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Enhancing Lake Effect Snowfall Estimates Using GOES and NEXRAD Data

This project aims to develop, test, and implement a GOES-based lake effect snowfall (LES) estimate product that augments NEXRAD coverage over the Great Lakes. By employing synergistic data applications, we will establish empirical relationships between GOES and NEXRAD data to improve real-time snowfall estimates, essential for NWS nowcasting applications. The project will focus on creating a comprehensive dataset on snowfall rates and facilitate operational transitions for improved forecasting and hazardous weather advisories throughout the region.

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Enhancing Lake Effect Snowfall Estimates Using GOES and NEXRAD Data

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  1. a. FY12-13 GIMPAP Project Proposal Title Page Title: : Using GOES and NEXRAD Data to Improve Lake Effect Snowfall Estimates Type: GOES Utilization Status: New Duration: 2 years Project Leads: Mark Kulie (CIMSS,Univ. of Wisconsin-Madison), mskulie@wisc.edu Other Participants: Andrew Heidinger (NOAA/NESDIS/STAR/ABSP), Andi Walther (CIMSS/UW-Madison), Ralf Bennartz (CIMSS/UW-Madison)

  2. b. Project Summary • Develop, test, and implement GOES lake effect snow (LES) snowfall estimate product • Augments NEXRAD coverage over Great Lakes • Synergistic GOES/NEXRAD data application • Utilizes CIMSS GOES-R Proving Ground • Empirical GOES/NEXRAD relationships • NWS nowcasting applications • Useful real-time product • LES advisories/warnings

  3. c. Motivation / Justification LES impacts: • Prodigious snow amounts • Hazardous weather • Great Lakes hydrology • Locally variable • Difficult to forecast • Radar detection deficiencies Marquette, MI NWS Web Page: http://www.crh.noaa.gov/images/mqt/0809snow.jpg

  4. c. Motivation / Justification NEXRAD 1734Z Terra MODIS 1725Z

  5. c. Motivation / Justification NEXRAD 1638Z Terra MODIS 1635Z

  6. d. Methodology • “Calibrate” GOES with NEXRAD (~100 km range) • Develop empirical relationships • NEXRAD snowfall rates (Z-S relationships) • Relate derived snowfall rate to GOES products • CIMSS GOES-R Proving Ground essential component

  7. e. Expected Outcomes • Create LES GOES/NEXRAD testing dataset • MQT initial focus • Enthusiastic initial response from MQT NWS WFO • GOES LES operational snowfall rate product (Great Lakes focus)

  8. e. Possible Path to Operations • Proof-of-concept, demonstration phase • CIMSS GOES-R Proving Ground • Outreach and feedback • NESDIS Satellite Analysis Branch • NWS offices (e.g., MKX, MQT, APX, GRR, BUF, etc.) • Operational implementation

  9. f. Milestones YEAR 1: • Compile case studies w/ MQT NWS WFO collaboration • Identify useful GOES cloud products (e.g., cloud top height, optical thickness, cloud phase, microphysics, etc.) • Develop NEXRAD/GOES snowfall rate relationships YEAR 2: • Process larger dataset (e.g., entire snowfall season) • Outreach/feedback from NESDIS/NWS • Hone methodology based on feedback • Prepare for operational transition

  10. g. Funding Request (K)

  11. g. Spending Plan FY12 • FY12 $55,000 Total Project Budget • Grant to CIMSS - $55,000 • % FTE - 40% (~$50,000) • Travel - $5,000 (Travel to NWS sites, NESDIS, conference) • Federal Travel – • Federal Publication Charges – • Federal Equipment - • Transfers to other agencies – • Other -

  12. g. Spending Plan FY13 • FY13 $60,000 Total Project Budget • Grant to CIMSS - $60,000 • % FTE - 40% (~$50,000) • Travel - $5,000 (Travel to NWS sites, NESDIS, conference) • Publication charge - $5,000 • Federal Travel – • Federal Publication Charges – • Federal Equipment - • Transfers to other agencies – • Other -

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