1. ScientificForecasting: How to Be a Better Forecaster courtesy of Prof. David Schultz University of Manchester

2. Will your job be replaced by automated forecasts?

3. Not if you beat the computer! www.arrowitod.net

4. Advice on how to beat the computer

5. 1. Embrace automation that saves you time - manual analyses for the TV and newspapers - gridded forecasts for public versus - manual analyses for your diagnosis - severe-weather forecasting - customer-oriented products

6. - two reasons why forecasts fail – –Poor initial conditions – –Model errors - how good are the initial conditions? - what are the strengths and weaknesses of the models? 2. Know how to beat the models

7. - real atmosphere - model atmosphere 3. Know climatology

9. High-resolution models may produce wonderfully detailed, but inaccurate, forecasts.

10. ensemble forecasting systems help quantify the likelihood of possibilities 4. Embrace high resolution, yet think probabilistically

11. - Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models by David Stensrud - what physical processes can we expect the model to forecast reasonably well? 5. Don’t treat the model as a black box

12. An Example Know what kind of convective parameterization scheme is in your model

13. If forecasting severe weather was as easy as the Extreme Forecast Index, then forecasters would be out of jobs. Ingredients-based forecasting Is physically based Focuses your attention on most important processes Limits the number of charts that you look at saves you time 6. Embrace ingredients-based forecasting techniques

14. The Three Ingredients for Deep, Moist Convection Instability Lift Moisture

16. Ingredients-based forecasting an ingredient is something necessary and sufficient for some event to occur based on whatever we understand and limited by what we don’t easily adapted to incorporate new scientific understanding (C. Doswell)

17. Understanding and forecasting convection is best viewed through an ingredients-based forecasting methodology. Johns and Doswell (1992)

18. Severe Convective Storms ≠ Severe Thunderstorms A convective storm producing hail, tornadoes or strong winds may not produce lightning.

19. deep, moist convection strong updrafts in the lower part of the mixed-phase region of the cloud Ingredients for Electrification

20. LCL (~cloud base) warmer than –10°C (ensures supercooled liquid water) Equilibrium level (~cloud top) colder than –20°C (ensures ice nucleation over adequate depth) CAPE > 100–200 J/kg in –10° to –20°C layer (ensures ascent > 6–7 m/s in lower mixed-phase region) Diagnostic Quantities for Electrification van den Broeke et al. (2005, WAF)

21. be open to alternative realities question your forecasts ask the right questions 7. Approach forecasting like a scientist.

22. Six Questions to Ask (from Bosart 2003, “Whither the Weather Analysis and Forecasting Process?”, Weather and Forecasting) 1. What happened? 2. Why did it happen? 3. What is happening? 4. Why is it happening? 5. What is going to happen? 6. Why is it going to happen? (Don’t be tempted to “cheat” and only consider #5!) (Courtesy of Russ Schumacher)

23. Why is there rain? A Philosophy of Diagnosis

24. A Philosophy of Diagnosis 1. QG thinking: advection of vorticity by thermal wind (e.g., vorticity advection, warm advection) 2. If not QG, then try frontogenesis at different levels. 3. If not frontogenesis, then something else: topography, PBL circulations, diabatic effects, etc.

25. A Philosophy of Diagnosis 1. QG thinking: advection of vorticity by thermal wind (e.g., vorticity advection, warm advection) 2. If not QG, then try frontogenesis at different levels. 3. If not frontogenesis, then something else: topography, PBL circulations, diabatic effects, etc. Note that assessing instability is also important, but secondary to this philosophy. Conditional stability or moist symmetric instability only modulates the response to the given forcing.

26. Today’s complex models require intelligent users 8. Develop your critical thinking skills

27. attend seminars UCAR MetEd, EUMETCAL, ECMWF www.meted.ucar.edu read the literature - know your history - keep abreast of recent developments 9. Seek enlightenment

28. continue your education testbeds? Masters degree? Ph.D.? actively pursue research that will make your jobs easier go to conferences and present your research 10. Grow professionally

29. Your job now will not be the same job in the future Your job now will not be the same job in the future.

30. For Further Reading How to Read and Critique a Scientific Paper – Pamela Heinselman How to Research and Write Effective Case Studies in Meteorology – David Schultz The Role of Diagnosis in Weather Forecasting – Charles Doswell and Robert Maddox On the Use of Models in Meteorology – Charles Doswell

31. Forecasting Workshop Prof. David Schultz University of Manchester Manchester, United Kingdom

32. Who am I? 1996: PhD University at Albany NOAA/National Severe Storms Laboratory and University of Oklahoma (1996–2006) Prof. at Finnish Meteorological Institute and University of Helsinki (2006–2010) Prof. at University of Manchester

33. David Schultz Chief Editor, Monthly Weather Review Co-founder and Assistant Editor, Electronic Journal of Severe Storms Meteorology (2006– 2013) www.ejssm.org Author, Eloquent Science www.eloquentscience.com

34. Past Training n n European Severe Storms Laboratory Testbed, 2012–14 n n Eumetcal Cyclone Week, Marine Forecasting, 2012–13 n n Summer School on Severe and Convective Weather, Nanjing, China, 2011 n n Eumetcal Numerical Weather Prediction Course, Helsinki, Finland, 2009 n n Observations and Numerical Weather Prediction of Severe Storms and Flash Flood Forecasting Summer School, Constanţa, Romania, 2006 n n Mediterranean Storms Driven Flash Floods, Montpezat, France, 2006 n n NOAA Advanced Warning Operations Course: Winter Weather, 2005–2006

35. My Research Interests Midlatitude cyclones FrontsWindstorms Convective storms Climatologies of European convective storms Operational forecasting

36. My Recommendation What resonates? Take the occasional note Dig deeper later Ask lots of questions

37. Monday: introductory lecture on scientific forecasting meet students learn about the forecasting challenges specific to your office tour of the forecast environment work with staff to prepare case studies for later in the week prepare exercises on the workstations Tuesday: lectures on numerical weather forecasting Wednesday: lectures and practicals on thermodynamic (skew T–logp) diagrams ingredients-based forecasting of convection Thursday: lectures and practicals on forecasting lightning and MCSs Friday: wrap up and case-study practicals