Adaptive tuning of uncertain parameters in a numerical weather prediction model based upon data assimilation

In numerical weather prediction models, near-surface quantities like 10-m wind speed (FF10M) or 2-m temperature (T2M) tend to exhibit significantly larger forecast errors than the related variables in the free troposphere. Besides representativeness issues of surface stations, this is primarily related to parametrization errors and insufficient knowledge of relevant physical properties of the soil and the surface layer. For instance, the vegetation roughness length is usually derived from land-cover classifications that may contain errors and reflect only part of the natural variability. This article describes a methodology implemented into Deutscher Wetterdienst's operational numerical weather prediction model ICON in order to improve the estimate of such parameters by using information from the data assimilation system. Building upon the condition that FF10M, T2M, and 2-m relative humidity are assimilated, time-filtered assimilation increments are calculated for the respective fields at the lowest model level. These are taken as proxies for the related model biases. For T2M, an additional weighted increment field is computed that indicates the model bias in the diurnal temperature amplitude. Based on these increment fields, several physical parameter fields and a few model tuning parameters are varied around their base values. This adaptive parameter adjustment is used operationally in the global and regional forecasting systems of Deutscher Wetterdienst. The ensuing reduction of the FF10M, T2M, and 2-m relative humidity errors typically lies on the order of 5% on a hemispheric average but has substantial regional and seasonal variability that depends on the original magnitude of the model error. A weaker but still statistically significant positive impact is seen in the radiosonde verification of wind speed, humidity, and temperature in the lower troposphere, giving confidence that the adaptive tuning indeed reduces model errors rather than pushing the model towards unrepresentative station observations.