Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN
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Published:2021-02-15
Issue:2
Volume:13
Page:435-446
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Fullhart Andrew T.,Nearing Mark A.,Armendariz Gerardo,Weltz Mark A.
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).
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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