Comparison of Spatial Precipitation Forecasts with a Satellite Dataset

Abstract

The purpose of this research paper is to analyze and compare global precipitation data from the Climate Forecast System Version 2 (CFSv2) with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) to improve forecast capabilities. The comparison of statistical parameters of the satellite dataset with the CFSv2 will provide a useful foundational analysis on global precipitation. The various forecast time frames will then be analyzed for accuracy, and a quantile mapping (QM) technique will be applied to correct precipitation amounts from the CFSv2. QM requires a training and test dataset of the CFSv2 with the statistics of the PERSIANN-CDR used for corrections. Finally, the forecast corrections result for the CFSv2 may be used by a broad community of users specifically for the management of flood and drought-prone areas along with the scientific modeling community.

Document Details

• Document Type: Technical Report
• Publication Date: Mar 01, 2021
• Accession Number: AD1166680

Entities

People

• Andrew C. Siebels

Organizations

• Air Force Institute of Technology

Tags

Communities of Interest

• Space

DTIC Thesaurus Topics

• Abstracts
• Accuracy
• Air Force
• Artificial Satellites
• Atmospheric Sciences
• Climate
• Climate Change
• Climatology
• Data Science
• Data Sets
• Department Of Defense
• Engineering
• False Alarms
• Floods
• Geosynchronous Satellites
• Information Science
• Low Earth Orbits
• Precipitation
• Signal Processing
• South America
• Southeast Asia
• Standards
• Statistical Analysis
• Statistics
• Tropical Regions
• United States

Fields of Study

• Environmental science

Readers

• Aerospace Engineering.
• Atmospheric Science/Meteorology
• Neural Network Machine Learning.

Technology Areas

• AI & ML
• AI & ML - Bayesian Inference
• AI & ML - Neural Networks
• Space