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contributor authorRidal, Martin
contributor authorDahlbom, Mats
date accessioned2017-06-09T16:51:37Z
date available2017-06-09T16:51:37Z
date issued2017
identifier issn1558-8424
identifier otherams-75423.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4217758
description abstractadar reflectivity observations have proven to be beneficial for improving the skill of numerical weather prediction (NWP) models. A few countries around the world use radar reflectivity observations in their operational runs. The majority of experiments and usage are still only including the local radar observations from the country in which the forecasts are made. The model domains on the other hand cover areas far greater than this and therefore observations from surrounding countries need to be included. As of today there is no central collection and re-distribution of volume data in Europe. Since a few years back there is a collection and an initiative to harmonize European radar observations but the re-distribution is only of centrally constructed composites. This study describes the efforts to collect volume reflectivity data from several data providers and make them available for use in a NWP model. A preprocessing of the reflectivity data has been set up in order to handle the different incoming data and to make a first data reduction in order for the NWP models to be able to include the new observations. Assimilation experiments have been performed that proves that it is possible to assimilate operational radar reflectivity data from several countries with a neutral to positive impact.
publisherAmerican Meteorological Society
titleAssimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept
typeJournal Paper
journal volume056
journal issue006
journal titleJournal of Applied Meteorology and Climatology
identifier doi10.1175/JAMC-D-16-0247.1
journal fristpage1739
journal lastpage1751
treeJournal of Applied Meteorology and Climatology:;2017:;volume( 056 ):;issue: 006
contenttypeFulltext


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