Acquiring Satellite Based Weather Data

A technique for estimating precipitation over Africa has been developed to augment the rainfall data available from the relatively sparse observational network of rain-gauge stations over this region. For the period 1998-2001, the data used by LEWS was the rainfall estimator (RFE) version 1.0 product. The method uses METEOSAT 5 satellite data, Global Telecommunication System (GTS) rain-gauge reports, model analyses of wind and relative humidity, and orography for computing daily estimates of accumulated rainfall (Herman et al., 1997; http://edcintl.cr.usgs.gov/adds/RFEPaper. php). Since January 1, 2001, the RFE version 2.0, which has been implemented by National Oceanic and Atmospheric Administration's Climate Prediction Center (Xie and Arkin, 1997) to replace RFE 1.0, has been used by LEWS. RFE 2.0 uses additional techniques to better estimate precipitation while continuing the use of cold cloud duration (derived from cloud top temperature) and station rainfall data. The METEOSAT 7 geostationary satellite is the primary satellite data source.

The LEWS system acquires rainfall data from ftp.ncep.noaa.gov/pub/ cpc/fews/newalgo_est/ site, minimum temperature from ftp.ncep.noaa.gov/ pub/cpc/fews/daily_gdas_avgs/tmin/ site, and maximum temperature from ftp.ncep.noaa.gov/pub/cpc/fews/daily_gdas_avgs/tmax/ site. These data are placed on the Web (http://cnrit.tamu.edu.edu/rsg/rainfall/rainfall.cgi) for public use. These geo-referenced values of rainfall, minimum/maximum temperature, and generated radiation data are linked to the PHYGROW model to provide weather data to derive the model in the automated system. Temperature provided is skin temperature, which is not the same as the typical 2-m shaded thermometer data, reported by most standard weather status, requiring modification of model equations to accommodate this type of temperature measure. There is a need for biophysical modelers to recognize the new emerging measures of temperature, wind, and humidity from satellites and explore new algorithms to take advantages of these geographically robust data.

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