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Observations from spaceborne microwave (MW) and infrared (IR) passive sensors are the backbone of current satellite meteorology, essential for data assimilation into modern numerical weather prediction and for climate benchmarking. While MW and IR observations from space offer complementary features with respect to cloud properties, their synergy for cloud investigation is currently underexplored, despite the presence of both MW and IR sensors on operational meteorological satellites such as the EUMETSAT Polar System (EPS) MetOp series. As such, several key cloud microphysical properties are not part of the operational products available from EPS MetOp sensors. In addition, the EPS Second Generation (EPS-SG) series, scheduled for launch starting from 2024 onward, will carry sensors such as the Microwave Sounder (MWS) and IASI Next Generation (IASI-NG), enhancing spatial and spectral resolutions and thus capacity to retrieve cloud properties. This article presents the Combined MWS and IASI-NG Soundings for Cloud Properties (ComboCloud) project, funded by EUMETSAT with the overall objective to specify, prototype, and validate algorithms for the retrieval of cloud microphysical properties (e.g., water content and drop effective radius) from the synergy of passive MW and IR observations. The article presents the synergy rationale, the algorithm design, and the results obtained exploiting simulated observations from EPS and EPS-SG sensors, quantifying the benefits to be expected from the MW–IR synergy and the new generation sensors.

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.

In this study, we investigate the ability of several convective initiation predictors based on satellite infrared observations to distinguish convective weak precipitation events from those leading to intense rainfall. The two types of precipitation are identified according to hourly rainfall, respectively less than 10 mm and greater than 30 mm. The analysis is conducted on a representative dataset containing 92 severe and weak precipitation events collected over the Italian peninsula in the period 2016–2019 over June-September. The events are selected to be short-lived (i.e., less than 12 h) and localized (i.e., less than 50×50km2). Italian National Radar Network products, namely the Vertical Maximum Intensity (VMI) and the Surface Rain Total (SRT) variables (from Dewetra Platform by CIMA, Italian Civil Protection Department), are used as indicators of convection (i.e., VMI greater than 35 dBZ echo intensity) and cumulated rainfall, respectively. The considered predictors are linear combinations of spectral infrared channels measured with the Rapid Scan Service (RSS) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard Meteosat Second Generation (MSG) geostationary satellites. We select a 5×5 SEVIRI pixel-box centered on the storm core and perform a statistical analysis of the predictors up to 2.5 h around the event occurrence. We demonstrate that some of the proxies—describing growth and glaciation storm properties—show few degrees contrast between severe and nonsevere precipitation cases, hence carrying significant information to help discriminate the two types. We design a threshold scheme based on the three most informative predictors to distinguish weak and strong precipitation events. This analysis yields accuracy higher than 0.6 and the probability of false detection lower than 0.26; in terms of reducing false alarms, this method shows slight better performances compared to related works, at the expense of a lower probability of detection. The overall results, however, show limited capability for these infrared proxies as stand-alone predictors to distinguish severe from nonsevere precipitation events. Nonetheless, these may serve as additional tools to reduce the false alarm ratio in nowcasting algorithms for convective orographic storms. This study also provides further insight into the correlation between early infrared fields signatures prior to convection and subsequent evolution of the storms, extending previous works in this field.

Reliable values of relative humidity are basic inputs for modelling in many disciplines and in the most disparate scientific fields. Unfortunately, humidity variables remain less focused than other meteorological parameters and generally suffer from considerable uncertainty mainly due to the fact that their observations are not widely available, fostering the use of the observations of other meteorological quantities to estimate them. The aim of this work is to assess the loss in daily maximum and minimum relative humidity accuracy when sampling interval becomes coarser than one hour. For this purpose, meteorological data from the ERA5 dataset, the most advanced reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), are used. Among the many advantages of ERA5 over the previous release ERA-Interim are the finer temporal resolution and data archived at the hourly time step. Near-surface relative humidity is derived using 1- and 3-hourly reanalysis data of 2-m temperature, 2-m dew-point temperature and surface pressure. Deviations from the actual values, as obtained from reference measures acquired at 15 minute intervals, are evaluated. Results show that the biases of the ERA5-based values are consistently reduced compared to its predecessor and that the performance of the calculated 1-hourly time resolution relative humidity data is almost equivalent to using observations. Reducing the sampling interval from three to one hour provides a significant improvement in data quality. The results indicate significant increases in errors in the estimates when the temporal resolution of the meteorological inputs becomes coarser than one hour, exceeding also the numerical and approximation errors due to simplifying assumptions in the theoretical and empirical formulas used. The positive impact of improving temporal resolution from ERA-Interim to ERA5 reanalysis is also quantified by the number of the correct relative humidity extremes increasing by 50%.

