Editorial Type:
Article
Article Type:
Research Article

Impact of Ingesting Satellite-Derived Cloud Cover into the Regional Atmospheric Modeling System

Ismail Yucel Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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W. James Shuttleworth Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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R. T. Pinker Department of Meteorology, University of Maryland, College Park, College Park, Maryland

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L. Lu Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona, and Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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S. Sorooshian Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona

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Print Publication:
01 Mar 2002
DOI:
https://doi.org/10.1175/1520-0493(2002)130<0610:IOISDC>2.0.CO;2
Page(s):
610–628
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Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and longwave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4 km × 4 km) fields of fractional cloud cover from visible band Geostationary Operational Environmental Satellite (GOES) data using a tracking procedure to determine the clear-sky composite image. Initial studies, in which RAMS surface shortwave radiation fluxes were replaced by estimates obtained by applying satellite-derived cloud cover in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model, revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming longwave radiation and precipitation fields. Consequently, in this study, the relationship between cloud albedo, optical depth, and water/ice content used in the UMD GEWEX/SRB model was applied instead to provide estimates of whole-column cloud water/ice that were ingested into RAMS. This potentially enhances the realism of the modeled short- and longwave radiation and precipitation. The ingested cloud image took the horizontal distribution of clouds from the satellite image but derives its vertical distribution from the fields simulated by RAMS in the time step immediately prior to assimilation. The resulting image was ingested every minute, with linear interpolation used to derive the 1-min cloud images between 15-min GOES samples. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) weather station network in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion was found to substantially improve the ability of the RAMS model to capture temporal and spatial variations in surface fields associated with cloud cover. An initial test suggests that cloud ingestion enhanced RAMS short-term forecast ability.

Corresponding author address: Ismail Yucel, Department of Hydrology and Water Resources, The University of Arizona, Harshbarger Building 11, Tucson, AZ 85721. Email: yucel@hwr.arizona.edu

Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and longwave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4 km × 4 km) fields of fractional cloud cover from visible band Geostationary Operational Environmental Satellite (GOES) data using a tracking procedure to determine the clear-sky composite image. Initial studies, in which RAMS surface shortwave radiation fluxes were replaced by estimates obtained by applying satellite-derived cloud cover in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model, revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming longwave radiation and precipitation fields. Consequently, in this study, the relationship between cloud albedo, optical depth, and water/ice content used in the UMD GEWEX/SRB model was applied instead to provide estimates of whole-column cloud water/ice that were ingested into RAMS. This potentially enhances the realism of the modeled short- and longwave radiation and precipitation. The ingested cloud image took the horizontal distribution of clouds from the satellite image but derives its vertical distribution from the fields simulated by RAMS in the time step immediately prior to assimilation. The resulting image was ingested every minute, with linear interpolation used to derive the 1-min cloud images between 15-min GOES samples. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) weather station network in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion was found to substantially improve the ability of the RAMS model to capture temporal and spatial variations in surface fields associated with cloud cover. An initial test suggests that cloud ingestion enhanced RAMS short-term forecast ability.

Corresponding author address: Ismail Yucel, Department of Hydrology and Water Resources, The University of Arizona, Harshbarger Building 11, Tucson, AZ 85721. Email: yucel@hwr.arizona.edu

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