Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)

Abstract

ABSTRACTStochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.

Keywords

• Agriculture
• climate
• hydrology
• soil erosion
• stochastic weather generator