HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Larsen, G. A. Articles by Pense, R. B. Search for Related Content PubMed Articles by Larsen, G. A. Articles by Pense, R. B. Agricola Articles by Larsen, G. A. Articles by Pense, R. B.

Stochastic Simulation of Daily Climatic Data for Agronomic Models¹

Many agronomic models require the input of daily climatic data. Simulated climatic data may be used when long series of historic data are not available or convenient, or when future data are needed. A stochastic weather simulation model was developed and validated for a wide range of climates. The model produces possible daily sequences of precipitation amount, maximum and minimum air temperature, and total solar radiation at the earth's surface for the entire year.

A first-order, two-state Markov chain is used to simulate the occurrence of wet and dry days. Probabilities are used to simulate the occurrence of trace precipitation amounts on wet days. A two-parameter gamma distribution conditioned by the precipitation status on the previous day is used to generate greater than trace amounts. Two bivariate normal distributions conditioned by the precipitation status on the current day are used to simulate current temperature deviations from long-term average temperature curves. A two-parameter gamma distribution simulates current solar radiation deviations from the calculated maximum clear day radiation on dry days. On wet days, the deviations are simulated with a two-parameter beta distribution.

The model was developed with data from Columbia, Mo. Model validation was done for Columbia, Albuquerque, N.M., Caribou, Maine, Medford, Ore., and Miami, Fla. Various statistical tests were done to detect significant differences in central tendency, dispersion, and distribution. Comparisons were made to the base period used for parameter estimation (ranged from 16 to 20 years) and also to the 80-year period of record available at Columbia. The validation showed that the model produced climatic data which generally did not differ significantly from the base period at any of the locations. At Columbia, it was determined that the 17-year base period was not long enough to adequately represent the 80 years of precipitation data.

Key Words: Markov chain • Probability distribution • Precipitation • Temperature • Solar radiation

² Mathematical statistician, USDA-SRS, P.O. Box E, Fort Collins, CO 80522.

³ Mathematical statistician, USDA-SRS, Room 4833, South Bldg., Washington, DC 20250

Received for publication September 14, 1981.