TRMM) Multisatellite Precipitation Analysis Research Articles

Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June–September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low‐pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.

Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA

This study focuses on the evaluation of 3-hourly 0.25° × 0.25° satellite-based rainfall estimates produced by the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). The evaluation is performed during six heavy rainfall events that were generated by tropical storms passing over Louisiana, United States. Two surface-based rainfall datasets from gauge and radar observations are used as a ground reference for evaluating the real-time (RT) version of the TMPA product and the post-real-time bias adjusted research version. The evaluation analysis is performed at the native temporal and spatial scales of the TMPA products, 3-hourly and 0.25° × 0.25°. Several graphical and statistical techniques are applied to characterize the deviation of the TMPA estimates from the reference datasets. Both versions of the TMPA products track reasonably well the temporal evolution and fluctuations of surface rainfall during the analyzed storms with moderate to high correlation values of 0.5–0.8. The TMPA estimates reported reasonable levels of rainfall detection especially when light rainfall rates are excluded. On a storm scale, the TMPA products are characterized by varying degrees of bias which was mostly within ± 25% and ± 50% for the research and RT products, respectively. Analysis of the error distribution indicated that, on average, the TMPA products tend to overestimate small rain rates and underestimate large rain rates. Compared to the real-time estimates, the research product shows significant improvement in the overall and conditional bias, and in the correlation coefficients, with slight deterioration in the probability of detecting rainfall occurrences. A fair agreement in terms of reproducing the tail of the distribution of rain rates (i.e., probability of surface rainfall exceeding certain thresholds) was observed especially for the RT estimates. Despite the apparent differences with surface rainfall estimates, the results reported in this study highlight the TMPA potential as a valuable resource of high-resolution rainfall information over many areas in the world that lack capabilities for monitoring landfalling tropical storms.

Development of the Microwave calibrated Infrared Split‐window Technique (MIST) for rainfall estimation

An infrared rainfall estimation technique that includes information from a split window is developed. The split window refers to the difference in the brightness between the far infrared (IR) channels situated at around 10 µm and 12 µm, which has been used to estimate atmospheric water vapour and for rain area detection. The technique, called the Microwave calibrated Infrared Split‐window Technique (MIST), can be considered an extension of the Adjusted GOES Precipitation Index (AGPI). IR rain rates are first estimated from an IR‐rain rate relation derived from matching the monthly histograms of combined microwave rain estimates (3B40RT) produced by the Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) and the infrared data observed from a geostationary satellite. The novelty is the inclusion of the split‐window information to eliminate non‐rainy pixels as a second step. The technique has been applied to Geostationary Meteorological Satellite (GMS) and Geostationary Operational Environmental Satellite (GOES) data and tested for a dry and a wet period. The results show that the MIST has comparable biases and better rain event detection skill than the TMPA, although the TMPA is constrained by the gauge analysis by design while the MIST has no direct gauge input.

New paradigm for statistical validation of satellite precipitation estimates: Application to a large sample of the TMPA 0.25° 3‐hourly estimates over Oklahoma

It is well acknowledged that space‐based and ground‐based radar estimates of rainfall are both affected by several sources of uncertainties; nevertheless, the latter are generally used as ground reference for the validation of the former. In this way, it is difficult to assess what portion of the observed discrepancies is solely due to the targeted satellite errors and what is the impact of the radar‐rainfall errors. The authors propose a novel approach for the validation of satellite‐based rainfall estimates, in which an empirically based radar‐rainfall error model is used to account for deterministic and random uncertainties in radar‐rainfall estimates. Using an easy‐to‐interpret metric, the approach can be used to answer probabilistic questions such as “Given radar and satellite rainfall estimates, what is the probability that the true rainfall is larger (smaller) than the satellite estimate”? The results are based on a large sample (6 years) of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 0.25‐degree 3‐hourly satellite‐rainfall estimates and hourly radar‐rainfall product (aggregated at the satellite space‐time scale) from the Oklahoma City WSR‐88D radar (KTLX). This study shows how the TMPA satellite product tends to overestimate the high rainfall values and underestimate the low values and how it tends to perform better during the hot season (June to September). Moreover, the authors demonstrate how the presence of errors in the ground‐based radar could significantly affect the results of the satellite product evaluation.

