【水科学讲坛】第25讲：黄河勘测规划设计研究院有限公司董事长、

党委书记张金良教高学术报告

报告简介：

首先介绍黄河调水调沙的基本情况，对调水调沙的缘由、定义、指导思想和总体目标以及调水调沙模式进行讲解。其次报告黄河调水调沙方案制定关键技术，黄河调水调沙21年以来取得的效果以及当前调水调沙存在的问题。最后展望黄河调水调沙前景。

爱尔兰都柏林圣三一大学殷硕教授学术报告通知

报告简介：

高性能金属构件是航空、航天、交通、能源、军工等现代工业的基石，且高端装备的服役性能很大程度上取决于构件的高性能。金属增材制造技术是近年来兴起的一种尖端制造技术，其独特的零件生产过程与传统的打印过程十分类似，因此又被称为金属3D打印技术。增材制造技术的诞生，为生产可定制化、高性能、结构复杂构件的制造提供了完美的解决方案。增材制造过程几乎不受零件复杂程度的影响，对于单件小批量生产和具有较高几何复杂性的零件，增材制造具有显著的竞争优势。传统的零件的制造受到零件本身复杂性的限制，往往在设计过程中并未完全实现功能优先的设计，结构上有很多冗余，浪费材料。增材制造可以通过对结构进行拓扑优化设计，实现构件的结构功能一体化、轻量化、高强度、耐极端载荷、超强散热、复杂内部流道、功能梯度等功能，极大的满足现代工业对难加工金属构件短周期、高精度、高性能制造的重大需求。

美国劳伦斯利弗摩尔国家实验室潘宝祥研究员学术报告

报告简介：

The earth climate system is featured by the chaotic geophysical fluid dynamics and the complicated interaction among various subsystems. This chaoticity and complexity raise the need to disentangle internal climate variability noise, external forcing, and model formulation deficiencies to answer climate-relevant questions, such as weather variability and climate adaptation. This talk discusses a self-supervised adversarial learning method for merging climate models and climate observations to disentangle different sources of uncertainties in climate prediction, therefore diagnosing, correcting, and improving our modeling of the earth system. We discuss the limitations of supervised (deep) learning in climate applications, and highlight the necessity of shifting toward novel learning paradigms to realize the power of modern machine learning techniques. We believe by replacing human model diagnosis experts with tireless machine "nitpickers" and "cleaners", we may soon reach a true "digital twin" of the earth climate system.

美国劳伦斯利弗摩尔国家实验室潘宝祥研究员学术报告

报告简介：

Deep neural networks, operate with large, high quality data, which together with proper computation resources, motivate an ongoing paradigm shift in scientific discovery and engineering practices. This talk is for domain experts who are interested in deep learning, and would like to apply deep learning to make predictions, explanations, or quickly explore research ideas. I will briefly review the technical history of deep learning, discuss six mindsets underpinning the data-driven modeling paradigm, use several application studies to illustrate the potential pitfalls and benefits for applying deep neural networks in specific problems. I encourage an open discussion of research frontiers, given that individuals could hardly follow the fast progress in this field. Finally, I will close the talk by providing useful resources for learning and tracking advances in this field.

美国劳伦斯利弗摩尔国家实验室潘宝祥研究员学术报告

报告简介：

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

华东师范大学孙勋研究员学术报告

报告简介：

Corn is the 1st economic field crop in the world, whose price stability guarantees sustainable and equitable food security. Most previous farm commodity price prediction model only focus on detecting the autoregression of historical transaction, while ignoring other factors. For agricultural commodities, different climate condition leads to different harvest situation, thus bringing volatility to prices. Therefore, it is reasonable to propose a method based on climate indices to measure the degree of their influence on price fluctuation.

A multiple regression model is developed for predicting corn price movements at the nation level. The June-September season is selected to target the essential growing stages of corn which are especially sensitive to drought, high temperature stress and water stress. In order to describe the movements of price, the price difference between June and September is chosen as the dependent variable. Daily climate data are obtained from PRISM which integrates both satellite and meteorological station observation data, and monthly price data are sourced from USDA. 39-year trend from 1981-2019 is explored to construct a predictive model. The results show that the accuracy of predicting up and down of price is 85%. Specifically, temperature in July has an identifiable effect on price movements which explains 36.99% price variation. These results imply that during the key growing period, climate indices occupy an important position on improving crop price forecast ability.

中国船舶科学研究中心彭晓星研究员学术报告

报告简介：

空化的尺度效应一般是指模型空化现象与原型的偏差，空化尺度效应的存在极大地阻碍了空化研究的工程应用。本报告首先介绍空化尺度效应的一般概念，从空化机理角度分析空化尺度效应的来源。然后从空化起始、云空化、空蚀等几个方面讨论空化尺度效应的修正方法和未来研究展望。