Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions
Won Chang, Michael L. Stein, Jiali Wang, V. Rao Kotamarthi, Elisabeth J. Moyer
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Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6-7%/K, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1-2 %/K in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. We develop here a new methodology for identifying individual rainstorms and studying their physical characteristics - including starting location, intensity, spatial extent, duration, and trajectory - that allows identifying that compensating mechanism. We apply this technique to precipitation over the contiguous U.S. from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In model studies, we find that the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller, in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. We show also that projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via "data-driven simulations" that apply model-projected changes to observational data. We therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.
Event-Aware Spatiotemporal Precipitation Forecasting with Geographic Context and Physics-Guided Regularization
Beijing Institute of Technology（北京理工大学）；Tangshan Research Institute, Beijing Institute of Technology（北京理工大学唐山研究院）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation
Fudan University（复旦大学）；Shanghai Central Meteorological Observation（上海中心气象台）
数值天气预报能够及时预报灾害性降水，减轻人类灾害。欧洲中期天气预报中心（European Centre for Medium Range Weather Forecasts，EC）是世界上最常用的数值天气预报中心。然而，由于人们对降水机制的认识还不充分，使得EC往往存在一定的偏差，因此有必要通过降水偏差修正（Bias Correcting on Precipitation，BCoP）来修正EC预测。现有的BCoPs受到有限的先验数据和固定的时空（ST）尺度。因此，我们提出了一个名为时空特征自动选择（STAS）模型的端到端深度学习BCoP模型，通过ST特征选择机制（SFM/TFM）从EC中选择最佳ST规则。根据不同的输入特征，这两种机制可以自动调整空间和时间尺度进行校正。在EC公共数据集上的实验表明，与8个已发表的BCoP方法相比，STAS在BCoP的几个标准上表现出最先进的性能，命名为威胁分数（TS）。烧蚀研究表明，SFM/TFM在提高BCoP的性能方面效果显著，尤其是在强降水天气中。
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd.（基础设施范式转换研究所，八千代工程株式会社）
最近，由于气候变化引起的前所未有的天气条件，洪水灾害已成为一个社会问题。对暴雨立即作出反应对于减轻经济损失和迅速恢复都很重要。时空降水预报可以提高大坝入库流量预测的准确性，提前6小时以上减轻洪水损失。然而，由于目标预测值与地面实况值之间存在不可约的偏差，普通的ConvLSTM在实际降水预报中存在着可预报范围大于3个时间步的局限性。本文提出了一种雨码的时空降水代码到代码的预测方法。我们提出了一种新的雨的功能，表示一个时间雨的过程，使用多帧融合的时间步长减少。我们基于标准ConvLSTM对各种术语范围进行了雨码研究。我们应用了一个坝区内的日本雨季小时降水数据，2006年至2019年下约12.7万小时，每年5月至10月。我们将雷达分析每小时的数据应用于面积为136 x 148 km 2的中央更广泛的区域。最后，我们在几个预报范围内提供了雨码大小和每小时准确度之间的敏感性研究。
VARENN: Graphical representation of spatiotemporal data and application to climate studies
Field Science Education and Research Center (FSERC), Kyoto University（京都大学 田野科学教育研究中心（FSERC））；Center for the Promotion of Interdisciplinary Education and Research, Kyoto University（京都大学 跨学科教育研究推进中心）
Evaluation of the performance of Euro-CORDEX RCMs for assessing hydrological climate change impacts in Great Britain: a comparison of different spatial resolutions and quantile mapping bias correction methods
Boundary-Induced Biases in Climate Networks of Extreme Precipitation and Temperature
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Connecticut（康涅狄格大学）；University of Maryland at Baltimore County（马里兰大学巴尔的摩县分校）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学的布罗德研究所）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Humboldt University of Berlin（柏林洪堡大学）
Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking
高分辨率区域气候模式的动态降尺度可能提供在气候模拟中真实再现降水和天气事件的可能性。当分辨率降到千米级时，使用显式对流而不是参数化对流可以提供更高的保真度。然而，这些增加的模型分辨率允许并且需要越来越复杂的诊断来评估模型保真度。在这项研究中，我们使用了一套动态缩小规模的模拟夏季美国（WRF驱动的NCEP）与系统的变化参数和治疗对流作为一个测试案例模型降水的评估。特别是，我们使用了一种新的暴雨识别和跟踪算法，该算法基本上将所有降雨分配给单个降水事件（Chang et al. 2016）。这种方法允许多方面的见解，包括，至少在这些运行中，模型湿偏差是由过度的面积范围的降水事件。偏差是时间依赖性的，产生过度的昼夜周期振幅。我们发现，这种效果是由新的生产的事件，但在白天的长期降水事件的过度放大，并在域平均，降水偏差出现最好的表示为添加剂的偏移。在所有评估的模型配置中，对流允许模拟最一致地减少了降水事件特征的偏差。
Assessment of Future Changes in Intensity-Duration-Frequency Curves for Southern Ontario using North American (NA)-CORDEX Models with Nonstationary Methods
Modelling the combined effects of land use and climatic changes: coupling bioclimatic modelling with markov-chain cellular automata in a case study in Cyprus