Assessing the Relative Performance of Microwave-Based Satellite Rain-Rate Retrievals Using TRMM Ground Validation Data

Abstract Spaceborne microwave sensors provide critical rain information used in several global multisatellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs 4 yr (2003–06) of satellite data to assess the relative performance and skill of the Special Sensor Microwave Imager [SSM/I (F13, F14, and F15], Advanced Microwave Sounding Unit [AMSU-B (N15, N16, and N17)], Advanced Microwave Scanning Radiometer for Earth Observing System [AMSR-E (Aqua)], and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from the TRMM Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ), and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain-rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB were further stratified into ocean, land, and coast categories using a 0.25° terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating–observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm h−1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data were analyzed on monthly scales. These results signal to developers of global rainfall products, such as the TRMM Multisatellite Precipitation Analysis (TMPA) and the Climate Data Center’s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates to provide the highest-quality estimates in their products.

Validation of satellite rainfall products over Greece

Six widely available satellite precipitation products were extensively validated and intercompared on monthly-to-seasonal timescales and various spatial scales, for the period 1998–2006, using a dense station network over Greece. Satellite products were divided into three groups according to their spatial resolution. The first group had high spatial (0.5°) resolution and consists only of Tropical Rainfall Measuring Mission (TRMM) products: the TRMM Microwave Imager (TMI) precipitation product (3A12) and the TRMM multisatellite precipitation analysis products (3B42 and 3B43). The second group comprised products with medium spatial (1°) resolution. These products included the TRMM 3B42 and 3B43 estimates (remapped to 1° resolution) and the Global Precipitation Climatology Project one-degree daily (GPCP-1DD) analysis. The third group consisted of low spatial (2.5°) resolution products and included the 3B43 product (remapped to 2.5° resolution), the GPCP Satellite and Gauge (GPCP-SG) product, and the National Oceanographic and Atmospheric Administration Climate Prediction Center (NOAA-CPC) Merged Analysis (CMAP). Rain gauge data were first gridded and then compared with monthly and seasonal precipitation totals as well as with long-term averages of the six satellite products at different spatial resolutions (2.5°, 1°, and 0.5°). The results demonstrated the excellent performance of the 3B43 product over Greece in all three spatial scales. 3B42 from the first and second group and CMAP from the third exhibited a reasonable skill.

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

Abstract This paper describes a comprehensive assessment of a new high-resolution, gauge–satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge- and satellite-based analyses of daily precipitation over land in regional domains.

An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data

Abstract The last several years have seen the development of a number of new satellite-derived, globally complete, high-resolution precipitation products with a spatial resolution of at least 0.25° and a temporal resolution of at least 3-hourly. These products generally merge geostationary infrared data and polar-orbiting passive microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. The Program to Evaluate High Resolution Precipitation Products (PEHRPP) was established to evaluate and intercompare these datasets at a variety of spatial and temporal resolutions with the intent of guiding dataset developers and informing the user community regarding the error characteristics of the products. As part of this project, the authors have performed a subdaily intercomparison of five high-resolution datasets [Climate Prediction Center morphing (CMORPH) technique; Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA); Naval Research Laboratory (NRL) blended technique; National Environmental Satellite, Data, and Information Service Hydro-Estimator; and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] with existing subdaily gauge data over the United States and the Pacific Ocean. Results show that these data are effective at representing high-resolution precipitation, with correlations against 3-hourly gauge data as high as 0.7 for CMORPH, which had the highest correlations with the validation data. Biases are relatively high for most of the datasets over land (apart from the TMPA, which is gauge adjusted) and ocean, with a general tendency to overestimate warm season rainfall over the United States and to underestimate rainfall over the tropical Pacific Ocean. Additionally, all the products studied faithfully resolve the diurnal cycle of precipitation when compared with the validation data.

