Potential of 4d-VAR for exigent forecasting of severe weather
Ross N. Hoffman, John M. Henderson, Thomas Nehrkorn
备注：
Some of this material was presented during the 4th General Assembly of EGU 2008 at the session NH1.02 titled "Extreme Events Induced by Weather and Climate Change: Evaluation, Forecasting and Proactive Planning."
Severe storms, tropical cyclones, and associated tornadoes, floods, lightning, and microbursts threaten life and property. Reliable, precise, and accurate alerts of these phenomena can trigger defensive actions and preparations. However, these crucial weather phenomena are difficult to forecast. The objective of this paper is to demonstrate the potential of 4d-VAR (four dimensional variational data assimilation) for exigent forecasting (XF) of severe storm precursors and to thereby characterize the probability of a worst-case scenario. 4d-VAR is designed to adjust the initial conditions (IC) of a numerical weather prediction model consistent with the uncertainty of the prior estimate of the IC while at the same time minimizing the misfit to available observations. For XF the same approach is taken but instead of fitting observations, a measure of damage or loss or an equivalent proxy is maximized or minimized. To accomplish this will require development of a specialized cost function for 4d-VAR. When 4d-VAR solves the XF problem a by-product will be the value of the background cost function that provides a measure of the likelihood of occurrence of the forecast exigent conditions. 4d-VAR has been previously applied to a special case of XF in hurricane modification research. A summary of a case study of Hurricane Andrew (1992) is presented as a prototype of XF. The study of XF is expected to advance forecasting high impact weather events, refine methodologies for communicating warning and potential impacts of exigent weather events to a threatened population, be extensible to commercially viable products, such as forecasting freezes for the citrus industry, and be a useful pedagogical tool.
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University of Electronic Science and Technology of China（电子科技大学）；Institute of Plateau Meteorology, China Meteorological Administration（中国气象局高原气象研究所）；Institute of Urban Meteorology, China Meteorological Administration（中国气象局城市气象研究所）
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Wuhan University（武汉大学）；Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere（湖北省陆气定量遥感重点实验室）；School of Remote Sensing and Information Engineering（遥感信息工程学院）；Hubei Luojia Laboratory（湖北珞珈实验室）
Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data
University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）；Intel Corp.（英特尔公司）；University of Delaware（特拉华大学）；Western Kentucky University（西肯塔基大学）；National Weather Office（国家气象局）；University of South Alabama（南阿拉巴马大学）；Tulane University（杜兰大学）；Auburn University（奥本大学）
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Cooperative Institute for Mesoscale Meteorological Studies（中尺度气象研究合作所）；NOAA/OAR/National Severe Storms Laboratory（美国国家海洋和大气管理局/海洋与大气研究/国家强风暴实验室）；School of Meteorology, University of Oklahoma（俄克拉荷马大学气象学院）；School of Computer Science, University of Oklahoma（俄克拉荷马大学计算机科学学院）
VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
Hong Kong University of Science and Technology (HKUST)（香港科学与技术大学）；Huawei Technologies Ltd.（华为技术有限公司）；Zhejiang University（浙江大学）；Shanghai AI Laboratory（上海人工智能实验室）
University of Cambridge（剑桥大学）；Vector Institute（矢量研究所）；University of Toronto（多伦多大学）；Microsoft Research（微软研究院）；British Antarctic Survey（英国南极调查局）；The Alan Turing Institute（阿兰·图灵研究所）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Google Deepmind（谷歌DeepMind）
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Meteorological Service（上海市气象局）
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
Beijing University of Posts and Telecommunications（北京邮电大学）；National Meteorological Center, China Meteorological Administration（中国气象局国家气象中心）
