2m Temperature Downscaling with Dynamic Lapse Rates

Application of a global downscaling method for 2m temperature is investigated, using forecasts of the ECMWF Integrated Forecast System. We work with a spatial resolution of 9km, which is destined to become the operational medium range ensemble resolution at ECMWF from June 2023. The downscaling technique is broadly based on Sheridan et al (Meteorological Applications, 2010), in which regression between model topographic elevation ("coarse grid") and model 2m temperature at an instant, in the vicinity of a gridbox in question, is the basis for lapse rate assignment for that gridbox. The rate so-derived is used to adjust temperatures onto a fine grid orography (e.g. 1km) for the said gridbox. Wherever, on the fine grid, elevation is out of the range of the coarse grid model topographic elevations in the vicinity then we use either extrapolation, for lower points, or model level data, for higher points. This overall approach should innately adapt to many meteorological scenarios - unstable, frontal, inversions, etc. - delivering much more weather-dependant lapse rate assignments for particular locations. The 2m temperatures so computed should verify much better than when using just a fixed rate assumption - e.g. -6.5K/km - as ECMWF does currently for it's very widely used meteogram products. The key attractions of this approach are: (i) the potential for large skill gains, (ii) that we exploit model "situational awareness" by indirectly incorporating the model's in-built physical processes, (iii) no training period or training data is needed, (iv) there is complete transparency for users because the lapse rate derivation is clearcut, and one can store lapse rate values on the grid in question. In this presentation we will describe the approach and discuss the results of early investigations, using unstable and stable examples. The stronger and weaker aspects will be highlighted, discussing how to deal with coastal effects and addressing the all-important questions of search radius for defining 'the vicinity', and the regression format. One innate weaker point is the absence of any bias correction; a proposal for how this could also be built in in future, using weather-type dependant gridbox-scale bias correction from ECMWF's ecPoint post-processing method, for 2m temperature, will also be touched on.