Abstract
Abstract. This dataset contains input parameters for 12 703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature, and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-, 20-, and 10-year record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the US, which were compared to a selection of stations from an existing US CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen (last access: 20 November 2020) and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020a).
Highlights
Essential climate variables defined by the World Meteorological Organization are physical, chemical, or biological variables, or groups of linked variables that critically contribute to the characterization of Earth’s climate (Bojinski et al, 2014)
CLImate GENerator (CLIGEN) is one such point-scale weather generator that produces daily outputs based on input parameters that are essentially observed monthly statistics
The 30, 20, and 10-year datasets are generally in close agreement, and in some cases, the methods used to create this dataset may offer an improvement over existing CLIGEN input files
Summary
Essential climate variables defined by the World Meteorological Organization are physical, chemical, or biological variables, or groups of linked variables that critically contribute to the characterization of Earth’s climate (Bojinski et al, 2014). Climate data reduced to monthly statistics may facilitate analysis of multi-decadal climate trends and serve as benchmarks of climate normals (Menne et al, 2012; Hollmann et al, 2013) In this paper, it is discussed how a stochastic weather generator may be parameterized with a new dataset of monthly climate statistics to simulate daily weather outputs for locations around the world. Fullhart et al.: Climate benchmarks and input parameters representing locations in 68 countries cal distributions of weather parameters (Kinnell, 2019) Such models include the Rangeland Hydrology and Erosion Model (RHEM), the Water Erosion Prediction Project (WEPP) model, and the Revised Universal Soil Loss Equation 2 (RUSLE 2) model. The parameters are validated using an existing CLIGEN input dataset for the United States, and differences are discussed
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