Tropical Rainfall Measuring Mission Rainfall Research Articles

Validation of TRMM and FEWS Satellite Rainfall Estimates with Rain Gauge Measurement over Ashanti Region, Ghana

Satellite rainfall estimates have predominantly been used for climate impact studies due to poor rain gauge network in sub-Saharan Africa. However, there are limited microscale studies within the sub-region that have assessed the performance of these satellite products, which is the focus of the present study. This paper therefore considers validation of Tropical Rainfall Measuring Mission (TRMM) and Famine Early Warning System (FEWS) satellite estimates with rain gauge measurements over Ashanti region of Ghana. First, a consistency assessment of the two gauge data products, the Automatic Rain Gauge (ARG) and Ghana Meteorological Agency (GMet) Standard Rain Gauge (SRG) measurements, was performed. This showed a very good agreement with correlation coefficient of 0.99. Secondly, satellite rainfall products from TRMM and FEWS were validated with the two gauge measurements. Validation results showed good agreement with correlation coefficients of 0.6 and 0.7 for TRMM and FEWS with SRG, and 0.87 and 0.86 for TRMM and FEWS with ARG respectively. Probability Of Detection (POD) and Volumetric Hit Index (VHI) were found to be greater than 0.9. Volumetric Critical Success Index (VCSI) was 0.9 and 0.8 for TRMM and FEWS respectively with low False Alarm Ratio (FAR) and insignificant Volumetric Miss Index (VMI). In general, relatively low biases and RMSE values were observed. The biases were less than 1.3 and 0.8 for TRMM and FEWS-RFE respectively. These indicate high rainfall detection capabilities of both satellite products. In addition, both TRMM and FEWS were able to capture the onset, peak and cessation of the rainy season, as well as the dry spells. Although TRMM and FEWS sometimes under/overestimated rainfall, they have the potential to be used for agricultural and other hydro-climatic impact studies over the region. The Dynamic-Aerosol-Cloud-Chemistry Interactions in West Africa (DACCIWA) project will provide an improved spatial gauge network database over the study area to enhance future validation and other climate impact studies.

Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates

The accurate assessment of spatiotemporal rainfall variability is a crucial and challenging task in many hydrological applications, mainly due to the lack of a sufficient number of rain gauges. The purpose of the present study is to investigate the spatiotemporal variations of annual and monthly rainfall over Fujian province in China by combining the Bayesian maximum entropy (BME) method and satellite rainfall estimates. Specifically, based on annual and monthly rainfall data at 20 meteorological stations from 2000 to 2012, (1) the BME method with Tropical Rainfall Measuring Mission (TRMM) estimates considered as soft data, (2) ordinary kriging (OK) and (3) cokriging (CK) were employed to model the spatiotemporal variations of rainfall in Fujian province. Subsequently, the performance of these methods was evaluated using cross-validation statistics. The results demonstrated that BME with TRMM as soft data (BME-TRMM) performed better than the other two methods, generating rainfall maps that represented the local rainfall disparities in a more realistic manner. Of the three interpolation (mapping) methods, the mean absolute error (MAE) and root mean square error (RMSE) values of the BME-TRMM method were the smallest. In conclusion, the BME-TRMM method improved spatiotemporal rainfall modeling and mapping by integrating hard data and soft information. Lastly, the study identified new opportunities concerning the application of TRMM rainfall estimates.

Estimation of Water Resources Availability and Mini-Hydro Productivity in High-Altitude Scarcely-Gauged Watershed

Streamflow prediction in high-altitude scarcely-gauged catchments is essential for efficient water resources management and hydropower generation. Aim of this study is to estimate water resources availability (WRA) by the application of a standard rainfall-runoff model and to study its impact on mini-hydropower production with application to Mangla basin. The Mangla basin is a scarcely gauged catchment situated in the snow- and glacier-fed Himalayan and Pir Panjal Range. Daily streamflow forecasting has been performed by the application of a GIS (Geographic Information System) based hydrological modeling system (HEC-HMS) with observed and TRMM (Tropical Rainfall Measuring Mission) rainfall data to cover the ungauged part of the catchment. The obtained results suggest that HEC-HMS can efficiently reproduce daily streamflows in snow-fed glacierized catchments with Nash-Sutcliffe (NS) coefficients in the range 0.71–0.80. The WRA was estimated at the Mangla dam to analyze its impact on mini-hydropower generation at Upper Jhelum Canal (UJC). The gross mini-hydropower potential energy on the UJC was found to be 196 and 360 GWH for average and design discharge, respectively, demonstrated that mini-hydro power generation could be a significant addition for the energy sector of Pakistan.

TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization

Abstract. The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998–2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5–10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10–35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

Assessment of Effective Seasonal Downscaling of TRMM Precipitation Data in Peninsular Malaysia

Precise spatio-temporal measurements of rainfall during seasonal monsoons are critical for accurate hydrologic analyses in the tropical regions of Southeast Asia. The use of satellite precipitation data is technologically sound but requires downscaling to minimize inherent uncertainties. The uncertainties at a local climate regime that are essential to be resolved are rarely reported; consequently, such work needs attention. To address this problem, we validated the Tropical Rainfall Measuring Mission (TRMM) precipitation data using high-resolution areal precipitation (0.125 deg.) at a seasonal scale. This study examined the performance of the monthly rainfall data product (TRMM 3B43) at the seasonal monsoon scale in the local climate region of Peninsular Malaysia. The high-resolution areal precipitation data (0.125 deg.) were derived from a dense rain gauge network ever collected in Peninsular Malaysia (n = 984). Three relevant performance elements were evaluated: (i) the ability to depict temporal rainfall variation (ii) the quantitative error between TRMM and ground rainfall; and (iii) the ability to estimate the actual rainfall amount. We found that the ability of monthly TRMM data to depict rainfall variation and its tendency to propagate large errors varied seasonally. The correlation between TRMM and ground rainfall was good during the wettest period in all local climate regions. The error was related to the northeast monsoon and inter-monsoon 2 (September–October). Meanwhile, the TRMM ratio varied regionally, rather than seasonally. Determining the local-scale uncertainties will facilitate future downscale activities using TRMM satellite data in this region.

Evaluating the suitability of TRMM satellite rainfall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China

The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission (TRMM) data with rain gauge data and further to use this TRMM data to drive a Distributed Time-Variant Gain Model (DTVGM) to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year (2001–2010) daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R 2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years (2001–2007) of data on daily hydrological processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamflow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.

Integrating TRMM and MODIS satellite with socio-economic vulnerability for monitoring drought risk over a tropical region of India

Drought is a recurring feature of the climate, responsible for social and economic losses in India. In the present work, attempts were made to estimate the drought hazard and risk using spatial and temporal datasets of Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS) in integration with socio-economic vulnerability. The TRMM rainfall was taken into account for trend analysis and Standardized Precipitation Index (SPI) estimation, with aim to investigate the changes in rainfall and deducing its pattern over the area. The SPI and average rainfall data derived from TRMM were interpolated to obtain the spatial and temporal pattern over the entire South Bihar of India, while the MODIS datasets were used to derive the Normalized Difference Vegetation Index (NDVI) deviation in the area. The Geographical Information System (GIS) is taken into account to integrate the drought vulnerability and hazard, in order to estimate the drought risk over entire South Bihar. The results indicated that approximately 36.90% area is facing high to very high drought risk over north-eastern and western part of South Bihar and need conservation measurements to combat this disaster.

Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

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

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.

Dry/Wet Conditions Monitoring Based on TRMM Rainfall Data and Its Reliability Validation over Poyang Lake Basin, China

Local dry/wet conditions are of great concern in regional water resource and floods/droughts disaster risk management. Satellite-based precipitation products have greatly improved their accuracy and applicability and are expected to offer an alternative to ground rain gauges data. This paper investigated the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall data for monitoring the temporal and spatial variation of dry/wet conditions in Poyang Lake basin during 1998–2010, and validated its reliability with rain gauges data from 14 national meteorological stations in the basin. The results show that: (1) the daily TRMM rainfall data does not describe the occurrence and contribution rates of precipitation accurately, but monthly TRMM data have a good linear relationship with rain gauges rainfall data; (2) both the Z index and Standardized Precipitation Index (SPI) based on monthly TRMM rainfall data oscillate around zero and show a consistent interannual variability as compared with rain gauges data; (3) the spatial pattern of moisture status, either in dry months or wet months, based on both the Z index and SPI using TRMM data, agree with the observed rainfall. In conclusion, the monthly TRMM rainfall data can be used for monitoring the variation and spatial distribution of dry/wet conditions in Poyang Lake basin.

Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate

The Tropical Rainfall Measuring Mission (TRMM) is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. In this study, the daily rainfall from TRMM has been utilized to simulate the soil moisture content up to 30 cm vertical soil profile of at an interval depth of 15 cm by using the HYDRUS 1D numerical model for the three plots. The simulated soil moisture content using ground-based rainfall and TRMM-derived rainfall measurements indicate an agreeable goodness of fit between the both. The Nash–Sutcliffe efficiency using ground-based and TRMM-derived rainfall was found in the range of 0.90–0.68 and 0.70–0.40, respectively. The input data sensitivity analysis of precipitation combined with different irrigation treatment indicates a high dependency of soil moisture content with rainfall input. The overall analysis reveals that TRMM rainfall is promising for soil moisture prediction in absence of ground-based measurements of soil moisture.

