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Weather-Related Impacts: Lessons Learned Analysis (MELLA) Tool

This article introduces the Meteorological Event Lessons Learned Analysis (MELLA) tool, which examines weather-related impacts and provides valuable insights for warning preparedness meteorologists. The article discusses the importance of understanding impacts and highlights the basic anatomy of a MELLA, including examples and comparisons. It also emphasizes the need to link weather conditions with their corresponding impacts.

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Weather-Related Impacts: Lessons Learned Analysis (MELLA) Tool

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  1. Meteorological Event Lessons Learned Analysis (MELLA) – A Tool on Weather-Related Impacts for the Warning Preparedness Meteorologists Program of EC Denis Gosselin National Coordinator, WPM Program Meteorological Service of Canada CRHNet Symposium Fredericton, October 2010

  2. Content • The Warning Preparedness Meteorologists (WPM) Program – In Short • Weather-Related Impacts: Why Bother? • MELLA – Beyond Weather Conditions • Basic Anatomy of a MELLA • Examples: What Have We Learned So Far? • What Else? – Comparing Cases • Linking Weather and Impacts

  3. The WPM Program – In Short • National program at the Meteorological Service of Canada (MSC) since 2003 (started as a regional pilot in 1998). • Modest workforce: 15 people coast-to-coast either on part-time (WPM and Outreach) or full-time basis. • WPM’s provide tailored high-impact weather information to various clients, namely: • EMO’s (provincial, territorial and municipal levels) • Federal partners • Media

  4. The WPM Program – In Short Focus is on High Impact Weather.

  5. The WPM Program – In Short • However, high-impact weather implies knowledge of impacts. • WPM’s are former weather forecasters and they were not trained to deal with impacts. • So, except for a few particular types of weather event (rain with melting snow, hurricanes), impact information that can be passed on to clients by WPM’s is very limited.

  6. Weather-Related Impacts: Why Bother? Average number of people reported killed, per million inhabitants and disaster origin 1991-2005 Source of data: EM-DAT: The OFDA/CRED International Disaster Database, UCL – Brussels, Belgium

  7. Weather-Related Impacts: Why Bother? Total amount of reported economic damages per continent and disaster origin (2005 US$ billion) 1991-2005 Source of data: EM-DAT: The OFDA/CRED International Disaster Database, UCL – Brussels, Belgium

  8. Weather-Related Impacts: Why Bother? • Auditor General of Canada – 2008 December Report of the Commissioner of the Environment and Sustainable Development – Chapter 2: Managing Severe Weather Warnings-Environment Canada • Paragraph 2.74 - Recommendation. Environment Canada should regularly assess the effectiveness of severe weather warnings from a user's perspective, especially the effectiveness of the methods of delivery to users and how well the warnings are understood by key users and the public.

  9. MELLA – Beyond Weather Conditions • The Meteorological Event Lessons Learned Analysis or MELLA is a tool that was designed to capture links between: • weather forecasts • expected results • factual realities • Capturing links = performance assessment. • MELLA ≠ forecast verification

  10. MELLA – Beyond Weather Conditions • A MELLA tries to determine answers to the following questions: • What happened? • What worked/did not work well? • Why did it/did it not work well? • What lessons can we learn from that event? • MELLA’s are/will be conducted on a pilot basis in the current fiscal year • Issue with resources • Standardizing/streamlining process as much as possible

  11. MELLA – Beyond Weather Conditions What happened – traditional approach (Case Studies, Storm Damage Surveys): aimed at weather forecast verification Forecasts Issued Other Issues Regular Forecasts Special Weather Bulletins Weather Watches Weather Warnings Others Performance of Atmospheric Models Technological Problems and Issues + VS Observations Regular Weather Observations On-Site Measurements/Estimates Radar/Satellite Imagery

  12. MELLA – Beyond Weather Conditions What happened – MELLA approach: aimed at forecast verification from a client’s perspective Other Issues Forecasts Issued Performance of Atmospheric Models Technological Problems and Issues Regular Forecasts Special Weather Bulletins Weather Watches Weather Warnings Others Impacts + Social Environmental Economic VS Observations Social Science Elements Regular Weather Observations On-Site Measurements/Estimates Radar/Satellite Imagery Risk Communication Risk Perception Client Response

