Screen-level non-GTS data assimilation in a limited-area mesoscale model

Abstract

Abstract. The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle). The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particu- Milelli (m.milelli@arpa.piemonte.it) larly improved by the new-data assimilation, but, on the other hand, it has not been destroyed. It has to be pointed out that a correct description of the planetary boundary layer, even only the lowest part of it, could be helpful to the forecasters and, in general, to the users, in order to deal with meteorological hazards such as snow (in particular snow/rain limit definition), or fog (description of temperature inversions).

On the Spin‐Up Period in WRF Simulations Over Europe: Trade‐Offs Between Length and Seasonality

“…Second, we computed the root-mean-square error (RMSE) between the 1982 5-day running mean daily series retrieved from each experiment and the reference series at the grid point level. In order to elucidate statistically significant changes (reductions or increases) in the RMSE values with longer spin-up periods, we applied a resampling method as in Milelli et al (2010). We rejected the null-hypothesis that there is no difference in the RMSE between two simulations with different spin-up periods with a 95% of confidence level according to the method.…”

“…Although this analysis accounts for the similarity among all quantiles in the distributions, specific impacts on their far-end right side were additionally evaluated by direct comparison of the 90th percentile values of the daily T2 and PR series (T2p90 and PRp90, respectively) between the various experiments 10.1029/2019MS001945 and the reference run. The statistical significance of differences in T2p90 and PRp90 were again assessed by 1000 bootstrap replications as detailed in Milelli et al (2010), imposing 95% confidence.…”

Precipitation response to aerosol–radiation and aerosol–cloud interactions in regional climate simulations over Europe

“…The statistical significance of the differences between the climatologies reproduced by the simulations was checked using a bootstrap method with 1000 repetitions and applying a p value < 0.05. Further details of this method can be found in Milelli et al (2010).…”

Precipitation response to Aerosol-Radiation and Aerosol-Cloud Interactions in Regional Climate Simulations over Europe

“…The statistical significance of the differences among the climatologies reproduced by the simulations is checked by using a Bootstrap method with 1000 repetitions and a p-value < 0.05 was applied. More details about the method can be found in Milelli et al (2010).…”