Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia

Abstract

An overwhelming number of applications depend on reliable precipitation estimations.However, over complex terrain in regions such as the Andes or the southwestern Amazon, the spatial coverage of rain gauges is scarce. Two reanalysis datasets, a satellite algorithm and a scheme that combines surface observations with satellite estimations were selected for studying rainfall in the following areas of Bolivia: the central Andes, Altiplano, southwestern Amazonia, and Chaco. These Bolivian regions can be divided into three main basins: the Altiplano, La Plata, and Amazon. The selected reanalyses were the Modern-Era Retrospective Analysis for Research and Applications, which has a horizontal resolution (~50 km) conducive for studying rainfall in relatively small precipitation systems, and the Climate Forecast System Reanalysis and Reforecast, which features an improved horizontal resolution (~38 km). The third dataset was the seventh version of the Tropical Rainfall Measurement Mission 3B42 algorithm, which is conducive for studying rainfall at a ~25 km horizontal resolution. The fourth dataset utilizes a new technique known as the Combined Scheme, which successfully removes 1 ACCEPTED MANUSCRIPT satellite bias. All four of these datasets were aggregated to a coarser resolution.Additionally, the daily totals were calculated to match the cumulative daily values of the ground observations. This research aimed to describe and compare precipitation in the two reanalysis datasets, the satellite-algorithm dataset, and the Combined Scheme with ground observations. Two seasons were selected for studying the precipitation estimates: the rainy season (December-February) and the dry season (June-August). The average, bias, standard deviation, correlation coefficient, and root mean square error were calculated. Moreover, a contingency table was generated to calculate the accuracy, bias frequency, probability of detection, false alarm ratio, and equitable threat score.All four datasets correctly depicted the spatial rainfall pattern. However, CFSR and MERRA overestimated precipitation along the Andes' eastern-facing slopes and exhibited a dry bias over the eastern Amazon; TRMM3B42 and the Combined Scheme depicted a more realistic rainfall distribution over both the Amazon and the Andes. When separating the precipitation into classes, MERRA and CFSR overestimated light to moderate precipitation (1-20 mm/day) and underestimated very heavy precipitation (>50 mm/day). TRMM3B42 and CoSch depicted behaviors similar to the surface observations; however, CoSch underestimated the precipitation in very intense systems (>50 mm/day) The statistical variables indicated that CoSch's correlation coefficient was highest for every season and basin. Additionally, the bias and RMSE values suggested that CoSch closely represented the surface observations.

Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

“…The NCEP-CFSR data resulted in significant overestimation for the 5-10 and 10-20 mm·day −1 precipitation classes in both basins. This is similar to the results reported by Blacutt et al [70], who also discovered the NCEP-CFSR overestimated precipitation at 3-20 mm·day −1 class in Bolivia. They further reported the NCEP-CFSR tended to overestimate precipitation during the annual precipitation season period.…”

“…Differences in climate and geographical conditions are the most likely explanation for such differences between the Jajarmizadeh et al [88] study and the results reported in this research and other previously cited studies. In addition, the streamflow overestimation that resulted from the use of the NCEP-CFSR data in this study could be related to possible problems that occur over tropical regions [70], including the effects of the satellite algorithms on precipitation estimation and the CFSR model parameterizations.…”

Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

“…During wet seasons for all considered regions, TMPA presents CC higher than 0.7, RMSE close to 50% and Bias values into the −10%-10% intervals (Table 2; Figure 3). These specific values were previously defined as objective values to ensure the good performance of SREs at monthly scale [9,10,19,30]. Results over the TDPS and for TMPA are in line with a previous study of the [2005][2006][2007] period [9] with similar CC, RMSE and Bias values.…”

Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

“…Guo et al, 2015;Blacutt et al, 2015;Ward et al, 2011;Scheel et al, 2011). Here, we further subdivide daily rainfall in five types of events, which are used to classify precipitation based on its daily intensity, ranging from no rain (dry day; < 1 mm d −1 ) to violent rain (> 40 mm d −1 ), as shown in Table 2.…”