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Meteorology in the Energy Sector: Process, Products, and Research
 

Meteorology in the Energy Sector: Process, Products, and Research

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  • I’m about to go into each of these in more depth but the place to start is by noticing the environment you are in and how your customers react to what you do. Different shops will need to handle these issue different from one another…
  • Knowing your customer doesn’t change the ANALYTIC process - it changes the COMMUNICATION process…
  • Numbers and data will speak more to more quantitative shops… probabilities, model spreads, etc…
  • I’m about to go into each of these in more depth but the place to start is by noticing the environment you are in and how your customers react to what you do. Different shops will need to handle these issue different from one another…
  • Here’s where we get to play Dr. Phil or have what I like to call Dr. Phil moments. Stop from time to time and identify your own professional traits. Catalog them - acknowledge them… identify strengths… identify challenge areas… make a list for self improvement…
  • I’m about to go into each of these in more depth but the place to start is by noticing the environment you are in and how your customers react to what you do. Different shops will need to handle these issue different from one another…
  • I struggle with distraction from e-mail and IM. Periods of time where it just needs to be shut down.
  • Intellect is what you get paid to do… not typing numbers into a spreadsheet. Software solutions? Simply be creative with your “starting point” --- starting from scratch takes a lot longer than adjusting automated data
  • Heur-Whaaat?? encouraging a person to learn, discover, understand, or solve problems on his or her own, as by experimenting, evaluating possible answers or solutions, or by trial and error. This is how most of us learn to forecast and “hone” our skills - heuristic processes are what allow us as meteorologsits to add value over the models and over automated systems. Observe current weather Compare against your experience Review models
  • Experience means - pattern recognition… rules of thumb… special sauce… gut feeling… the “art” of forecasting
  • Recognize Heuristic Pitfalls Limited number of observations You are basing this on the things YOU have seen or rules of thumb that your colleagues/mentors have seen. I’ll ask the quesiton of someone friendly in the audience… how many strong El Nino’s have you observed in your forecasting career? How many “polar pigs”? How many GFS busts during shallow cold-air intrusions? Limited View / Limited Scope We can “over focus.” Example - forecast shows a strongly negative NAO in mid-January - MASSIVE greenland blocking. Aha - I’ve seen this many tmes in my careeer!! Rules of thumb tell me what this means!! It’s gonna get CRAZY cold!! BUT… what aren’t you looking at? Assume you’ve seen 10 instances and in 7 of those 10 there has been a solid snowcover from DCA to ORD. Assume this time there is no such snowcover. Assume 8 of those 10 were in ENSO neutral years but this is a La Nina. Assume this instance is coincident with a negative PNA (not positive). If your observational dataset is too limited or if your focus is too narrow - you’ll miss something important. Subjective Unanticipated Biases We argue over this one a lot and there can be a Doctor Phil element here too. Assume you know your group is extremely short NG in the winter and you’ve seen a very zonal/warm jet stream pattern for several weeks. Models begin to show ampification. How many runs do you need to see before you change your forecast? Do you react faster or slower if your group is massively long NG and you’ve had a warm forecast?? Should you know the position or remain objective?? Bottom line… you are not a computer - you will react differently uunder different circumstances. Try to account for this… try to control this. . T least try to acknowledge this. Taleb’s “Narration Fallacy” As humans - we like explanation and we tend to see patterns and offer stories where none actually exist. Ther eis hard science behind this (tell the split brain story). The fact we find a story makes it easier for others to accept our explanation and can lead to an over confidence - believing something that’s far less certain that we want to think. This is dangerous!! Human Factor Environmenal factors will affect how we link things doing a heuristic analysis. It’s been a long week and you are looking for analogs for the summer forecast. As you review the la nina versus prevous years - will fatigue play a factor? Will you miss a year you might have included if you were paying closer attention? If you have 4-5 analogs - missing one more can make a HUGE difference to the mean. Put simply - It works until it doesn’t work
