Evaluation of Sampling Errors of Precipitation from Spaceborne and Ground Sensors

Charles E. Graves Climate System Research Program, Texas A&M University, College Station, Texas

Search for other papers by Charles E. Graves in
Current site
Google Scholar
PubMed
Close
,
Juan B. Valdés Department of Civil Engineering and Climate System Research Program, Texas A&M University, College Station, Texas

Search for other papers by Juan B. Valdés in
Current site
Google Scholar
PubMed
Close
,
Samuel S. P. Shen Department of Mathematics, University of Alberta, Edmonton, Alberta, Canada

Search for other papers by Samuel S. P. Shen in
Current site
Google Scholar
PubMed
Close
, and
Gerald R. North Climate System Research Program, Texas A&M University, College Station, Texas

Search for other papers by Gerald R. North in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The spatial and temporal characteristics of rainfall over Oklahoma and Kansas are analyzed in this paper using the raingage data collected during the Preliminary Regional Experiment for STORM-Central (PRESTORM). The autocorrelation function and the spectrum are obtained directly from both processing the raingage data and using a theoretical stochastic model of space–time precipitation. This theoretical model serves as an intermediate step to obtain more information from the raingage records. The spectra obtained are then compared with those obtained from oceanic precipitation in the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) and with that obtained from analyzing raingage records in east Texas. Finally, the spectra are used to evaluate the sampling errors that are due to the spatial gaps in measurements. The sampling error is expressed as an integral over the product of the spectral density of the stochastic rain field and a filter function. This filter function solely depends on the space–time configuration of the measurement instruments. The values of the analytical and numerical results on the sampling error are obtained for ground, spaceborne, and combined sensors of precipitation for several aggregation levels in space and time and alternative spacing and visiting times. It was found that sampling errors of land precipitation are higher than those reported for ocean precipitation.

Abstract

The spatial and temporal characteristics of rainfall over Oklahoma and Kansas are analyzed in this paper using the raingage data collected during the Preliminary Regional Experiment for STORM-Central (PRESTORM). The autocorrelation function and the spectrum are obtained directly from both processing the raingage data and using a theoretical stochastic model of space–time precipitation. This theoretical model serves as an intermediate step to obtain more information from the raingage records. The spectra obtained are then compared with those obtained from oceanic precipitation in the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) and with that obtained from analyzing raingage records in east Texas. Finally, the spectra are used to evaluate the sampling errors that are due to the spatial gaps in measurements. The sampling error is expressed as an integral over the product of the spectral density of the stochastic rain field and a filter function. This filter function solely depends on the space–time configuration of the measurement instruments. The values of the analytical and numerical results on the sampling error are obtained for ground, spaceborne, and combined sensors of precipitation for several aggregation levels in space and time and alternative spacing and visiting times. It was found that sampling errors of land precipitation are higher than those reported for ocean precipitation.

Save