Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios

Summary This study investigates the performance of merging radar and rain gauge data for different high temporal resolutions and rain gauge network densities. Three different geostatistical interpolation techniques: Kriging with external drift, indicator kriging with external drift and conditional merging were compared and evaluated by cross validation. Ordinary kriging was considered as the reference method without using radar data. The study area is located in Lower Saxony, Germany, and covers the measuring range of the radar station Hanover. The data used in this study comprise continuous time series from 90 rain gauges and the weather radar that is located near Hanover over the period from 2008 until 2010. Seven different temporal resolutions from 10 min to 6 h and five different rain gauge network density scenarios were investigated regarding the interpolation performance of each method. Additionally, the influence of several temporal and spatial smoothing-techniques on radar data was evaluated and the effect of radar data quality on the interpolation performance was analyzed for each method. It was observed that smoothing of the gridded radar data improves the performance in merging rain gauge and radar data significantly. Conditional merging outperformed kriging with an external drift and indicator kriging with an external drift for all combinations of station density and temporal resolution, whereas kriging with an external drift performed similarly well for low station densities and rather coarse temporal resolutions. The results of indicator kriging with an external drift almost reached those of conditional merging for very high temporal resolutions. Kriging with an external drift appeared to be more sensitive in regard to radar data quality than the other two methods. Even for 10 min temporal resolutions, conditional merging performed better than ordinary kriging without radar information. This illustrates the benefit of merging rain gauge and radar data even for very high temporal resolutions.

[1]  Grégoire Dubois,et al.  Spatial Interpolation Comparison 97. , 1998 .

[2]  U. Haberlandt,et al.  Spatial interpolation of hourly rainfall - effect of additional information, variogram inference and storm properties , 2010 .

[3]  W. F. Krajewski,et al.  Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data , 1989 .

[4]  Uwe Haberlandt,et al.  Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event , 2007 .

[5]  P. Goovaerts.  Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall , 2000 .

[6]  Michael F. Hutchinson,et al.  Interpolation of Rainfall Data with Thin Plate Smoothing Splines - Part II: Analysis of Topographic Dependence , 2002 .

[7]  Ashish Sharma,et al.  Radar rainfall error variance and its impact on radar rainfall calibration , 2003 .

[8]  J. Jaime Gómez-Hernández,et al.  A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data , 2009 .

[9]  Daniel Sempere-Torres,et al.  Radar rainfall: Separating signal and noise fields to generate meaningful ensembles , 2011 .

[10]  Witold F. Krajewski,et al.  Cokriging radar‐rainfall and rain gage data , 1987 .

[11]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[12]  Michael F. Hutchinson,et al.  Interpolation of Rainfall Data with Thin Plate Smoothing Splines - Part I: Two Dimensional Smoothing of Data with Short Range Correlation , 1998 .

[13]  Ashish Sharma,et al.  Merging gauge and satellite rainfall with specification of associated uncertainty across Australia , 2013 .

[14]  Laurent Delobbe,et al.  Evaluation of radar-gauge merging methods for quantitative precipitation estimates , 2009 .

[15]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[16]  Urs Germann,et al.  Variograms of Radar Reflectivity to Describe the Spatial Continuity of Alpine Precipitation , 2001 .

[17]  James W. Wilson,et al.  Radar Measurement of Rainfall—A Summary , 1979 .

[18]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[19]  Quanxi Shao,et al.  An improved statistical approach to merge satellite rainfall estimates and raingauge data. , 2010 .

[20]  A. Bárdossy,et al.  Interpolation of precipitation under topographic influence at different time scales , 2013 .

[21]  D. Bae,et al.  Quantitative Comparison of the Spatial Distribution of Radar and Gauge Rainfall Data , 2012 .

[22]  Dong-Jun Seo,et al.  Real-time estimation of mean field bias in radar rainfall data , 1999 .

[23]  G. Pegram,et al.  Combining radar and rain gauge rainfall estimates using conditional merging , 2005 .

[24]  Ashish Sharma,et al.  Application of Scaling in Radar Reflectivity for Correcting Range-Dependent Bias in Climatological Radar Rainfall Estimates , 2004 .

[25]  Alexis Berne,et al.  Temporal and spatial resolution of rainfall measurements required for urban hydrology , 2004 .