scholarly journals An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

2014 ◽  
Vol 27 (1) ◽  
pp. 273-284 ◽  
Author(s):  
Jian-Jian Wang ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
David Bolvin
Keyword(s):  
Pacific Ocean ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Mean Value ◽  
Passive Microwave ◽  
Control Measures ◽  
Tropical Ocean ◽  
Tropical Rainfall ◽  
Uncertainty Estimates ◽  
Microwave Imager

Abstract An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primary TRMM instruments: the TRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation σ among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3 mm day−1 for the deep tropical ocean belt between 10°N and 10°S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by σ is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

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 Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References

2008 ◽  
Vol 47 (12) ◽  
pp. 3170-3187 ◽  
Author(s):  
Xin Lin ◽  
Arthur Y. Hou
Keyword(s):  
United States ◽  
Tropical Rainfall Measuring Mission ◽  
Passive Microwave ◽  
Earth Observing System ◽  
Temporal Sampling ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Cross Track ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.

scholarly journals Adequacy of Using a 1/3-Degree Special Sensor Microwave Imager Dataset to Estimate Climate-Scale Rainfall

2000 ◽  
Vol 39 (5) ◽  
pp. 680-685 ◽  
Author(s):  
Qihang Li ◽  
Ralph Ferraro ◽  
Norman Grody
Keyword(s):  
Data Processing ◽  
Spatial Resolution ◽  
Information Service ◽  
Global Precipitation Climatology Project ◽  
Spatial Density ◽  
Tropical Ocean ◽  
Full Resolution ◽  
Toga Coare ◽  
Microwave Imager ◽  
Climate Scale

Abstract Until recently, monthly rainfall products using the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service Office of Research and Applications Special Sensor Microwave Imager (SSM/I) rainfall algorithm have been generated on a global 2.5° × 2.5° grid. The rainfall estimates are based on a subsampled set of the full-resolution SSM/I data, with a resulting spatial density of about one-third of what is possible at SSM/I’s highest spatial resolution. The reduction in the spatial resolution was introduced in 1992 as a compromise dictated by data processing capabilities. Currently, daily SSM/I data processing at full resolution has been established and is being operated in parallel with the subsampled set. Reprocessing of the entire SSM/I time series based on the full-resolution data is plausible but requires the reprocessing of over 10 yr of retrospective data. Because the Global Precipitation Climatology Project is considering the generation of a daily 1° × 1° rainfall product, it is important that the effects of using the reduced spatial resolution be reexamined. In this study, error due to using the reduced-resolution versus the full-resolution SSM/I data in the gridded products at 2.5° and 1° grid sizes is examined. The estimates are based on statistics from radar-derived rain data and from SSM/I data taken over the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar site. SSM/I data at full resolution were assumed to provide rain estimates with 12.5-km spacing. Subsampling with spacings of 25, 37.5 (which corresponds to the present situation of ⅓° latitude–longitude spatial resolution), and 50 km were considered. For the instantaneous 2.5° × 2.5° product, the error due to subsampling, expressed as a percentage of the gridbox mean, was estimated using radar-derived data and was 6%, 10%, and 15% at these successively poorer sampling densities. For monthly averaged products on a 2.5° × 2.5° grid, it was substantially lower: 3%, 4%, and 7%, respectively. Subsampling errors for monthly averages on a 1° × 1° grid were 8%, 16%, and 23%, respectively. Estimates based on SSM/I data at full resolution gave errors that were somewhat larger than those from radar-based estimates. It was concluded that a rain product of monthly averages on a 1° × 1° grid must use the full-resolution SSM/I data. More work is needed to determine how applicable these estimates are to other areas of the globe with substantially different rain statistics.

scholarly journals Relating Passive 37-GHz Scattering to Radar Profiles in Strong Convection

2011 ◽  
Vol 50 (1) ◽  
pp. 233-240 ◽  
Author(s):  
Daniel J. Cecil
Keyword(s):  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
The United States ◽  
Phase Layer ◽  
Tropical Ocean ◽  
Convective Systems ◽  
Freezing Level ◽  
Brightness Temperatures ◽  
Microwave Imager ◽  
Ice Water

