Spatial interpolation of two-metre temperature over Norway based on the combination of numerical weather prediction ensembles and in situ observations

Abstract

Accurate hourly two-metre temperature gridded fields available in near real-time are valuable products for numerous applications, such as civil protection and energy production planning. An analysis ensemble of temperature is obtained from the combination of a numerical weather prediction ensemble (background) and in situ observations. At the core of the flow-dependent spatial interpolation method lies the analysis step of the local ensemble transform Kalman filter (LETKF). A scaling factor and a localization procedure have been added to correct for deficiencies of the background. Each observation is characterized by its own representativeness, which is allowed to vary in time. We call the method described here an Ensemble-based Statistical Interpolation (EnSI) scheme for spatial analysis and it has been integrated into the operational post-processing systems in use at the Norwegian Meteorological Institute (MET Norway). The benefits of the analysis are assessed over a 1-year time period (July 2017–July 2018) and a case-study is presented for a challenging situation over complex terrain. EnSI gives more accurate results than an interpolation method based exclusively on observations. The analysis ensemble provides a more informative representation of the uncertainty than a spatial analysis based on a single-field background. EnSI reduces the number of large prediction errors in the analysis compared to the background by almost 50%, reduces the ensemble spread and increases its accuracy.