Temporal Consistency of the UERRA Regional Reanalysis: Investigating the Forecast Skill

Sammanfattning: Weather forecasting has improved greatly since the middle of the 20th century, thanks to better forecasting models, an evolved weather observing system, and improved ways of assimilating the observation data. However, these large systematical improvements make it difficult to use the weather data for climatological studies. Furthermore, observations are scarce, and they cannot be made everywhere. One way to solve this problem is to produce reanalyses, where a fixed version of a numerical weather prediction (NWP) model is used to produce gridded analysis and forecast data with detailed descriptions of the weather by assimilating observation data for a determined time period. One of the newest regional reanalyses is UERRA (Uncertainties in Ensembles of Regional Re-Analyses), which spans over the time period 1961-2015 and covers the whole Europe. By using a fixed NWP model, the only two factors that might influence the temporal quality of a regional reanalysis dataset are the varying number and quality of weather observations, and the quality of the global driving model which gives information about boundaries and large-scale features. In this report, data from one of the UERRA products has been used with the aim to investigate the temporal consistency of the 30-hour forecast skill regarding three parameters; temperature at 2 meters height (t2m), wind speed at 100 meters height (ws100) and 500 hPa geopotential (Φ500). The work has been focused on only land points over Europe during winters and summers, as this enables to investigate the model behaviour at the lowest and highest temperatures. The 30-hour forecast skill was estimated throughout the time period from how well it performed compared to the 6-hour forecast. Temporal inconsistencies were found throughout the reanalysis, with the largest temporal differences being present for Φ500, followed by ws100. UERRA shifts its global driving model in 1979 from ERA-40 (ECMWF Re-Analysis 40) to ERA-Interim (ECMWF Interim Re-Analysis), which ends up as a significant improvement of forecast skill for all investigated parameters. Furthermore, ws100 also shows a significant skill improvement in wintertime from 1979 onwards, while Φ500 shows a systematical improvement for both seasons. In general, the forecast skill is lower in wintertime than in summertime, which might be a result from higher natural variability of the weather in winters. A quick study of forecast data from ERA-Interim shows that the same improving trend in Φ500 can be seen also in that dataset, while the two model drifts differ completely. It was concluded that the addressed issues with temporal inconsistency should be communicated to end users utilizing the UERRA datasets, as knowledge about this can be greatly beneficial when studying climatological trends and patterns and when using the model to reforecast weather events.