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Data assimilation of radar reflectivity volumes in a LETKF scheme

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, 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 (S), amplitude (A)
and location (L). 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
(roe). 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 roe introduces imbalances into
the analyses, resulting in a severe degradation of forecast accuracy,
especially when very short assimilation cycles are used.