  • Control Heuristic Pitfalls (as much as you can) Beware the “boundaries” of the analysis Assume you are wanting to do an analysis regarding a 15 day window beginning January 8th. Should you simply slice Jan 8-21 for every year on record? Perhaps then you’ll have nearly 40 datapoints. Does the atmosphere understand Jan 7 is different than Jan 8? Consider taking rolling 15-day windows for each period between December 1 and February 28? Bounce your conclusions off people you trust Quantify as much as you can Engage “pure” R&D Admit the stability of the ground beneath you Dr. Phil diagnosis - KNOW yourself - Human days versus Computer Days
  • Control Heuristic Pitfalls (as much as you can) Bounce your conclusions off people you trust Quantify as much as you can Engage “pure” R&D Admit the stability of the ground beneath you Quantify the “gut factor” - even if it’s just subjectively to yourself. Dr. Phil diagnosis - KNOW yourself - Human days versus Computer Days
  • 1) Quantify Forecast Skill Beyond the standard metrics MAE and RMSE only mean so much and while they are valid metrics for long term verification they may do very little to tell you about skill. Think about: Skill as opposed to Verfication - Skill is a measure of how your forecast compares to some other type of forecast as oppsed to simply versus reality. Think about Heidiki Skill Scoring and Kuiper Skill Scoring Link skill to your customer’s sensitivities. Regional skill? Per station skill? National skill? Break skill out to seasonal or subseaonal time scales. There can be value to “conditional skill” - testing how your forecast performs in different situations or in comparison to different external forecasts. Ex. When Esat shows a major event (MA or MB) what’s your skill relative to theirs? 2) Quantify/Qualify Confidence Be consistent and meaningful This one is tricky and could be very very complicated. But beware of the simple 1-10 confidence scale. What makes a 2 different than a 3? Also - it loses it’s value if it’s always a 5 and/or if it never goes to 1 or 10. Think about using some sort of skill metrics on your confidence scales. How often do you have high skill when your confidence is “high”? How often do you have high skill when your confidence is “low”? Theoretically there should be a difference. Addressing the two things above go a LONG way toward helping your user/customer assess how to apply your forecast and how to value it. 3) Team-Up to Work with Market Data Quantify Market Impact
  • It’s confident - it says ACT ON ME!! But is that really the message you intend to send? Be careful of SCREAMING because it may imply confidence in a forecast where you don’t really intend to communicate such confidence… This is essentially what a “screamer” communicator will do…
  • There are a lot of probabilities here - which give very good information. But the “quant” meteorologist can sometimes take a long time analyzing the data to come up with a forecast like this. It also fails to give any experiential or heuristic or “gut” assessment of how things might continue to trend. Do YOU believe that there is a 1 in 5 chance of MB from DCA to NY?? If so… say so… which implies that your forecast would be “below” instead of MB, N or A.
  • You’ve covered all the bases here but not given any real assessment for the likelihood of anything. How would someone use this “hedger” forecast?
  • As a licensed attorney I am responsible for legal recommendations - event to non-clients. My professional duty is to be current on legal issues or to simply shut up. Consider this example…. Assume I’m walking down the street after this conference and I witness a car accident. One car turned right at a red light and was struck by a car coming through the opposing green. Coincidentally - the right-turning car’s insurance agent happens to be standing next to me on the curb. I turn to him and say “wow - did you see that guy run that red light - you can’t turn right on red in Texas - it’s against the law!!” He say’s “how do you know” - I say - I’m a lawyer!! I may not practice law but I know it’s illegal to turn right on red in Texas. Based on my comment the insurance agent pays the claim on behalf of his client - assume the other driver was seriously hurt and the total property and medical claims are 500k. But… I’m wrong… right on red is illegal in CALIFORNIA but not Texas. I’m liable - (1) to the insurance company who paid a claim they should not have paid - 500k out of my pocket; (2) to the Bar Association - sanction or license revocation.
