English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. A reflectivity forward operator and its associatedtangent linear and adjoint operators (together named RadarVar) weredeveloped for variational data assimilation (DA). RadarVar can analyze bothrainwater and ice-phase species (snow and graupel) by directly assimilatingradar reflectivity observations. The results of three-dimensionalvariational (3D-Var) DA experiments with a 3 km grid mesh setting of theWeather Research and Forecasting (WRF) model showed that RadarVar waseffective at producing an analysis of reflectivity pattern and intensitysimilar to the observed data. Two to three outer loops with 50–100iterations in each loop were needed to obtain a converged 3-D analysis ofreflectivity, rainwater, snow, and graupel, including the melting layerswith mixed-phase hydrometeors. It is shown that the deficiencies in theanalysis using this operator, caused by the poor quality of the backgroundfields and the use of the static background error covariance, can bepartially resolved by using radar-retrieved hydrometeors in a preprocessingstep and tuning the spatial correlation length scales of the backgrounderrors. The direct radar reflectivity assimilation using RadarVar alsoimproved the short-term (2–5 h) precipitation forecasts compared to thoseof the experiment without DA.

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