TRMM) Multisatellite Precipitation Analysis Research Articles

Evaluation of several rainfall products used for hydrological applications over West Africa using two high‐resolution gauge networks

AbstractThe evaluation of rainfall products over the West African region will be an important component of the Megha‐Tropiques (MT) Ground Validation (GV) plan. In this paper, two dense research gauge networks from Benin and Niger, integrated in the MT GV plan, are presented and are used to evaluate several currently available global or regional satellite‐based rainfall products. Eight products—the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Center Morphing method (CMORPH), Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) 3B42 real‐time and gauge‐adjusted version, Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) African Rainfall Estimate (RFE), Estimation des Precipitation par SATellite (EPSAT), and Global Precipitation Climatology Project One Degree Daily estimate (GPCP‐1DD)—are compared to the ground reference. The comparisons are carried out at daily, 1° resolution, over the rainy season (June–September), between the years 2003 and 2010. The work focuses on the ability of the various products to reproduce salient features of the rainfall regime that impact the hydrological response. The products are analysed on a multi‐criteria basis, focusing in particular on the way they distribute the rainfall within the season and by rain rate class. Standard statistical diagnoses such as the correlation coefficient, bias, root mean square error and Nash skill score are computed and the inter‐annual variability is documented. Two simplified hydrological models are used to illustrate how the nature and structure of the product error impact the model output in terms of runoff (calculated using the Soil Conservation Service method, SCS, in Niger) or outflow (calculated with the ‘modèle du Génie Rural à 4 paramètres Journalier’, GR4J model, in Benin). Copyright © 2013 Royal Meteorological Society

Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals

ABSTRACTThe European Centre for Medium‐range Weather Forecasts (ECMWF) reanalysis ERA‐40, ERA‐Interim, University of Washington (UW) data, APHRODITE's Water Resources (APHRODITE), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation estimates are compared with each other and with the corrected gauge observations over the Tibetan Plateau (TP) at both basin and plateau scales. The ERA‐40 generally can capture the broad spatial and temporal distributions in the gauge‐based precipitation estimates over the TP. However, the ERA‐40 shows little agreement with the gauge‐based precipitation in annual variations for the years before 1979. The anticipated improvements in the ERA‐Interim precipitation relative to ERA‐40 have not been realized in this study. It greatly overestimates the Corrected‐China Meteorological Administration (CMA) (by 74–290%) and other datasets, although the ERA‐Interim has a better correspondence than ERA‐40 with the Corrected‐CMA data at both annual and monthly scales among the selected basins. All the products can detect the large‐scale precipitation regime, including the monsoon‐dominated precipitation in summer and the westerly‐wind‐induced precipitation in winter. The Corrected‐CMA and APHRODITE estimates generally show decreasing trends in summer and increasing trends in spring and winter precipitation during 1961–2007 at both basin and plateau scales. However, the Corrected‐CMA shows larger values in trends and more cases with significance than the APHRODITE, suggesting the effects of the undercatch corrections on the precipitation trends. The use of precipitation derived from current reanalysis projects is less preferable for hydrology analysis than the TP observational data at basin scales. However, using gauge‐based precipitation datasets as hydrologic model forcings should be careful in the river basins where gauge station network is spare, such as in the Yarlung zangbo river basin. Satellite products still hold a great potential for providing high‐resolution precipitation information in remote regions such as the western TP, although more evaluations are needed on the feasibility of satellite precipitation products on the TP where the topography is complex and rainfall rate is highly variable.

