Abstract

An Observing System Simulation Experiment (OSSE) was designed and developed to assess the potential benefit of the Infrared Sounding on the Meteosat Third Generation (MTG-IRS) geostationary meteorological satellite system to regional forecasts. In the proposed OSSE framework, two different models, namely, the MM5 and WRF models, were used in a nature run and data assimilation experiments, respectively, to reduce the identical twin problem. The 5-day nature run, which included three convective storms that occurred during the period from 11 to 16 June 2002 over US Great Plains, was generated using MM5 with a 4 km. The simulated “conventional” observations and MTG-IRS retrieved temperature and humidity profiles, produced from the nature run, were then assimilated into the WRF model. Calibration experiments showed that assimilating real or simulated “conventional” observations yielded similar error statistics in analyses and forecasts, indicating that the developed OSSE system worked well. On average, the MTG-IRS retrieved profiles had positive impact on the analyses and forecasts. The analyses reduced the errors not only in the temperature and the humidity fields but in the horizontal wind fields as well. The forecast skills of these variables were improved up to 12 hours. The 18 h precipitation forecast accuracy was also increased.

Highlights

  • Sensed satellite observations play an important role in modern data assimilation and forecast systems [1, 2]

  • Comparing nature run with the National Center for Environment Prediction (NCEP) Eta model 40 km analyses, it is found that nature run gives a good simulation of large scale and mesoscale weather systems

  • An Observing System Simulation Experiment (OSSE) system was designed and conducted to document the added value of temperature and water vapor observations derived from the Meteosat Third Generation (MTG)-IRS to regional forecasts, especially to precipitation prediction

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Summary

Introduction

Sensed satellite observations play an important role in modern data assimilation and forecast systems [1, 2]. The variational data assimilation technique has been pursued in research communities and operational centres, with the main focus on large scale and mesoscale forecast, to assimilate clear sky and cloudy radiance. At the European Centre for Medium-Range Weather Forecasts (ECMWF), an all-sky approach [3] has been developed to assimilate Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer for the Earth Observing system (AMSR-E) data. Cloudy infrared radiance assimilation at convection-resolving scale using a 4-dimensional variational data assimilation system was studied by Vukicevic et al [5, 6]. Overall their results indicate that the analyzed atmospheric fields are improved after assimilation

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