Abstract
Abstract. Cyclones affecting the Mediterranean region, sometimes related to severe weather events, are often not well represented enough in numerical model predictions. Assessing the quality of the forecast of these cyclonic structures would be a significant advance in better knowing the goodness of the weather forecast in this region, and particularly the quality of predictions of high impact phenomena. In order to estimate the cyclone forecast uncertainty in operational models, in this work we compare two cyclone databases for the period 2006–2007: one from the operational analyses of the T799 ECMWF deterministic model; and the other from the forecasts provided by the same model in three ranges, H+12, H+24, and H+48. The skill of the model to detect cyclones and its accuracy in describing their features are assessed. An index is presented as an indicator of the quality of the prediction, derived from the frequency distribution of errors in the prediction of four characteristics of the cyclone: position, central pressure value, geostrophic circulation, and domain. Some sub-indexes are derived to verify each of the variables separately in order to analyse the most frequent sources of error. Other sub-indexes are also defined to indicate possible biases in the numerical prediction model.
Highlights
An essential aspect in the weather prediction process is verification, that is, a comparison of predicted weather against observed weather or a good estimate of true outcome
Verification is a key step to improving the forecast process, as information concerning the scale and features of forecast errors is obtained and possible sources of error can be identified, in order to monitor forecast quality and to compare the quality of different forecast systems (Brooks and Doswell, 1996; Wernli et al, 2008; JWGFVR, 2008)
Many variables can be used to describe the weather and the most representative ones should be selected. These variables can derive from observations or from numerical model analyses; all of them entail an associated error
Summary
An essential aspect in the weather prediction process is verification, that is, a comparison of predicted weather against observed weather or a good estimate of true outcome. Many variables can be used to describe the weather and the most representative ones should be selected These variables can derive from observations (e.g. remote sensing, surface observations) or from numerical model analyses; all of them entail an associated error. Another difficulty is how to handle so much information. Some suitable statistical skill scores can be selected, or created if necessary, to combine the large quantity of information obtained from this process These indexes can be useful to quantify the variation of the skill of numerical models in forecasting these events and to assess the improvement of the model over time (Charles et al, 2009). The resultant methodology has been applied to investigate the performance of the ECMWF T799 operational model in predicting surface cyclones
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