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Weather station

About: Weather station is a research topic. Over the lifetime, 1789 publications have been published within this topic receiving 42864 citations. The topic is also known as: meteorological station & meteorological observation post.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: Inverse-distance weighting, ordinary kriging, and regression Kriging and apply these indices to insurance against heat damage to corn in Illinois and Iowa, showing that heat index insurance reduces relative risk premiums by 27-29% and that interpolated indices outperform the nearest-neighbor index by around 2-3% in terms of relative risk reduction.
Abstract: Extreme heat events cause periodic damage to crop yields and may pose a threat to the income of farmers. Weather index insurance provides payouts to farmers in case of measurable weather extremes to keep production going. However, its viability depends crucially on the accuracy of local weather indices to predict yield damages from adverse weather conditions. So far extreme heat indices are poorly represented in weather index insurance. In this study we construct indices of extreme heat using observations at the nearest weather station and estimates for each county using three interpolation techniques: Inverse-distance weighting, ordinary kriging, and regression kriging. Applying these indices to insurance against heat damage to corn in Illinois and Iowa, we show that heat index insurance reduces relative risk premiums by 27-29% and that interpolated indices outperform the nearest-neighbor index by around 2-3% in terms of relative risk reduction. Further, we find that the advantage of interpolation over a nearest-neighbor index in terms of relative risk reduction increases as the sample of weather stations is reduced. These findings suggest that heat index insurance can work even when weather data is spatially sparse, which delivers important implications for insurance practice and policy makers. Further, our public code repository provides a rich toolbox of methods to be used for other, perils, crops and regions. Our results are therefore not only replicable but also constitute a cornerstone for projects to come.

11 citations

Journal ArticleDOI
TL;DR: In this article, a Central-European nowcasting system for use in mountainous terrain is tested in the Whistler/Vancouver area as part of the SNOW-V10 experiment.
Abstract: A Central-European nowcasting system which has been developed for use in mountainous terrain is tested in the Whistler/Vancouver area as part of the SNOW-V10 experiment. The integrated nowcasting through comprehensive analysis system provides hourly updated gridded forecasts of temperature, humidity, and wind, as well as precipitation forecasts which are updated every 15 min. It is based on numerical weather prediction (NWP) output and real-time surface weather station and radar data. Verification of temperature, relative humidity, and wind against surface stations shows that forecast errors are significantly reduced in the nowcasting range compared to those of the driving NWP model. The main contribution to the improvement comes from the implicit bias correction due to use of the latest observations. Relative humidity shows the longest lasting effect, with >50 % reduction of mean absolute error up to +4 h. For temperature and wind speed this percentage is reached after +2 and +3 h, respectively. Two cases of precipitation nowcasting are discussed and verified qualitatively.

11 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared the ERA5 reanalysis monthly precipitation data of European Centre for Medium-range Weather Forecasts over South Siberia with observed precipitation data records from 132 ground weather stations for 1979-2015.

11 citations

Patent
08 Feb 2000
TL;DR: In this article, the spatial synoptic classification (SSC) is modified by taking seed days within sliding periods so that seed days are selected from the warmest and coldest periods of the year and two intermediate periods.
Abstract: The spatial synoptic classification (SSC) is modified by taking seed days within sliding periods so that seed days are selected from the warmest and coldest periods of the year and two intermediate periods. Artificial seed days are produced to represent typical weather patterns on other days of the year. From the selected seed days and the artificial seed days, a classification technique is developed to categorize a weather type for any day. Once the seed days are selected for a weather station, those seed days can be used to select seed days for nearby weather stations.

11 citations

Journal ArticleDOI
TL;DR: The toolkit reformats the extracted GHCN-Daily data into a more accessible structure to facilitate data mining and research on a large scale, and can be challenging for novice users and may dissuade from the uptake of this valuable dataset.

11 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202347
202293
2021124
2020123
2019131
2018131