Daily Indian Precipitation Analysis Formed from a Merge of Rain-Gauge Data with the TRMM TMPA Satellite-Derived Rainfall Estimates

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) have enhanced the accuracy of rainfall estimation from satellites over ocean and land. An algorithm to merge TRMM Multi-satellite Precipitation Analysis (TMPA) satellite estimates with the India Meteorological Department (IMD) rain-gauge values is tested for the Indian monsoon region. A daily merged gauge and satellite data product (NMSG) at 1° latitude-longitude resolution for the Indian monsoon region is prepared to depict the large-scale aspects of monsoon rainfall. The satellite product used as a first guess is the TRMM TMPA for daily estimates. Incorporation of IMD gauge data corrects the mean biases of the TMPA values. TMPA alone is able to depict the space-time distribution of monsoon rainfall patterns. The merging of gauge data enhances the value of the satellite information; therefore, the NMSG is more representative than TMPA. Daily, monthly, and seasonal fields are prepared and compared with the land-only gridded data of the India Meteorological Department National Climate Centre (IMDNCC) at the same resolution. This inter-comparison with another independent dataset confirms the utility of the NMSG, produced by this objective analysis algorithm. The comparison of the merged data with the TMPA data reveals the regions where the satellite estimates have mean biases. Objective statistical scores also confirm the goodness of NMSG. The NMSG data are meant for use in verification of large-scale rainfall features from numerical models for the monsoon region.

Integrating point-of-care and automated methods for cardiac testing

Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015.The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond.

Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and Its Utility in Hydrologic Prediction in the La Plata Basin

Abstract Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage provide a potential alternative source of forcing data for hydrological models in regions where conventional in situ precipitation measurements are not readily available. The La Plata basin in South America provides a good example of a case where the use of satellite-derived precipitation could be beneficial. This study evaluates basinwide precipitation estimates from 9 yr (1998–2006) of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA; 3B42 V.6) through comparison with available gauged data and the Variable Infiltration Capacity (VIC) semidistributed hydrology model applied to the La Plata basin. In general, the TMPA estimates agreed well with the gridded gauge data at monthly time scales, most likely because of the monthly adjustment to gauges performed in TMPA. The agreement between TMPA and gauge precipitation estimates was reduced at daily time scales, particularly for high rain rates. The TMPA-driven hydrologic model simulations were able to capture the daily flooding events and to represent low flows, although peak flows tended to be biased upward. There was a good agreement between TMPA-driven simulated flows in terms of their reproduction of seasonal and interannual streamflow variability. This analysis shows that TMPA has potential for hydrologic forecasting in data-sparse regions.

Diurnal Variation of Summer Rainfall over the Tibetan Plateau and Its Neighboring Regions Revealed by TRMM Multi‐Satellite Precipitation Analysis

AbstractThis paper investigates the diurnal variation of summertime precipitation over the Tibetan Plateau and its neighboring areas using the TMPA (TRMM Multi‐satellite Precipitation Analysis) data during 2002~2006. The TMPA precipitation product is compared with rain gauge observations from 643 meteorological sites in China to confirm its applicability and fidelity. Both composite and harmonic analyses are applied to quantify the diurnal cycles of precipitation intensity and frequency. The harmonic amplitude indicates pronounced daily variability over the Tibetan Plateau and its nearby regions, with the strongest diurnal signal over the central Plateau and Indian Peninsula southwest of the Plateau. The harmonic phase displays that the timing of the maximum precipitation amount and frequency has considerably geographical dependence. Overall, a late‐afternoon‐evening maximum and a morning minimum are dominant in the central Plateau, whereas a late‐night maximum is prevalent around the Plateau and in the Sichuan Basin, and a morning and afternoon maximum appear in the upper and mid‐lower reaches of the Yangtze River, respectively. There is a coherent diurnal variation pattern east of the Plateau, characterized by systematically delayed precipitation maximum away from the Plateau. The significant nocturnal rainfall in the Sichuan Basin is likely associated with eastward‐propagating convective systems originated over the Tibetan Plateau.

