Spontaneous aggregation of deep convection is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid-scale processes. This study focuses on the role played by mixing and entrainment, either provided by the turbulence model or the implicit numerical dissipation. We have analyzed the results of two different models, WRF and SAM, and compared different configurations by varying the turbulence models, the initial conditions and the horizontal spatial resolution. At coarse grid resolution (3 km), the removal of turbulent mixing prevents the occurrence of Convective Self-Aggregation (CSA) in models with low numerical diffusivity, while it is preserved in models with high numerical diffusivity. When the horizontal grid resolution is refined to 1 km (thus reducing the implicit numerical dissipation), CSA is only achieved by increasing the explicit turbulent mixing. In this case, CSA was found to occur even with a small amount of shallow clouds. Therefore, this study suggests that the sensitivity of CSA to horizontal grid resolution is not primarily due to the corresponding decrease in shallow clouds. Instead, it is found that turbulent mixing and dissipation at small scales regulate the amplitude of initial humidity perturbations introduced by convection in the free troposphere: the greater the dissipation at small scales, the greater the size and the strength of humidity perturbations in the free troposphere that can destabilize the RCE state.

Given the regional surface network of the Umbria region, a mountainous area located in central Italy, the observed hourly temperature time series from 2010 to 2017 have been analyzed applying  basic and extended quality control (QC) procedures following the World Meteorological Organization (WMO) standards. The validation procedure consisted of automatic QC, producing validated data with metadata subsequently recorded in NetCDF format. After these controls, data have been manually checked and an extended procedure has been applied to reconstruct the temperature time series for missing data. The spatiotemporal method used to reconstruct the data has been linear interpolation for 1-hour gaps, and the Empirical Orthogonal Function algorithm (EOF) for the 2-hour and longer gaps. The introduction of a complete and homogeneous data set of hourly reanalysis ERA5 (ECMWF) allowed the reconstruction of the longest gaps with statistical and physical consistency. The final product of this study is a continuous station time series of hourly temperatures that will be available to the public by the end of 2020; a daily version of the original time series is already available at the regional website (https://annali.regione.umbria.it/).Keywords --- Agrometeorology, Quality control, Missing data, ERA5, Empirical Orthogonal Function

Spontaneous aggregation of deep convection is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid-scale processes. This study focuses on the role played by mixing and entrainment, either provided by the turbulence model or the implicit numerical dissipation. We have analyzed the results of two different models, WRF and SAM, and we have compared different configurations by varying the turbulence models, the numerical schemes and the horizontal spatial resolution. At coarse grid resolution (3 km), removing turbulent mixing prevents the occurrence of Convective Self-Aggregation (CSA) in low numerical diffusion models, while delaying it in high numerical diffusion models. When the horizontal grid resolution is refined to 1 km (thus reducing the implicit numerical dissipation), CSA is achieved only by increasing the explicit turbulent mixing. In this case, CSA was found to occur even with a small amount of shallow clouds. Therefore, this study suggests that the sensitivity of CSA to horizontal grid resolution is not primarily due to the corresponding decrease in shallow clouds. Instead, it is found that turbulent mixing and dissipation at small scales regulate the amplitude of humidity perturbations introduced by convection in the free troposphere: the greater the dissipation at small scales, the greater the size and the strength of humidity perturbations in the free troposphere that can destabilize the RCE state.

The Radiative-Convective Equilibrium (RCE) of two models exhibiting convective aggregation has been compared. The goal of the work, following the suggestion from the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP), is to identify key parameters controlling self-aggregation in RCE for both models and discuss the processes controlled by these parameters in order to find the simulations similarities and to test their differences. The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), have different physical and numerical formulations. This allowed us to compare the sensitivity to processes related to self-aggregation. When self-aggregation occurs, the two models present similar statistics for what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction (leading to an “Iris effect’), within the spread of the RCEMIP values. On the other hand, they differ both in the degree of organization and the organization feedback: SAM is strongly organized (is on the highest quartile of the RCEMIP for the Iorg Index) and the convective organization is achieved by cloud-radiative feedback; ARPS is weakly organized (on the multi-model average of the RCEMIP for the Iorg Index) and the moisture-convection feedback is leading to the convective organization. The prevalence of one mechanism over the other has been found in the interaction between the microphysics and the sub-cloud layer properties. This comparison suggests that, in order to have a robust measure of climate sensitivity, climate models should include both types of convective organization mechanisms as shown by the two models.

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 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