The Role of Mesoscale Convective Complexes in Southern Africa Summer Rainfall

AbstractA combination of numerous factors, including geographic position, regional orography, and local sea surface temperatures, means that subtropical southern Africa experiences considerable spatial and temporal variability in rainfall and is prone to both frequent flooding and drought events. One system that may contribute to rainfall variability in the region is the mesoscale convective complex (MCC). In this study, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data is used to document the precipitation produced by MCCs over southern Africa for the 1998–2006 period.Most of the rainfall associated with MCCs is found to occur over central Mozambique, extending southward to eastern South Africa. High precipitation totals associated with these systems also occur over the neighboring southwest Indian Ocean, particularly off the northeast coast of South Africa. MCCs are found to contribute up to 20% of the total summer rainfall (November–March) in parts of the eastern region of southern Africa. If the month of March is excluded from the analysis, then the contribution increases up to 24%. In general, the MCC summer rainfall contribution for most of the eastern region is approximately between 8% and 16%. Over the western interior and Botswana and Namibia, the MCC contribution is much less (<6%). It is also evident that there is considerable interannual variability associated with the contribution that these systems make to the total warm season rainfall.

Precipitation Contribution of Tropical Cyclones in the Southeastern United States from 1998 to 2009 Using TRMM Satellite Data

Abstract The objective of this paper is to characterize the precipitation amounts originating from tropical cyclones (TCs) in the southeastern United States during the tropical storm season from June to November. Using 12 years of precipitation data from the Tropical Rainfall Measurement Mission (TRMM), the authors estimate the TC contribution on the seasonal, interannual, and monthly precipitation budget using TC information derived from the International Best Track Archive for Climate Stewardship (IBTrACS). Results derived from the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 showed that TCs accounted for about 7% of the seasonal precipitation total from 1998 to 2009. Rainfall attributable to TCs was found to contribute as much as 8%–12% for inland areas located between 150 and 300 km from the coast and up to 15%–20% for coastal areas from Louisiana to the Florida Panhandle, southern Florida, and coastal Carolinas. The interannual contribution varied from 1.3% to 13.8% for the period 1998–2009 and depended on the TC seasonal activity, TC intensity, and TC paths as they traveled inland. For TCs making landfall, the rainfall contribution could be locally above 40% and, on a monthly basis, TCs contributed as much as 20% of September rainfall. The probability density functions of rainfall attributable to tropical cyclones showed that the percentage of rainfall associated with TC over land increased with increasing rain intensity and represent about 20% of heavy rainfall (>20 mm h−1), while TCs account for less than 5% of all seasonal precipitation events.

Evaluation of TRMM 3B42 Precipitation Product using Rain Gauge Data in Meichuan Watershed, Poyang Lake Basin, China

This study evaluated Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product i.e. TRMM 3B42 data, using data from 52 rain gauge stations around the Meichuan watershed, which is a representative watershed of Poyang Lake basin in China. Both the latest Version 7 (V7) and previous Version 6 (V6) of TRMM 3B42 data were compared and evaluated for a 9-year period covering 2001–2005 and 2007–2010. The evaluations were conducted at different spatial (grid and watershed) and temporal (daily, monthly and annual) scales. For evaluation at grid scale, the Thiessen polygon method was used to transform pointed-based rain gauge data to areal precipitation at the same grid scale (0.25°) as TRMM 3B42 data. The results showed that there was little difference in performances of V6 and V7 TRMM 3B42 products. Overall, both V6 and V7 products slightly overestimated precipitation with a bias of 0.04. At daily scale, both V6 and V7 data were considered to be unreliable with large relative RMSE (135%–199%) at the two spatial scales, and they were deficient in capturing large storms. These results suggest that local calibration with rain gauge data should be conducted before V6 and V7 TRMM 3B42 data are used at daily scale. At monthly and annual scales, V6 and V7 TRMM 3B42 data match the rain gauge data well (R2 = 0.91–0.99, relative RMSE = 4%–23%) at both grid and watershed scale and thus have good potential for hydrological applications.

