The TRMM Multi-Satellite Precipitation Analysis (TMPA) (original) (raw)
Related papers
2007
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) pro-vides 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 re...
Journal of Hydrometeorology, 2007
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°ϫ 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.
2006
Launched in 1997, the Tropical Rainfall Measuring Mission (TRMM) satellite is a joint effort of NASA and the Japanese Space Agency to improve the estimation and characterization of tropical rainfall. The TRMM satellite carries two instruments for measuring precipitation: a multifrequency passive microwave radiometer (TRMM Microwave Imager – TMI) and a 13.8 GHz frequency precipitation radar (PR) (Kummerow et al. 1998). These instruments on the TRMM satellite allow for both simultaneous and independent measurement of storm characteristics between 35o N and 35o S latitude.
The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit
Journal of Applied Meteorology, 2000
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
The Tropical Rainfall Measuring Mission (TRMM) Progress Report
Recognizing the importance of rain in the tropics and the accompanying latent heat release, NASA for the U.S. and NASDA for Japan have partnered in the design, construction and flight of an Earth Probe satellite to measure tropical rainfall and calculate the associated heating. Primary mission goals are 1) the understanding of crucial links in climate variability by the hydrological cycle, 2) improvement in the large-scale models of weather and climate 3) Improvement in understanding cloud ensembles and their impacts on larger scale circulations. The linkage with the tropical oceans and landmasses are also emphasized. The Tropical Rainfall Measuring Mission (TRMM) satellite was launched in November 1997 with fuel enough to obtain a four to five year data set of rainfall over the global tropics from 37°N to 37°S. This paper reports progress from launch date through the spring of 1999. The data system and its products and their access is described, as are the algorithms used to obtain the data. Some exciting early results from TRMM are described. Some important algorithm improvements are shown. These will be used in the first total data reprocessing, scheduled to be complete in early 2000. The reader is given information on how to access and use the data. 1. Introduction The Tropical Rainfall Measuring Mission ffRMM) satellite has yielded important interim results after nearly two years of successful flight operations since launch in late 1997. This paper summarizes the mission science goals, instruments, algorithm development; some early results using the "at launch" algorithms, as well as ongoing efforts to validate the TRMM products. Section 2 contains the mission science goals, a brief summary of the joint project between Japan and the United States, and a table of the instruments. Section 3 describes the selected TRMM products, the algorithms developed to obtain the products, and the TRMM data system. Section 4 is a progress report on validation efforts. Section 5 presents some highlights of TRMM products during the first months after launch and their use in several research activities. Section 6 furnishes a brief overview of the planned satellite system (Global Precipitation Mission) to succeed TRMM in measuring precipitation from space. Section 7 contains concluding remarks for this stage of the mission's lifetime. 2. The goals, the TRMM Project and the Instrument complement 2.1 The importance of tropical rainfall: TRMM goals Tropical rainfall is important in the hydrological cycle and to the lives and welfare of humans. Three-fourths of the energy that drives the atmospheric wind circulation comes from the latent heat released by tropical precipitation. It varies greatly in space and time. Often severe droughts are succeeded by deadly floods. Many scales are involved in the rain processes and their impacts on global circulations. The rain-producing cloud systems may last several hours or days. Their dimensions range from 10 km to several hundred km, so that they cannot yet be treated explicitly in the large-scale weather and climate models. Until the end of 1997, precipitation in the global tropics was not known to within a factor of two. Regarding "global warming", the various large-scale models differed among themselves in the predicted magnitude of the warming and in the expected regional effects of these temperature and moisture changes. Accurate estimates of tropical precipitation and the associated latent heat release were urgently needed to improve these models. The agreed upon science goals of TRMM as presented in the first major report (Simpson, Ed., 1988) are shown in Table 2.1 9/9/99 3:03 PM IV. TO HELP UNDERSTAND, DIAGNOSE AND PREDICT THE ONSET AND DEVELOPMENT OF THE EL NINO, SOUTHERN OSCILLATION AND THE PROPAGATION OF THE 30-60 DAY OSCILLATION IN THE TROPICS V. TO HELP UNDERSTAND THE EFFECT THAT RAINFALL HAS ON THE OCEAN THERMOHALINE CIRCULATIONS & THE STRUCTURE OF THE UPPER OCEAN VI. TO ALLOW CROSS-CALIBRATION BETWEEN TRMM AND OTHER SENSORS WITH LIFE EXPECTANCIES BEYOND THAT OF TRMM ITSELF. VII. TO EVALUATE THE DIURNAL VARIABILITY OF TROPICAL RAINFALL GLOBALLY VIII. TO EVALUATE A SPACE-BASED SYSTEM FOR RAINFALL MEASUREMENT 2.2 The TRMM instruments To meet the science goals, within limited resources, the final instruments are shown in Table 2.2.1. Their scanning patterns are illustrated in Figure 2.2.1. passive microwave instruments would thus be able calibrate the surface rain estimations made empirically from operational geosynchronous IR sensors. Using this method with geosynchronous products obviated the restricted sampling by TRMM alone, which would overfly a given 5°by 5°grid box only about twice in 24 hr. The radar and radiometer combination enables high quality precipitation profiles. The small cloud drops that play an integral part in the latent heat release process, however, would not be observable with sufficient accuracy to construct profiles of the latent heat release. It was therefore planned from the start to use results of a cloud-resolving numerical model in retrieving the important latent heat profiles.