  • I can speak about the laws of California if that’s where my experience lies - no worries here. Or - I could offer my “informed opinion.” This starts by researching and finding that the law says right on red is legal. Then I’d go on to research court cases and legislative intent in california to establish an argument to challenge the law. Sure it’s legal but I find that the courts often rule against drivers who turn right on red. There is good precedent to suggest that this driver would LOSE in court. Therefore - even though it’s legal - the insurance company should still pay the claim. This is an “informed opinion” - based on my own work. Time consuming - but still a valid opinion. Or - I could simply go with the stated law. It’s legal to turn right on red. No trouble if I say that. Otherwise - I have to simply say “I don’t know” Consider this in your daily job. How do you respond to questions about climate change? Many of you would claim expertise in the weather window out to 15 days - maybe out to 90 days but few would claim to be climate scientists. How do you respond? How do you formulate this response?? If you disagree that climate change is occurring - is it because you’ve read the research on both sides? Have you had the opportunity to meet the research teams or speak with recognized experts that have either conclusion? If you’ve made an honest and thorough attempt to learn climate science and believe there is credible data to refute the notion of AGW - then you are professionally ethical in stating your informed opinion. OR - if you haven’t had time to look at the issue thoroughly you are acting in a professional manner by citing the major authorities. The IPCC, AMS, AGU, AAS and others all have position statements that are concurrent with one another. It’s professional to simply quote those. OR - if you are skeptical about climate change but have not had the time to access the articles - determine credible sources - digest data on both sides of the issue - read the IPCC - etc. But you’ve seen comments from folks like Dr. Gray and Dr. Singer that might be reasonable. MIGHT BE - but you can’t verify or corroborate with experts. You are acting properly by simply declining to comment. You are NOT ACTING professionally, however by voicing the opinions of non-experts that track with your own “gut feel” on the issue until/unless you’ve spent time trying to learn the issue from all (credible) sides.

Meteorology in the Energy Sector: Process, Products, and Research Meteorology in the Energy Sector: Process, Products, and Research Presentation Transcript

    • Reminiscences of an Energy-Met:
    • Products, Process and Professionalism
    • Stephen Bennett,
    • Scripps Director of Business Development
    • April 2009
  • Perhaps More Aptly Titled: Ramblings of a Wx Weenie
  • Ramblings of a Wx Weenie Much of this presentation is "soft science," comprised of observations I've made while working as a proprietary operational meteorologist and while managing "weather research" teams for energy trading and reinsurance underwriting.  I'm also weaving in even "softer science" by discussing some philosophy gleamed from recent bestselling business publications, communication training and leadership theory. While I consider myself to be a “hard” scientist - I believe the “soft stuff” is where we stand to make the quickest improvements in our daily jobs. Those improvements also lead to some of the greatest return on our time in professional development. Disclaimer #1
  • Ramblings of a Wx Weenie There are many who say the world has changed. Markets may not currently act/react to weather as they used to. I begin with the basic assumption that market fundamentals (i.e. weather driven energy demand) will return as a market driver. Now is the time to prepare. Exploit the opportunity to learn and improve your techniques while the intensity/focus on weather is diminished. Disclaimer #2
    • Operational Met: Standard Route
      • Basic Education
      • Basic Experience
    • Transition to Trading Support
      • Leadership Roles
      • Professional Degree
    • Recent Moves
      • Academic Universe
      • Bridge to Business
    Quick Introduction
  • Starting Exercise…
    • Please Stand if your primary job involves:
      • Forecasting the Weather
      • Informing and Advising Decision Makers
  • Starting Exercise…
    • Sit down and become anonymous…
      • From the list below:
      • Please note your most significant regular-working challenge
    A: Time to Complete Tasks B: Time to Fully Analyze Data C: The Forecast Itself D: If They’d Only Listen! E: None of the Above
  • Starting Exercise…
    • Please Stand if your primary job involves:
      • Utilizing Forecast Information from Meteorologists
      • Making Decisions and Deploying Capital
  • Starting Exercise…
    • Sit down and become anonymous…
      • From the list below:
      • Your most significant challenge re: weather information
    F: Difficult to Understand G: Difficult to Apply H: Confidence Issues (Over/Under Confident Met) I: Difficult to Value J: None of the Above
  • Please Wait While your friendly WxBuggers Tabulate…
    • Time Management
      • Time to Complete
      • Workload
      • Organization
      • Distraction
      • Time Bombs
      • Style
    Know your customer!! Cross Cutting Themes
    • Forecast & Analysis Process
      • Time to Fully Analyze
      • The Forecast Itself
      • Difficult to Apply
      • Difficult to Value
      • Efficiency
      • Heuristic Pitfalls
      • Scientific Limitations
      • Area of Expertise
      • Remaining Current
    • Communication
      • If They’d Only Listen!
