Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

2013 ◽  
Vol 52 (12) ◽  
pp. 2809-2827 ◽  
Author(s):  
Joseph P. Zagrodnik ◽  
Haiyan Jiang
Keyword(s):  
Tropical Cyclones ◽  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Light Rain ◽  
Hourly Rainfall ◽  
Wide Range ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates ◽  
Rainfall Estimates

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

Verification of TMI-Adjusted Rainfall Analyses of Tropical Cyclones at ECMWF Using TRMM Precipitation Radar

2005 ◽  
Vol 44 (11) ◽  
pp. 1677-1690 ◽  
Author(s):  
A. Benedetti ◽  
P. Lopez ◽  
E. Moreau ◽  
P. Bauer ◽  
V. Venugopal
Keyword(s):  
Tropical Cyclones ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Radar Data ◽  
Skill Score ◽  
Precipitation Radar ◽  
Statistical Measures ◽  
Model Background ◽  
Trmm Precipitation ◽  
Rain Rates

Abstract A validation of passive microwave–adjusted rainfall analyses of tropical cyclones using spaceborne radar data is presented. This effort is part of the one-dimensional plus four-dimensional variational (1D+4D-Var) rain assimilation project that is being carried out at the European Centre for Medium-Range Weather Forecasts (ECMWF). Brightness temperatures or surface rain rates from the Tropical Rainfall Measuring Mission (TRMM) satellite are processed through a 1D-Var retrieval to derive values of total column water vapor that can be ingested into the operational ECMWF 4D-Var. As an indirect validation, the precipitation fields produced at the end of the 1D-Var minimization process are converted into equivalent radar reflectivity at the frequency of the TRMM precipitation radar (13.8 GHz) and are compared with the observations averaged at model resolution. The averaging process is validated using a sophisticated downscaling/upscaling approach that is based on wavelet decomposition. The precipitation radar measurements are ideal for this validation exercise, being approximately collocated with but completely independent of the TRMM Microwave Imager (TMI) radiometer measurements. Qualitative and statistical comparisons between radar observations and retrievals from the TMI-derived surface rain rates and from TMI radiances are made using 17 well-documented tropical cyclone occurrences between January and April of 2003. Several statistical measures, such as bias, root-mean-square error, and Heidke skill score, are introduced to assess the 1D-Var skill as well as the model background skill in producing a realistic rain distribution. Results show a good degree of skill in the retrievals, especially near the surface and for medium–heavy rain. The model background produces precipitation in the domain that is sometimes in excess with respect to the observations, and it often shows an error in the location of precipitation maxima. Differences between the two 1D-Var approaches are not large enough to make final conclusions regarding the advantages of one method over the other. Both methods are capable of redistributing the rain patterns according to the observations. It appears, however, that the brightness temperature approach is in general more effective in increasing precipitation amounts at moderate-to-high rainfall rates.

scholarly journals A case study for cyclone ‘Aila’ for forecasting rainfall using satellite derived rain rate data

MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 77-82
Author(s):  
HABIBURRAHAMAN BISWAS ◽  
P.K. KUNDU ◽  
D. PRADHAN
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Storm Track ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Cyclone Aila ◽  
Potential Trap ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates

caxky dh [kkM+h esa cuus ,oa tehu ls Vdjkus okys pØokrh; rwQkuksa ds  ifj.kkeLo:i  Hkkjh o"kkZ dh otg ls if’pe caxky ds rV lesr Hkkjr ds iwohZ rV ds yksxksa dh tku eky dks dkQh [krjk jgrk gSA tehu ls Vdjkus okys m".kdfVca/kh; pØokrh rwQkuksa dh otg ls gksus okyh o"kkZ dh ek=k dk iwokZuqeku djuk cgqr dfBu gSA m".kdfVca/kh; pØokrh; rwQkuksa ds nk;js esa vkus okys o"kkZ okys {ks=ksa esa laHkkfor pØokrh; rwQku ls gksus okys o"kkZ lap;u dk iwokZuqeku djus ds fy, mixzg ls izkIr o"kkZ njksa dk mi;ksx fd;k tk ldrk gSA bl 'kks/k i= esa ‘vkbyk’ ds m".kdfVca/kh; o"kkZ ekiu fe’ku ¼Vh- vkj- ,e- ,e-½] mixzg o"kkZ nj vk¡dM+ksa rFkk rwQku ds ns[ks x, ekxZ dk mi;ksx djrs gq, m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ls 24 ?kVsa igys rVh; LVs’kuksa ij o"kkZ dk vkdyu djus dk iz;kl fd;k x;k gSA la;qDr jkT; vesfjdk esa fodflr lqifjfpr rduhd ds vk/kkj ij  m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ds 24 ?kaVs igys m".kdfVca/kh; o"kkZ foHko ¼Vh- vkj- ,- ih-½ iwokZuqeku fo’ks"k :i  ls rwQku dh fn’kk ds lkeus vkus okys rVh; {ks=ksa ds fy, vPNh o"kkZ dk iwokZuqeku miyC/k djkrk gSA Major threat to the life and property of people on the east coast of India, including West Bengal Coast, is due to very heavy rainfall from landfalling tropical cyclones originated over Bay of Bengal. Forecasting magnitude of rainfall from landfalling tropical cyclones is very difficult. Satellite derived rain rates over the raining areas of tropical cyclones can be used to forecast potential tropical cyclone rainfall accumulations. In the present study, an attempt has been made to estimate 24 hours rainfall over coastal stations before landfall of tropical Cyclone ‘Aila’ using Tropical Rainfall Measuring Mission (TRMM) satellite rain rates data and observed storm track of Aila. Forecast Tropical Rainfall Potential (TRaP), 24 hours prior to landfall for the tropical cyclone ‘Aila’ based on well known technique developed in USA, provides a good rainfall forecast especially for the coastal areas lying at the head of direction of the storm.

The Tropical Rainfall Potential (TRaP) Technique. Part II: Validation

2005 ◽  
Vol 20 (4) ◽  
pp. 465-475 ◽  
Author(s):  
Ralph Ferraro ◽  
Paul Pellegrino ◽  
Michael Turk ◽  
Wanchun Chen ◽  
Shuang Qiu ◽  
...  
Keyword(s):  
Information Service ◽  
Storm Track ◽  
Stage Iv ◽  
Retrieval Algorithm ◽  
Maximum Rainfall ◽  
Tropical Rainfall ◽  
Hourly Rainfall ◽  
Potential Trap ◽  
Eta Model ◽  
Rainfall Estimates

Abstract Satellite analysts at the Satellite Services Division (SSD) of the National Environmental, Satellite, Data, and Information Service (NESDIS) routinely generate 24-h rainfall potential for all tropical systems that are expected to make landfall within 24 to at most 36 h and are of tropical storm or greater strength (>65 km h−1). These estimates, known as the tropical rainfall potential (TRaP), are generated in an objective manner by taking instantaneous rainfall estimates from passive microwave sensors, advecting this rainfall pattern along the predicted storm track, and accumulating rainfall over the next 24 h. In this study, the TRaPs generated by SSD during the 2002 Atlantic hurricane season have been validated using National Centers for Environmental Prediction (NCEP) stage IV hourly rainfall estimates. An objective validation package was used to generate common statistics such as correlation, bias, root-mean-square error, etc. It was found that by changing the minimum rain-rate threshold, the results could be drastically different. It was determined that a minimum threshold of 25.4 mm day−1 was appropriate for use with TRaP. By stratifying the data by different criteria, it was discovered that the TRaPs generated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, with its optimal set of measurement frequencies, improved spatial resolution, and advanced retrieval algorithm, produced the best results. In addition, the best results were found for TRaPs generated for storms that were between 12 and 18 h from landfall. Since the TRaP is highly dependent on the forecast track of the storm, selected TRaPs were rerun using the observed track contained in the NOAA/Tropical Prediction Center (TPC) “best track.” Although some TRaPs were not significantly improved by using this best track, significant improvements were realized in some instances. Finally, as a benchmark for the usefulness of TRaP, comparisons were made to Eta Model 24-h precipitation forecasts as well as three climatological maximum rainfall methods. It was apparent that the satellite-based TRaP outperforms the Eta Model in virtually every statistical category, while the climatological methods produced maximum rainfall totals closer to the stage IV maximum amounts when compared with TRaP, although these methods are for storm totals while TRaP is for a 24-h period.

