic and microphysical processes. Therefore, realistically simulating interactions between precipitation particles and the atmosphere is important when diagnosing the WPT. In the present study, we analyze the performance of a modified version of the one-dimensional spectral bin model (SBM; version 1DSBM-19M) of Carlin and Ryzhkov (2019), which simulates the change in the physical characteristics of precipitation particles of various sizes as they fall from the cloud top to the ground and diagnoses surface WPTs. We compare the performance of the SBM and four other diagnostic methods that use the following variables: (1) atmospheric thickness, (2) wet-bulb temperature, (3) temperature and relative humidity, and (4) wet-bulb temperature and low-level lapse rate. Three reference WPTs (snow (SN), rain (RA), and RASN) are obtained from particle size velocity (PARSIVEL) disdrometer data using a newly proposed decision tree algorithm. The results show that the SBM has the highest overall hit rate for all cases among five diagnostic methods. In contrast, the hit rate of the SBM for each WPT shows lower performance for RA than for the other methods. These results indicate that the SBM simulations tend to underestimate melting compared to observations. We thus explore the effects of the SBM's microphysics scheme on the extent of melting in cases of misdiagnosed RA. An optimized SBM that uses the climatological snow density–diameter relationship for the Pyeongchang region produces an increased amount of melting and achieves improved skill scores compared to the current SBM, which uses a snow density–diameter relationship for the Colorado region.

Satellite and gauge-based precipitation dataset assessment in Thua Thien Hue (Vietnam)

2025-06-18
Nguyễn Tiáșżn ThĂ nh , Nguyễn ThĂĄi SÆĄn | Bulletin of Geography Physical Geography Series
Thua Thien Hue, a central province of Vietnam, has a monsoon tropical climate and complex interaction of weather patterns and topography and, particularly, very sparse of in-situ precipitation observations to 
 Thua Thien Hue, a central province of Vietnam, has a monsoon tropical climate and complex interaction of weather patterns and topography and, particularly, very sparse of in-situ precipitation observations to model the hydrological characteristics for flood monitoring. So, this study evaluates the performance of four satellite-based precipitation datasets (CHIRPS, GSMaP, GPM and MSWEP) against gauge-based precipitation observations in Thua Thien Hue from 2020 to 2023. Tthe accuracy of each dataset is evaluated based on Taylor diagrams, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation coefficient (R), Critical Success Index (CSI), Probability Of Detection (POD) and False Alarm Ratio (FAR). Results show that MSWEP exhibits the highest correlation (R=0.58), lowest RMSE (23 mm/day), and best agreement with observed rainfall, making it the most reliable dataset. GSMaP follows, with strong correlation (R=0.63) but higher RMSE, indicating good temporal alignment but greater variability in extreme events. In contrast, CHIRPS and GPM have weaker correlations (R&lt;0.40) and higher RMSE (&gt;50 mm/day), leading to frequent underestimation of precipitation. The findings highlight systematic biases in satellite precipitation estimates and emphasize the need for regional calibration and bias correction. The study suggests that MSWEP is a useful source of data and should be prioritized for hydrological modeling in the region.

Data-driven techniques in rainfall forecasting using CMIP6 simulation outputs and ground-observed data

2025-06-17
Varun Dubey , Vikas Kumar Vidyarthi | Acta Geophysica

Understanding drivers and uncertainty in projected African precipitation

2025-06-17
Thierry N. Taguela , Akintomide A. Akinsanola , Tolulope E. Adeliyi +2 more | npj Climate and Atmospheric Science

Enhancing Rainfall Estimates in East Africa by Integrating TAHMO Ground Data and Satellite Rainfall Products

