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<span style="font-family:Helvetica;font-size:12px;orphans:auto;widows:auto" class="ContentPasted0">This is an announcement of Elena Orlova's MS Presentation</span>
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Candidate: Elena Orlova</div>
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Date: Friday, November 11, 2022</div>
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Time: 9 am CST</div>
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Remote Location:<span class="Apple-converted-space ContentPasted0"> </span><a href="https://uchicago.zoom.us/j/96838766484?pwd=UkZlVTYrWnVCMUplRzJKL09zQlNQQT09" class="ContentPasted0">https://uchicago.zoom.us/j/96838766484?pwd=UkZlVTYrWnVCMUplRzJKL09zQlNQQT09</a> </div>
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Location: JCL 390</div>
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M.S. Paper Title: Enhancing Subseasonal Climate Forecasting with Climate Model Ensembles and Machine Learning</div>
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Abstract: Producing high-quality forecasts of key climate variables such as temperature and precipitation on sub-seasonal time scales has long been a gap in operational forecasting. Recent studies have shown promising results using machine learning (ML) models
to advance sub-seasonal forecasting (SSF), but several open questions remain. First, several past approaches use the average of an ensemble of physics-based forecasts as an input feature of these models. However, ensemble forecasts contain information that
can aid prediction beyond only the ensemble mean. Second, past methods have focused on average performance, whereas forecasts of extreme events are far more important for planning and mitigation purposes. Third, climate forecasts correspond to a spatially-varying
collection of forecasts, and different methods account for spatial variability in the response differently. Trade-offs between different approaches may be mitigated with model stacking. This paper describes the application of a variety of ML methods to predicting
monthly average precipitation and two meter temperature using physics-based predictions (ensemble members) and observational data such as relative humidity, pressure at sea level or geopotential height two weeks in advance for the whole continental U. S. Regression
and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models are considered. The proposed models outperform common baselines such as historical averages and ensemble averages. This paper further includes
an investigation of feature importance and trade-offs between using the full ensemble or only the ensemble average.</div>
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Advisors: Rebecca Willett</div>
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<span style="font-family:Helvetica;font-size:12px;orphans:auto;widows:auto" class="ContentPasted0">Committee Members: Rebecca Willett, Ian Foster, and Yuxin Chen</span><br>
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