Machine learning to improve assimilation of snow observations for (sub)seasonal hydrologic forecasts

机器学习可改善（次）季节水文预报中雪观测的同化

• 批准号: 538084-2019
• 负责人: Fletcher, Christopher
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680859
• 关键词: Machine; learning; improve; assimilation; snow

Forecasts of streamflow in Canadian rivers between two weeks and two months ahead are of tremendous value to individuals, governments and industry. Aquanty Inc.would like to improve its capability to produce these forecasts, and a major challenge is obtaining accurate information about the quantity of water stored in the surface snowpack at the beginning of the forecast period. This proposal uses novel statistical and modelling techniques to produce a new optimal snow data product that can be used as an input for hydrologic forecasting. The new snow data product will be produced in real-time from an optimal combination of station snow surveys, remote sensing and reanalysis data, and will have great value to water managers. The impact of using this improved snow data to initialise hydrologic forecasts will be measured and reported in the scientific literature, where it will be of considerable interest to developers of similar forecast systems across the world.

加拿大河流未来两周至两个月的流量预测对于个人、政府和行业具有巨大价值。 Aquanty Inc. 希望提高其生成这些预测的能力，主要挑战是获取有关预测期开始时表面积雪中储存的水量的准确信息。该提案使用新颖的统计和建模技术来生成新的最佳雪数据产品，可用作水文预报的输入。新的降雪数据产品将通过站点降雪调查、遥感和再分析数据的优化组合实时生成，将对水资源管理者具有巨大的价值。使用改进的降雪数据来初始化水文预报的影响将在科学文献中进行衡量和报告，世界各地类似预报系统的开发人员将对此产生极大的兴趣。