The effect of 3DVAR data assimilation and convection-permitting scales on the simulation of precipitation and temperature over Western Europe

Data assimilation techniques can improve the simulation of regional precipitation and temperature over complex regions. Nowadays, regional climate simulations using convection-permitting scales are becoming available, but the number of those simulations including an additional data assimilation scheme is rather small because of their high computational costs. Hence, it is important to evaluate the effect of data assimilation schemes for such convection-permitting simulations, and to determine if data assimilation produces any improvement in the simulation of temperature or precipitation fields.

To investigate this, we employ the Weather Research and Forecasting model (WRF; version 3.8.1) to dynamically downscale the state-of-the-art ERA5 reanalysis over Western Europe. A 3 km spatial resolution grid is employed, together with 51 vertical levels. The temporal resolution of the WRF outputs is one hour. Two model configurations are tested in two experiments spanning the period 2010-2020 after a one-year spin-up. In the first experiment (NoDA), after the initialization of the model, the boundary conditions drive the model. The second experiment (DA) is configured the same way as NoDA, but the additional 3DVAR data assimilation step (WRFDA) is run every six hours (00, 06, 12 and 18 UTC – analysis times). Observations obtained from the PREPBUFR dataset (NCEP ADP Global Upper Air and Surface Weather Observations) are employed, and only those included inside a 120 min window around analysis times were assimilated. For DA, monthly varying background error covariance matrices were created. In both cases, the model uses the Noah-MP land surface model, and high-resolution daily-varying SST fields from the NOAA OI SST v2 data set instead of the SST field from ERA5.

The results of this study show that both experiments produce similar monthly precipitation patterns to those from observational data sets such as IMERG and CHIRPS, or the reanalysis ERA5. However, in general, and particularly during summer months, DA produces larger amounts of precipitation than NoDA. These amounts are in line with those from CHIRPS. In terms of temperature, DA show colder temperatures than NoDA in most of the months, which again are similar to those from observational data sets such as CRU or EOBS. The monthly temperature patterns of both experiments are similar to those from both observational data sets. These results highlight the fact that NoDA already is able to generate reliable precipitation and temperature fields compared to diverse gridded observational data sets, but the 3DVAR data assimilation can additionally improve the performance of the regional model when convection-permitting scales are employed.