  13. Basic Anatomy of a MELLA • Summary of Meteorological Event • Chronology of Weather Conditions (Forecast and Observed) and Related Events • Event Determinants • Lessons Learned (Meteorological and Social Perspectives) • Documented knowledge on weather-impacts links • Documented “best practice” requirements to maintain/improve performance

  14. Basic Anatomy of a MELLA Summary of Weather Event *Emergency Operation Center

  15. Basic Anatomy of a MELLA Chronology

  16. Basic Anatomy of a MELLA Event Determinants • Meteorological Elements • Early Synoptic Signature • Precipitation (type, amount, duration) • Wind • Temperature • Etc. • Vulnerabilities • Impacts • Risk Communication Elements • Risk Perception Elements • Response Elements • Natural Catastrophe Ranking • And more…

  17. Basic Anatomy of a MELLA

  18. Basic Anatomy of a MELLA

  19. Basic Anatomy of a MELLA Lessons Learned

  20. Basic Anatomy of a MELLA Lessons Learned

  21. Examples: What Have We Learned So Far?

  22. Examples: What Have We Learned So Far?

  23. Examples: What Have We Learned So Far? Population for Windsor – Leamington – Essex County: ~ 390 000

  24. Lessons Learned

  25. Examples: What Have We Learned So Far?

  26. Examples: What Have We Learned So Far?

  27. Examples: What Have We Learned So Far? • Heat Wave and Severe Thunderstorms – Early July in Montreal • Special Weather Statement issued 2-3 days prior to first day of heat wave • Risk of severe thunderstorms at end of heat wave included in SWS • Regular contacts between EMO’s and WPM’s for updates during event (exchange of information) • Feedback indicates positive response from EMO’s (health authorities, municipal authorities) • Clear demonstration of impact mitigation

  28. What Else? – Comparing Cases Meteorologically similar cases

  29. What Else? – Comparing Cases Meteorologically different cases

  30. What Else? – Comparing Cases • With a database (Meteorological Event Analysis Database – MEvA DBase) • Project proposal submitted a few months ago, development work ongoing • Boolean-type (truth table) • Could be built from MELLA’s and from former regional case studies with the addition of data on impacts and vulnerabilities • Potential benefits • Establish factual relationships between weather elements and related impacts • MEvA DBase would not only be an analytical tool but also a knowledge management and knowledge sharing tool • Available at operational desks (compatible with operational workstations that are used by forecasters)

  31. What Else? – Comparing Cases • Some examples of the database’s qualitative determinants • Weather Elements • Forecast Verification and Lead Time • Geographical Elements • Impacts • Natural Catastrophe Ranking • Risk Communication Elements • Risk Communication Response • Work in progress: determinants will be added as project unfolds

  32. What Else? – Comparing Cases Qualitative Comparative Analysis (QCA) • Summarize data • Check for data consistency • Corroborate current assumptions and theories • Elaborate and test new assumptions and theories • Establish potential causation links between a series of successive elements and a particular impact

  33. What Else? – Comparing Cases Assumptions and Theories • Are the 4 cognitive determinants of response to threats important in hazardous weather risk communication? • Are there “configurational causation” relationships between the dynamic or synoptic signature of the weather event, the “determinants” (components) of the weather event, the ensuing impact(s)? • Which determinant(s) is (are) always present when a particular outcome is present/absent?

  34. What Else? – Comparing Cases Assumptions and Theories • Are current « binary » weather warnings from EC adequate in helping Canadians to make informed safety-related decisions? • Can EC observe a clear response to weather warnings from clients (does EC have appropriate tools to measure it)? • Would an early warning system (such as Vigilance) be more efficient in giving rise to a response from clients?

  35. Linking Weather and Impacts X 700 X 600 X 500 25% increase in peak gusts = 650% increase in structural damages Increase in damages X 400 X 300 X 200 X 100 >37. 04 km/h >74.08 km/h >92.06 km/h >111.12 km/h Peak Gusts

  36. Thank you Merci denis.gosselin@ec.gc.ca

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