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首页> 外文期刊>Journal of hydrometeorology >Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets
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Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets

机译：Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets

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摘要

Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we evaluated the use of a spatial regression method to estimate the uncertainty in precipitation and temperature fields of existing deterministic gridded meteorological datasets. Taking the widely used North American Land Data Assimilation System 2 (NLDAS-2) precipitation and temperature dataset as an example, we used the deterministic NLDAS-2 values to generate ensemble estimates for daily precipitation, mean temperature, and the diurnal temperature range. Our method is a form of ensemble dressing. Nine variations were tested to assess the impacts of sampling density on the estimates of the mean and uncertainty, and one strategy was selected to generate 100 ensemble members at 1/8 degrees and daily resolution for the period 1979-2019, termed as the Ensemble Dressing of NLDAS-2 (EDN2). Compared with an independent station-based ensemble dataset, the ensemble dressing method produces reasonable uncertainty patterns for precipitation and underestimates uncertainty for temperature. For precipitation, the uncertainty increases with the increase in daily accumulation. For temperature, the uncertainty is relatively small in the warm season and large in the cold season. This ensemble dressing method is applicable to other deterministic gridded meteorological datasets. The generated spatiotemporally varying uncertainty information could support applications such as land surface and hydrologic modeling, data assimilation, and forecasting, especially where application models are tied to a specific meteorological dataset.

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