Concept of the 2 km ICON-LAM reanalysis based on conventional observations for climate applications at Central Europe

Reanalysis data sets are becoming increasingly popular for a broad spectrum of applications such as climate adaptation and mitigation, renewable energy, agriculture or hydrology, including the assessment of meteorological hazards and extremes. Recently, the high relevance of reanalysis data sets has been increased further due to their value as a basis for training AI-based NWP model emulators. Most of these applications require much finer grid spacing compared to ERA5 (31 km), ERA6 (14 km), or even ICON-DREAM (dual resolution reanalysis for emulators, applications, and monitoring, 13 and 6.5 km). Therefore, regional reanalysis is designed and produced for limited geographical regions, allowing for the provision of high-quality and high-resolution data sets.

 This presentation is a companion work with colleagues from the research and development department of DWD, who introduce the new concept for regional reanalysis and the adaptation to their new regional reanalysis product ICON-FORCE. We aim to present the sparse-input version of ICON-FORCE, named ICON-FORCE-c, which is designed for the needs of DWD's Climate and Environment business area. For this product, we will use only two types of observation data, which are 1) conventional observations and as a future update 2) SEVIRI radiometer satellite data.

ICON-FORCE-c will be produced using the operational 2 km ICON-LAM numerical weather prediction (NWP) model framework. Its operational data assimilation cycle comprises the KENDA LETKF-based data assimilation scheme at hourly intervals, complemented by a snow analysis every 6 hours, and T2M, SST and soil moisture analysis every 24 hours (at 00 UTC). The background error covariances are provided by a 20 member ensemble at the same 2.1 km resolution of the deterministic run. The boundary conditions come from the ICON-DREAM European nest domain.

As the concept summary, we introduce two versions of ICON-FORCE, 1) the full-input and 2) the sparse-input regional reanalyses. While full-input reanalyses aim to provide the best description of the Earth system, with the modern regional observational network, the sparse input reanalyses aims to provide the best possible climate state for the area, thus focusing on climate trends and its consistency. We demonstrate how changes in the observational system, which includes the introduction of new observations within a reanalysis, have the potential to cause artifical trends. 

In this work, we present in more detail the sparse-input regional reanalysis, whose period extent will be determined by the availability of the ICON-DREAM boundary data. We present first evaluation results comparing the performance of ICON-FORCE-c to the full-input version as well as ICON-DREAM.