Proceedings ArticleDOI
Automatic Rain and Wind Measurement Fault Identification in Mesoscale Weather Station Networks
Vesa Hasu,Heikki N. Koivo +1 more
- pp 489-494
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TLDR
In this paper, the authors proposed several automatic rain and wind measurement fault identification algorithms for surface weather station networks with station spacing around 10-20 km, where the fault identification tests must be tailor-made for dense measurement networks.Abstract:
Future increase in short-term weather forecasting, i.e. nowcasting, products requires denser surface weather station networks than in before. Thus number of measurement stations and points increases also and their fault identification must be more accurate - the fault identification tests must be tailor-made for dense measurement networks. This paper proposes several automatic rain and wind measurement fault identification algorithms for surface weather station networks with station spacing around 10-20 km.read more
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References
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Journal ArticleDOI
Quality Assurance Procedures in the Oklahoma Mesonetwork
Mark A. Shafer,Christopher A. Fiebrich,Derek S. Arndt,Sherman E. Fredrickson,Timothy W. Hughes +4 more
TL;DR: The process of developing high quality data in one network, the Oklahoma Mesonetwork (Mesonet), is detailed in this paper, which consists of an instrument laboratory, field visits, automated computer routines, and manual inspection.
Journal ArticleDOI
The Impact of Unique Meteorological Phenomena Detected by the Oklahoma Mesonet and ARS Micronet on Automated Quality Control
TL;DR: In this article, the authors used automated quality assurance (QA) software developed by the Oklahoma Mesonetwork (Mesonet) provides an efficient means to sift through over 500 000 observations ingested daily from the Mesonet and from a Micronet sponsored by the United States Department of Agriculture (USDA).
Journal ArticleDOI
A Quality Control Program for Surface Mesometeorological Data
TL;DR: In this paper, a program is described which has been used to verify the quality of surface mesonet data collected during the Cooperative Convective Precipitation Experiment (CCOPE).
Journal ArticleDOI
Estimation of Wind Speed Distribution Using Markov Chain Monte Carlo Techniques
TL;DR: Markov chain Monte Carlo techniques are used to carry out a Bayesian estimation procedure using wind speed data obtained from the Observatory of Hong Kong, which shows the method is extremely flexible.