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 resulting in distortion of simulated agricultural or hydrological variables. This study presents a stochastic weather generator that attempts to improve interannual variability characteristics by perturbing monthly parameters using a low-frequency stochastic model, and evaluates the effectiveness of the low-frequency component on interannual variability of generated monthly climate and simulated crop variables. Effectiveness of the low-frequency correction was tested by comparing results based on observed weather sequences to those generated from the same underlying stochastic model without and with the low-frequency component. For monthly precipitation and maximum and minimum temperatures at 25 locations in the continental USA, the low-frequency correction reduced total error and eliminated negative bias of interannual variability, and reduced the number of station-months with significant differences 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 interannual variability of monthly climatic means found in some stochastic weather generators, and improve crop simulation applications of stochastically-generated weather. Further refinement is needed to better represent interannual variability of both precipitation occurrence and intensity processes, and to rectify over-correction of interannual temperature variability.