Coping with model structural uncertainty in medium-term hydro-climatic forecasting

Ashish Sharma; Shahadat Chowdhury

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Coping with model structural uncertainty in medium-term hydro-climatic forecasting

机译：中期水文气候预测中应对模型结构不确定性

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This paper reviews two alternatives for reducing structural uncertainty in medium-term hydro-climatic forecasting. The first is a static ensemble average, illustrated here using the Multiple Reservoir Inflow Forecasting System, a nonparametric probabilistic forecasting model that relates streamflow to climate predictors, and generates monthly sequences of multi-site flow from the present for the coming 12 months. Instead of forming a single predictive relationship, multiple constituent models, each having their own unique predictor variable sets, are formed. A weighted probabilistic combination of these constituent models completes the static ensemble average. The second alternative is a dynamic ensemble average that allows constituent models to change importance with time, model weights evolving as a function of these weights at preceding time steps. Dynamic model combination is demonstrated here for first combining multiple sea surface temperature anomaly forecasts to produce a global sea surface temperature anomaly field, and then using the dynamically combined sea surface temperature anomaly (SSTA) field to concurrently ascertain inflows at multiple locations in a semi-arid Australian catchment. The paper concludes by identifying scenarios under which one would expect to see improvements as a result of static or dynamic model combination, and provides suggestions for further research in this area.

机译：本文回顾了两种减少中期水文气候预测中结构不确定性的方法。第一个是静态总体平均数，在此使用多水库入水量预测系统进行说明。该系统是一种非参数概率预测模型，该模型将流量与气候预测因子相关联，并在未来12个月内按月生成多站点流量的月度序列。代替形成单个预测关系，形成了多个组成模型，每个模型具有各自的独特预测变量集。这些组成模型的加权概率组合完成了静态总体平均。第二种选择是动态综合平均数，它允许组成模型随时间改变重要性，模型权重根据这些权重在前面的时间步长演变而来。这里展示了动态模型组合，该模型首先组合多个海面温度异常预报以产生一个全球海面温度异常场，然后使用动态组合的海面温度异常（SSTA）场同时确定半个海域中多个位置的流入量。干旱的澳大利亚流域。本文最后通过确定可以通过静态或动态模型组合实现预期改进的方案，并为该领域的进一步研究提供了建议。

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来源
《Nordic hydrology》 |2011年第3期|p.113-127|共15页
作者
Ashish Sharma; Shahadat Chowdhury;
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作者单位

School of Civil and Environmental Engineering,The University of New South Wales,Sydney, nsw 2052,Australia;

School of Civil and Environmental Engineering,The University of New South Wales,Sydney, NSW 2052,Australia;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
dynamic combination; model uncertainty; probabilistic forecasting; rainfall; seasonal forecasting; streamflow;

机译：动态组合模型不确定性概率预报降雨季节预报流量;

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1. Coping with model structural uncertainty in medium-term hydro-climatic forecasting [J] . Ashish Sharma and Shahadat Chowdhury Hydrology Research . 2011,第2a3期

机译：中期水文气候预测中应对模型结构不确定性
2. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market [J] . George P. Papaioannou, Christos Dikaiakos, Anargyros Dramountanis, Energies . 2016,第8期

机译：使用混合流形学习主成分模型的短期和中期负荷预测的分析和建模，并与古典统计模型（SARIMAX，指数平滑）和人工智能模型（ANN，SVM）进行比较：希腊电力市场
3. Global Climate Model Selection for Analysis of Uncertainty in Climate Change Impact Assessments of Hydro-Climatic Extremes [J] . Patrick A. Breach, Slobodan P. Simonovic, Zhiyong Yang American Journal of Climate Change . 2016,第4期

机译：全球气候模式选择，用于分析极端水文气候变化影响评估中的不确定性
4. On the Use of Data Uncertainty in Hydro-climatic Modeling: a Bayesian Approach [C] . Shivam Tripathi, Rao S Govindaraju World environmental and water resources congress . 2009

机译：在水文气候模拟中使用数据不确定性：贝叶斯方法
5. Statistical Models for Characterizing and Reducing Uncertainty in Seasonal Rainfall Pattern Forecasts to inform Decision Making [D] . AlMutairi, Bandar Saud. 2017

机译：用于表征和减少季节降雨模式的不确定性的统计模型预测，以告知决策
6. Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty identifiability and forecasts [O] . Gerardo Chowell 2017

机译：将动态模型拟合到具有不确定性的流行病暴发：参数不确定性可识别性和预测的入门
7. Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market [O] . George P. Papaioannou, Christos Dikaiakos, Anargyros Dramountanis, 2016

机译：使用混合流形学习主成分模型和与经典统计模型（saRImaX，指数平滑）和人工智能模型（aNN，sVm）的比较分析和建模中短期负荷预测：希腊电力市场案例

Coping with model structural uncertainty in medium-term hydro-climatic forecasting

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