2025-06-17
Vincent Hoogelander , Rolf Hut , Camille Le Coz +2 more | Journal of Hydrometeorology
Abstract East Africa relies heavily on satellite-based rainfall estimates due to a lack of in-situ data. However, satellite rainfall products often perform poorly in this region. In this study, data 
 Abstract East Africa relies heavily on satellite-based rainfall estimates due to a lack of in-situ data. However, satellite rainfall products often perform poorly in this region. In this study, data from the Trans-African Hydro-Meteorological Observatory (TAHMO) were used to build a regional rainfall product in East Africa based on the SM2Rain algorithm. Subsequently, this regional product was merged with a reanalysis product (ERA5) and two microwave (MW)/infrared (IR)-based rainfall products (IMERG and CHIRPS) based on the Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER). Within this framework, merging weights are derived from the error variances of the rainfall products determined from quadruple collocation on a pixel-to-pixel basis. The merged and individual products are evaluated using data from individual TAHMO stations. We tested SUPER with various inter-product dependency assumptions and found that, in the best-performing configuration, IMERG contributed the most to the merged product, followed by CHIRPS, ERA5, and SM2Rain. SM2Rain showed performance comparable to other rainfall products but is more useful for detecting the offset of the rainy season in drier climates and less reliable in wet conditions. The findings indicated that the merged product outperforms the individual products in most performance metrics. Additionally, we demonstrated the importance of comparing satellite and ground-measured precipitation time-series, alongside evaluating performance metrics. The ultimate goal of this study is to develop a workflow to enhance the accuracy of rainfall measurements in East Africa by leveraging information from TAHMO data and different existing products, contributing to the improvement of satellite-based rainfall estimates in East Africa.

Responses of Convective Heavy Rainfall to Atmospheric Moisture Amplification: Implications for Probable Maximum Precipitation Estimation

2025-06-16
Yusuke Hiraga , Ryotaro Tahara | Journal of Hydrometeorology
Abstract Mesoscale convective systems (MCSs) can produce extreme precipitation and flash flooding, emphasizing its importance for Probable Maximum Precipitation (PMP). To estimate PMP using a numerical weather prediction model, the 
 Abstract Mesoscale convective systems (MCSs) can produce extreme precipitation and flash flooding, emphasizing its importance for Probable Maximum Precipitation (PMP). To estimate PMP using a numerical weather prediction model, the Relative Humidity Maximization (RHM) method has been most commonly used, in which atmospheric grids in the modeling domain are fully saturated to maximize moisture availability for the storm. However, such drastic saturation practice may weaken thermodynamic gradients that are essential for MCSs development, potentially reducing rather than maximizing precipitation. This study investigated the response of MCS-induced heavy rainfall to various atmospheric moisture maximization strategies using the Weather Research and Forecasting (WRF) model, focusing on an extreme rainfall event in August 2022 in Japan. Applying RHM to all grids in the atmosphere resulted in weakened convective organization, reduced precipitation magnitude, and altered spatial rainfall distribution due to atmospheric stabilization and diminished low-level convergence. To address this, we selectively applied RHM to lower atmospheric layers and high-humidity regions to maintain a strong thermodynamic gradient, based on our understanding of the storm mechanism. This selective RHM approach successfully increased atmospheric moisture during the storm while maintaining (even enhancing) convective organization. The RHM practice below 900 hPa with the threshold of relative humidity &gt; 90% preserved strong vertical and horizontal thermodynamic gradients and moisture flux convergence, leading to a 25.8% increase in 48-hour maximum precipitation over the Arakawa Basin. Thus, both vertical and horizontal gradients in thermodynamics matter for maximizing precipitation of MCSs. Further studies should apply similar methodologies to a broader range of MCS events to generalize our findings and to develop an effective PMP estimation methodology for MCSs.

