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, 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. <br><br> 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. <br><br> 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. <br><br> 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

Evaluation of satellite-derived agro-climate variables in the Northern Great Plains of the United States

“…However, RMSD for non-growing season data appeared to be lower than that for the growing season for precipitation (Table 3), possibly due to seasonal distribution of precipitation, with lower amounts of precipitation during the cold season. At the same time, relatively better FAR, POD and TS scores for the growing season (Table 2), consistent with earlier studies (Scheel et al 2010), suggest better performance of TMPA in the growing season.…”

“…This is not surprising, because the inherent uncertainty in the daily product was lowered through averaging in the monthly product (Scheel et al 2010). The poor correlation of daily precipitation could possibly be due to the inherent random errors in rain gauge measurements (Ciach 2003) and due to the nature of TMPA algorithm, which includes an adjustment of the satellite-derived estimations of precipitation to match the raingauge measurements at a monthly scale (Huffman et al 2007), which may not improve the daily or hourly estimates (Scheel et al 2010). The poor performance of TMPA daily Figure 2 for explanation of the frequency bias index (FBI), false alarm ratio (FAR), probability of detection (POD) and threat score (TS).…”

“…The poor performance of TMPA daily Figure 2 for explanation of the frequency bias index (FBI), false alarm ratio (FAR), probability of detection (POD) and threat score (TS). 620 R. Lemons et al precipitation could be due to errors at the sensors and product algorithm (Scheel et al 2010). For example, the global algorithm of infrared data processing does not consider the distance between the cloud top and earth's surface.…”

Evaluation of reanalysis and satellite-based precipitation datasets in driving hydrological models in a humid region of Southern China

“…Information about precipitation rates, amounts and distribution is indispensable for a wide range of applications including agronomy, hydrology, meteorology and climatology (Scheel et al 2011). The spatial and temporal distribution of precipitation greatly influences land surface hydrological fluxes and states (Gottschalck et al 2005).…”

Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) Products and Their Potential Hydrological Application at an Arid and Semiarid Basin in China

“…Of all these existing High Resolution Precipitation Products (HRPP), TMPA is aimed at combining all of the available individual estimates to form a single, best estimate of precipitation. Two categories of satellite-based precipitation data are merged into TMPA products: one is dataset collected by PMW sensors aboard on a list of Low Earth Orbiting (LEO) satellites, including the TRMM Microwave Imager (TMI) on TRMM, Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) on Defense Meteorological Satellite Program (DMSP) satellite series (DMSP F13-F17), Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E) on Aqua, the Advanced Microwave Sounding Unit B (AMSU-B) on the National Oceanic and Atmospheric Administration (NOAA) satellite series (NOAA [15][16][17] and the Microwave Humidity Sounder (MHS) on later NOAA-series satellite (NOAA 18), and the European Operational Meteorological (Metop) satellites. The other data source for TMPA is precipitation estimations from IR sensors on both LEO and geostationary satellites.…”