      • Difficult to Understand
      • Confidence Issues
      • Style
      • Culture
      • Recognize Limitations
      • Recognize Value
      • Flexibility
  • Know Your Customer!!
  • The Quant Shop…
  • Surf Shop… (or choose your analogy)
  • Lone Guns vs. Group Think
  • Or Simply…
    • Time Management
      • Time to Complete
      • Workload
      • Organization
      • Distraction
      • Time Bombs
      • Style
    Know yourself!! Cross Cutting Themes
    • Forecast & Analysis Process
      • Time to Fully Analyze
      • The Forecast Itself
      • Difficult to Apply
      • Difficult to Value
      • Efficiency
      • Heuristic Pitfalls
      • Scientific Limitations
      • Area of Expertise
      • Remaining Current
    • Communication
      • If They’d Only Listen!
      • Difficult to Understand
      • Confidence Issues
      • Style
      • Culture
      • Recognize Limitations
      • Recognize Value
      • Flexibility
  • Know Yourself!!
    • Time Management
      • Time to Complete
      • Workload
      • Organization
      • Distraction
      • Time Bombs
      • Style
    Cross Cutting Themes
    • Forecast & Analysis Process
      • Time to Fully Analyze
      • The Forecast Itself
      • Difficult to Apply
      • Difficult to Value
      • Efficiency
      • Heuristic Pitfalls
      • Scientific Limitations
      • Area of Expertise
      • Remaining Current
    • Communication
      • If They’d Only Listen!
      • Difficult to Understand
      • Confidence Issues
      • Style
      • Culture
      • Recognize Limitations
      • Recognize Value
      • Flexibility
    • 1) Workload: Are you doing too much??
      • What are you doing?? Look at your products…
        • Create a “time inventory.”
        • Categorize the Inventory. (ex - Short Range Fcst; Demand Model; Seasonal; etc) .
        • Quantify Perceived Value per Category - Simple Survey
        • Quantify Skill Per Product
        • Calculate Actual Value per Product
          • Perceived Value x Skill = Actual Value
        • Calculate Time Value Per Product
          • Actual Value x Total Time = Time Value
        • Sort and Review!!
        • Adjust if Necessary
          • Do it… Outsource it… Kill it…
    • I struggle just to complete all of the products my traders require!!
    Time Management Issues
    • I struggle just to complete all of the products my traders require!!
    • 2) How Are You Organized?
      • Incoming Information Streams - Technology Solutions?
      • Product Queue - Maximize Parallel Processes?
    • 3) Tips from Doctor Phil
      • Keep a fully accurate (and totally private) “time log”
      • Time analyzing data?
      • Time producing products, reports, briefings, etc?
      • Time interacting with other mets and traders?
        • Data gathering versus “other”…
      • Time spent on “other?”
    Minimize Collateral Damage Time Management Issues
    • Give me more time with the models!!
    • It’s a hard forecast!!
    • Great weather forecast - what do I do with it??
    • Great weather forecast - but how important is it??
    Forecast and Analysis Process
    • Give me more time with the models!!
    • 1) How efficient are you??
      • Time spent on “monkey work” vs. “intellect”
      • Technology solutions?
      • What’s your “starting point”?
    Forecast and Analysis Process
    • Give me more time with the models!!
    • 1) How efficient are you??
      • Time spent on “monkey work” vs. “intellect”
      • Technology solutions?
      • What’s your “starting point”?
    • 2) Recognize Heuristic Pitfalls
      • Heur-whaaaat???
    • It’s a hard forecast!!
    Forecast and Analysis Process
  • Observe Current and Recent Weather Analyze Model Output Additional Research Forecast
    • Observe Reality
    • Experience
    • Review the Models
    • Experience
    • Write the Forecast
    Heuristic Analysis
    • It’s a hard forecast!!
    • 2) Recognize Heuristic Pitfalls
      • Limited number of observations
      • Limited View / Limited Scope
      • Subjective Unanticipated Biases
      • Taleb’s “Narration Fallacy”
      • Correlation versus Causation
      • Human Factor
      • Put simply - It works well until it doesn’t work at all
    Forecast and Analysis Process
    • It’s a hard forecast!!
