Correcting low-frequency variability bias in stochastic weather generators

Stochastic weather generators used with agricultural simulation models tend to under predict interannual variability of generated climate, often resul ting in distortion of simulated agricultural or hydrological variables. Thi s study presents a stochastic weather generator that attempts to improve in terannual variability characteristics by perturbing monthly parameters usin g a low-frequency stochastic model, and evaluates the effectiveness of the low-frequency component on interannual variability of generated monthly cli mate and simulated crop variables. Effectiveness of the low-frequency corre ction was tested by comparing results based on observed weather sequences t o those generated from the same underlying stochastic model without and wit h the low-frequency component. For monthly precipitation and maximum and mi nimum temperatures at 25 locations in the continental USA, the low-frequenc y correction reduced total error and eliminated negative bias of interannua l variability, and reduced the number of station-months with significant di fferences between observed and generated interannual variability, but over- represented variability of precipitation frequency. For 11 crop scenarios, the low-frequency correction reduced the number of instances in which mean simulated yields and development times differed for observed and generated weather, and improved all measures of interannual variability of simulated yields and development times. We conclude that the approach presented here to disaggregate and separately model the high- and low-frequency components of weather variability can effectively address the negative bias of intera nnual variability of monthly climatic means found in some stochastic weathe r generators, and improve crop simulation applications of stochastically-ge nerated weather. Further refinement is needed to better represent interannu al variability of both precipitation occurrence and intensity processes, an d to rectify over-correction of interannual temperature variability. (C) 20 01 Elsevier Science B.V. All rights reserved.