Medicane Ianos: 4D-Var Data Assimilation of Surface and Satellite Observations into the Numerical Weather Prediction Model WRF

Abstract

This work investigates extreme weather events such as the onset of medicanes, which can cause severe socioeconomic impacts, along with their predictability. In order to accurately forecast such events, the Weather Research and Forecasting (WRF) model and its state-of-the-art data assimilation modeling framework (WRFDA) were set up to produce high-resolution forecasts for the case study of Medicane Ianos, which affected Greece between 17 and 19 September 2020. Information from weather stations and the satellite precipitation product IMERG was blended with the background model information from the Global Forecast System (GFS) using the 4D variational data assimilation (4D-Var) technique. New fields in an 18 km spatial resolution domain covering Europe were generated and utilized as improved initial conditions for the forecast model. Forecasts were issued based on these improved initial conditions at two nested domains of 6 km and 2 km spatial resolution, with the 2 km domain enclosing Greece. Denial experiments, where no observational data were assimilated in the initial boundary conditions, showed that the temperature fields benefited throughout the forecasting horizon from the assimilation (ranging from a 5 to 10% reduction in the average MAE values), while neutral to slightly positive (ranging from a 0.4 to 2% reduction in the average MAE values) improvement was found for wind, although not throughout the forecast horizon. The increase in spatial resolution did not significantly reduce the forecast error, but was kept at the same small order of magnitude. A tendency of the model to overpredict precipitation regardless of assimilation was observed. The assimilation of the IMERG data improved the precipitation forecasting ability up to the 18th hour of forecast. When compared to assimilation experiments that excluded IMERG data, the assimilation of IMERG data produced a better representation of the spatial distribution of the precipitation fields.

Aeolus Data Validation for an Extreme Precipitation Event in Greece with the COSMO NWP Model

“…For this point, the difference is more than 60 km, with the Aeolus point located in the Kyparissian Golf in western Peloponnese and the non-Aeolus point in the Messinian Gulf in southern Peloponnese. With the exception of this point, there is generally good agreement with those provided by the literature, especially the sources listed in [14,16,17]. The differences may be attributed to the fact that these works were based on different analyses (e.g., final analysis of NCEP, ERA-5 reanalysis, and the operational ECMWF analyses).…”

“…Due to its exceptional scientific challenge as well as its most serious social impact, this is a notably well-studied case from many perspectives. These aspects have been thoroughly addressed in several current works regarding its meteorological and remote-sensing characteristics as well considerations regarding the socioeconomic hazards that resulted [12][13][14][15][16][17] and references therein. This study's goal is to investigate the existence of any qualitative and quantitative differences and improvements, especially regarding precipitation, resulting from the assimilation of Aeolus data, using the last version of the state-of-the-art non-hydrostatic regional COSMO model [18][19][20][21].…”

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

“…WRF is a mesoscale weather forecast model developed by the National Center for Atmospheric Research of the United States. This model has been successfully applied to data assimilation research [29,30], air quality modeling [31,32], and regional climate simulations, such as short−term rainfall forecasts [33] and typhoon simulation [34]. The WRF was dynamically scaled down using region nesting to improve the simulation resolution.…”