Validation of TMPA and GPCP 1DD against the ground truth rain‐gauge data for Indian region

ABSTRACTThe satellite datasets, namely, the Tropical Rainfall Measuring Mission (TRMM) MultiSatellite Precipitation Analysis (TMPA) product, 3B42 version 6 and Global Precipitation Climatology Project (GPCP) one‐degree‐daily (1DD) from NASA Goddard Space Flight Center, have been validated against the ‘ground truth’ rain‐gauge data from the India Meteorological Department for all‐India during the period 1998–2007. The comparisons reveal that both the satellite datasets are good enough in representing the spatial rainfall distribution over India. The categorical statistics provides an insight in evaluating the satellite's ability and its accuracy in estimating the precipitation. The average bias along the heavy‐rainfall regions (e.g. Western coast, Arunachal Pradesh, Sub‐Himalayan West Bengal, Sikkim, and east Uttaranchal) for the TMPA (GPCP 1DD) is −8.03 (−8.29) mm d−1, whereas the mean is 7.47 (6.97) mm d−1, which signifies that the satellite datasets underestimate the Indian summer monsoon rainfall in sharp rainfall regions. Albeit, the TMPA excels over GPCP 1DD where the rainfall is mainly attributed to orography, yet both fail in capturing the extreme rainfall year, 2005. The satellite datasets do capture the intraseasonal oscillations as is evident from spectral analysis, whereas the empirical orthogonal function analysis of standardized daily and seasonal anomalies exemplifies that the satellite datasets also capture the dominant modes. Additionally, the active and the break spells calculated for the months of July and August for the satellite datasets are in agreement with the ones obtained for the rain‐gauge.

Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China

Using statistical methods and contingency table method, this paper evaluates the accuracy of 12 years (1998–2009) Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) daily-accumulated precipitation products within a year, the dry season, and rain season for each of the five subbasins and for each grid point (0.25 × 0.25°) in the Lancang River basin by comparing the results with data from the 35 rain gauges. The results indicate that TMPA daily precipitation estimates tend to show an underestimation comparing to the rain gauge daily precipitations under any scenarios, especially for the middle stream in the dry season. The accuracy of TMPA-averaged precipitation deteriorates with the increase of elevation at both basin and grid scale, with upstream and downstream having the worst and best accuracy, respectively. A fair capability was shown when using daily TMPA accumulations to detect rain events at drizzle rain and this capability improves with the increase of elevation. However, the capability deteriorates when it is used to detect moderate rain and heavy rain events. The accuracy of TMPA precipitation estimate products is better in the rain season than in the dry season at all scenarios. Time difference and elevation are the main factors that have impact on the accuracy of TMPA daily-accumulated precipitation products.

Evaluation of Global Flood Detection Using Satellite-Based Rainfall and a Hydrologic Model

Abstract A new version of a real-time global flood monitoring system (GFMS) driven by Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall has been developed and implemented using a physically based hydrologic model. The purpose of this paper is to evaluate the performance of this new version of the GFMS in terms of flood event detection against flood event archives to establish a baseline of performance and directions for improvement. This new GFMS is quantitatively evaluated in terms of flood event detection during the TRMM era (1998–2010) using a global retrospective simulation (3-hourly and ⅛° spatial resolution) with the TMPA 3B42V6 rainfall. Four methods were explored to define flood thresholds from the model results, including three percentile-based statistical methods and a Log Pearson type-III flood frequency curve method. The evaluation showed the GFMS detection performance improves [increasing probability of detection (POD)] with longer flood durations and larger affected areas. The impact of dams was detected in the validation statistics, with the presence of dams tending to result in more false alarms and greater false-alarm duration. The GFMS validation statistics for flood durations >3 days and for areas without dams vary across the four methods, but center around a POD of ~0.70 and a false-alarm rate (FAR) of ~0.65. The generally positive results indicate the value of this approach for monitoring and researching floods on a global scale, but also indicate limitations and directions for improvement of such approaches. These directions include improving the rainfall estimates, utilizing higher resolution in the runoff-routing model, taking into account the presence of dams, and improving the method for flood identification.

