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Statistical Interpretation of High-Resolution Weather Model for Severe Weather Forecasting

This study aims to develop a model-based system for short-range severe weather forecasting using high-resolution numerical weather model data. The Neighbourhood Method is applied to transform deterministic forecasts into probabilistic products, allowing for better interpretation of small-scale structures and the derivation of exceedance probabilities for threshold values. The study also evaluates the reliability and quality of probabilistic products for precipitation forecasts.

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Statistical Interpretation of High-Resolution Weather Model for Severe Weather Forecasting

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  1. Interpretation of the new high-resolution model LMK Heike Hoffmann heike.hoffmann@dwd.de Volker Renner volker.renner@dwd.de Susanne Theis susanne.theis@dwd.de

  2. Aims • High-resolution numerical weather forecasts include noticeable stochastic elements already in the short range • Direct model output for deterministic forecasts should therefore be transformed to suppress essentially unpredictable small scale structures • Probability information for the exceedance of given thresholds and/or warning events should be derived by statistical means

  3. Method We plan a two step approach 1. Using information of a single model forecast by applying the Neighbourhood Method (NM) 2. Using information resulting from LMK forecasts that are started every 3 h (LAF-Ensemble)

  4. Goals: • Development of a model-based NWP-system for very short range forecasts (+18h) of severe weather events especially related to • deep moist convection • interactions with fine-scale topography • Current LMK-configuration: • x=2.8 km grid spacing, t=16 sec. • 421 x 461 grid points, 50 layers Planned operational use: end 2006

  5. Neighbourhood Method Assumption: LMK-forecasts within a spatiotemporalneighbourhood are assumed to constitute a sample of the forecast at the central grid point

  6. x x Definition of Neighbourhood I y t

  7. Definition of Neighbourhood II Size of Area Form of Area hs Linear Regressions

  8. Products • Smoothed fields for deterministic forecasts • Expectation Values from spatiotemporal neighbourhood • simple averaging over quadratic grid boxes • Probabilistic Products • Exceedance Probabilities for certain threshold values for different parameters, especially for hazardous weather warnings

  9. Verification results for precipitation Data LMK forecasts; 3.-17.01.2004; 13.-27.07.2004; 1 h values, 00 UTC and 12 UTC starting time; 7-18 h forecast time all SYNOPs available from German stations comparison with nearest land grid point Neighbourhood-Method-Parameters vers_01: 3 time levels (3 h); 10 s ( 28 km) vers_02: 3 time levels; 5 s vers_03: 3 time levels; 15 s Averaging square areas of different sizes (5x5,15x15)

  10. Conclusions for deterministic precipitation forecasts • scores in winter are much better than in summer, but in summer there is more effect in postprocessing • expectation values of NM do not integrate into simple averaging, they show some advantages for intermediate thresholds • there is, however, no obvious overall improvement by using the NM instead of simple spatial averaging • therefore, simple averaging over 5x5 domain will be applied followed by a re-calibration of the distributions of the smoothed field towards the distribution of the original field

  11. LMK Total Precipitation [mm/h] 13. Jan. 2004, 00 UTC, vv=17-18h [mm/h]

  12. Exceedance Probability of 1mm/h, 13. Jan. 2004, 00 UTC, vv=17-18h [%] calculated with the NM with radius 10 grid steps, 3 time intervals (t-1, t, t+1)

  13. Preliminary Conclusions for precipitation probabilities Reliability suffers from the well known effect of “overconfidence”; it can only slightly be improved by increasing the spatial neighbourhood BSS indicates only a small advantage with respect to the “climate” of the relative periods which, however, is not known in advance BSS with DMO as reference shows significant improvement of postprocessed fields, in July more than in January Quality of probabilistic products improves with increasing size of the spatiotemporal neighbourhood; the optimal neighbourhood has not yet been determined Increasing temporal neighbourhood size leads to better results than increasing spatial neighbourhood (with same number of points in the neighbourhood)

  14. Outlook - Continue development of probabilistic products based on the NM - Develop a new weather-interpretation for the LMK - Improve products by use of the information from the LMK LAF-Ensemble

  15. Acknowledgements Many thanks to ... U. Damrath, J. Förstner, T. Hanisch, W. Peyinghaus, K. Stephan and the colleagues in the Projects 2 and 3 of the Aktionsprogramm 2003

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