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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: Random, but Uniform Please: Requirements for Synthetic Weather Generation

Author
item Garbrecht, Jurgen

Submitted to: Journal Hydrologic Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 5, 2012
Publication Date: January 31, 2012
Citation: Garbrecht, J.D. 2012. Random, but uniform please: requirements for synthetic synthetic precipitation generation for computer simulations in agriculture. Journal Hydrologic Engineering. 28(2):207-217.

Interpretive Summary: Observed weather records are often too short or contain long periods of missing weather data that preclude their used for hydrologic and environmental investigations. Computer generated weather records can complement and extend the observed weather records. Computer programs that generate weather records are called synthetic weather generation models. These models rely on standard uniform random numbers (RN) to simulate stochastic aspects of weather. As the name indicates a sequence of RNs is supposed to be uniformly distributed between 0 and 1. However, short sequences of RNs, needed to generate certain weather variables, are not necessarily uniformly distributed, and may lead to unrepresentative synthetic weather. A modified RN generator was developed that ensures that sequences as short as 50 RNs meet the uniformity requirement of the weather generation model. The RNs produced by the modified RN generator were thoroughly tested and met the requirements of uniformly distributed RNs. An example application demonstrated that precipitation values generated with these RNs reproduced the characteristics of observed precipitation. It was concluded that the modified RN generator is a good choice for source of RNs because the RNs are more compatible with assumptions of the weather generation model and lead to higher-quality synthetic daily precipitation records, especially for short records of 50 years.

Technical Abstract: Synthetic weather generation models often rely on standard uniform random numbers (RN) to simulate stochastic aspects of weather. However, short sequences of RNs, needed to generate certain weather variables, are not necessarily uniformly distributed, as assumed by weather generation models, and may lead to unrepresentative synthetic weather. A modified random number generator (MRNG) was developed that ensures that sequences as short as 50 RNs meet the uniformity requirement of the weather generation model. The procedure consists of compositing a population of 30 numbers that meet the uniform distribution requirements, and sampling, one at a time, at random and without replacement, from this population. As more RNs are needed a different population of 30 numbers is composited, and so forth. Tests of the MRNG revealed that sequences of about 50 RNs from the MRNG consistently approximated both mean and standard deviation of the uniform distribution within ±5%. Sequence lengths of 50 RNs and longer were shown to be serially uncorrelated and distribution invariant. Generation of daily precipitation with RNs produced by the MRNG led to better approximations of observed daily precipitation statistics than with RNs from a conventional RNG. A frequency analysis of generated annual peak daily precipitation showed good correspondence with observed precipitation for all peak-precipitation events with return period less than 2 years, and a slight overestimation for higher frequency peak events. It was concluded that the MRNG is a better choice for source of RNs because the RNs are more compatible with assumptions of the weather generation model and lead to higher-quality synthetic daily precipitation records, especially short records of 50 years.

Last Modified: 4/18/2014
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