Multi-scale accuracy assessment of meteorological satellite precipitation products in the dry-hot valley of Jinsha River

2025-06-16
Junxin Feng , Guokun Chen , Xingwu Duan +4 more | Journal of Hydrology Regional Studies

Precipitation characteristics estimated from the data given by recording raingauges and automatic weather stations

2025-06-16
Ladislav Holko , Michal Danko , Martin Jančo +2 more | Acta hydrologica Slovaca

Comparative Analysis of GMI and DPR Precipitation Measurements over Global Oceans During Summer Season

2025-06-15
Eun‐Kyoung Seo | Geosciences
This study provides a comprehensive comparison between Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) measurements through analysis of collocated precipitation at the 19 GHz footprint 
 This study provides a comprehensive comparison between Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) measurements through analysis of collocated precipitation at the 19 GHz footprint scale for pixels during hemispheric summer seasons (JJA for Northern Hemisphere and DJF for Southern Hemisphere). Precipitation pixels exceeding 0.2 mm/h are categorized into convective, stratiform, and mixed types based on DPR classifications. While showing generally good agreement in spatial patterns, the GMI and DPR exhibit systematic differences in precipitation intensity measurements. The GMI underestimates convective precipitation intensity by 13.8% but overestimates stratiform precipitation by 12.1% compared to DPR. Mixed precipitation shows the highest occurrence frequency (47.6%) with notable differences between instruments. While measurement differences for convective precipitation have significantly improved from previous Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) estimates (62% to 13.8%), the overall difference has increased (from 2.6% to 12.6%), primarily due to non-convective precipitation. Latitudinal analysis reveals distinct precipitation regimes: tropical regions (below ~30°) produce intense convective precipitation that contributes about 40% of total precipitation despite lower frequency, while mid-latitudes (beyond 30°) shift toward stratiform-dominated regimes where stratiform precipitation accounts for 60–90% of the total. Additionally, geographical variation in GMI-DPR differences shows a see-saw pattern across latitude bands, with opposite signs between tropical and mid-latitude regions for convective and stratiform precipitation types. A fundamental transition in precipitation characteristics occurs between 30° and 40°, reflecting changes in precipitation mechanisms across Earth’s climate zones. Analysis shows that tropical precipitation systems generate approximately three times more precipitation per unit area than mid-latitude regions.

Performance Analysis of SMOTE and SMOTEN Techniques for Daily Rainfall Classification using XGBoost

2025-06-14
Najwa Laila Anggraini , Basuki Rahmat , Achmad Junaidi | Journal of Artificial Intelligence and Engineering Applications (JAIEA)
The vital function that rainfall patterns fulfill in diverse sectors of life, including agriculture, water management, and disaster mitigation, has engendered the necessity for an accurate rainfall classification system to 
 The vital function that rainfall patterns fulfill in diverse sectors of life, including agriculture, water management, and disaster mitigation, has engendered the necessity for an accurate rainfall classification system to facilitate early warning and decision-making. However, the development of a classification system is often encumbered by various obstacles, with data imbalance being a prominent one. The objective of this study is to analyze two data resampling techniques, namely SMOTE and SMOTEN, with the aim of improving the performance of the XGBoost classification model. The dataset utilized is accessible on the BMKG website and is classified into five categories. Subsequent to the preprocessing stage, the data is divided by two schemes: 70:30 and 80:20. The determination of the sensitivity of each dataset is achieved through variations in the number of folds in cross validation and the use of learning rates. The experimental results indicate that the SMOTE configuration, with a data division proportion of 80:20 using 10 folds and a learning rate of 0.15, attains the maximum accuracy value of 92.92%. This represents a substantial enhancement from the original dataset accuracy result of 75.36% and surpasses the SMOTE experimental results with an accuracy of 90.58%. Consequently, SMOTEN was found to be superior and effective in managing the imbalance of numerical and categorical datasets, thereby enhancing the performance of the XGBoost model in daily rainfall classification.

