Open AccessSpatial techniques applied to precipitation ensemble forecasts: from verification results to probabilistic products
Author(s) -
Ben Bouallègue Zied,
Theis Susanne E.
Publication year - 2014
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1435
Subject(s) - probabilistic logic , smoothing , computer science , probabilistic forecasting , quantitative precipitation forecast , ensemble forecasting , consensus forecast , meteorology , precipitation , data mining , environmental science , econometrics , mathematics , artificial intelligence , geography , computer vision
Spatial techniques have been developed to quantify the performance of a system beyond the classical point‐to‐point comparison with observations. Including spatial neighbourhood information in the verification process, the quality of a forecast can be better characterized. Guidance for the interpretation of deterministic forecasts can also be delivered. This paper investigates the application of spatial techniques to ensemble forecasts. The aim is to assess ensemble forecast skills better and to provide improved guidance to the forecasters in the form of refined probabilistic products. Two spatial techniques are applied to precipitation forecasts derived from an ensemble system at the convective scale ( COSMO‐DE‐EPS ). The first technique is a smoothing method which enlarges the ensemble sample size by neighbouring forecasts. The resulting forecasts are called fuzzy probabilistic forecasts. The second method is an upscaling procedure which modifies the reference area of the probabilities. Fuzzy and upscaled probabilistic forecasts are assessed over a 3 month period covering summer 2011. The impact of smoothing and upscaling is investigated for a range of neighbourhood sizes and spatial scales respectively. Based on the verification results, recommendations are drawn how to use these techniques in optimally presenting COSMO‐DE‐EPS probabilistic products to forecasters who issue weather warnings.