A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data

• Published in: Advances in water resources Vol. 32; no. 7; pp. 986 - 1002
• Main Authors: Velasco-Forero, Carlos A., Sempere-Torres, Daniel, Cassiraga, Eduardo F., Jaime Gómez-Hernández, J.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2009; Elsevier
• Subjects: Blending; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; estimation; Exact sciences and technology; Geostatistics; Hydrology; Hydrology. Hydrogeology; measurement; Merging; meteorological data; Pollution, environment geology; radar; Radar rainfall; rain; Rain gauges; Rainfall estimation; rainfall fields; Iberian Peninsula; Southern Europe; Catalonia Spain; Barcelona Spain; Europe; Spain; Radar rainfall; Blending; Merging; Rain gauges; Geostatistics; Rainfall estimation; geostatistics; models; rainfall; time variations; maps; cokriging; kriging; rain water; case studies; automated analysis; surface water; water resources; correlation; semivariograms; radar methods; performances; flow
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2008.10.004

Quantitative estimation of rainfall fields has been a crucial objective from early studies of the hydrological applications of weather radar. Previous studies have suggested that flow estimations are improved when radar and rain gauge data are combined to estimate input rainfall fields. This paper reports new research carried out in this field. Classical approaches for the selection and fitting of a theoretical correlogram (or semivariogram) model (needed to apply geostatistical estimators) are avoided in this study. Instead, a non-parametric technique based on FFT is used to obtain two-dimensional positive-definite correlograms directly from radar observations, dealing with both the natural anisotropy and the temporal variation of the spatial structure of the rainfall in the estimated fields. Because these correlation maps can be automatically obtained at each time step of a given rainfall event, this technique might easily be used in operational (real-time) applications. This paper describes the development of the non-parametric estimator exploiting the advantages of FFT for the automatic computation of correlograms and provides examples of its application on a case study using six rainfall events. This methodology is applied to three different alternatives to incorporate the radar information (as a secondary variable), and a comparison of performances is provided. In particular, their ability to reproduce in estimated rainfall fields (i) the rain gauge observations (in a cross-validation analysis) and (ii) the spatial patterns of radar fields are analyzed. Results seem to indicate that the methodology of kriging with external drift [KED], in combination with the technique of automatically computing 2-D spatial correlograms, provides merged rainfall fields with good agreement with rain gauges and with the most accurate approach to the spatial tendencies observed in the radar rainfall fields, when compared with other alternatives analyzed.