Precipitation Changes near Three Gorges Dam, China. Part I: A Spatiotemporal Validation Analysis

Abstract In October 2010, the water level upstream of the Three Gorges Dam (TGD) reached the designated 175-m level. The associated inundation and land use–land cover changes have important implications for water resource management, agriculture, ecosystems, and the hydroclimate. Ultimately, it is important to quantify whether the dam-related changes have altered precipitation patterns. Since rain gauges are limited in the region, satellite-based methods are viable. This study is the first to validate NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) data from 1998 to 2009 using 34 national meteorological rain gauges in the Three Gorges region. Areal average satellite estimates are first verified with areal average rain gauge data both annually and seasonally. Then based on empirical orthogonal functions, the study area is divided into two subregions, and similar validation procedures are performed for both subregions. TMPA data are found to have high correlations with rain gauge data for the whole study area, and correlations for the subregions are only slightly lower. The seasonal analysis yields the lowest correlations for winter. Compared with the gauge data, rainfall is slightly overestimated by about 3 mm month−1. At daily scale, satellite data show good agreement with gauge data for all rain intensity categories except light rain (<1 mm day−1). Spatially, the point-source gauge data are gridded using Thiessen polygons for comparison with satellite data, and the results suggest the satellite-based product may overestimate rainfall in mountainous areas near the reservoir, especially in spring and summer. Overall, the validation results yield strong statistical support for applying satellite rainfall data for hydroclimate studies in this region.

The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin

Drought is one of the most destructive disasters in the Lancang River Basin, which is an ungauged basin with strong heterogeneity on terrain and climate. Our validation suggested the version-6 monthly TRMM multi-satellite precipitation analysis (TMPA; 3B43 V.6) product during the period 1998 to 2009 is an alternative precipitation data source with good accuracy. By using the standard precipitation index (SPI), at the grid point (0.25°×0.25°) and sub-basin spatial scales, this work assessed the effectiveness of TMPA in drought monitoring during the period 1998 to 2009 at the 1-month scale and 3-months scale; validated the monitoring accuracy of TMPA for two severe droughts happened in 2006 and 2009, respectively. Some conclusions are drawn as follows. (1) At the grid point spatial scale, in comparison with the monitoring results between rain gauges (SPI1g) and TMPA grid (SPI1s), both agreed well at the 1-month scale for most of the grid points and those grid points with the lowest critical success index (CSI) are distributed in the middle stream of the Lancang River Basin. (2) The same as SPI1s, the consistency between SPI3s and SPI3g is good for most of the grid points at the 3-months scale, those grid points with the lowest were concentrated in the middle stream and downstream of the Lancang River Basin. (3) At the 1-month scale and 3-months scale, CSI ranged from 50% to 76% for most of the grid points, which demonstrated high accuracy of TMPA in drought monitoring. (4) At the 3-months scale, based on TMPA basin-wide precipitation estimates, though we tended to overestimate (underestimate) the peaks of dry or wet events, SPI3s detected successfully the occurrence of them over the five sub-basins at the most time and captured the occurrence and development of the two severe droughts happened in 2006 and 2009. This analysis shows that TMPA has the potential for drought monitoring in data-sparse regions.

Intercomparison of High-Resolution Precipitation Products over Northwest Europe

AbstractSatellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons.

Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

A feasibility of six-hourly rainfall forecast over central India using model output and remote sensing data

Rainfall forecast has prime importance in an agrarian country like India, wherein the agricultural production is solely dependent on monsoon rainfall. In this paper, an artificial neural network (ANN) technique is used to construct a non-linear mapping between output data from global forecast system (GFS) and rainfall from tropical rainfall measuring mission (TRMM) satellite measurements. The objective of the present study is to generate region-specific six-hourly quantitative rainfall forecast over central India using ANN and resilient propagation learning algorithm. Meteorological variables from the GFS model and precipitation product from TRMM multisatellite precipitation analysis (TMPA) are used as input data for training the network, which generate rainfall forecast for the next time step. The test was performed for central India during the summer monsoon period of 2010. In order to evaluate the potential of rainfall forecast skill over the studied region, the forecast precipitation has been intercompared with TMPA-3B42, and Kalpana-1 derived precipitation products and a statistical analysis was performed. The linear correlation between ANN forecast and TMPA-3B42 rainfall was 0.58, whereas it was 0.52 with Kalpana-1 derived precipitation estimates. The results show that the predicted precipitation by the present technique performs better than GFS model precipitation forecast, and the system indicates a potential for more accurate rainfall forecasting.

