<html><head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head>
<body>
<p>Colleagues,</p>
<p>Aaron Hill, our 3rd MET faculty candidate is speaking today at
3:15 in EOAS 1044. Hopefully you can be there in person. If not
the zoom link for his talk is <br>
</p>
<p><a class="moz-txt-link-freetext" href="https://fsu.zoom.us/j/95943330514?pwd=VmZkOUN6WXlLR2kwRnBaLy9nbEdDQT09">https://fsu.zoom.us/j/95943330514?pwd=VmZkOUN6WXlLR2kwRnBaLy9nbEdDQT09</a><br>
<br>
Meeting ID: 959 4333 0514<br>
Passcode: 632310</p>
<p>Title: <span id="docs-internal-guid-19b67e9e-7fff-a240-7777-146a79851180" class=""><span id="docs-internal-guid-d391794c-7fff-69f2-8f02-0e7be9e4bf59" class=""><span style="font-size: 11pt; font-family: Arial; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Advancing High-Impact Weather Hazard Forecasting with Machine Learning</span></span></span></p>
<p><span id="docs-internal-guid-19b67e9e-7fff-a240-7777-146a79851180" class=""><span id="docs-internal-guid-d391794c-7fff-69f2-8f02-0e7be9e4bf59" class=""><span style="font-size: 11pt; font-family: Arial; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Speaker: Dr. Aaron Hill (Colorado State University)
</span></span></span></p>
<p><span id="docs-internal-guid-19b67e9e-7fff-a240-7777-146a79851180" class=""><span id="docs-internal-guid-d391794c-7fff-69f2-8f02-0e7be9e4bf59" class=""><span style="font-size: 11pt; font-family: Arial; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Abstract: </span></span></span><span id="docs-internal-guid-19b67e9e-7fff-a240-7777-146a79851180" class=""><span id="docs-internal-guid-d391794c-7fff-69f2-8f02-0e7be9e4bf59" class=""></span></span><span style="font-size: 11pt; font-family: Arial; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Weather hazards associated with deep convection (e.g., excessive rainfall, tornadoes, large hail, and severe wind) are often the most costly natural disasters annually. Prediction of these hazards is hindered by their localized nature and the inability of sophisticated numerical weather prediction (NWP) models to explicitly represent processes that result in hazardous weather. Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged recently as alternative methods to forecast high-impact weather hazards, and they have proven particularly skillful and valuable in their ability to explicitly forecast hazard occurrence and location (e.g., 40% chance of hail within 40 km of a point). One specific area that AI and ML have been used in the meteorology domain is postprocessing of NWP model output, taking advantage of the mathematical and statistical properties of the ML methods and their ability to process large and complex datasets, to create prediction systems capable of generating real-time forecasts of weather hazards. One example is the Colorado State University Machine Learning Probabilities (CSU-MLP) prediction system which is trained to relate historical records of high-impact weather with simulated environments from an NWP model. The ML system identifies the well-studied synoptic patterns that support high-impact weather and can be examined to better understand the ingredients for these events. The CSU-MLP has undergone significant development over recent years and is now being routinely used in operational forecasting environments, including the Weather Prediction Center, Storm Prediction Center, and local National Weather Service offices. </span></p>
<br class="">
<div style="line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;" class=""><span style="font-size: 11pt; font-family: Arial; font-variant-ligatures: normal; font-variant-east-asian: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;" class="">Underlying the development of the CSU-MLP, and ML prediction systems more generally, is a decision about how to define weather events. Whereas tornadoes, severe hail, and severe wind have clear definitions (e.g., severe hail is >1” in diameter) and historical records reflect these definitions, excessive rainfall and flash flooding are ill defined. Does 2 inches of rain in 3 hours produce the same impacts in Idaho as it does in Florida? Reports of flash flooding are also inconsistently reported across the country due to varying definitions of events. As a result, development of the CSU-MLP forecast system has considered a number of definitions of excessive rainfall, including radar-derived average recurrence intervals (ARIs) and local storm reports, to produce a skillful forecast system. This talk will focus on these developments and provide a brief overview of the CSU-MLP system and forecast skill derived from high-resolution NWP model output as well as a coarser global model (i.e., the Global Ensemble Forecast System). Interpretability and explainability methods will be introduced to demonstrate what can be learned about the forecast problem, including how the ML models are learning relevant synoptic and mesoscale dynamics that we know exist, which is vital to building trustworthy products. Finally, the highlights/challenges of transitioning AI research to operational forecast centers will be discussed. </span></div>
<p></p>
</body>
</html>