scholarly journals TRMM Calibration of SSM/I Algorithm for Overland Rainfall Estimation

2006 ◽  
Vol 45 (6) ◽  
pp. 875-886 ◽  
Author(s):  
Tufa Dinku ◽  
Emmanouil N. Anagnostou
Keyword(s):  
South Asia ◽  
Amazon Basin ◽  
Tropical Rainfall Measuring Mission ◽  
Retrieval Algorithm ◽  
Calibration Data ◽  
Error Statistics ◽  
Microwave Imager ◽  
Rain Rates ◽  
Rain Retrieval

Abstract This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals.

scholarly journals Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

2005 ◽  
Vol 22 (4) ◽  
pp. 365-380 ◽  
Author(s):  
David B. Wolff ◽  
D. A. Marks ◽  
E. Amitai ◽  
D. S. Silberstein ◽  
B. L. Fisher ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Marshall Islands ◽  
Error Statistics ◽  
Tropical Rainfall ◽  
Nasa Goddard Space ◽  
Ground Validation ◽  
Microwave Imager ◽  
Rain Rates ◽  
Good Agreement

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent validation techniques show good agreement between gauge-measured and radar-estimated rainfall. A comparison of the NASA GV products and those developed independently by the University of Washington for a subset of data from the Kwajalein Atoll site also shows good agreement. A comparison of NASA GV rain intensities to satellite retrievals from the TRMM Microwave Imager (TMI), precipitation radar (PR), and Combined (COM) algorithms is presented, and it is shown that the GV and satellite estimates agree quite well over the open ocean.

scholarly journals Investigating Seasonal PR–TMI Calibration Differences

2008 ◽  
Vol 25 (7) ◽  
pp. 1228-1237
Author(s):  
Tufa Dinku ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Amazon Basin ◽  
Southeastern United States ◽  
Tropical Rainfall Measuring Mission ◽  
Validation Dataset ◽  
Precipitation Radar ◽  
Seasonal Differences ◽  
Geographic Regions ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Retrieval

Abstract Seasonal differences in the calibration of overland passive microwave rain retrieval are investigated using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Four geographic regions from southern Africa, South Asia, the Amazon basin, and the southeastern United States are selected. Three seasons are compared for each region. Two scenarios of algorithm calibration are considered. In the first, the parameter sets are derived by calibrating the TMI algorithm with PR in each season. In the second scenario, common parameter sets are derived from the combined dataset of all three seasons. The parameter sets from both scenarios are then applied to the validation dataset of each season to determine the effect of seasonal calibration. Furthermore, calibration parameters from one season are also applied to another season, and results are compared against those derived using the season’s own parameters. Appreciable seasonal differences are observed for the U.S. region, while there are no significant differences between using individual seasonal calibration and the all-season calibration for the other regions. However, using one season’s parameter set to retrieve rainfall for another season is associated with increased uncertainty. It is also shown that the performance of the retrieval varies by season.

scholarly journals Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm

2018 ◽  
Vol 10 (11) ◽  
pp. 1770 ◽  
Author(s):  
Ruanyu Zhang ◽  
Zhenzhan Wang ◽  
Kyle Hilburn
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Earth Observing System ◽  
Mesoscale Structures ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates

A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10–15 mm/h category; however, with maximum errors around −22% at the lowest (0–0.5 mm/h) and highest rain rates (25–30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals.

scholarly journals Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting

2013 ◽  
Vol 52 (6) ◽  
pp. 1379-1393 ◽  
Author(s):  
Robert J. Kuligowski ◽  
Yaping Li ◽  
Yu Zhang
Keyword(s):  
Flash Flood ◽  
Tropical Rainfall Measuring Mission ◽  
Low Earth Orbit ◽  
Low Latency ◽  
False Alarms ◽  
Calibration Data ◽  
Calibration Dataset ◽  
Data Refresh ◽  
Microwave Imager ◽  
Rain Rates

AbstractData from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

2005 ◽  
Vol 22 (7) ◽  
pp. 909-929 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Christian D. Kummerow
Keyword(s):  
A Priori ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Dimensional Structure ◽  
Retrieval Algorithm ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation

Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.

