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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATION OF CLIMATE VARIABILITY AND FORECASTS INTO RISK-BASED MANAGEMENT TOOLS FOR AGRICULTURE PRODUCTION AND RESOURCE CONSERVATION

Location: Great Plains Agroclimate and Natural Resources Research Unit

Title: Generating synthetic daily precipitation realizations for seasonal precipitation forecasts

Authors
item GARBRECHT, JURGEN
item ZHANG, XUNCHANG

Submitted to: Journal Hydrologic Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 20, 2012
Publication Date: N/A

Interpretive Summary: Computer generated daily weather data are often used to expand short records of past weather observations. Extension of the methodology to enable generation of daily weather for a seasonal climate forecast would broaden agricultural applications to include pro-active soil and water resources management, better prediction of achieving production targets, and weather-related risk assessment. In this study, an analytical method was developed that allows generation of precipitation for seasonal forecasts up to twelve months ahead. The methodology was implemented in weather generator SYNTOR and tested with precipitation data from the USDA-ARS weather station at Temple, TX, and a hypothetical forecast of wetter than normal precipitation for April through July. The analytical method for generating daily precipitation for seasonal forecast was successfully tested. It validated the approach, analytical solution, and implementation of the method in experimental climate generator SYNTOR. The forward looking capability of the method complements existing weather generation models that are based on and replicate weather statistics of the past. The method is well suited to deliver a large number of precipitation realizations to perform weather-related risk assessments. Water resource managers, farm loan officers, agricultural consultants, and risk management agencies are among those that would most benefits from synthetic weather generation of forecast conditions.

Technical Abstract: Synthetic weather generation models that depend on statistics of past weather observations are often limited in their applications to issues that depend upon historical weather characteristics. Enhancing these models to take advantage of increasingly available and skillful seasonal climate outlook products would broaden applications to include pro-active soil and water resources management, better prediction of achieving production targets, and weather-related risk assessment. Here, an analytical method was developed that enables generation of daily precipitation time-series for seasonal forecasts up to twelve months ahead. The method uses historical weather observations to establish reference precipitation statistics (monthly precipitation amount, number of rainy days per month, wet-wet and dry-wet day transition probabilities) and subsequently adjusts these statistics to reflect the forecast departures from long-term average conditions. This “reference & forecast departure” approach ensures that generated precipitation is consistent and compatible with the forecast and the local climate characteristics as well. The method was tested with precipitation data from the USDA-ARS weather station at Temple, TX, for a hypothetical seasonal precipitation forecast. Several 100-year time-series of generated daily precipitation reproduced average monthly precipitation within ±6% of expected forecast values and had a Mean Absolute Error (MAE) of less than 3%, wet/dry day transition probabilities within 5% and had a MAE of less than 2%, and average number of rainy days per calendar month within ±2% and had a MAE of less than 1%. The successful testing of the method validated the approach, analytical solution, and implementation of the method in experimental climate generator SYNTOR. This forward looking capability of synthetic weather generation will benefit water resource managers, farm loan officers, agricultural consultants, risk management agencies, and anyone relying on seasonal climate forecast information for decision making.

Last Modified: 7/28/2014
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