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1 of 30 Tom.Pagano@por.usda.gov 503 414 3010 Introduction to Forecasts and Verification

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2 of 30 Forecasts and decision-making What is a forecast? What kinds of forecasts exist? What makes a forecast good?

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3 of 30 Predictions are everywhere! Weather forecasts

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4 of 30 Predictions are everywhere! Weather forecasts Climate forecasts

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5 of 30 Predictions are everywhere! Weather forecasts Climate forecasts River Forecasts

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6 of 30 Whether you use a “forecast” product or not, almost all resource decisions assume something about the future (and not just for climate…)

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7 of 30 Whether you use a “forecast” product or not, almost all resource decisions assume something about the future (and not just for climate…) Population Budgets

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8 of 30 The focus of today: Seasonal water supply volume forecasts (available in a variety of formats) NRCS formats:

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9 of 30 The focus of today: Seasonal water supply volume forecasts (available in a variety of formats) NRCS formats:

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10 of 30 NWS formats:

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11 of 30 NWS formats:

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12 of 30 Location Elements of a forecast: Location, Time, Magnitude, Probability

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13 of 30 Location Time Period Historical Average Elements of a forecast: Location, Time, Magnitude, Probability

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14 of 30 Location Time Period “Most Probable”* Water Volume Historical Average Error Bounds * Term retired in 2005 Elements of a forecast: Location, Time, Magnitude, Probability

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15 of 30 Ways of expressing a forecast: Categorical – “It will be warm” Deterministic – “It will be 103% of normal” Probabilistic – “There is a 50% chance of being more than 23 kaf”

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16 of 30 Ways of expressing a forecast: Categorical – “It will be warm” Deterministic – “It will be 103% of normal” Probabilistic – “There is a 50% chance of being more than 23 kaf” Ways of verifying forecasts: Categorical – Did you predict the right category? Deterministic – Was the observed amount very far from the forecast? Probabilistic – Was the outcome unlikely?

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17 of 30 Water supply forecasts are probabilistic at their core, but most products are deterministic

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18 of 30 Climate forecasts are probabilistic too, but mostly they’re discussed categorically Tilts in the odds

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19 of 30 What is the “Climatology” or “Equal Chances” forecast? The safest forecast in the absence of skill or a signal… “The outcome will be between zero and infinity”

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20 of 30 What is the “Climatology” or “Equal Chances” forecast? The safest forecast in the absence of skill or a signal… “The outcome will be between zero and infinity” Try a little harder… “The range of temperatures observed on earth” Keep trying… “The typical range of temperatures in Durango in January”

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21 of 30 What is the “Climatology” or “Equal Chances” forecast? The safest forecast in the absence of skill or a signal… “The outcome will be between zero and infinity” Try a little harder… “The range of temperatures observed on earth” Keep trying… “The typical range of temperatures in Durango in January” But what is “typical”? Standard definition is the 30-year normal, 1971-2000 Long enough to be stable, recent enough to be relevant “Naïve baselines” are useful for determining how much “better” a forecasting system is, comparatively Interestingly, there is no “Equal Chances” forecast in hydrology…

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22 of 30 Resolution versus Reliability Resolution/Sharpness: How confident are your forecasts? How much do they differ from climatology? How much do you go out on a limb?

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23 of 30 Resolution versus Reliability Resolution/Sharpness: How confident are your forecasts? How much do they differ from climatology? How much do you go out on a limb? 2002 2003 2004 2005 2006 2007 2008 Feb-Apr precipitation forecasts Issued Jan 15 Never says anything interesting Forecast often relatively strong

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24 of 30 Resolution versus Reliability Reliability: Are you going out on the right limb? When you make a statement, is it generally correct? e.g. When you say there’s a 30% chance of rain, does it rain 30% of the time?

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25 of 30 Resolution versus Reliability Reliability: Are you going out on the right limb? When you make a statement, is it generally correct? e.g. When you say there’s a 30% chance of rain, does it rain 30% of the time? Percent of time the observed should fall in different categories 10% 20% 10%

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26 of 30 Forecast “Appropriateness” Is this the most appropriate forecast given the information at hand?

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27 of 30 Forecast “Appropriateness” Is this the most appropriate forecast given the information at hand? Year 2006 2007 Apr 1 snowApr-Jul obs Animas at Durango Apr-Jul fcst 68% 89% Fcst - Obs 0% -18% 57% 54% 07M31S Was 2007 a “bad” forecast?

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28 of 30 Forecast “Appropriateness” Is this the most appropriate forecast given the information at hand? Year 2006 2007 Apr 1 snowApr-Jul obs Animas at Durango Apr-Jul fcst 68% 89% Fcst - Obs 0% -18% 57% 54% 07M31S Was 2007 a “bad” forecast? In 2007, Apr-Jun precipitation was >400% what it was in 2006. 2006 Apr-Jul precip 3 rd lowest on record. Maybe 2006 was the “bad” forecast with a lucky outcome?

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29 of 30 Skill and Value are different Consider also: Timeliness Cost to produce Understandability Relevance Specificity Transparency Credibility Ability to use Skill is probably one of the hardest things on the list to improve

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30 of 30 Summary 3 Classes of Forecasts: Categorical, Deterministic, Probabilistic At their core, all forecasts are probabilistic, but derived products are not. Resolution: How often do you show up for “exams”? Reliability: What is your score on exams you do take? Appropriateness: Are you using the best information on hand? Skill is only one part of value and is one of the hardest things to improve

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