Abstract Tropical Rainfall Measuring Mission (TRMM) Microwave Imager and precipitation radar measurements are examined for strong convective systems. Storms having similar values of minimum 37-GHz polarization-corrected temperature (PCT) are grouped together, and their vertical profiles of maximum radar reflectivity are composited. Lower 37-GHz PCT corresponds to stronger radar profiles (high reflectivity through a deep layer), but characteristic profiles for a given 37-GHz PCT are different for deep tropical ocean, deep tropical land, subtropical ocean, and subtropical land regions. Tropical oceanic storms have a sharper decrease of reflectivity just above the freezing level than storms from other regions with the same brightness temperature. Storms from subtropical land regions have the slowest decrease of reflectivity with height and the greatest mixed-phase-layer ice water content (IWC). Linear fits of 37-GHz PCT versus IWC for each region are used to scale the brightness temperatures. Counts of storms with these scaled brightness temperatures below certain thresholds suggest that not as many of the strongest storms occur in central Africa as in subtropical parts of South America, the United States, and central Asia.

scholarly journals Differences of Rainfall Estimates over Land by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI)—Dependence on Storm Height

2005 ◽  
Vol 44 (3) ◽  
pp. 367-383 ◽  
Author(s):  
Fumie A. Furuzawa ◽  
Kenji Nakamura
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Local Times ◽  
Precipitation Radar ◽  
Tropical Rainfall ◽  
Convective Rain ◽  
Brightness Temperatures ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Orbit Data

Abstract It is well known that precipitation rate estimation is poor over land. Using the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI), the performance of the TMI rain estimation was investigated. Their differences over land were checked by using the orbit-by-orbit data for June 1998, December 1998, January 1999, and February 1999, and the following results were obtained: 1) Rain rate (RR) near the surface for the TMI (TMI-RR) is smaller than that for the PR (PR-RR) in winter; it is also smaller from 0900 to 1800 LT. These dependencies show some variations at various latitudes or local times. 2) When the storm height is low (<5 km), the TMI-RR is smaller than the PR-RR; when it is high (>8 km), the PR-RR is smaller. These dependencies of the RR on the storm height do not depend on local time or latitude. The tendency for a TMI-RR to be smaller when the storm height is low is more noticeable in convective rain than in stratiform rain. 3) Rain with a low storm height predominates in winter or from 0600 to 1500 LT, and convective rain occurs frequently from 1200 to 2100 LT. Result 1 can be explained by results 2 and 3. It can be concluded that the TMI underestimates rain with low storm height over land because of the weakness of the TMI algorithm, especially for convective rain. On the other hand, it is speculated that TMI overestimates rain with high storm height because of the effect of anvil rain with low brightness temperatures at high frequencies without rain near the surface, and because of the effect of evaporation or tilting, which is indicated by a PR profile and does not appear in the TMI profile. Moreover, it was found that the PR rain for the cases with no TMI rain amounted to about 10%–30% of the total but that the TMI rain for the cases with no PR rain accounted for only a few percent of the TMI rain. This result can be explained by the difficulty of detecting shallow rain with the TMI.

Intercalibrated Passive Microwave Rain Products from the Unified Microwave Ocean Retrieval Algorithm (UMORA)

2008 ◽  
Vol 47 (3) ◽  
pp. 778-794 ◽  
Author(s):  
K. A. Hilburn ◽  
F. J. Wentz
Keyword(s):  
Spatial Patterns ◽  
Liquid Water ◽  
Rain Rate ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Global Precipitation Climatology Project ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Surface Rain Rate ◽  
Microwave Imager

Abstract The Unified Microwave Ocean Retrieval Algorithm (UMORA) simultaneously retrieves sea surface temperature, surface wind speed, columnar water vapor, columnar cloud water, and surface rain rate from a variety of passive microwave radiometers including the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The rain component of UMORA explicitly parameterizes the three physical processes governing passive microwave rain retrievals: the beamfilling effect, cloud and rainwater partitioning, and effective rain layer thickness. Rain retrievals from the previous version of UMORA disagreed among different sensors and were too high in the tropics. These issues have been fixed with more realistic rain column heights and proper modeling of saturation and footprint-resolution effects in the beamfilling correction. The purpose of this paper is to describe the rain algorithm and its recent improvements and to compare UMORA retrievals with Goddard Profiling Algorithm (GPROF) and Global Precipitation Climatology Project (GPCP) rain rates. On average, TMI retrievals from UMORA agree well with GPROF; however, large differences become apparent when the instantaneous retrievals are compared on a pixel-to-pixel basis. The differences are due to fundamental algorithm differences. For example, UMORA generally retrieves higher total liquid water, but GPROF retrieves a higher surface rain rate for a given amount of total liquid water because of differences in microphysical assumptions. Comparison of UMORA SSM/I retrievals with GPCP shows similar spatial patterns, but GPCP has higher global averages because of greater amounts of precipitation in the extratropics. UMORA and GPCP have similar linear trends over the period 1988–2005 with similar spatial patterns.

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