Version 1
: Received: 27 June 2023 / Approved: 28 June 2023 / Online: 28 June 2023 (13:40:41 CEST)
How to cite:
Zhang, Z.; Mao, W.; Wang, M.; Zhang, W.; Ji, C.; Aidaituli, M.; An, D. Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China. Preprints2023, 2023062043. https://doi.org/10.20944/preprints202306.2043.v1
Zhang, Z.; Mao, W.; Wang, M.; Zhang, W.; Ji, C.; Aidaituli, M.; An, D. Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China. Preprints 2023, 2023062043. https://doi.org/10.20944/preprints202306.2043.v1
Zhang, Z.; Mao, W.; Wang, M.; Zhang, W.; Ji, C.; Aidaituli, M.; An, D. Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China. Preprints2023, 2023062043. https://doi.org/10.20944/preprints202306.2043.v1
APA Style
Zhang, Z., Mao, W., Wang, M., Zhang, W., Ji, C., Aidaituli, M., & An, D. (2023). <strong>Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China</strong>. Preprints. https://doi.org/10.20944/preprints202306.2043.v1
Chicago/Turabian Style
Zhang, Z., Mushajiang Aidaituli and Dawei An. 2023 "<strong>Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China</strong>" Preprints. https://doi.org/10.20944/preprints202306.2043.v1
Abstract
The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusion live data Chinese Land Data Assimilation System (CLDAS,0.05°× 0.05°,hourly data) were used as real data, and Central Meteorological Observatory guidance forecast (SCMOC,0.05°× 0.05 °,forecasting the following 10 days in 3 h intervals) was used as forecast data, both issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products SCMOC.Based on conventional temperature forecast information,we designed four temperature rise prediction algorithms for essential factors affecting snowmelt.Temperature rise prediction algorithms included the daily maximum temperature algorithm, daily temperature rise range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm.Four temperature rise prediction algorithms values were calculated of each prediction product.Root-mean-square error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution.Comprehensive tests show that the forecast product revised by the average filter algorithm was superior to revised by the dynamic linear regression algorithm as well as the original forecast product.Through these algorithms, the more suitable temperature rise forecast value for each grid point in the study area can be obtained at different prediction times. The comprehensive and accurate temperature forecast value in mountainous snowmelt season can provide an accurate theoretical basis for effective prediction of the runoff in snowmelt areas and prevention of snowmelt flooding.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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