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 A Comparison of Gamma and Lognormal Distributions for Characterizing Satellite Rain Rates from the Tropical Rainfall Measuring Mission

2004 ◽  
Vol 43 (11) ◽  
pp. 1586-1597 ◽  
Author(s):  
Hye-Kyung Cho ◽  
Kenneth P. Bowman ◽  
Gerald R. North
Keyword(s):  
Gamma Distribution ◽  
Lognormal Distribution ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rainfall Data ◽  
Retrieval Algorithm ◽  
Sample Mean ◽  
Tropical Rainfall ◽  
Lognormal Distributions ◽  
Rain Rates

Abstract This study investigates the spatial characteristics of nonzero rain rates to develop a probability density function (PDF) model of precipitation using rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The minimum χ2 method is used to find a good estimator for the rain-rate distribution between the gamma and lognormal distributions, which are popularly used in the simulation of the rain-rate PDF. Results are sensitive to the choice of dynamic range, but both the gamma and lognormal distributions match well with the PDF of rainfall data. Comparison with sample means shows that the parametric mean from the lognormal distribution overestimates the sample mean, whereas the gamma distribution underestimates it. These differences are caused by the inflated tail in the lognormal distribution and the small shape parameter in the gamma distribution. If shape constraint is given, the difference between the sample mean and the parametric mean from the fitted gamma distribution decreases significantly, although the resulting χ2 values slightly increase. Of interest is that a consistent regional preference between two test functions is found. The gamma fits outperform the lognormal fits in wet regions, whereas the lognormal fits are better than the gamma fits for dry regions. Results can be improved with a specific model assumption depending on mean rain rates, but the results presented in this study can be easily applied to develop the rainfall retrieval algorithm and to find the proper statistics in the rainfall data.

scholarly journals Microwave Rainfall Estimation over Coasts

2005 ◽  
Vol 22 (5) ◽  
pp. 497-512 ◽  
Author(s):  
Jeffrey R. McCollum ◽  
Ralph R. Ferraro
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Radar Rainfall ◽  
The Public ◽  
Earth Observing System ◽  
Temperature Criterion ◽  
Previous Algorithm ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates ◽  
Microwave Data

Abstract The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.

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.

The Diurnal Cycle of Rainfall and Convective Intensity according to Three Years of TRMM Measurements

2003 ◽  
Vol 16 (10) ◽  
pp. 1456-1475 ◽  
Author(s):  
Stephen W. Nesbitt ◽  
Edward J. Zipser
Keyword(s):  
Diurnal Cycle ◽  
Tropical Rainfall Measuring Mission ◽  
Early Morning ◽  
Convective Systems ◽  
Rain Gauges ◽  
Mesoscale Convective ◽  
Late Evening ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates

Abstract The Tropical Rainfall Measuring Mission (TRMM) satellite measurements from the precipitation radar and TRMM microwave imager have been combined to yield a comprehensive 3-yr database of precipitation features (PFs) throughout the global Tropics (±36° latitude). The PFs retrieved using this algorithm (which number nearly six million Tropicswide) have been sorted by size and intensity ranging from small shallow features greater than 75 km2 in area to large mesoscale convective systems (MCSs) according to their radar and ice scattering characteristics. This study presents a comprehensive analysis of the diurnal cycle of the observed precipitation features' rainfall amount, precipitation feature frequency, rainfall intensity, convective–stratiform rainfall portioning, and remotely sensed convective intensity, sampled Tropicswide from space. The observations are sorted regionally to examine the stark differences in the diurnal cycle of rainfall and convective intensity over land and ocean areas. Over the oceans, the diurnal cycle of rainfall has small amplitude, with the maximum contribution to rainfall coming from MCSs in the early morning. This increased contribution is due to an increased number of MCSs in the nighttime hours, not increasing MCS areas or conditional rain rates, in agreement with previous works. Rainfall from sub-MCS features over the ocean has little appreciable diurnal cycle of rainfall or convective intensity. Land areas have a much larger rainfall cycle than over the ocean, with a marked minimum in the midmorning hours and a maximum in the afternoon, slowly decreasing through midnight. Non-MCS features have a significant peak in afternoon instantaneous conditional rain rates (the mean rain rate in raining pixels), and convective intensities, which differs from previous studies using rain rates derived from hourly rain gauges. This is attributed to enhancement by afternoon heating. MCSs over land have a convective intensity peak in the late afternoon, however all land regions have MCS rainfall peaks that occur in the late evening through midnight due to their longer life cycle. The diurnal cycle of overland MCS rainfall and convective intensity varies significantly among land regions, attributed to MCS sensitivity to the varying environmental conditions in which they occur.

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