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Lessons learnt fromthe operational 1D +4D‐Var assimilationof rain‐ andcloud‐affected SSM/Iobservations at ECMWF Alan GeerPéter BauerPhilippe Lopez2008 год

Lessons learnt from the operational 1D + 4D‐Var assimilation of rain‐ and cloud‐affected SSM/I observations at ECMWF
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Аннотация: Abstract Rain‐ and cloud‐affected Special Sensor Microwave/Imager (SSM/I) observations are assimilated operationally at the European Centre for Medium‐Range Weather Forecasts (ECMWF). The four‐dimensional variational analysis (4D‐Var) assimilates total column water vapour (TCWV) derived from one‐dimensional variational retrievals (1D‐Var). From the SSM/I radiances, 1D‐Var retrieves surface wind and the vertical profiles of temperature, humidity, cloud and precipitation. The main shortcoming of the ‘1D + 4D‐Var’ technique is that, of all this information, only TCWV gets into the 4D‐Var analysis. More information could be used: the rainwater path agrees well, in an instantaneous comparison, with observations from the precipitation radar on the Tropical Rainfall Measuring Mission. There are other issues, however: the simplified moist physics operators used in 1D‐Var produce roughly twice the observed amount of rain, but the problem is masked by a sampling bias, which comes from applying 1D + 4D‐Var when the observations are cloudy or rainy, but not when the first guess is rainy or cloudy and the observations are clear. The shortcomings of 1D + 4D‐Var will be addressed by moving to a direct 4D‐Var assimilation which includes all SSM/I observations, whether clear, cloudy or rainy, in the same stream. Copyright © 2008 Royal Meteorological Society
Год издания: 2008
Авторы: Alan Geer, Péter Bauer, Philippe Lopez
Издательство: Wiley
Источник: Quarterly Journal of the Royal Meteorological Society
Ключевые слова: Meteorological Phenomena and Simulations, Precipitation Measurement and Analysis, Climate variability and models