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 (T_B) 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.

This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

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.

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.

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%.