Distance in spatial interpolation of daily rain gauge data

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.

[1]  Henry F. Diaz,et al.  Creating a Serially Complete, National Daily Time Series of Temperature and Precipitation for the Western United States , 2000 .

[2]  F. Rubel,et al.  BALTEX 1/6-degree daily precipitation climatology 1996–1998 , 2001 .

[3]  I. Orlanski.  A rational subdivision of scales for atmospheric processes , 1975 .

[4]  Witold F. Krajewski,et al.  Conditional Bias in Radar Rainfall Estimation. , 2000 .

[5]  Anton Kruger,et al.  An evaluation of NEXRAD precipitation estimates in complex terrain , 1999 .

[6]  Ronald B. Smith.  The Influence of Mountains on the Atmosphere , 1979 .

[7]  D. Shepard,et al.  Computer Mapping: The SYMAP Interpolation Algorithm , 1984 .

[8]  E. Martin,et al.  A meteorological estimation of relevant parameters for snow models , 1993 .

[9]  M. Palecki,et al.  Storm Precipitation in the United States. Part I: Meteorological Characteristics. , 2005 .

[10]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[11]  V. Singh,et al.  Mathematical models of small watershed hydrology and applications. , 2002 .

[12]  C. Obled,et al.  Objective analyses and mapping techniques for rainfall fields: An objective comparison , 1982 .

[13]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[14]  Scott M. Robeson,et al.  Daily Precipitation Grids for South America , 2006 .

[15]  R. Adler,et al.  Intercomparison of global precipitation products : The third Precipitation Intercomparison Project (PIP-3) , 2001 .

[16]  V. Singh,et al.  Mathematical models of large watershed hydrology , 2002 .

[17]  Michele Brunetti,et al.  A new instrumental precipitation dataset for the greater alpine region for the period 1800–2002 , 2005 .

[18]  C. Schär,et al.  A PRECIPITATION CLIMATOLOGY OF THE ALPS FROM HIGH-RESOLUTION RAIN-GAUGE OBSERVATIONS , 1998 .

[19]  Ronald B. Smith.  A linear upslope-time-delay model for orographic precipitation , 2003 .

[20]  Bodo Ahrens,et al.  Impact of nesting strategies in dynamical downscaling of reanalysis data , 2004 .

[21]  Dennis P. Lettenmaier,et al.  Dynamic modeling of orographically induced precipitation , 1994 .

[22]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[23]  B. Sevruk.  REGIONAL DEPENDENCY OF PRECIPITATION-ALTITUDE RELATIONSHIP IN THE SWISS ALPS , 1997 .

[24]  M. Palecki,et al.  Storm Precipitation in the United States. Part II: Soil Erosion Characteristics , 2005 .

[25]  F. Rubel,et al.  Correction of Daily Rain Gauge Measurements in the Baltic Sea Drainage Basin , 1999 .

[26]  P. Paufler,et al.  Numerical Data and Functional Relationships in Science and Technology - New Series. , 1994 .

[27]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.