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Mesoscale numerical weather prediction models are frequently utilized for wind speed analysis and forecasting in the planning and support of wind power generation. However, high computational costs only allow for routine use up to a kilometer scale, which is sometimes too coarse to support onshore wind power generation in areas with complex orography. To address this, an algorithm was developed in southern Italy to downscale the wind fields output using the weather research and forecasting (WRF) model for the first 250 m above ground level. The algorithm is based on artificial neural networks (ANNs) and uses the WRF model outputs on a 1.2 km regular grid, and the land surface height and orientation on a 240 m regular grid to downscale wind fields to a 240 m regular grid. To train the ANNs, a WRF simulation dataset in large eddy simulation (LES) mode was developed. Particular attention was paid to defining the ANN architectures and analyzing inputs to mitigate overfitting risk while maintaining manageable computation costs. The evaluation of outcomes conducted using independent test datasets from WRF-LES simulations reveals that the wind speed root-mean-square difference (RMSD) is 0.5 m/s over land and 0.2 m/s over the sea surface, respectively, at a spatial resolution of approximately 800 m. These figures are lower than the RMSD values of 1.6 m/s over land and 1.0 m/s over the sea surface, accompanied by a spatial resolution of 1.8 km, which were obtained through comparison with the spline interpolation method.

This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument—scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1—has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A/MHS model evaluation. The results clearly show the NN algorithm’s high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A/MHS)-simulated dataset, respectively.

The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface.

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

The purpose of this work is to explore the effect of temporal sampling on the accuracy of the hourly mean Surface Solar Irradiance (SSI) estimation. An upgraded version of the Advanced Model for the Estimation of Surface Solar Irradiance from Satellite (AMESIS), exploiting data from the Meteosat Second Generation Rapid Scanning Service (MSG-RSS), has been used to evaluate the SSI. The assessment of the new version of AMESIS has been carried out against data from two pyranometers located in Southern (Tito) and Northern (Ispra) Italy at an altitude of 760 m and 220 m, respectively. The statistical analysis of the comparison between hourly mean SSI estimates based on temporal sampling every five minutes shows a quantitative improvement compared to those based on 15-minute sampling. In particular, for the whole dataset in Tito, the correlation increases from 0.979 to 0.998, the Root Mean Square Error (RMSE) decreases from 45.16 W/m2 to 13.19 W/m2 and the Mean Bias Error (MBE) is reduced from −0.67 W/m2 to −0.02 W/m2. For the whole dataset in Ispra, the correlation increases from 0.995 to 0.998, the RMSE decreases from 24.85 W/m2 to 15.59 W/m2, whereas the MBE increases from 3.84 W/m2 to 4.58 W/m2. This preliminary assessment shows that higher temporal sampling can improve SSI monitoring over areas featuring frequent and rapid solar irradiance variation.

This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.

This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.

A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.

The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m2 and 0.995, respectively) than SSI_VIS (−33.69 W/m2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel.

In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and implemented. MSG-SEVIRI SatFog provides the indication of the presence of fog in near real time and at the high spatial resolution of the HRV channel. The HRV resolution is useful for detecting small scale daytime fog that would be missed in the MSG-SEVIRI low spatial resolution channels. By combining textural, physical and tonal tests, a distinction between fog and low stratus is performed for pixels identified as low/middle clouds or clear by the Classification-MAsk Coupling of Statistical and Physical Methods (C-MACSP) cloud detection algorithm. Suitable thresholds have been determined using a specific dataset covering different geographical areas, seasons and time of the day. MSG-SEVIRI SatFog is evaluated against METeorological Aerodrome Reports (METAR) data observations. Evaluation results in an accuracy of 69.9%, a probability of detection of 68.7%, a false alarm ratio of 31.3% and a probability of false detection of 30.0%.

Detection of clouds over polar areas from satellite radiometric measurements in the visible and IR atmospheric window region is rather difficult because of the high albedo of snow, possible ice covered surfaces, very low humidity, and the usual presence of atmospheric temperature inversion. Cold and highly reflective polar surfaces provide little thermal and visible contrast between clouds and the background surface. Moreover, due to the presence of temperature inversion, clouds are not always identifiable as being colder than the background. In addition, low humidity often causes polar clouds to be optically thin. Finally, polar clouds are usually composed of a mixture of ice and water, which leads to an unclear spectral signature. Single and bi-spectral threshold methods are sometimes inappropriate due to a large variability of surface emissivity and cloud conditions. The objective of this study is to demonstrate the crucial role played by surface emissivity in the detection of polar winter clouds and the potential improvement offered by infrared hyperspectral observations, such as from the Infrared Atmospheric Sounding Interferometer (IASI). In this paper a new approach for cloud detection is proposed and validated exploiting active measurements from satellite sensors, i.e., the CloudSat cloud profiling radar (CPR) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For a homogenous IASI field of view (FOVs), the proposed cloud detection scheme tallies with the combined CPR and CALIOP product in classifying 98.11% of the FOVs as cloudy and also classifies 97.54% of the FOVs as clear. The Hansen Kuipers discriminant reaches 0.95.