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Convection‐permitting ensembles: Challenges related to their design and use
Author(s) -
Frogner IngerLise,
Singleton Andrew T.,
Køltzow Morten Ø.,
Andrae Ulf
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3525
Subject(s) - predictability , precipitation , quantitative precipitation forecast , environmental science , climatology , ensemble average , lead time , lead (geology) , convection , meteorology , scale (ratio) , added value , value (mathematics) , atmospheric sciences , mathematics , statistics , geology , geography , economics , operations management , cartography , finance , geomorphology
Challenges related to the design and use of a convection‐permitting ensemble (CPEPS) are discussed. In particular the scale‐dependent predictability of precipitation and the use of a CPEPS as well as its potential added value over global ensemble prediction systems (EPS) are investigated. Forecasts of precipitation from the operational CPEPS in Finland, Norway and Sweden (MEPS) are used for the investigations. It is found that predictability for scales smaller than ∼60 km is lost rapidly within the first 6 h of the forecast with the smallest predictable scale growing more slowly to ∼100 km over the following 18–24 h. However, there is large case‐to‐case variability and the ensemble perturbations fail to become fully saturated, especially in winter, suggesting a weakness in the design of the ensemble. The added value of CPEPS over deterministic forecasts and coarser resolution EPSs is discussed with summary statistics and case‐studies. It is shown that the added value varies between seasons and lead times. For precipitation there is an added value for both severe precipitation events and for precipitation/no precipitation decisions. The added value is higher in summer compared to winter and for shorter lead times compared to longer lead times.