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Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation
SMHI, Research Department, Meteorology.ORCID iD: 0000-0003-4870-5459
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2009 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 61, no 2, 278-287 p.Article in journal (Refereed) Published
Abstract [en]

An observation operator for Doppler radar radial wind measurements is developed further in this article, based on the earlier work and considerations of the measurement characteristic. The elementary observation operator treats radar observations as point measurements at pre-processed observation heights. Here, modelling of the radar pulse volume broadening in vertical and the radar pulse path bending due to refraction is included to improve the realism of the observation modelling. The operator is implemented into the High Resolution Limited Area Model (HIRLAM) limited area numerical weather prediction (NWP) system. A data set of circa 133 000 radial wind measurements is passively monitored against the HIRLAM six-hourly background values in a 1-month experiment. No data assimilation experiments are performed at this stage. A new finding is that the improved modelling reduces the mean observation minus background (OmB) vector wind difference at ranges below 55 km, and the standard deviation of the radial wind OmB difference at ranges over 25 km. In conclusion, a more accurate and still computationally feasible observation operator is developed. The companion paper (Part II) considers optimal super-observation processing of Doppler radar radial winds for HIRLAM, with general applicability in NWP.

Place, publisher, year, edition, pages
2009. Vol. 61, no 2, 278-287 p.
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
URN: urn:nbn:se:smhi:diva-629DOI: 10.1111/j.1600-0870.2008.00380.xISI: 000263258300008OAI: oai:DiVA.org:smhi-629DiVA: diva2:807240
Available from: 2015-04-23 Created: 2015-04-21 Last updated: 2016-11-22Bibliographically approved

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