Elisabetta Ricciardelli

An Advanced Model for the Estimation of the Surface Solar Irradiance Under All Atmospheric Conditions Using MSG/SEVIRI Data

A new advanced model for estimation of surface solar irradiance from satellite (AMESIS), designed to estimate with better accuracy the incident solar radiation at the surface from the spinning enhanced visible and infrared imager (SEVIRI) satellite measurements, has been developed. The new generations of sensors such as SEVIRI payload on board the geostationary meteosat second generation gives an opportunity to improve the solar irradiance estimation at surface with accuracy as well as the high spatial and time resolution for a large geographical area according to the needs of solar energy applications. The model developed takes into account the effect of aerosol, the overcast and partially cloudy coverage, and provides irradiance solar maps every 15 min both for monitoring purposes and for monthly, annual averages of surface solar irradiance. Cloud and aerosol microphysical parameters are retrieved by using VIS and IR SEVIRI channels, while surface solar irradiance is retrieved through the high-resolution broadband visible channel. Comparisons with the Global Atmosphere Watch station ground-based measurements of incoming solar radiation agree with the values retrieved with AMESIS model. The results show a very good correlation of about 0.99, a root mean square and a bias ranging, respectively, between 1 and 2.7 J/cm2 and -0.6 and 0.4 J/cm2 depending on the station.

Analysis of Catania Flash Flood Case Study by Using Combined Microwave and Infrared Technique

AbstractIn this paper, the analysis of an extreme convective event atypical for the winter season, which occurred on 21 February 2013 on the east coast of Sicily and caused a flash flood over Catania, is presented. In just 1 h, more than 50 mm of precipitation was recorded, but it was not forecast by numerical weather prediction (NWP) models and, consequently, no severe weather warnings were sent to the population. The case study proposed is first examined with respect to the synoptic situation and then analyzed by means of two algorithms based on satellite observations: the Cloud Mask Coupling of Statistical and Physical Methods (MACSP) and the Precipitation Evolving Technique (PET), developed at the National Research Council of Italy. Both of the algorithms show their ability in the near-real-time monitoring of convective cell formation and their rapid evolution. As quantitative precipitation forecasts by NWP could fail, especially for atypical convective events like in Catania, tools like MACSP and PET...

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

A Technique for Classifying Uncertain MOD35/MYD35 Pixels Through Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Observations

This paper describes a technique that uses the information gathered from the geostationary instrumentation [Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI)] to investigate the pixels detected as ¿uncertain¿ by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-mask algorithm. This technique analyzes the uncertain MODIS areas by using a time series of MSG-SEVIRI images taken at infrared (IR) and visible (VIS) wavelengths. In order to classify the uncertain pixels related to the granules acquired during the daytime and completely included in the high-resolution visible (HRV) image, the spectral and textural features derived from a time series of HRV images are used as inputs in a K-nearest neighbor (K-NN) classifier. For the areas not included in the HRV image and for those acquired during nighttime, the input parameters are determined from a time series of IR/VIS and IR images, respectively. The K-NN classifier detected 52.0%, 48.7%, and 37.0% of the MOD35/MYD35 uncertain pixels analyzed over land and 54.5%, 45.4%, and 49.7% of those analyzed over sea as cloud free, when using HRV, IR, and IR/VIS features as inputs, respectively. Percentages of 39.8%, 31.8%, and 37.3% of the pixels analyzed over land and 40.7%, 47.4%, and 38.0% of those analyzed over sea were classified as cloudy when using HRV, IR, and IR/VIS features as inputs, respectively. The remaining uncertain pixels were classified as low confidence cloudy or cloud free by the K-NN classifier. A set of comparisons was made with cloud-profiling radar/cloud-aerosol lidar with orthogonal polarization 2B-Geometrical Profiling-Lidar product results.

The Role of Emissivity in the Detection of Arctic Night Clouds

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.

Nowcasting Surface Solar Irradiance with AMESIS via Motion Vector Fields of MSG-SEVIRI Data

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.

Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible 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%.

Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data

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.