Abstract
This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges.
Highlights
Knowledge of the spatio-temporal variations in the distribution of precipitation is of critical importance to hydrological/hydrometeorological modeling
Rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges
A two-stage geostatistical downscaling approach was presented for integrating datasets from different supports and adjusting the errors in the satellite precipitation product
Summary
Knowledge of the spatio-temporal variations in the distribution of precipitation is of critical importance to hydrological/hydrometeorological modeling. Two data sources are routinely used to obtain reliable estimates of precipitation. The spatio-temporal estimates of precipitation are routinely obtained by applying various interpolation algorithms to the rain gauge. If the downscaled satellite-derived precipitation estimates with the fine resolution auxiliary variables are integrated with rain gauge data, the final prediction results at the fine resolution might not show improved performance in some areas with sparse rain gauges. The second possible integration approach is to use the auxiliary variables at the fine resolution as additional inputs for multivariate kriging algorithms as in this study. CM has been applied to integrate only one type of radar or satellite precipitation product, so the applicability of CM is not clear By considering these issues, integrating datasets from both different supports and multiple sources should be tested extensively in future research
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