Short Courses on June 5, Monday
Tian-You Yu (University of Oklahoma)
David Bodine (University of Oklahoma)
Weather radar has played a critical roles in monitoring hazardous and severe storms, precipitation measurements, understanding of weather processes, etc. In this short course, basic radar operation principals will be first introduced. Subsequently, weather radar system, subsystems, their functionality and design trade-offs will be presented. Basic polarimetric weather radar products including spectral moments and dual-polarization variables will be defined and discussed. High-level signal processing to generate those radar products will be presented. Advanced topics in weather radar technologies including pulse compression, phased array, etc. will be introduced.
Haonan Chen (Colorado State University)
V. Chandrasekar (Chandra) (Colorado State University)
It is long believed that polarimetric radar observations are rich in information content and conventional methods of utilizing them have only been able to extract part of the information due to the nature of the analytical tools. For example, we still rely on empirical algorithms such as Z-R relations or other polarimetric relations to estimate rainfall. These parametric relations are generally derived using power- law fitting between rainfall rates and radar observables, and they often need to be adjusted since raindrop size distribution (DSD) varies in different precipitation regimes or even within a single storm system. In addition, it is challenging to remove the inherent parameterization error in such “fixed” parametric relations, even if the relations are fine-tuned with local DSD data. The multi-parameter multi-dimensional radar observations are ideal candidates for modern artificial intelligence (AI) applications in extracting quantitative precipitation information. This short course will familiarize participants with fundamentals of AI and machine learning. A particular emphasis will be placed on quantitative precipitation estimation and short-term prediction (nowcasting) and different aspects of weather radar data sciences.
Race Clark (NOAA/National Severe Storms Laboratory)
Jian Zhang (NOAA/National Severe Storms Laboratory)
Steve Martinaitis (CIWRO)
The Multi-Radar Multi-Sensor (MRMS) system is a suite of hydrometeorological tools developed at the National Severe Storms Laboratory and run operationally by the U.S. National Weather Service. MRMS ingests information from a wide variety of sensors, including weather radars in the U.S. and Canada, rain gauges, and output from numerical weather prediction models. MRMS outputs are produced on a shared 1-km x 1-km Cartesian grid every 2 minutes, over the contiguous U.S. and southern Canada, Alaska, Hawaii, and the Caribbean. In this short course, MRMS developers will explain the overall system and provide in-depth detail on the quantitative precipitation estimates produced within MRMS. The short course will include a demonstration of the MRMS real-time product viewers.
Jonathan J. Gourley (NOAA/National Severe Storms Laboratory)
Humberto Vergara (Cooperative Institute for Severe and High-Impact Weather Research and Operations - CIWRO)
Jorge Duarte (Cooperative Institute for Severe and High-Impact Weather Research and Operations - CIWRO)
EF5 is a distributed hydrologic modeling framework that runs within the Multi-Radar Multi-Sensor (MRMS) system and utilizes the 2-min/1-km radar-only rainfall product. It has been transitioned to the U.S. National Weather Service where it runs operationally and supplies meaningful products to guide the issuance of flash flood warnings. This course will begin with a brief, conference-style overview of the modeling framework. Participants will then access the open-source modeling software, inputs from MRMS rainfall estimates, parameter maps, etc., to run simulations for an actual case of flash flooding. The model results will be displayed in QGIS and assessed using Local Storm Reports and other information they will retrieve online.