Investigating the similarity of satellite rainfall error metrics as a function of Köppen climate classification

This study addressed the question: How much similarity exists in uncertainty of space-borne precipitation products for similar Köppen climate zones in different and distant landmasses? Various metrics of satellite rainfall uncertainty were identified using a six year (2002–2007) archive of NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) data product called 3B42V6 for two large distant landmasses that share many similar Köppen climate zones: 1) United States and 2) Australia. The level of quantitative similarity in error metrics for the same Köppen climate zones was then investigated. It was found that the bias and root mean squared error exhibited very close levels of similarity for similar Köppen climate zones in the US and Australia. However, similar inferences could not be drawn for other (higher-ordered) error metrics such as Probability of Detection (POD). The contrasting nature of the ground validation (GV) data (i.e., NEXRAD-radar in US and point gauge in Australia) for characterizing uncertainty may be one of the reasons for this observed lack of similarity. Using a dense gauge network of 42 gauges over a standard 3B42V6 grid box (~0.25°) as a ground validation benchmark, the dependence of uncertainty as a function of gauge density was quantified. These relationships were then cast in the context of our Köppen climate similarity experiment to identify the minimum level of gauge density that would be needed to resolve more accurately the actual level of similarity of error metrics for distant landmasses.

Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali

Research has been conducted to compare daily, monthly and seasonal rain rates derived from Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) using rain gauge analysis from 1998 to 2002. Three rain gauges in the Bali islands were employed. Statistical analysis was used to analyse the relationship of the TMPA product with the rain gauge data. Resulting statistical measures consisted of the linear correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The results of these analyses indicate that satellite data have lower values than the gauge estimation values. The validation analysis showed a very good relationship with the gauge data on monthly timescales. However, a poor relationship was found between the gauge data and the daily data analysis from the TMPA. The 3B42 and 3B43 products showed the same levels of relationship during the wet season and dry season. The correlation in the dry season was better than during the wet season. Statistical error levels during the wet season were better than in the dry season. The 3B43 showed slight improvement in these values when compared with the 3B42 (both the random error measurement and the scatter of the estimates were reduced). In general, the data from TMPA are potentially usable to replace rain gauge data, especially to replace the monthly data, if inconsistencies and errors are taken into account.

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

AbstractThis study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

Validation of high-resolution TRMM-3B43 precipitation product using rain gauge measurements over Kyrgyzstan

This paper presents the validation of monthly precipitation using Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA)—3B43 product with conventional rain gauge observations for the period 1998–2007 over Kyrgyzstan. This study is carried out to quantify the accuracy of TMPA-3B43 product over the high latitude and complex orographic region. The present work is quite important because it is highly desirable to compare the TMPA precipitation product with the ground truth data at a regional scale, so that the satellite product can be fine-tuned at that scale. For the validation, four different types of spatial collocation have been used: station wise, climatic zone wise, topographically and seasonal. The analysis has been done at the same spatial and temporal scales in order to eliminate the sampling biases in the comparisons. The results show that TMPA-3B43 product has statistically significant correlation (r = 0.36–0.88) with rain gauge data over the most parts of the country. The minimum linear correlation is observed around the large continental water bodies (e.g., Issyk-Kul lake; r = 0.17–0.19). The overall result suggests that the precipitation estimated using TMPA-3B43 product performs reasonably well over the plain regions and even over the orographic regions except near the big lake regions. Also, the negative bias suggests the systematic underestimation of high precipitation by TMPA-3B43 product. The analyses suggest the need of a better algorithm for precipitation estimation over this region separately to capture the different types of rain events more reliably.

