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A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

Abstract. A reflectivity forward operator and its associated
tangent linear and adjoint operators (together named RadarVar) were
developed for variational data assimilation (DA). RadarVar can analyze both
rainwater and ice-phase species (snow and graupel) by directly assimilating
radar reflectivity observations. The results of three-dimensional
variational (3D-Var) DA experiments with a 3 km grid mesh setting of the
Weather Research and Forecasting (WRF) model showed that RadarVar was
effective at producing an analysis of reflectivity pattern and intensity
similar to the observed data. Two to three outer loops with 50–100
iterations in each loop were needed to obtain a converged 3-D analysis of
reflectivity, rainwater, snow, and graupel, including the melting layers
with mixed-phase hydrometeors. It is shown that the deficiencies in the
analysis using this operator, caused by the poor quality of the background
fields and the use of the static background error covariance, can be
partially resolved by using radar-retrieved hydrometeors in a preprocessing
step and tuning the spatial correlation length scales of the background
errors. The direct radar reflectivity assimilation using RadarVar also
improved the short-term (2–5 h) precipitation forecasts compared to those
of the experiment without DA.