Contribution of TRMM rainfall data to the study of natural systems and risk assessment. Cases of application in SW Angola

Researchers studying exogenous processes in developing countries have to deal with the problem of scarcity of rainfall data. With satellite measurements, such as those provided by the Tropical Rainfall Measuring Mission (TRMM), it may be possible to overcome this limitation. In this work we link the TRMM rainfall measurements with two examples of exogenous processes from southwest Angola: landslide processes, focused on the events at the Leba road in early 2011, and flow conditions, based on the water level determined in gauging stations from rivers Cavaco and Catumbela. It is demonstrated that major mass flow movements occur when specific TRMM rainfall thresholds are reached. Regarding the flow conditions, the water level in two gauging stations is strongly conditioned by other factors beside the atmospheric precipitation in their watersheds (retention and release of water from reservoirs, channel obstruction, etc.), but short term oscillations are closely linked with the rainfall in the proximity. TRMM data is found to be very useful for the analysis of specific extreme events or the patterns of behavior of natural systems and, consequently, constitute a valuable tool in natural risks assessment.

Changes in terrestrial water storage versus rainfall and discharges in the Amazon basin

AbstractThis study examines how the interannual variability of rainfall impacts the land water storage in the Amazon basin during the 2003–2010 time span at monthly time‐scale using respectively, Tropical Rainfall Measuring Mission (TRMM) and Gravity Recovery And Climate Experiment (GRACE) satellite observations. Monthly estimates of GRACE‐based terrestrial water storage (TWS) are compared to (1) TRMM rainfall, (2) in situ discharges at the outlet of the major sub‐basins of the Amazon over 2003–2010 to characterize the redistribution of precipitation on land water. The time‐variations of land water storage derived from GRACE are consistent with those of rainfall and discharges at basin and sub‐basin scales even at interannual time‐scales (correlation generally greater than 0.7). The study of the relationship between these two quantities reveals large differences in terms of rainfall amount, water storage, time delays, resulting of the water transport among the sub‐basins of the Amazon. The analysis of GRACE data has enabled identification of the signature of the recent extreme climatic events (droughts of 2005 and 2010, flood of 2009) on the land water storage, in terms of spatial patterns and intensity. These results are in good agreement with what was observed on independent datasets (water levels and discharges, vegetation activity, forest fires, and drought index), highlighting the interest of gravimetry from space missions for the characterization of the interannual variability of the TWS. GRACE data offer the unique opportunity to monitor the hydrological cycle in ungauged basins where reliable observations of rainfall and discharges are missing.

Global Distribution of Extreme Precipitation and High-Impact Landslides in 2010 Relative to Previous Years

Abstract It is well known that extreme or prolonged rainfall is the dominant trigger of landslides worldwide. While research has evaluated the spatiotemporal distribution of extreme rainfall and landslides at local or regional scales using in situ data, few studies have mapped rainfall-triggered landslide distribution globally because of the dearth of landslide data and consistent precipitation information. This study uses a newly developed global landslide catalog (GLC) and a 13-yr satellite-based precipitation record from Tropical Rainfall Measuring Mission (TRMM) data. For the first time, these two unique products provide the foundation to quantitatively evaluate the co-occurrence of precipitation and rainfall-triggered landslides globally. Evaluation of the GLC indicates that 2010 had a large number of high-impact landslide events relative to previous years. This study considers how variations in extreme and prolonged satellite-based rainfall are related to the distribution of landslides over the same time scales for three active landslide areas: Central America, the Himalayan arc, and central eastern China. Several test statistics confirm that TRMM rainfall generally scales with the observed increase in landslide reports and fatal events for 2010 and previous years over each region. These findings suggest that the co-occurrence of satellite precipitation and landslide reports may serve as a valuable indicator for characterizing the spatiotemporal distribution of landslide-prone areas in order to establish a global rainfall-triggered landslide climatology. This study characterizes the variability of satellite precipitation data and reported landslide activity at the global scale in order to improve landslide cataloging and attempt to quantify landslide triggering at daily, monthly, and yearly time scales.

SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Abstract. Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and R2 of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and R2 of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and R2 indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales.

TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis

Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. This might be one of the only options for analysing rainfall patterns at different temporal and spatial scales in data-scarce environments, particularly in developing countries. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall estimation over the tropics. Rainfall estimates from the TRMM satellite exhibit inaccuracies over topographically complex regions, thus warranting suitable corrections. Multi-resolution analysis (MRA) was applied to improve TRMM 3B42 daily rainfall estimation at 19 meteorological stations located over the Andean Plateau. The detailed signal from each meteorological station was added to the trend signal of each TRMM data cell. Comparing raw and corrected TRMM with gauged rainfall revealed that wavelet-based correction of TRMM 3B42 on average improved several metrics: entropy difference (15.45−1.32), determination coefficient (0.07−0.92), bias (0.68−1.01) and relative mean absolute error (RMAE, 0.86−0.59). The entropy difference of corrected TRMM and gauged rainfall was less than 5%, even when TRMM correction was performed with noise from a station located up to 565 km away from the TRMM cell. This entropy difference corresponded to an average bias of less than 10% in the rainfall estimation.

Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin

Summary Spatial rainfall is a key input to distributed hydrological models, and its precisions heavily affect the accuracy of stream flow predictions from a hydrological model. Traditional interpolation techniques which obtain the spatial rainfall distribution from rain gauge data have some limitations caused by data scarcity and bad quality, especially in developing countries or remote locations. Satellite-based precipitation products are expected to offer an alternative to ground-based rainfall estimates in the present and the foreseeable future. For this purpose, the quality and usefulness of satellite-based precipitation products need to be evaluated. The present study compares the difference of Tropical Rainfall Measuring Mission (TRMM) rainfall with rain gauges data at different time scales and evaluates the usefulness of the TRMM rainfall for hydrological processes simulation and water balance analysis at the Xinjiang catchment, located in the lower reaches of the Yangtze River in China. The results show at daily time step TRMM rainfall data are better at determining rain occurrence and mean values than at determining the rainfall extremes, and larger difference exists for the maximal daily and maximal 5-day rainfalls. At monthly time scale, good linear relationships between TRMM rainfall and rain gauges rainfall data are received with the determination coefficients (R2) varying between 0.81 and 0.89 for the individual stations and 0.88 for areal average rainfall data, respectively. But the slope of regression line ranges between 0.74 for Yingtan and 0.94 for Yushan, indicating that the TRMM satellite is inclined to underestimate the monthly rainfall in this area. The simulation of daily hydrological processes shows that the Water Flow Model for Lake Catchment (WATLAC) model using conventional rain gauge data produces an overall good fit, but the simulation results using TRMM rainfall data are discontented. The evaluation results imply that the TRMM rainfall data are unsuited for daily stream flow simulation in this study area with desired precisions. However, good performance can be received using TRMM rainfall data for monthly stream flow simulations. The comparison of the simulated annual water balance components shows that the different rainfall data sources can change the volume value and proportion of water balance components to some extent, but it generally meets the need of practical use.

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km2) to 6.1% (post-calibration at 3125 km2) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km2) was decreased to 15.4% (post-calibration at 3125 km2). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.

Rainfall statistics evaluation of ECMWF model and TRMM data over Bangladesh for flood related studies

AbstractQuantitative precipitation estimates from the European Centre for Medium‐Range Weather Forecasts (ECMWF) modelling system and the Tropical Rainfall Measuring Mission (TRMM) may be used for areas where rainfall data are not available or where the number of rainfall stations is inadequate. In this study, ECMWF 40 years re‐analysis (ERA40) and TRMM satellite estimated daily rainfall data have been analysed at three station locations, Dinajpur, Rangpur, and Sylhet, in the Bangladesh territories of Ganges, Brahmaputra and Meghna basins, respectively. Statistical verifications (visual verification, yes/no dichotomous verification and continuous variables verification methods) have been applied to these data. Bangladesh Meteorological Department (BMD) rainfall data are used as the reference data.The continuous variables verification methods show the better accuracy of the TRMM data. The probability of occurrence of TRMM rainfall is close to the BMD‐observed data. It implies that the TRMM data can be used for modelling of design flood in areas where observed rainfall data are not available. However, in the case of the TRMM rainfall data, the chances of missing a flood event are higher compared to the ECMWF data. Yes/no dichotomous verification methods show that ECMWF rainfall data are safe to use for flood‐forecasting‐related planning purposes because rain events are estimated better in ECMWF. The probability of occurrence of ECMWF rainfall shows that these data can also be used to estimate the design flood flow. Both ECMWF and TRMM rainfall data may be used where locally observed rainfall data are not available. Copyright © 2011 Royal Meteorological Society

Validation of TRMM Data in the Black Volta Basin of Ghana

AbstractThis study was conducted in the Black Volta basin, Ghana, to determine how Tropical Rainfall Measuring Mission (TRMM) satellite-derived rainfall compares with ground-measured values and the possibility of using it to complement ground-measured rainfall. Bilinear interpolation was used to resample 0.25°×0.25° TRMM 3B42V6 monthly rainfall grids to determine site-specific rainfall of the basin based on rain gauge locations and analyzed statistically. The study showed that the correlation between the monthly datasets ranged from 0.73 to 0.88. A plot of the average monthly data of all the stations gave a correlation coefficient (R) of 0.94. The overall catchment rainfall was well represented by TRMM data. However, the annual station rainfalls were either underestimated or overestimated. The underestimations and overestimations were mostly below 20% and 10%, respectively, of the raingauge measurements. Although the TRMM rainfall data did not perfectly match with the ground measurements, it can still be ...