    • 2) Recognize Heuristic Pitfalls
      • Limited number of observations
      • Limited View / Limited Scope
      • Subjective Unanticipated Biases
      • Taleb’s “Narration Fallacy”
      • Correlation versus Causation
      • Human Factor
      • Put simply - It works well until it doesn’t work at all
    • 3) Control Heuristic Pitfalls (as much as you can)
      • Beware the “boundaries” of the analysis
      • Bounce your conclusions off people you trust
      • Quantify as much as you can
      • Engage “pure” R&D
      • Admit the stability of the ground beneath you
      • Dr. Phil diagnosis - KNOW yourself - Human days versus Computer Days
    Forecast and Analysis Process
    • It’s a hard forecast!!
    • 4) Recognize and Acknowledge Scientific Limitation
      • Spot “the line” between scientific analysis and science fiction
      • Don’t cross it!!
      • Stay current to understand where the line is moving
    • 5) Assess your own Expertise and Knowledge Base
      • Spot “the line” where your expertise ends
      • Cross with caution
        • Learning Curves Take Time
        • Learning & Error: Package Deal
        • Customer Awareness/Reliance
    Forecast and Analysis Process
    • 1) Quantify Forecast Skill
      • Beyond the standard metrics
    • 2) Quantify/Qualify Confidence
      • Be consistent and meaningful
    • 3) Team-Up to Work with Market Data
      • Quantify Market Impact
    • Great weather forecast - what do I do with it??
    • Great weather forecast - but how important is it??
    Forecast and Analysis Process
    • I’m a great forecaster - if they’d only LISTEN to me!!
    • What the heck are the weather-folk talking about?!?
    • Overconfidence - the met never verifies…
    • Under-confidence - we miss the big moves!
    Communication
    • I’m a great forecaster - if they’d only LISTEN to me!!
    • What the heck are the weather-folk talking about?!?
    • Overconfidence - the met never verifies…
    • Under-confidence - we miss the big moves!
    • 1) Dr. Phil Moment - Think about Your Communication Style
      • “ The Screamer”
      • “ The Quant”
      • “ The Hedger”
    Communication
      • A strong trough will dig into the West during the period bringing exceptional late season cold weather from the Rockies to California while a new ridge expands into the Midwest. This resembles the recent “Omega Block” pattern with troughing remaining in the Northeast along with unseasonably cool weather. The Northeast could get a taste of late winter if today’s operational runs verify.
    The Screamer Think about style…
      • The general pattern implies that a trough-west / ridge-central / trough-east scenario will emerge during the forecast period. The 00z operational run appears overdone with cold in the East. 50% of model solutions are below normal in the NYC to DCA corridor while 20% indicate much below normal. 20% of the solutions offer a normal scenario and 10% show above normal.
    The Quant Think about style…
      • It is possible that a new trough will develop in the West causing a Ridge to pop in the Central U.S. but keeping a trough in the East during the forecast period. This scenario would lead to B or MB in the West and A to MA in the Rockies and Plains. It’s important to note, however, that operational models tend to overamplify the pattern and ensemble runs are not as cool in the East. Additionally, there is some indication that the pattern could progress more quickly than expected which would favor a burst of warmer weather in Midwest and the East.
    The Hedger Think about style…
    • I’m a great forecaster - if they’d only LISTEN to me!!
    • What the heck are the weather-folk talking about?!?
    • Overconfidence - the met never verifies…
    • Under-confidence - we miss the big moves!
    • 2) Be Honest About Your Limitations and Be FIRM About It.
      • “ I don’t know.”
      • “ The science just isn’t solid enough to answer that question.”