Thailand Daily Rainfall and Comparison with TRMM Products

Abstract Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

A comparison study of summer-time synoptic-scale waves in South China and the Yangtze River basin using the TRMM Multi-Satellite Precipitation Analysis daily product

Summer-time synoptic-scale waves in South China and the Yangtze River basin are quantified and compared by means of analyzing the 6-year (1998–2004) TRMM Multi-Satellite Precipitation Analysis (TMPA) daily product. An innovative 3-dimensional spectrum analysis method is applied. The results indicate that synoptic-scale waves appearing in South China prominently propagate westward within a zonal wavenumber range of 9–21 and a frequency range of −0.12–−0.22 cycles day−1, while those in the Yangtze River basin primarily move eastward with the same characteristic wavenumbers of 9–21, but within a frequency range of 0.2–0.29 cycles day−1. Zonal and meridional distributions, and seasonal variations of these waves are further explored and compared. It shows that summer-time synoptic-scale waves in the South China result from the northward migration and oscillation of the Intertropical Convergence Zone (ITCZ) in the western Pacific, whereas the ones in the Yangtze River basin are generally related to the synoptic troughs within the westerly flow, originating from the Qinghai-Tibet Plateau.

Satellite-based Flood Modeling Using TRMM-based Rainfall Products.

Increasingly available and a virtually uninterrupted supply of satellite-estimatedrainfall data is gradually becoming a cost-effective source of input for flood predictionunder a variety of circumstances. However, most real-time and quasi-global satelliterainfall products are currently available at spatial scales ranging from 0.25o to 0.50o andhence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scaleflood events. This study assesses the question: what are the hydrologic implications ofuncertainty of satellite rainfall data at the coarse scale? We investigated this question onthe 970 km² Upper Cumberland river basin of Kentucky. The satellite rainfall productassessed was NASA's Tropical Rainfall Measuring Mission (TRMM) Multi-satellitePrecipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real timewith a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data canimprove application in flood prediction to some extent with the trade-off of more falsealarms in peak flow. However, a more rational and regime-based adjustment procedureneeds to be identified before the use of satellite data can be institutionalized among floodmodelers.

The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales

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.

A comparison of TRMM to other basin-scale estimates of rainfall during the 1999 Hurricane Floyd flood

The volumetric rainfall attributed to Hurricane Floyd in 1999 was computed for the bulk of the Tar, Neuse, and Cape Fear River Basins in eastern North Carolina, USA from the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) research product, and compared with volumes computed using kriged gauge data and one centrally located radar. TMPA showed similar features in the band of heaviest rainfall with kriged and radar data, but was higher in the basin-scale integrations. Furthermore, Floyd’s direct runoff volumes were computed and divided by the volumetric rainfall estimates to give runoff coefficients for the three basins. The TMPA, having the larger storm totals, would suggest greater infiltration during Floyd than the gauge and radar estimates would. Finally, we discuss a concept for adjusting the United States Department of Agriculture Natural Resources Conservation Service rainfall-runoff model when predicting discharge values from real-time TMPA in ungauged river basins.

Validasi Nilai Erosivitas Hujan Dari Data Penginderaan Jauh TRMM 3B42 Di Bali Selatan

Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energydetermines the erosivity and is in turn greatly dependent on rainfall intensity. Research hasbeen conducted to validate monthly rainfall erosivity derived from the Tropical Rainfall MeasuringMission (TRMM) Multisatellite Precipitation Analysis(TMPA)3B43 version 7 usingraingauge data analysis from 2003 to 2012. Rain gauge located in the south Bali regions wereemployedto monitor erosivity value from two different methods that are base on Bols (1978)andAbdurachman(1989). Therelationship of erosivity and their other factor from TRMM3B43andrain gauge data statistical analysis measures consisted of the linear correlation coefficient,themean bias error (MBE), and the root mean square error (RMSE). Data validation wasconductedwith point-by-point analysis. The results of these analyses indicate that satellitedatahave lower values than the gauge estimation values. The point-by-point analysis indicatedsatellite data values of high to very high correlation, while values of MBE and RMSEtendedto indicate underestimations with high square errors. Moreover,monthly rainfall erosivityderived from TRMM give high correlation from both methods, with has high bias androot-mean-squareerror. In general, the data from TRMM3B43 version 7 are potentially usabletoreplace rain gauge data based on erosivity estimation, but after inconsistencies and errorsaretaken into account.