Abstract. In this paper, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (very short-term forecast, 3 h in this study) for two severe weather events that occurred in Italy. The first case refers to a moderate and localized rainfall over central Italy that occurred on 16 September 2017. The second case occurred on 9 and 10 September 2017 and was very intense and caused damages in several geographical areas, especially in Livorno (Tuscany) where nine people died. The first case study was missed by several operational forecasts, including that performed by the model used in this paper, while the Livorno case was partially predicted by operational models. We use the RAMS@ISAC model (Regional Atmospheric Modelling System at Institute for Atmospheric Sciences and Climate of the Italian National Research Council), whose 3D-Var extension to the assimilation of radar reflectivity factor is shown in this paper for the first time. Results for the two cases show that the assimilation of lightning and radar reflectivity factor, especially when used together, have a significant and positive impact on the precipitation forecast. For specific time intervals, the data assimilation is of practical importance for civil protection purposes because it changes a missed forecast of intense precipitation (≥40 mm in 3 h) to a correct one. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its usefulness is partially reduced by the increase in false alarms, especially when both datasets are assimilated.

This study shows how satellite-based passive and active microwave (MW) sensors can be used in conjunction with high-resolution Numerical Weather Prediction (NWP) simulations to provide insights of the precipitation structure of the tropical-like cyclone (TLC) Numa, which occurred on 15-19 November 2017. The goal of the paper is to characterize and monitor the precipitation at the different stages of its evolution from development to TLC phase, throughout the storm transition over the Mediterranean Sea. Observations by the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) and by the GPM constellation of MW radiometers are used, in conjunction with the Regional Atmospheric Modeling System (RAMS) simulations. The GPM-CO measurements are used to analyze the passive MW radiometric response to the microphysical structure of the storm, while the comparison between successive MW radiometer overpasses shows the evolution of Numa precipitation structure from its early development stage on the Ionian Sea into its TLC phase, as it persists over southern coast of Italy (Apulia region) for several hours. Measurements evidence stronger convective activity at the development phase compared to the TLC phase, when strengthening or weakening phases in the eye development, and the occurrence of warm rain processes in the areas surrounding the eye, are identified. The weak scattering and polarization signal at and above 89 GHz, the lack of scattering signal at 37 GHz, and the absence of electrical activity in correspondence of the rainbands during the TLC phase, indicate weak convection and the presence of supercooled cloud droplets at high levels. RAMS high-resolution simulations support what inferred from the observations, evidencing Numa TLC characteristics (closed circulation around a warm core, low vertical wind shear, intense surface winds, heavy precipitation), persisting for more than 24 h. Moreover, the implementation of DPR 3D reflectivity field in the RAMS data assimilation system shows a small (but non negligible) impact on the precipitation forecast over the sea up to a few hours after the DPR overpass.

Abstract. In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

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This paper shows the performance of an operational forecasting system, based on the regional atmospheric modeling system (RAMS), at 3 km horizontal resolution over southern Italy. The model is initialized from the 12 UTC operational analysis/forecasting cycle of the European Centre for Medium range Weather Forecasts (ECMWF). The forecast is issued for the following three days. The performance is evaluated for a whole year for the surface parameters: temperature, relative humidity, wind speed and direction, and precipitation. The verification has been performed against SYNOP stations over southern Italy. A dense non-GTS network over Calabria is used for precipitation. Results show that RMSE is about 2-3 K for temperature, 12–16% for relative humidity, 2.0–2.8 m/s for wind speed, and 55–75° for wind direction, the performance varying with the season and with the forecasting time. The error increases between the first and third forecast days. The verification of the rainfall forecast shows that the model underestimates the area of the precipitation. The model output statistics (MOS) is applied to all parameters but precipitation. Results show that the MOS reduces the RMSE by 0–30%, depending on the forecasting time, on the season and on the meteorological parameter.

The knowledge of water vapour distribution is a key element in atmospheric modeling and considerable information, also at the local scale, can be derived from the GPS-ZTD (global positioning system-Zenith total delay) data.This paper shows the assimilation of GPS-ZTD data into the RAMS@ISAC (Regional Atmospheric Modeling System at Institute of Atmospheric Sciences and Climate of the National Research Council) to improve the representation of the water vapour in the meteorological model. The data assimilation system is based on 3D-Var (three-dimensional variational assimilation system) and it is applied to a network of 29 receivers located within the Lazio Region, Central Italy. All collected data are processed using the PPP (precise point positioning) method through RTKLIB, an open source program package for GNSS (Global Navigation Satellite Systems) Positioning. Among the GPS receivers, three are single frequency receivers, able to acquire L1 frequency only, so that it is necessary a preliminary reconstruction of L2 synthetic observations, which is achieved by a new original ground-based augmentation strategy. Results show remarkably that the single frequency receivers can be used the same way as geodetic receivers.The RAMS@ISAC is run at 4 km horizontal resolution over central Italy and is nested, using one-way nesting, into a 10 km horizontal resolution run of the same model. The experiment was performed along to two months, from 28 July to 28 September 2017.Results show that the GPS-ZTD data, assimilated by 3D-Var, have an important impact on the analysis of the water vapour field and the RMSE of ZTD and IWV (vertically integrated water vapour) is roughly halved for the analysis compared to the background.The impact of the GPS-ZTD data assimilation is also evaluated for the very short term (VSF) forecast (1-3 h), obtaining an improvement of the ZTD and IWV RMSE for all three hours of forecast.

We present the first macroscopical model for charge transport in compound semiconductors to make use of analytic ellipsoidal approximations for the energy dispersion relationships in the neighbours of the lowest minima of the conduction bands. The model considers the main scattering mechanisms charges undergo in polar semiconductors, that is the acoustic, polar optical, intervalley non-polar optical phonon interactions and the ionized impurity scattering. Simulations are shown for the cases of bulk 4H and 6H-SiC.

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