A Bayesian kriging approach for blending satellite and ground precipitation observations. Issue 2 (11th February 2015)

• Record Type: Journal Article
• Authors: Verdin, Andrew; Rajagopalan, Balaji; Kleiber, William; Funk, Chris
• Is Part Of: Water resources research. Volume 51:Issue 2(2015:Feb.)
• Page Start: 908
• Page End: 921
• Publication Date: 2015-02-11
• Subjects: Bayesian -- precipitation -- satellite estimates -- kriging -- Central America -- blending; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2014WR015963
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite‐derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite‐derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite‐derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite‐derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the "true" observed precipitation value at each grid cell. Key Points: Blending observed and estimated precipitation creates a more complete and accurate product Drought and flood management practices require accurate precipitation estimates Bayesian approaches provide more complete information and uncertainty