84th AMS Annual Meeting

Wednesday, 14 January 2004: 2:00 PM
Using Surface Raingage Data and the Noncontiguous Rainfall (NCR) Technique to Determine Bias in the Global Precipitation Climatology Project (GPCP) Rainfall Algorithm
Room 618
Andrew J. Reader, Oklahoma Climatological Survey, Norman, OK
The purpose of this study is to determine biases in the Global Precipitation Climatology Project (GPCP) rainfall algorithm using surface raingage data and the noncontiguous rainfall (NCR) technique. In particular, the Oklahoma Mesonet daily rainfall data will be heavily used in the study. In addition to the Oklahoma Mesonet data, ASOS data will also be utilized for this project. The presentation given will discuss the motives, methodology, and preliminary results of the conducted research.

The Oklahoma Mesonet provides a network of 115 automated weather stations across the state of Oklahoma. The GPCP data provides a daily rainfall value with a resolution of 1˚ by 1˚ latitude-longitude. The comparison of the data sets will offer daily (or monthly) scatter plots from which standard errors can be calculated. The largest source of the standard error caused by raingage data is the sampling error. This error can be greatly reduced with the use of the NCR technique. The NCR technique is a statistical method which used sparsely distributed raingage measurement to obtain calibrations of satellite rainfall algorithm [Morrissey 1991]. The NCR technique reduces sampling error by taking the statistical characteristics of observed raingage data, and models additional data points. With the addition of data points, the standard error between the GPCP data and the Mesonet data can be greatly reduced. With the reduction of sampling error, the GPCP algorithm bias can be more easily identified. Although the study is based solely over the state of Oklahoma, the results from the data comparison can be used to modify GPCP algorithms and aid in weather forecasting.

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