SC-PREC4SA: A serially complete daily precipitation dataset for South America

2025-06-14
Adrian Huerta , Roberto Serrano‐Notivoli , Stefan Brönnimann | Scientific Data
This study introduces the serially complete precipitation dataset for South America (SC-PREC4SA), a daily precipitation dataset (1960-2015) designed to address observational gaps and ensure temporal consistency across diverse climates. The 
 This study introduces the serially complete precipitation dataset for South America (SC-PREC4SA), a daily precipitation dataset (1960-2015) designed to address observational gaps and ensure temporal consistency across diverse climates. The raw dataset underwent quality control, gap-filling, and homogenization procedures. Applied robust quality control highlighted common but also overlooked issues, enhancing data reliability. Gap-filling achieved a mean accuracy of 70 % (60 %) in the prediction on wet/dry days (wet-day magnitude). These metrics highlight the reliability of the gap-filling process, particularly in mixed climates, where station networks are sparse. The homogenization algorithm, focused primarily on wet days, effectively reduced inhomogeneities while preserving precipitation variability across South America. By integrating a unified framework and multiple outputs from 7794 stations, SC-PREC4SA provides a robust dataset that captures daily precipitation patterns with high to moderate accuracy and consistency. It offers a valuable resource for climate research, hydrological modeling, and water resource management, addressing longstanding challenges in precipitation data availability and quality for South America.

Comparison and Evaluation of Rain Gauge, CMORPH, TRMM PR and GPM DPR KuPR Precipitation Products over South China

2025-06-13
Rui Wang , Huiping Li , Hao Huang +1 more | Remote Sensing
Remote sensing precipitation products are essential for the systematic analysis of precipitation characteristics and changes. This study conducts a comparative evaluation of precipitation products from rain gauge stations, Climate Prediction 
 Remote sensing precipitation products are essential for the systematic analysis of precipitation characteristics and changes. This study conducts a comparative evaluation of precipitation products from rain gauge stations, Climate Prediction Center morphing technique (CMORPH), Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) version 7 and Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar Ku band (DPR KuPR) version 6 orbital observations during the common observational period (April–September 2014) across South China. The spatial patterns and probability density function of rain rates from four precipitation products show similar features. However, average rain rates from CMORPH (0.2–2.6 mm/h) tend to be smaller than those from rain gauge (0.1–4.4 mm/h) in temporal and spatial distribution. Conversely, average rain rates from TRMM PR and GPM KuPR (0.4–10.0 mm/h) are generally larger and exhibit more pronounced monthly changes. Despite notable differences in the number of detection samples, TRMM and GPM exhibit comparable spatiotemporal distributions and vertical structures, including rain-rate profiles, storm top heights and liquid (ice) water path. This confirms the consistency of space-borne precipitation radars and provides a foundation for analyzing long-term precipitation trends. Further analysis reveals that light rain rates from CMORPH have relatively small deviations, while rain rates generally tend to underestimate the rain rate compared to rain gauge. In contrast, TRMM PR and GPM KuPR tend to generally overestimate rain rates. Meanwhile, CMORPH (1.5–6.0 mm/h) shows larger deviations from rain gauge than TRMM and GPM, and the bias progressively increases as rain rates rise, as indicated by root mean square error results. Several statistical metrics suggest that although the missing detection rates of TRMM and GPM are higher than those of CMORPH (probability of detection 10–60%), their false detection rates are spatially lower (false alert ratio 10–30%) in Middle-East China. This study aims to provide valuable insights for enhancing precipitation retrieval algorithms and improving the applicability of remote sensing precipitation products.

Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning

2025-06-13
Marshall Rosenhoover , John Rushing , John Beck +2 more | Sensors
Accurate, real-time estimation of rainfall from Doppler radars remains a challenging problem, particularly over complex terrain where vertical beam sampling, atmospheric effects, and radar quality limitations introduce significant biases. In 
 Accurate, real-time estimation of rainfall from Doppler radars remains a challenging problem, particularly over complex terrain where vertical beam sampling, atmospheric effects, and radar quality limitations introduce significant biases. In this work, we leverage citizen science rain gauge observations to develop a deep learning framework that corrects biases in radar-derived surface precipitation rates at high temporal resolution. A key step in our approach is the construction of piecewise-linear rainfall accumulation functions, which align gauge measurements with radar estimates and allow for the generation of high-quality instantaneous rain rate labels from rain gauge observations. After validating gauges through a two-stage temporal and spatial consistency filter, we train an adapted ResNet-101 model to classify rainfall intensity from sequences of surface precipitation rate estimates. Our model substantially improves precipitation classification accuracy relative to NOAA’s operational radar products within observed spatial regions, achieving large gains in precision, recall, and F1 score. While generalization to completely unseen regions remains more challenging, particularly for higher-intensity rainfall, modest improvements over baseline radar estimates are still observed in low-intensity rainfall. These results highlight how combining citizen science data with physically informed accumulation fitting and deep learning can meaningfully improve real-time radar-based rainfall estimation and support operational forecasting in complex environments.