scholarly journals Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones

2016 ◽  
Vol 33 (7) ◽  
pp. 1539-1556 ◽  
Author(s):  
Paula J. Brown ◽  
Christian D. Kummerow ◽  
David L. Randel
Keyword(s):  
Tropical Cyclones ◽  
Vertical Profile ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Initial Assessment ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
One Year ◽  
Trmm Microwave Imager ◽  
Rain Rates

AbstractThe Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.

scholarly journals Satellite Microwave Surface Observations in Tropical Cyclones

2010 ◽  
Vol 138 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Yves Quilfen ◽  
Bertrand Chapron ◽  
Jean Tournadre
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Surface Wind Speed ◽  
Microwave Measurements ◽  
Dual Frequency

Abstract Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.

scholarly journals Rainfall estimation of landfalling tropical cyclones over Indian coasts through satellite imagery

MAUSAM ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 193-202
Author(s):  
CHARAN SINGH ◽  
SUNIT DAS ◽  
R.B. VERMA ◽  
B. L. VERMA ◽  
B.K. BANDYOPADHYAY
Keyword(s):  
Tropical Cyclones ◽  
Heavy Rain ◽  
Maximum Rainfall ◽  
Cyclonic Storm ◽  
Convective Clouds ◽  
Main Emphasis ◽  
Slow Moving ◽  
Strong Convection ◽  
Landfalling Tropical Cyclones ◽  
The Right

One of the most significant impacts of landfalling tropical cyclones is caused by the copiousrainfall associated with it. The main emphasis of present study is to provide some guidance to the operational forecastersfor indicating the possible rainfall over the areas likely to be affected by the cyclones after landfall. Study of 14 pastlandfalling cyclones reveals that the maximum rainfall occurred in the first forward quadrant of tropical cyclonemovement, followed by the second quadrant and the areas near the track of the cyclones. Isohyetal analysis of 24 hoursrainfall for each cyclone reveals that occurrence of heavy rainfall is generally confined up to 150 kms radius from thestorm centre and rainfall is found to generally extend up to 300 kms with gradual decrease in amount. The rainfallreceiving areas are mostly covered with convective clouds with cloud top temperatures of -80 to -60 ºC, prior to and afterthe landfall of the systems. In 93% of tropical cyclones out of the 14 cases studied, 70 % convection lay to the right of thetrack. To examine the rainfall asymmetry due to asymmetry in distribution of convection, cloud top temperatures derivedfrom satellite infrared imagery data have been taken as the proxy of strong convection. It is also revealed in the study thatthe slow moving tropical cyclones cause heavy rain rather than fast moving tropical cyclones. The Bay of Bengalcyclones which crossed coast as cyclonic storm and very severe cyclonic storm caused 71.4% rainfall within the range 0-10 cm, 22.8% rainfall in the range 11-20 cm and 4.3% rainfall within the range 21-30 cm in the area of radius of 300 kmsfrom the centre of the cyclonic storms. For the Arabian Sea tropical cyclones, in general, about 70% rainfall occurredwithin the range 16-25 cm in 24 hours.

scholarly journals Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships

2013 ◽  
Vol 52 (1) ◽  
pp. 213-229 ◽  
Author(s):  
Weixin Xu ◽  
Robert F. Adler ◽  
Nai-Yu Wang
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Convective System ◽  
Specific Rate ◽  
Lightning Flash ◽  
Convective Rainfall ◽  
Light Rain ◽  
Flash Rate ◽  
Pixel Scale

