Estimating 1min rain rate distributions from numerical weather prediction

Paulson, Kevin S.

Abstract

Internationally recognized prognostic models of rain fade on terrestrial and Earth-space EHF links rely fundamentally on distributions of one-minute rain rates. Currently, in Rec. ITU-R P.837-6, these distributions are generated using the Salonen Poiares-Baptista method where one-minute rain rate distributions are estimated from long-term average annual accumulations provided by Numerical Weather Products (NWP). This paper investigates an alternative to this method based on the distribution of six-hour accumulations available from the same NWPs. Rain rate fields covering the UK, produced by the Nimrod network of radars, are integrated to estimate the accumulations provided by NWP and these are linked to distributions of fine scale rain rates. The proposed method makes better use of the available data. It is verified on 15 NWP regions spanning the UK and the extension to other regions is discussed.

Citation

Paulson, K. S. (2017). Estimating 1min rain rate distributions from numerical weather prediction. Radio science, 52(1), 176-184. https://doi.org/10.1002/2016rs006111

• Acceptance Date: Dec 30, 2016
• Online Publication Date: Jan 26, 2017
• Publication Date: 2017-01
• Deposit Date: Jan 23, 2017
• Publicly Available Date: Jan 26, 2017
• Journal: Radio science
• Print ISSN: 0048-6604
• Publisher: American Geophysical Union
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 1
• Pages: 176-184
• DOI: https://doi.org/10.1002/2016rs006111
• Keywords: Electrical and Electronic Engineering; General Earth and Planetary Sciences; Condensed Matter Physics
• Public URL: https://hull-repository.worktribe.com/output/447407
• Publisher URL: http://onlinelibrary.wiley.com/doi/10.1002/2016RS006111/abstract;jsessionid=7F955A797C562B832A475985D9E45C4E.f03t01
• Additional Information: Copy of article: Paulson, K. S. (2017), Estimating 1 min rain rate distributions from numerical weather prediction, Radio Sci., 52,176–184, doi:10.1002/2016RS006111.
• Contract Date: Jan 23, 2017