On the use of disdrometer data for characterization of precipitation episodes in the Basque Country

2025-06-12
Santiago Gaztelumendi , Joseba Egaña , José Antonio Aranda | Advances in science and research
Abstract. Measurements are essential to provide information on the actual state of the atmosphere in order to improve our understanding of atmospheric processes and their role in water cycle and 
 Abstract. Measurements are essential to provide information on the actual state of the atmosphere in order to improve our understanding of atmospheric processes and their role in water cycle and the climate system. In this paper we focus on measurements from optical disdrometers which seek to improve our understanding of complexity of precipitations processes at surface. In this work, we present a study focused on analyzing the key characteristics of precipitation episodes in the Basque Country. For this purpose 1 min data from disdrometers are aggregated into precipitation episodes. This analysis involves incorporating information derived from various aggregated statistics applied to various episodes variables, including duration, number of particles, rain intensity and total rainfall segmented by precipitation type, total rain amount, season and others. Finally, some comparison in between disdrometer precipitation episodes and tipping-bucket rain-gauge episodes has been done.

The Hydraulic Assessment of a New Portable Rainfall Simulator Using Different Nozzle Models

2025-06-12
Maria Angela Serio , Roberto Caruso , Francesco Giuseppe Carollo +3 more | Water
Research on water erosion often uses rainfall simulators, as these instruments allow for controlling the characteristics of the erosive agent and carrying out replications of experimental runs over brief time 
 Research on water erosion often uses rainfall simulators, as these instruments allow for controlling the characteristics of the erosive agent and carrying out replications of experimental runs over brief time periods. In this paper, the early-step assessment of a new pressurized rainfall simulator equipped with nozzles differing in their spray angle and flow rate is developed. Experimental runs were performed to determine operative information about its functioning and rainfall intensity distribution. The investigated pressure–flow rate pairs, corresponding to values differing from those provided by the manufacturer, suggested that these nozzles are affected by their technological variability. Therefore, the use of the nozzles in pressure ranges that the producer did not investigate requires the testing of the manufacturer’s characteristic curves. The developed analysis on the variability of the measured average rainfall intensities and Christiansen’s Uniformity coefficient with the distance from the nozzle orifice demonstrates that the best simulation was obtained for the high-flow-rate nozzles at 120° and 90° with a pressure of 0.5 bar. These two simulation conditions allowed for the obtainment of rainfall intensities equal to 50 and 70 mm/h, respectively, and excellent uniform spatial distributions within a circular area with a diameter of 1.5 m. Moreover, to change the rainfall intensity during the simulation, the more effective approach was to maintain a constant pressure and modify the nozzle type, thereby modifying the nozzle spray angle. This finding underlines the importance of verifying the manufacturer’s nozzle characteristic curves and testing the rainfall intensity spatial distribution for selecting the nozzles most suitable for the simulation aims.

A Quality Control Scheme for Weather Radar Radial Speed toward Data Assimilation

2025-06-12
Yin Liu , Mingyue Su , Hong Zhao +1 more | Journal of Environmental & Earth Sciences
In order to further enhance the numerical application of weather radar radial velocity, this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data 
 In order to further enhance the numerical application of weather radar radial velocity, this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation. The proposed scheme is based on the WRFDA (Weather Research and Forecasting Data Assimilation) system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data. A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process. The scheme is characterized by its simplicity, fast implementation, and ease of maintenance. By determining an appropriate threshold for quality control, the percentage of outliers identified by the scheme remains highly stable over time. Moreover, the mean errors and standard deviations of the O-B (observation-minus-background) values are significantly reduced, improving the overall data quality. The main information and spatial distribution features of the data are preserved effectively. After quality control, the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution. This adjustment is beneficial for the subsequent data assimilation process, contributing to more accurate numerical weather predictions. Thus, the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.