Scientific Data, 2020
The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatol...
A Ten-Year Tropical Rainfall Climatology Based on a Composite of TRMM Products
Journal of the Meteorological Society of Japan, 2009
A new climatology of tropical surface rain is described based on a composite of ten years of precipitation retrievals and analyses from the Tropical Rainfall Measuring Mission (TRMM). This TRMM Composite Climatology (TCC) consists of a combination of selected TRMM rainfall products over both land and ocean. This new climatology will be useful as a summary of surface rain estimates from TRMM (not replacing the individual products) and should be useful as a ready comparison with other non-TRMM estimates and for comparison with calculated precipitation from general circulation models. The TCC mean precipitation for each calendar month and for the annual total is determined by a simple mean of the three chosen products (slightly different combination of products over land and ocean). Over ocean areas, the three TRMM products are those based on the passive microwave (2A12), radar (2A25) and combined retrievals (2B31). Over land, the multi-satellite product (3B43) is substituted for the passive microwave product. The standard deviation (σ) at each point among the three estimates gives a measure of dispersion, which can be used as an indicator of confidence and as an estimate of error. The mean annual precipitation over the TRMM domain of 35°N to 35°S in the new climatology is 2.68 mm d-1 (ocean and land combined) with a σ of .05 mm d-1 , or 2.0%. The ocean (land) value is 2.74 mm d-1 (2.54) with a σ/mean of 2.1% (5.4%). The larger dispersion (and assumed error) over land is due to the greater difficulty of satellite rain retrieval over land, especially with passive microwave techniques and especially in mountains and along coasts. The maps of σ and σ/mean indicate these regions of less confidence, including areas over the ocean such as the eastern Pacific Ocean. Examples of values for different latitude bands, seasonal variations, and relations of the individual inputs to the composite mean are given. Comparison with analyses from the Global Precipitation Climatology Project (GPCP) indicates lower values than GPCP for the TRMM composite in middle latitudes over the ocean and over northern Australia and India during their respective summer monsoons.
Hydrology and Earth System Sciences, 2011
Satellite-based precipitation products are expected to offer an alternative to ground-based rainfall estimates in the present and the foreseeable future. In this paper, we evaluate the performance of TRMM 3B42 precipitation products in the Yangtze River basin for the period of 2003∼2010. The results are as follows: (1) the performance of RTV7 (V7) products is generally better than that of RTV6 (V6) in the Yangtze River basin, and the percentage of best performance (bias ranging within −10%∼10%) for the annual mean precipitation increases from 21.72% (54.79%) to 36.70% (59.85%) as the RTV6 (V6) improved to the RTV7 (V7); (2) the TMPA products have better performance in the wet period than that in the dry period in the Yangtze River basin; (3) the performance of TMPA precipitation has been affected by the elevation and a downward trend can be found with the increasing elevation in the Yangtze River basin. The average CC between the V7 and observed precipitation in July decreases from 0.71 to 0.40 with the elevation of gauge stations increasing from 500 m below to 4000 m above in the Yangtze River basin. More attention should be paid to the influence of complex climate and topography.
2012
The real-time availability of satellite-derived precipitation estimates provides hydrologists an opportunity to improve current hydrologic prediction capability for medium to large river basins. Due to the availability of new satellite data and upgrades to the precipitation algorithms, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time estimates (TMPA-RT) have been undergoing several important revisions over the past ten years. In this study, the changes of the relative accuracy and hydrologic potential of TMPA-RT estimates over its three major evolving periods were evaluated and inter-compared at daily, monthly and seasonal scales in the high-latitude Laohahe basin in China. Assessment results show that the performance of TMPA-RT in terms of precipitation estimation and streamflow simulation was significantly improved after 3 February 2005. Overestimation during winter months was noteworthy and consistent, which is suggested to be a consequence from interference of snow cover to the passive microwave retrievals. Rainfall estimated by the new version 6 of TMPA-RT starting from 1 October 2008 to present has higher correlations with independent gauge observations and tends to perform better in detecting rain compared to the prior periods, although it suffers larger mean error and relative bias. After a simple bias correction, this latest data set of TMPA-RT exhibited the best capability in capturing hydrologic response among the three tested periods. In summary, this study demonstrated that there is an increasing potential in the use of TMPA-RT in hydrologic streamflow simulations over its three algorithm upgrade periods, but still with significant challenges during the winter snowing events.