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Communicating Forecast Quality with Linear Probability Score (LPS)

Understanding and applying Linear Probability Score (LPS) to evaluate operational seasonal climate forecasts efficiently. Learn how LPS can enhance forecast quality assessment for non-specialists based on guidance from expert Simon J. Mason. The score ranges from 100% for perfect forecasts to 0% for entirely incorrect ones. Find out how to determine the expected score for 3-category forecasts and interpret forecast probabilities effectively.

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Communicating Forecast Quality with Linear Probability Score (LPS)

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  1. Linear probability score(LPS) Based on: Guidance on Verification of Operational Seasonal Climate Forecasts By Simon J. Mason (IRI), prepared under the auspices of the World Meteorological Organization (WMO), Commission for Climatology XIV, Expert Team on CLIPSOperations, Verification and Application Service

  2. Linear probability score (LPS) Option for communicating forecast quality of individualmaps for non-specialists LPS (Wilson et al, 1999) n: number of points (locations i) on the map at which the forecasts are to be verified m: number of categories j yi,j: is 1 if the observation at location i was in category j, and is 0 otherwise pi,j: is the forecast probability for category j at location i Interpretation: Average forecast probability assigned to the verifying categories Range: from 100% for perfect forecasts (100% probability assigned to the observed category at each of the locations) to 0% for perfectly bad forecasts (0% probability assigned to the observed category at each of the locations) A “good” forecast will score more than a strategy of using the climatological forecasts, and will beat the expected score from guessing. Question: What is this expected score for 3 category forecasts?

  3. Example forecasts and observations for 3 equi-probable categories [below-normal (B), normal (N), and above-normal (A)] Forecast probabilities for the 3 categories

  4. Example forecasts and observations for 3 equi-probable categories [below-normal (B), normal (N), and above-normal (A)] Forecast probabilities for the 3 categories 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 Blue numbers: 1 indicates the observed category 0 indicates non-observed category

  5. LPS example for tercile probability forecasts Average probabiliy about 6% more than climatology

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