Performance analysis of IMDAA and ERA5 reanalysis in reproducing monsoon precipitation extremes over Eastern India

2025-06-12
Javed Akhter , Sudipta Sarkar , Ratul Roy Choudhury +2 more | Theoretical and Applied Climatology

Urban precipitation dynamics risk assessment across Indian smart cities

2025-06-12
Vijay Jain , Shekhar Singh , Manish Goyal | Environmental Research Letters
Abstract The rising urban flood events with variable precipitation patterns across Indian cities make them vulnerable to precipitation extremes and cause huge economic losses. Considering this, the Indian Smart Cities 
 Abstract The rising urban flood events with variable precipitation patterns across Indian cities make them vulnerable to precipitation extremes and cause huge economic losses. Considering this, the Indian Smart Cities Mission was initiated in 2015 to make cities resilient to climate change and promote the nation's growth. Thus, we quantify the historical precipitation variability in precipitation amount and days. Further, we assessed the nonstationary influence of the ENSO, IOD, and AMO on future precipitation extremes projections for 10-, 25-, 50-, and 100-year return levels. Based on this, we computed the hazard due to precipitation dynamics across Indian cities and found it to be maximum for Silvassa city. Subsequently, we assessed the vulnerability of the cities based on the urban infrastructure and development and exposure to the regional population and economy to compute the risk for Indian cities. The study revealed that Ahmedabad, Thane, and Surat cities observed maximum risk due to precipitation dynamics in a changing climate. Further, we observed that risk was induced due to precipitation dynamics and associated extremes observed to be maximum in the Northwest, West Central, and Central Northeast precipitation zones. It assists policymakers in enhancing the urban climate action plan to make cities resilient to urban flood events.

Impact of Acoustic Waves on Rainfall Distribution in Acoustics-induced Artificial Rainfall Technology

2025-06-01
Mengyao Wang , Jiaye Li , Ji Chen +2 more | Results in Engineering

Microphysics of warm season heavy precipitation in 2024 over China: A preliminary investigation based on FY-3G PMR observations

2025-06-01
Long Wen , Zhaoyang Fu , Peiling Fu +2 more | Atmospheric Research

Strategy for WiFi Interference Detection in Weather Radar Applications

2025-06-01
Ana MarĂ­a DĂ­az RodrĂ­guez , Jorge Cogo , Juan Pablo Pascual | Radio Science
Abstract In the current context of intensive spectrum use by communications systems, WiFi systems have been allowed to use bands previously reserved for weather radars, as opportunity users. Some drawbacks 
 Abstract In the current context of intensive spectrum use by communications systems, WiFi systems have been allowed to use bands previously reserved for weather radars, as opportunity users. Some drawbacks in spectrum management make WiFi systems a source of interference that degrades the quality of observables obtained by C‐band weather radars. In this work we present a strategy to detect these interfering WiFi packets at the output signal of the radar matched filter. The strategy is based on a delay and correlate algorithm that exploits the periodic structure of the WiFi packets preamble, periodicity that remains unchanged even though the signal is distorted when passing through the radar reception stages. We formulate the detection strategy as a hypothesis test that uses the squared modulus of the auto‐correlation as the statistic, extended to a constant false alarm (CFAR) formulation to cope with the unknown noise power. We evaluate analytically and through numerical simulations the performance of the test in terms of detection probability. We also perform a series of controlled experiments using real‐world weather radar data collected by Argentinian C‐band RMA radars. The results show a high detection rate both when WiFi interference is in regions where there is only noise and when it is in regions where there is also a meteorological target.

Augmenting precipitation datasets: A comparative study of topographic-based corrections and collocation techniques considering orographic effects

2025-06-01
Mehran Besharatifar , Shadi Kamrani , Mohsen Nasseri | Atmospheric Research