This paper describes the first official release (v1.0) of RTTOV-gb. RTTOV-gb is a FORTRAN 90 code developed by adapting the atmospheric radiative transfer code RTTOV, focused on satellite-observing geometry, to the ground-based observing geometry. RTTOV-gb is designed to simulate ground-based upward-looking microwave radiometer (MWR) observations of atmospheric downwelling natural radiation in the frequency range from 22 to 150 GHz. Given an atmospheric profile of temperature, water vapor, and, optionally, cloud liquid water content, and together with a viewing geometry, RTTOV-gb computes downwelling radiances and brightness temperatures leaving the bottom of the atmosphere in each of the channels of the sensor being simulated. In addition, it provides the sensitivity of observations to the atmospheric thermodynamical state, i.e., the Jacobians. Therefore, RTTOV-gb represents the forward model needed to assimilate ground-based MWR data into numerical weather prediction models, which is currently pursued internationally by several weather services. RTTOV-gb is fully described in a previous paper (De Angelis et al., 2016), while several updates are described here. In particular, two new MWR types and a new parameterization for the atmospheric absorption model have been introduced since the first paper. In addition, estimates of the uncertainty associated with the absorption model and with the fast parameterization are given here. Brightness temperatures (TB) computed with RTTOV-gb v1.0 from radiosonde profiles have been compared with ground-based MWR observations in six channels (23.8, 31.4, 72.5, 82.5, 90.0, and 150.0 GHz). The comparison shows statistics within the expected accuracy. RTTOV-gb is now available to licensed users free of charge from the Numerical Weather Prediction Satellite Application Facility (NWP SAF) website, after registration. Coefficients for four MWR instrument types and two absorption model parameterizations are also freely available from the RTTOV-gb support website.

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.

The extraordinary rainfall event that affected the center of Italy on 9th and 10th September 2017 was studied by examining the synoptic analysis, radar network and rain gauges' measurements. The main precipitation event took place in the area around Livorno, where more than 200 mm of precipitation was recorded in 24 hours. The case study is analyzed using Weather Research and Forecasting (WRF) model and two algorithms based on satellite observations: the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique and the cloud Classification Mask Coupling of Statistical and Physics Methods (C-MACSP). The analysis shows that WRF is able to forecast the event, though with errors in actual structure, location, and time. For this reason, the combined use of different observational tools could support the WRF simulation to provide a better characterization of the event.

The Advanced Technology Microwave Sounder (ATMS) is a cross-track scanning microwave (MW) radiometer useful to retrieve Temperature (T) and Water Vapor (WV) atmospheric vertical profiles. Using spatial and temporal coincidences between ATMS observations and two different datasets of vertical T and WV, a global training dataset for machine learning purpose was built for the whole 2016. For each ATMS Brightness Temperatures acquisition, 32 levels of T (between 10 and 1000 hPa), and 23 levels of WV (between 200 and 1000 hPa), are used to train an algorithm based on Random Forests regression technique. A single RF was trained for each level and atmospheric variable, using the evaluation on the Out of Bag error to optimize the number of random selection of the input variables at each node splitting step, the number of trees in each forest and the minimum leaf size parameter, to avoid overfitting problem and obtain an accurate retrieval. Considering that the sounding below the precipitation level becomes unreliable, the precipitation-affected observations were removed from the training dataset by means of a pre-screening test based on BT. The results show an overall ability of the algorithm to retrieve T and WV vertical profiles in line with expectations.

