3.1 A 1D Var Reanalysis of ERA5 Assimilating Raman Lidar Measurements of Temperature and Relative Humidity

Wednesday, 15 January 2020: 8:30 AM
210C (Boston Convention and Exhibition Center)
Shayamila Mahagammulla Gamage, The University of Western Ontario, London, ON, Canada; and A. Haefele, G. Martucci, and R. J. Sica

We present a 1 dimensional variational (1D Var) reanalysis of fifth generation European centre for medium-range weather forecast atmospheric reanalyses (ERA5) temperature and relative humidity profiles above Payerne, Switzerland, assimilating raw backscatter measurements from the MeteoSwiss Raman Lidar for Operational Meteorology, RALMO. The reanalysis is called ERA5-reRH. We use an optimal estimation method (OEM) to perform the 1D Var data assimilation. The forward model combines the Raman lidar equation with the Hyland and Wexler expression for water vapor saturation pressure to produce a complete set of Raman lidar signals from profiles of temperature and relative humidity. The error covariance matrix of ERA5 (background) was derived from the differences between ERA5 and a set of 50 special radiosoundings which have not been assimilated into ERA5. We validated ERA5-reRH, ERA5 and RALMO temperature and relative humidity profiles against the same set of special radiosoundings and found the best agreement was for ERA5-reRH with a bias of less than 2 %RHw and a spread of less than 8 %RHw below 8 km in terms of relative humidity. In terms of temperature, improvements for ERA5-reRH were only found in the boundary layer because ERA5 assimilates a large number of upper air temperature observations. The OEM provides a full uncertainty budget of the reanalyzed temperature and relative humidity on a profile-by-profile basis.
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