Statistically combining rainfall characteristics estimated from remote‐sensed and rain gauge data sets in the Brazilian Amazon‐Tocantins Basins

Abstract

[1] This paper explores the use of a parametric geostatistical model for combining rainfall characteristics derived from rain gauge data with the same characteristics derived from remote-sensed data sets. Hypotheses can then be tested about which predictors significantly increase precision of an estimated characteristic. Although applicable wherever ground-level data and remote-sensed data are to be combined, the statistical procedure set out in the paper is developed for two examples of rainfall characteristics: (i) G, the mean annual rainfall at an ungauged site, conditional on knowledge of two predictor variables T (the mean annual rainfall calculated from the TRMM 3B42 data set for 1998-2009), and C (mean annual rainfall derived from the CMORPH data set for 2003-2009); (ii) the mean annual maximum 1 day rainfall H, interpolated using the same modeling procedure with predictor variables T and C derived from annual maximum 1 day rainfalls in the same remote-sensed data sets. Prediction errors showed no bias, skewness of distribution, or spatial heterogeneity. The model's generality means that it could be used with any predictors other than T and C, possibly derived from other satellite data sets or radar. Provided that predictor variables are correlated with the variable to be predicted, it is not necessary for the model relating them to be fitted using data from identical periods nor for the grid spacing of T and C to be identical. Model performance was evaluated by using a "leave-one-site-out" procedure, which showed that the root mean square error (RMSE) of model predictions at omitted sites was smaller than RMSEs obtained from five other well-known spatial predictors.Citation: Clarke, R. T., and D. C. Buarque (2013), Statistically combining rainfall characteristics estimated from remotesensed and rain gauge data sets in the Brazilian Amazon-Tocantins Basins,

Performance evaluation of rainfall estimates by TRMM Multi‐satellite Precipitation Analysis 3B42V6 and V7 over Brazil

“…Juárez et al [2009] found that, although the annual rainfall pattern is well captured by satellite products, these data sets estimates did not correlate well with observations over the northwestern Amazon. The analyzed period in both studies was less than a decade (3 and 8 years, respectively), which was also the case for other recent papers Clarke et al, 2011;Camparotto et al, 2013;Oliveira et al, 2014] [Juárez et al, 2009;Franchito et al, 2009;Buarque et al, 2011;Clarke et al, 2011;Clarke and Buarque, 2013;Camparotto et al, 2013;Oliveira et al, 2014] (see Table S1).…”

“…[] found smaller errors in TMPA estimates during winter. Despite different analysis time periods and sample sizes, all of the reported studies for the Brazilian territories considered only monthly or annual precipitation and/or only using previous versions of TRMM (V5 or V6) products [ Juárez et al ., ; Franchito et al ., ; Buarque et al ., ; Clarke et al ., ; Clarke and Buarque , ; Camparotto et al ., ; Oliveira et al ., ] (see Table S1).…”

Bias‐corrected data sets of climate model outputs at uniform space–time resolution for land surface modelling over Amazonia

“…It needs to be noted that, although not ideal, using only one rain gauge for obtaining μ is the best choices that can be made given the lack of a dense and reliable long‐term ground‐based observation network in the study area. Clark and Buarque () discuss several precipitation stations, however, the gauges described in that paper only have daily records. The method used here requires both continuous and hourly rainfall records.…”

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

“…Among them, Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) has been used, since the launch of TRMM in November 1997, to produce a range of quasi-global precipitation products. These products are widely used in various climate and hydrological applications [17,19,[21][22][23][24], and have been considered for the spatial assessment of erosivity [20,25,26]. Numerous studies have evaluated the performance of TRMM by comparing precipitation estimates from TRMM products with rain gauge precipitations or ground-based radar estimates [27][28][29].…”