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About the Project

COSMO Priority project AWARE: Appraisal of "Challenging WeAther" FoREcasts.

Task 1. Challenges in observing HIW

            The task considers which observations are necessary to verify HIW forecasts, as well as issues related to observation sparseness, quality, and thresholds. The effort is given to identify observation requirements for monitoring the selected hazards and/or for assessing forecast accuracy as well as to quantify the role of observation uncertainty.

Task 2: Overview of appropriate verification measures for HIW

Task 2.1 Survey for assessment of proper verification of phenomena

Task 2.2 Role of Stable Equitable Error in Probability Space (SEEPS) and the extreme dependency family of scores (EDI, SEDI) for the evaluation of extreme precipitation forecasts.

Task 2.3 Extreme Value Theory (EVT) approach- Fitting precipitation object characteristics to different distributions

Task 3: Verification applications (with a focus on spatial methods) to HIW

Task 3.1 Verification of forecasts of intense convective phenomena (thunderstorms w. lightning)

Task 3.2 Lightning potential index (LPI) in mountain regions.

Task 3.3 CRA (Contiguous rain area) and FSS analysis on intense precipitation.

Task 3.4 Distributional methodology (DIST) tuned on high-threshold events for flash floods forecast evaluation.

Task 3.5 LPI verification and correlation of convective events with microphysical and thermodynamical indices.

Task 3.6 Work on the comparative verification of NWC and NWP results using spatial verification methods.

Task 4. Overview of forecast methods, representation and user-oriented products linked to HIW

          The task studies how well is HIW is represented in postprocessing, what are the pros/cons of DMO vs. PostPro with respect to HIW phenomena predictions, what is the current   predictive skill, and the user’s interpretation of forecast value in high-impact weather situations (observed and/or forecast).

Task 4.1. Postprocessing vs. direct model output for HIW.

Task 4.2 Improving existing post-processing methods: Use of multi-linear regression (MLR), adaptive (recursive) least squares (A-RLS) and/or artificial neural networking (ANN) techniques.

Task 4.3 QPF evaluation approaches at ARPAE for hydrological purpose and for the issuing of Civil Protection alerts.

Task 4.4 Representing and communicating HIW forecast for decision making.

Task 4.5: Product generation and calibration of convection-permitting ensemble

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