Data assimilation of radar reflectivity volumes in a LETKF scheme

<p>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 a...

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Main Authors: T. Gastaldo, V. Poli, C. Marsigli, P. P. Alberoni, T. Paccagnella
Format: Article
Language:English
Published: Copernicus Publications 2018-11-01
Series:Nonlinear Processes in Geophysics
Online Access:https://www.nonlin-processes-geophys.net/25/747/2018/npg-25-747-2018.pdf
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author T. Gastaldo
T. Gastaldo
V. Poli
C. Marsigli
C. Marsigli
P. P. Alberoni
T. Paccagnella
author_facet T. Gastaldo
T. Gastaldo
V. Poli
C. Marsigli
C. Marsigli
P. P. Alberoni
T. Paccagnella
author_sort T. Gastaldo
collection DOAJ
description <p>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, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate 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-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (<i>S</i>), amplitude (<i>A</i>) and location (<i>L</i>). Results obtained assimilating both conventional data and 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 observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (<i>roe</i>). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of <i>roe</i> introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.</p>
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spelling doaj.art-136727ac5b42491687be37b120fd2fd82022-12-22T02:56:55ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462018-11-012574776410.5194/npg-25-747-2018Data assimilation of radar reflectivity volumes in a LETKF schemeT. Gastaldo0T. Gastaldo1V. Poli2C. Marsigli3C. Marsigli4P. P. Alberoni5T. Paccagnella6Arpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, ItalyDepartment of Physics and Astronomy, University of Bologna, Bologna, ItalyArpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, ItalyArpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, Italynow at: Deutscher Wetterdienst, Offenbach, GermanyArpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, ItalyArpae Emilia-Romagna Hydro-Meteo-Climate Service, Bologna, Italy<p>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, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate 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-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (<i>S</i>), amplitude (<i>A</i>) and location (<i>L</i>). Results obtained assimilating both conventional data and 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 observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (<i>roe</i>). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of <i>roe</i> introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.</p>https://www.nonlin-processes-geophys.net/25/747/2018/npg-25-747-2018.pdf
spellingShingle T. Gastaldo
T. Gastaldo
V. Poli
C. Marsigli
C. Marsigli
P. P. Alberoni
T. Paccagnella
Data assimilation of radar reflectivity volumes in a LETKF scheme
Nonlinear Processes in Geophysics
title Data assimilation of radar reflectivity volumes in a LETKF scheme
title_full Data assimilation of radar reflectivity volumes in a LETKF scheme
title_fullStr Data assimilation of radar reflectivity volumes in a LETKF scheme
title_full_unstemmed Data assimilation of radar reflectivity volumes in a LETKF scheme
title_short Data assimilation of radar reflectivity volumes in a LETKF scheme
title_sort data assimilation of radar reflectivity volumes in a letkf scheme
url https://www.nonlin-processes-geophys.net/25/747/2018/npg-25-747-2018.pdf
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