Stochastic 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...Stochastic 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.展开更多
Information about extreme rainfall is lacking in regions of South America and Africa.This study attempts to fill this scientific gap by use of a gridded parameterization for the stochastic weather generator,CLIGEN,to ...Information about extreme rainfall is lacking in regions of South America and Africa.This study attempts to fill this scientific gap by use of a gridded parameterization for the stochastic weather generator,CLIGEN,to map depth-duration-frequency(DDF)relationships.Analysis of 500-year point-scale precipitation time series generated at each grid point allowed maps of return period precipitation to be produced for a se-lection of sixteen durations ranging from 10-min to 1-year and for nine return periods from 2 to 500 years.The generalized extreme value(GEV)probability distribution was fitted for all durations,and given GEV quantiles,an interpolation method was applied to produce maps at 0.1° resolution that better resolve small-scale spatial climate gradients.In addition to uncertainties related to GEV fitting,this study quantifies prediction intervals based on ground validation.This validation was important for identifying biases in CLIGEN,although uncertainties were not always satisfactorily defined due to sampling design and other factors.For daily/multi-day durations,100 stations with daily observations and ≥50-year records were selected for validation against the 0.1° CLIGEN map series,resulting in a median and average absolute error of 13%and 16%,respectively.For sub-daily durations,prediction errors were larger overall.An analogy using available U.S.data established the degree of bias in CUGEN for sub-daily durations,and three records in Brazil with high temporal resolutions were used to confirm that applied bias adjustments resulted in error ranges similar to the daily/multi-day cases.This atlas is freely available for study of extreme precipitation.展开更多
文摘Stochastic 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.
文摘Information about extreme rainfall is lacking in regions of South America and Africa.This study attempts to fill this scientific gap by use of a gridded parameterization for the stochastic weather generator,CLIGEN,to map depth-duration-frequency(DDF)relationships.Analysis of 500-year point-scale precipitation time series generated at each grid point allowed maps of return period precipitation to be produced for a se-lection of sixteen durations ranging from 10-min to 1-year and for nine return periods from 2 to 500 years.The generalized extreme value(GEV)probability distribution was fitted for all durations,and given GEV quantiles,an interpolation method was applied to produce maps at 0.1° resolution that better resolve small-scale spatial climate gradients.In addition to uncertainties related to GEV fitting,this study quantifies prediction intervals based on ground validation.This validation was important for identifying biases in CLIGEN,although uncertainties were not always satisfactorily defined due to sampling design and other factors.For daily/multi-day durations,100 stations with daily observations and ≥50-year records were selected for validation against the 0.1° CLIGEN map series,resulting in a median and average absolute error of 13%and 16%,respectively.For sub-daily durations,prediction errors were larger overall.An analogy using available U.S.data established the degree of bias in CUGEN for sub-daily durations,and three records in Brazil with high temporal resolutions were used to confirm that applied bias adjustments resulted in error ranges similar to the daily/multi-day cases.This atlas is freely available for study of extreme precipitation.
