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Discussion papers | Copyright
https://doi.org/10.5194/npg-2018-27
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Apr 2018

Research article | 26 Apr 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

Data assimilation of radar reflectivity volumes in a LETKF scheme

Thomas Gastaldo1,2, Virginia Poli1, Chiara Marsigli1, Pier Paolo Alberoni1, and Tiziana Paccagnella1 Thomas Gastaldo et al.
  • 1Arpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, Italy
  • 2University of Bologna, Bologna, Italy

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, very fewattempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and to several open issues, like the arise of imbalances in the analyses and the estimation of the observational error. In this work, it is evaluated the impact of the assimilation of radar reflectivity volumes employing a Local Ensemble Transform Kalman Filter (LETKF), implemented for the convection permitting model of the COnsortium for Small-scale Modelling (COSMO). A 4 days test case on February 2017 is considered and QPF is evaluated in terms of the SAL technique, an object-based method which allows to evaluate structure, amplitude and location of precipitation fields Results obtained assimilating radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna region (Arpae-SIMC), in which only conventional data are employed. Furthermore, some sensitivity tests are performed to evaluate the impact of the additive inflation, of the lenght of assimilation windows and of the reflectivity observational error. Finally, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields.

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Accuracy of numerical weather prediction forecasts is strongly related to the quality of initial conditions employed. To improve them, it seems advantageous to use radar observations because of their high resolution both spatial and temporal. This is tested in a high resolution model which domain covers Italy. Results show that the improvement in using radar observations is very slight, probably because many parameters that affect the assimilation of these observations have still to be optimized.
Accuracy of numerical weather prediction forecasts is strongly related to the quality of initial...
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