In this study, we compare different nowcasting techniques based upon the calculation of motion vector fields derived from spectral channels of Meteosat Second Generation—Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI). The outputs of the nowcasting techniques are used as inputs to the Advanced Model for Estimation of Surface solar Irradiance from Satellite (AMESIS), for predicting surface solar irradiance up to 2 h in advance. In particular, the first part of the methodology consists in projecting the time evolution of each MSG-SEVIRI channel (for every pixel in the spatial domain) through extrapolation of a displacement vector field obtained by matching similar patterns within two successive MSG-SEVIRI data images. Different ways to implement the above method result in substantial differences in the predicted trajectory, leading to different performances depending on the time interval of interest. All the nowcasting techniques considered here systematically outperform the simple persistence method for all MSG-SEVIRI channels and for each case study used in this work; importantly, this occurs across the entire 2 h period of the forecast. In the second part of the algorithm, the predicted irradiance maps computed with AMESIS from the forecasted radiances, are shown to be in good agreement with irradiances derived from MSG measured radiances and improve on numerical weather model predictions, thus providing a feasible alternative for nowcasting surface solar radiation. The results show that the mean values for correlation, bias, and root mean square error vary across the time interval, ranging between 0.94, −1 W/m 2 , 61 W/m 2 after 15 min, and 0.73, −18 W/m 2 , 147 W/m 2 after 2 h, respectively.

Previsione a breve termine dell'irradianza solare mediante tecniche di Cloud Motion Vector applicate ai dati MSG-SEVIRI

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.

Solar power generation is highly variable due to its dependence on meteorological conditions. The integration of this fluctuating resource into the energy supply system requires reliable forecasts of the expected power production as a basis for management and operation strategies. This is one of the aims of the Solar Cloud project (funded by the Italian Ministry of Economic Development): to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar industry.

Solar power generation is highly variable due to its dependence on meteorological conditions. The integration of this fluctuating resource into the energy supply system requires reliable forecasts of the expected power production as a basis for management and operation strategies. This is one of the aims of the Solar Cloud project (funded by the Italian Ministry of Economic Development): to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar industry. To improve the prediction of solar irradiance a data assimilation experiment is performed. The study focuses on the evaluation of the impact on GHI forecast at high temporal and horizontal resolution of the assimilation of the SEVIRI radiance

Solar photovoltaic (PV) power production is strictly dependent on the global irradiance that reaches the PV panels plane. Solar power production thus depend heavily on the different meteorological conditions. In particular, under fog, the balance between direct and diffuse components of solar irradiance varies depending on the thickness of the fog layer. This modifies the amount of power generated by the solar panels. Information about the presence of fog can improve solar PV power output simulation, thus reducing the associated uncertainty and related costs for the grid operators. Research in satellite meteorology has shown that satellite imagery can be useful in detecting and forecasting fog (Gultepe et al., 2007). Different fog monitoring algorithms have already been developed using the narrowband visible and infrared channels of Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) (Cermak et al., 2008; Bendix, 2000). In this study fog daytime detection (physical and statistical tests) has been implemented using the High Resolution Visible (HRV) channel of MSG-SEVIRI. This channel has a spatial resolution of 1 km at sub satellite point and this allows to detect localized fog on small valleys. At the same time, the high temporal resolution of HRV data permits to monitor the fog evolution. Combining textural and threshold tests, a distinction between fog and low clouds has been conducted for that pixels identified as low or middle clouds by the C_MACSP cloud detection algorithm (Ricciardelli et al., 2008). Optimal thresholds have been calculated using a specific training dataset built with data collected in different areas of the satellite coverage, seasons of the year, and times of the day. The fog detection algorithm has been validated against METeorological Aerodrome Reports (METAR) observations. Beyond fog detection, another information useful to improve solar PV power production estimation is the probability of fog occurrence. A model to forecast fog occurrence has been implemented using several weather variables from a numerical weather prediction (NWP) model (surface and dew point temperatures, relative humidity, wind speed). The probability of fog occurrence is expressed by an index ranging from 0 to 1. The validation of the forecasted probability of fog occurrence is currently ongoing .