      • “ Use ‘all paths’”
    • 3) Recognize the Floor’s Culture - Adapt
      • When in Rome…
      • Gradually Educate
    • 4) Be Open Minded and Flexible
    Communication
  • Professional Standards in the Legal Profession Every state in the United States has a regulatory body (usually called a state bar association) that polices lawyer conduct. When lawyers are licensed to practice in a state, those lawyers subject themselves to this authority. Lawyers who fail to comply with local rules of ethics may be subjected to discipline ranging from … reprimand to disbarment. In the United States, the American Bar Association has promulgated model rules that have been influential in many jurisdictions. The model rules address [many things including] maintaining the integrity of the profession . American law schools are required to offer a course in professional responsibility , which encompasses both legal ethics and matters of professionalism that do not present ethical concerns. Finally - Professionalism
  • Professional Excellence for Meteorologists Private sector (and broadcast) meteorologists are often the primary “science communicators” for professional and public audiences. We owe it to ourselves, our customers, and our profession to achieve a level of professional excellence. Finally - Professionalism
    • 1) For Information Within Your Area of Expertise
      • Use Your Experience Base
      • Use Operational Research
      • Remain Current
    • 2) For Information Outside Your Area of Expertise
      • Provide “Informed” Opinions Based on Your Research
        • Engage Experts
        • Access Fields of Research
        • Learn It Before you Teach It
        • Realize - This is a TIME CONSUMING PROCESS!!
      • --OR--
      • Acknowledge “State of the Science”
        • Access research summaries
        • Look to professional associations
        • Identify experts and compile/communicate consensus
      • --OR--
      • Decline Comment
        • “ I don’t know…”
    Finally - Professionalism
    • Time Management
      • Big Calls vs. Forecast Details
      • Real Value vs. Gotta Get Done
    • Forecast Analytics
      • Met Intellect vs. Monkey Work
      • Correlation, Causality and Scientific Limits
    • Communication
      • Know Yourself
      • Know Your Customer
    • Professionalism
      • Learn It, Accept It, or Shut It
    In Conclusion - Main Points
  • The newly created "Office of Business Development" at Scripps has three primary initiatives: (1) Executive education, focused on the link from earth sciences to business (including weather, climate, natural hazards and biological applications); (2) Research partnerships, including the Scripps Partnership for Hazards and Environmental Applied Research (SPHEAR); and (3) Scripps innovation, invention, and new venture creation. http://sio.ucsd.edu/business/ Scripps is one of the worl d’s oldest, largest, and preeminent centers for ocean, earth and atmosphere research. Scripps science ranges from seminal work in Global Climate Change (including the Keeling Curve and a share of the Nobel Prize as part of the IPCC) to understanding El Nino's impact on North American weather to new discoveries in biofuels and biopharmaceuticals from the ocean. Scripps is also at the forefront of geophysical research ranging from studies geared toward oil/gas exploration to gaining a better understand catastrophic earthquakes.    www.sio.ucsd.edu
    • Stephen Bennett, SPHEAR Executive Director
    • Direct:  (858) 246-0065
    • Mobile: (312) 590-6360
    In the first quarter of 2009, a group of SPHEAR corporate partners teamed with Scripps researchers to launch a pilot project that will deliver insights on weather extremes as applied to energy trading. Scripps is proud to be working with Chesapeake Energy , Susquehanna International Group , and Citadel Investment Group on our first project that focuses on cold weather outbreaks and predicting their impact to the energy complex . The project: (1) uses a comprehensive definition to examine the variability of regional cold extremes; (2) uses powerful statistical tools to investigate causality and demonstrate skillful seasonal-to-interannual predictability of the seasonal probability and other features of regional cold outbreaks; (3) examines synoptic causes and precursors of individual regional cold events; and (4) expects to use this information to improve the lead-time and skill of their extended range weather forecasts by tapping into relevant precursor weather information and conditioning the weather forecast on contemporaneous large-scale climate information. The Scripps Partnership for Hazards and Environmental Applied Research (SPHEAR) is designed to provide member companies an opportunity to work directly with today’s leading scientific minds. Corporate partners expect a return-on-investment and academic partners expect to break new scientific ground. Both sides work closely together to set a research direction then, in return for their capital contribution, corporate partners interact directly with teams of earth, ocean, and atmospheric scientists throughout the entire scientific process. Corporate partners seek to enhance their risk management practices, investing strategies, product lines, product pipelines, and corporate governance practices by applying Scripps research findings. http://sio.ucsd.edu/special/sphear/ Thank You