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This work analyses 10 years (from 2009 to 2018) of long‐range Saharan dust advections (SDAs) and their correlation with the precipitation and radiative budget over the Central Mediterranean region. The identification of the SDAs is based on back‐trajectories (BTs) of air mass and complementary measurements of the dust deposited at the site of Monte Martano in Central Italy. The associated synoptic circulation weather types (CWT), precipitation and radiation variables have been estimated by using ERA5 and CAMS reanalysis, satellite data and raingauge observations. It is found that the 50% of all SDAs occur under a CWT characterized by an upper level trough over the Western Mediterranean and a high‐pressure system over the Eastern Mediterranean. Strong southerly winds, large vertical integral of water vapour transport and a positive anomaly of 2 m temperature are associated with dust uplift and transport. The effect of dust on rainfall intensity puts forward the important modulation of the aerosol effects on the radiative budget with a latitudinal dependence. On dusty days, intense rainfall increases over the northern Central Mediterranean and light rainfall is suppressed in the southern Central Mediterranean, pointing out a relevant correlation between dust and the intensification of extreme events. Other than affecting rainfall intensity, the stronger surface heating over the southern Central Mediterranean, which is correlated to a higher dust optical depth, causes a local maximum of sea surface temperature (SST) and near‐surface temperature anomaly. [ABSTRACT FROM AUTHOR]

It is highly likely that the Mediterranean region will experience increased aridity and hydrological droughts. Therefore, seasonal forecasts of soil moisture can be a valuable resource for groundwater management. However, their accuracy in this region has not been evaluated against observations. This paper presents an evaluation of soil moisture in the Central Mediterranean region during the period 2001–2021 using the seasonal forecast system SEAS5. Standardized anomalies of soil moisture are compared with observed values in ERA5 reanalysis. In terms of the average magnitude of the forecast error and the anomaly correlation coefficient, the forecasts demonstrate good performance only in certain regions of the domain for the deepest soil layer at 289 cm, the most interesting for groundwater management. No clear overlap with specific land features such as orography, land cover, or distance from the coast has been observed with respect to the forecast performance. Accordingly, seasonal forecasts can be used to detect wet and dry events for the deepest soil layer in certain regions, with lead-times of up to 6 months. In these regions, the area under the Relative Operating Characteristic (ROC) curve can reach values larger than 0.8. Dry events are generally better captured than wet events for all soil layers. We also analyzed the effectiveness of seasonal forecasts in predicting wet and dry events in Northern and Central Italy for the 2012–2013 period, with a lead-time of 6 months. We found that seasonal forecasting has great potential for groundwater management in certain areas of the Central Mediterranean. However, improvements are needed in observations, data assimilation methods, and the seasonal forecasting system to ensure reliable forecasts for upper soil layers and other regions. [ABSTRACT FROM AUTHOR]

Mediterranean Tropical-Like Cyclones, called "medicanes", present a multiscale nature and their track and intensity have been recognized as highly sensitive to large-scale atmospheric forcing and to diabatic heating as represented by the physical parameterizations in numerical weather prediction. Here, we analyse the structure and investigate the predictability of medicanes with the aid of the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) ensemble forecasting system with 25 perturbed members at 9 km horizontal resolution (compared to the 16 km operational resolution). The IFS ensemble system includes the representation of initial uncertainties from the ensemble data assimilation (EDA) and a recently developed uncertainty representation of the model physics with perturbed parameters (Stochastically Perturbed Parameterizations, SPP). The focus is on three medicanes, Ianos, Zorbas and Trixie that have been among the strongest in recent years. In particular, we have carried out separate ensemble simulations with initial perturbations, full physics SPP, and with a reduced set of SPP, where only convection is perturbed to highlight the convective nature of medicanes. It is found that compared to the operational analysis and satellite rainfall data, the forecasts reproduce the tropical-like features of these cyclones. Furthermore, the SPP simulations compare to the initial condition perturbation ensemble, in terms of tracking, intensity, precipitation and more generally in terms of ensemble skill and spread. Moreover, the study confirms that similar processes are at play in the development of the investigated three medicanes, in that the predictability of these cyclones is linked not only to the prediction of the precursor events (namely the deep cut-off low) but also to the interaction of the upper-level dynamically driven Potential Vorticity (PV) streamer with the tropospheric PV anomaly that is driven by surface heating and stratiform and convective condensational heating. [ABSTRACT FROM AUTHOR]

In the EFFS Project, an attempt has been made to develop a general framework to study the predictability of severe convective rainfall events in the presence of orography. Convective activity is embedded in orographic rainfall and can be thought as the result of several physical mechanisms. Quantifying its variability on selected area and time scales requires choosing the best physical representation of the rainfall variability on these scales, The main goal was (i) to formulate a meaningful set of experiments to compute the oscillation of variance due to convection inside model forecasts in the presence of orography and (ii) to give a statistical measure of it that might be of value in the operational use of atmospheric data. The study has been limited to atmospheric scales that span the atmosphere from 2 to 200 km and has been focused on extreme events with deep convection. Suitable measures of the changing of convection in the presence of orography have been related to the physical properties of the rainfall environment. Preliminary results for the statistical variability of the convective field are presented. [ABSTRACT FROM AUTHOR]

During the analysis - funded by The European Agricultural Fund for Rural Development (EAFRD) that has financed the EU's contribution to rural development program in Umbria (Italy) – of meteorological data from automatic weather stations following the WMO requirements [1], it has become evident the need of using the newest global climatic data (ERA5, ECMWF [2]) to compute indices to perform an extended quality control over data and to give climate information to the end users. The peculiar regional environment with strong orographic modulation of the Umbria region and the consequent impact over the precipitation field together with the signal of a seasonal and intra-seasonal change of the temperature distribution, show a complex impact of the climate change over the Umbria region and broadly over Central Italy. This work is trying to show how to account for the impact of the climate change over the phenology of vineyards using different indices as SPI, SPEI, and Winkler starting from the new reanalysis dataset with enhanced resolution in time and space. Moreover within this project agronomists and plant pathologists are cooperating with meteorological scientists to analyze and relate weather epidemic development in order to reduce the economic impact and environmental effects of airborne plant disease epidemics and ultimately to make the end-users timely decisions about the effective and economical application of fungicides and about other tactics to manage plant diseases.[1] WMO. Guide to agricultural meteorological practices (wmo-no. 134). World Meteorological Organization: Geneva, Switzerland, 2010.[2] https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5 [ABSTRACT FROM AUTHOR]