The morphological characteristics of the ground have a strong influence in solar resource assessment, especially in terrain with complex orography. The use of accurate Digital Model Terrain (DTM) plays a key role in assessing the amount of solar energy that reaches the earth surface. Complex terrain and areas with local micro-climatic characteristics introduces significant intra-cell variations of irradiance compared to satellite-derived measurements affected by limited spatial resolution. This is mainly due to terrain-related effects such as elevation gradient, obstruction and shadowing by horizon (natural or antropic). Influence of terrain features has been already described by several authors (Ruiz-Arias et al, 2010, Bosch et al, 2010). These terrain-related features (elevation, terrain aspect and slope, horizon angles and sky view factor) can be considered as static boundary condition as they do not change in time. In the framework of the SolarCloud project (funded by the Italian Ministry of Economic Development) this activity has the objective to quantify the dowscaling improvement in satellite-derived irradiance at territory scale using DTM rising resolution products. It is acknowledged that surface morphology is just one of the ancillary data in addition to the satellite observations that are needed in order to get more accurate result in the solar energy assessment. For this purpose, we used satellite-derived irradiance processed by AMESIS model (Advanced Model for the Estimation of the Surface Solar Irradiance, Geraldi et al, 2012) at low (3 km) and high resolution (1 km). These data are downscaled using different DTM resolution and compared with ground based stations measurements. The analysis has been developed using R open source software exploiting SRTM3 (Shuttle Radar Topography Mission 3 Arc-Second Global), SRTM1 (respectively with cell size of ~90 m and ~30 m) and higher resolution DTM generated by the local government (with cell size of 20x20 m and 5x5 m). The horizon elevation angle and sky view factor have been computed up to 25 km distance for each grid point and azimuthal angle (at 1 degree resolution). Issues related with computational time have been addressed by implementing into the algorithm a floating window which subdivided the whole study area in subareas with increasing resolution.

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.

We demonstrate the feasibility of generation of quasi-stable counter-propagating solitonic structures in an atomic Bose-Einstein condensate confined in a realistic toroidal geometry, and identify optimal parameter regimes for their experimental observation. Using density engineering we numerically identify distinct regimes of motion of the emerging macroscopic excitations, including both solitonic motion along the azimuthal ring direction, such that structures remain visible after multiple collisions even in the presence of thermal fluctuations, and snaking instabilities leading to the decay of the excitations into vortical structures. Our analysis, which considers both mean field effects and fluctuations, is based on the ring trap geometry of Murray et al. Phys. Rev. A 88 053615 2013., changes incorporating referee reports. higher resolution images and videos available upon request

We perform an ab initio analysis of the temperature dependence of the phase coherence length of finite temperature, quasi-one-dimensional Bose gases measured in the experiments of Richard et al. (Phys. Rev. Lett. 91, 010405 (2003)) and Hugbart et al. (Eur. Phys. J. D 35, 155-163 (2005)), finding very good agreement across the entire observed temperature range ($0.8, Comment: Amended manuscript with improved agreement to experiment, following some additional clarifications by Mathilde Hugbart and Fabrice Gerbier and useful comments by the reviewer; accepted for publication in Physical Review A

The stochastic Gross-Pitaevskii equation is shown to be an excellent model for quasi-one-dimensional Bose gas experiments, accurately reproducing the in situ density profiles recently obtained in the experiments of Trebbia et al. [Phys. Rev. Lett. 97, 250403 (2006)] and van Amerongen et al. [Phys. Rev. Lett. 100, 090402 (2008)], and the density fluctuation data reported by Armijo et al. [Phys. Rev. Lett. 105, 230402 (2010)]. To facilitate such agreement, we propose and implement a quasi-one-dimensional stochastic equation for the low-energy, axial modes, while atoms in excited transverse modes are treated as independent ideal Bose gases., Comment: 10 pages, 5 figures; updated figures with experimental data