AbstractThis study quantifies the relationships among lightning activity, convective rainfall, and associated cloud properties on both convective-system scale (or storm scale) and satellite-pixel scale (~5 km) on the basis of 13 yr of Tropical Rainfall Measuring Mission measurements of rainfall, lightning, and clouds. Results show that lightning frequency is a good proxy to separate storms of different intensity, identify convective cores, and screen out false convective-core signatures in areas of thick anvil debris. Significant correlations are found between storm-scale lightning parameters and convective rainfall for systems over the southern United States, the focus area of the study. Storm-scale convective rainfall or heavy-precipitation area has the best correlation (coefficient r = 0.75–0.85) with lightning-flash area. It also increases linearly with increasing lightning-flash rate, although correlations between convective/heavy rainfall and lightning-flash rate are somewhat weaker (r = 0.55–0.75). Statistics further show that active lightning and intense precipitation are not well collocated on the pixel scale (5 km); for example, only 50% of the lightning flashes are coincident with heavy-rain cores, and more than 20% are distributed in light-rain areas. Simple positive correlations between lightning-flash rate and precipitation intensity are weak on the pixel scale. Lightning frequency and rain intensity have positive probabilistic relationships, however: the probability of heavy precipitation, especially on a coarser pixel scale (~20 km), increases with increasing lightning-flash density. Therefore, discrete thresholds of lightning density could be applied in a rainfall estimation scheme to assign precipitation in specific rate categories.

scholarly journals Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales

2008 ◽  
Vol 47 (8) ◽  
pp. 2215-2237 ◽  
Author(s):  
David B. Wolff ◽  
Brad L. Fisher
Keyword(s):  
Rain Rate ◽  
Spatial Scales ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Global Ocean ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Events ◽  
Trmm Microwave Imager ◽  
Rain Rates

Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.

scholarly journals Observations of Extreme Short-Term Precipitation Associated with Supercells and Mesovortices

2019 ◽  
Vol 148 (1) ◽  
pp. 159-182 ◽  
Author(s):  
Erik R. Nielsen ◽  
Russ S. Schumacher
Keyword(s):  
Flash Flood ◽  
Stage Iv ◽  
The United States ◽  
Surface Boundary ◽  
Short Term ◽  
Central Plains ◽  
Hourly Rainfall ◽  
High Precipitation ◽  
Rain Rates ◽  
Term Rain

Abstract Extreme hourly rainfall accumulations (e.g., exceeding 75 mm h−1) in several noteworthy flash flood events have suggested that the most intense accumulations were attendant with discrete mesoscale rotation or rotation embedded within larger organized systems. This research aims to explore how often extreme short-term rain rates in the United States are associated with storm-scale or mesoscale vortices. Five years of METAR observations and three years of Stage-IV analyses were obtained and filtered for hourly accumulations over 75 and 100 mm, respectively, clustered into events, and subjectively identified for rotation. The distribution of the short-term, locally extreme events shows the majority of the events were located along the Atlantic and Gulf of Mexico coastlines with additional events occurring in the central plains and into the Midwest. Nearly 50% of the cases were associated with low-level rotation in high-precipitation supercells or mesoscale vortices embedded in organized storm modes. Rotation events occurred more clearly in the warm sector, while nonrotation events tended to occur along a surface boundary. The rotation events tended to produce higher hourly accumulations over a larger region, but were associated with somewhat stronger synoptic-to-mesoscale forcing for ascent and more total column moisture. These results support recent modeling results suggesting that rotationally induced dynamic vertical pressure perturbations should not be ignored when it comes to extreme precipitation and can potentially enhance the short-term rain rates.

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

2011 ◽  
Vol 12 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Jiahu Wang ◽  
Humberto Vergara ◽  
...  
Keyword(s):  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Efficiency Score ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Rain Gauges ◽  
Distributed Hydrologic Model ◽  
Radar Satellite ◽  
Rainfall Estimates

Abstract This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

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