Forensic Analysis of Two Contrasting Satellite Rainfall Products for Detection of the July 2002 Flooding in South-Central Texas

This study is a forensic investigation of how two well-known satellite rainfall products, available at native scales that are much coarser than that ideally needed for detecting heavy rainfall-induced localized flooding, might have performed for July 2002 flooding in south-central Texas. These two products are: 1) the Global Precipitation Climatology Project (GPCP) rainfall product available at 1° and daily resolution; and 2) the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42RT available at 0.25 degree and 3 hourly resolution. Both products are considered sufficiently coarse for the flooding that evolved in the typically Hortonian (infiltration-excess) catchments of south-central Texas. An error assessment was performed on these rainfall datasets based on ground validation (GV) data. Compared with the hydrographs of gauge points located within the flooding region, both satellite rainfall products showed promise in detecting the rising and receding trends of the flood events. However, compared with flood inundation maps from Dartmouth Flood Observatory, 3B42RT showed higher agreement in matching with flow accumulation pattern at the subgrid scale. This study shows that merged satellite precipitation products that are emerging today from the constellation of low-earth orbit passive microwave sensors have adequate information content at the native scale for application in providing guidance on localized flooding over ungauged basins.

Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution

Abstract. Climate time series are of major importance for base line studies for climate change impact and adaptation projects. However, for instance, in mountain regions and in developing countries there exist significant gaps in ground based climate records in space and time. Specifically, in the Peruvian Andes spatially and temporally coherent precipitation information is a prerequisite for ongoing climate change adaptation projects in the fields of water resources, disasters and food security. The present work aims at evaluating the ability of Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to estimate precipitation rates at daily 0.25° × 0.25° scale in the Central Andes and the dependency of the estimate performance on changing spatial and temporal resolution. Comparison of the TMPA product with gauge measurements in the regions of Cuzco, Peru and La Paz, Bolivia were carried out and analysed statistically. Large biases are identified in both investigation areas in the estimation of daily precipitation amounts. The occurrence of strong precipitation events was well assessed, but their intensities were underestimated. TMPA estimates for La Paz show high false alarm ratio. The dependency of the TMPA estimate quality with changing resolution was analysed by comparisons of 1-, 7-, 15- and 30-day sums for Cuzco, Peru. The correlation of TMPA estimates with ground data increases strongly and almost linearly with temporal aggregation. The spatial aggregation to 0.5°, 0.75° and 1° grid box averaged precipitation and its comparison to gauge data of the same areas revealed no significant change in correlation coefficients and estimate performance. In order to profit from the TMPA combination product on a daily basis, a procedure to blend it with daily precipitation gauge measurements is proposed. Different sources of errors and uncertainties introduced by the sensors, sensor-specific algorithm aspects and the TMPA processing scheme are discussed. This study reveals the possibilities and restrictions of the use of TMPA estimates in the Central Andes and should assist other researchers in the choice of the best resolution-accuracy relationship according to requirements of their applications.

Application of a Medium-Range Global Hydrologic Probabilistic Forecast Scheme to the Ohio River Basin

Abstract A 10-day globally applicable flood prediction scheme was evaluated using the Ohio River basin as a test site for the period 2003–07. The Variable Infiltration Capacity (VIC) hydrology model was initialized with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis temperatures and winds, and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation up to the day of forecast. In forecast mode, the VIC model was then forced with a calibrated and statistically downscaled ECMWF Ensemble Prediction System (EPS) 10-day ensemble forecast. A parallel setup was used where ECMWF EPS forecasts were interpolated to the spatial scale of the hydrology model. Each set of forecasts was extended by 5 days using monthly mean climatological variables and zero precipitation in order to account for the effects of the initial conditions. The 15-day spatially distributed ensemble runoff forecasts were then routed to four locations in the basin, each with different drainage areas. Surrogates for observed daily runoff and flow were provided by the reference run, specifically VIC simulation forced with ECMWF analysis fields and TMPA precipitation fields. The hydrologic prediction scheme using the calibrated and downscaled ECMWF EPS forecasts was shown to be more accurate and reliable than interpolated forecasts for both daily distributed runoff forecasts and daily flow forecasts. The initial and antecedent conditions dominated the flow forecasts for lead times shorter than the time of concentration depending on the flow forecast amounts and the drainage area sizes. The flood prediction scheme had useful skill for the 10 following days at all sites.