Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Abstract

Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km).Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields.Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. ForCorrespondence to: S. Federico (s.federico@isac.cnr.it) the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 • C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 • C, respectively, for the fourth-day forecast.Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error.The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensiti...

Real-time weather forecasting in the Western Mediterranean Basin: An application of the RAMS model

“…Although RAMS has been implemented for a concrete area within the Western Mediterranean Basin, due to its similar climate and physical characteristics, we strongly believe that the results found is this study could be projected as well to other areas in the east coast of the Iberian Peninsula. In addition, the results reproduced in the present paper are analogous to those found in other Mediterranean Regions, using the RAMS model (Pasqui et al, 2004;Federico, 2011), and using other real-time mesoscale models (Bartzokas et al, 2010). Likewise, considering other areas with Mediterranean-type climate regimes, it has been found that atmospheric humidity is the main cause of elevated minimum temperatures in the summer (Gershunov et al, 2009).…”

“…More information about the software developed may be found in Gómez et al (2013). (Papanastasiou, 2010;Federico, 2011;Kotroni, 2011;Hernández-Ceballos et al, 2013). The statistical calculations carried out in both cases include the mean bias, root mean square error (RMSE) and the index of agreement (IoA) for the near-surface temperature, relative humidity and wind speed.…”

Operational forecasting of daily summer maximum and minimum temperatures in the Valencia Region

“…This overestimation was strongest for inland areas, where bias values up to 6 ºC were observed. Federico (2011), using an optimal interpolation analysis, reproduced low RMSE errors, around 2 ºC for minimum temperatures and slightly higher values for the maximum. In this study, the highest values both for Bias and RMSE were also located inland.…”

“…Within the Mediterranean area, different systems have been implemented to forecast extreme temperature, such as that of Bartzokas et al (2010) for northwest Greece, or the one implemented by Federico (2011) for the Calabria region (southern Italy). In the first case, using the MM5 model (Dudhia, 1993), the daily maximum air temperature was slightly overestimated by the model, while minimum temperature was significantly overestimated.…”

Forecasting wind power production from a wind farm using the RAMS model

“…The R3 and R12 models share the same physical and dynamical parameterizations, which are as in Federico (2011), the only differences being their grids and horizontal resolutions. The R3 model uses two two-ways nested grids: the first grid has a horizontal resolution of 12 km and covers the Central part of the Mediterranean Basin, while the second grid extends over Central Italy with 3 km horizontal resolution.…”