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Reduction of Tropical Cloud and Precipitation Biases in Global High Resolution Models

Sector: Advanced Electronics • Location: United States of America

Source: Grants.gov

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The Office of Biological and Environmental Research (BER) of the Office of Science (SC), U.S. Department of Energy (DOE), hereby announces its interest in receiving applications for research grants on the topic of Reduction of Tropical Cloud and Precipitation Biases in Global High Resolution Models under the Regional and Global Climate Modeling (RGCM) and Atmospheric System Research (ASR) programs

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The project “Reduction of Tropical Cloud and Precipitation Biases in Global High Resolution Models” is an infrastructure initiative in the Advanced Electronics sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

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The Office of Biological and Environmental Research (BER) of the Office of Science (SC), U.S. Department of Energy (DOE), hereby announces its interest in receiving applications for research grants on the topic of Reduction of Tropical Cloud and Precipitation Biases in Global High Resolution Models under the Regional and Global Climate Modeling (RGCM) and Atmospheric System Research (ASR) programs. Simulation of climate in the tropics has improved considerably over the past decade; nevertheless global and regional climate models have biases in tropical precipitation and clouds. This Funding Opportunity Announcement (FOA) invites applications to examine, and improve our understanding of tropical clouds and precipitation in uniform high or variable resolution global models, global cloud resolving models, and/or regional models, by improving the process representations in the context of tropical Atmospheric Radiation Measurement (ARM) campaigns (http://www.arm.gov/campaigns/table ) and/or data from the ARM Tropical Western Pacific (TWP) http://www.arm.gov/sites/twp fixed sites. The intent of this FOA is to reduce tropical biases of precipitation and clouds in global climate models by fostering interactions between atmospheric process and global modelers.

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