Meteosat Second Generation Research Articles

Numerical simulations of windblown dust over complex terrain: the Fiambalá Basin episode in June 2015

Abstract. On 13 June 2015, the London Volcanic Ash Advisory Centre (VAAC) warned the Buenos Aires VAAC about a possible volcanic eruption from the Nevados Ojos del Salado volcano (6879 m), located in the Andes mountain range on the border between Chile and Argentina. A volcanic ash cloud was detected by the SEVIRI instrument on board the Meteosat Second Generation (MSG) satellites from 14:00 UTC on 13 June. In this paper, we provide the first comprehensive description of this event through observations and numerical simulations. Our results support the hypothesis that the phenomenon was caused by wind remobilization of ancient pyroclastic deposits (ca. 4.5 ka Cerro Blanco eruption) from the Bolsón de Fiambalá (Fiambalá Basin) in northwestern Argentina. We have investigated the spatiotemporal distribution of aerosols and the emission process over complex terrain to gain insight into the key role played by the orography and the condition that triggered the long-range transport episode. Numerical simulations of windblown dust were performed using the ARW (Advanced Research WRF) core of the WRF (Weather Research and Forecasting) model (WRF-ARW) and FALL3D modeling system with meteorological fields downscaled to a spatial resolution of 2 km in order to resolve the complex orography of the area. Results indicate that favorable conditions to generate dust uplifting occurred in northern Fiambalá Basin, where orographic effects caused strong surface winds. According to short-range numerical simulations, dust particles were confined to near-ground layers around the emission areas. In contrast, dust aerosols were injected up to 5–6 km high in central and southern regions of the Fiambalá Basin, where intense ascending airflows are driven by horizontal convergence. Long-range transport numerical simulations were also performed to model the dust cloud spreading over northern Argentina. Results of simulated vertical particle column mass were compared with the MSG-SEVIRI retrieval product. We tested two numerical schemes: with the default configuration of the FALL3D model, we found difficulties to simulate transport through orographic barriers, whereas an alternative configuration, using a numerical scheme to more accurately compute the horizontal advection in abrupt terrains, substantially improved the model performance.

Satellite-based high-resolution mapping of rainfall over southern Africa

Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.

An Enhanced Satellite-Based Algorithm for Detecting and Tracking Dust Outbreaks by Means of SEVIRI Data

Dust outbreaks are meteorological phenomena of great interest for scientists and authorities (because of their impact on the climate, environment, and human activities), which may be detected, monitored, and characterized from space using different methods and procedures. Among the recent dust detection algorithms, the RSTDUST multi-temporal technique has provided good results in different geographic areas (e.g., Mediterranean basin; Arabian Peninsula), exhibiting a better performance than traditional split window methods, in spite of some limitations. In this study, we present an optimized configuration of this technique, which better exploits data provided by Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation (MSG) satellites to address those issues (e.g., sensitivity reduction over arid and semi-arid regions; dependence on some meteorological clouds). Three massive dust events affecting Europe and the Mediterranean basin in May 2008/2010 are analysed in this work, using information provided by some independent and well-established aerosol products to assess the achieved results. The study shows that the proposed algorithm, christened eRSTDUST (i.e., enhanced RSTDUST), which provides qualitative information about dust outbreaks, is capable of increasing the trade-off between reliability and sensitivity. The results encourage further experimentations of this method in other periods of the year, also exploiting data provided by different satellite sensors, for better evaluating the advantages arising from the use of this dust detection technique in operational scenarios.

Clouds over the summertime Sahara: an evaluation of Met Office retrievals from Meteosat Second Generation using airborne remote sensing

Abstract. Novel methods of cloud detection are applied to airborne remote sensing observations from the unique Fennec aircraft dataset, to evaluate the Met Office-derived products on cloud properties over the Sahara based on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on-board the Meteosat Second Generation (MSG) satellite. Two cloud mask configurations are considered, as well as the retrievals of cloud-top height (CTH), and these products are compared to airborne cloud remote sensing products acquired during the Fennec campaign in June 2011 and June 2012. Most detected clouds (67 % of the total) have a horizontal extent that is smaller than a SEVIRI pixel (3 km × 3 km). We show that, when partially cloud-contaminated pixels are included, a match between the SEVIRI and aircraft datasets is found in 80 ± 8 % of the pixels. Moreover, under clear skies the datasets are shown to agree for more than 90 % of the pixels. The mean cloud field, derived from the satellite cloud mask acquired during the Fennec flights, shows that areas of high surface albedo and orography are preferred sites for Saharan cloud cover, consistent with published theories. Cloud-top height retrievals however show large discrepancies over the region, which are ascribed to limiting factors such as the cloud horizontal extent, the derived effective cloud amount, and the absorption by mineral dust. The results of the CTH analysis presented here may also have further-reaching implications for the techniques employed by other satellite applications facilities across the world.

Analysis of geostationary satellite-derived cloud parameters associated with environments with high ice water content

Abstract. We present an evaluation of the ability of passive broadband geostationary satellite measurements to detect high ice water content (IWC > 1 g m−3) as part of the European High Altitude Ice Crystals (HAIC) project for detection of upper-atmospheric high IWC, which can be a hazard for aviation. We developed a high IWC mask based on measurements of cloud properties using the Cloud Physical Properties (CPP) algorithm applied to the geostationary Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Evaluation of the high IWC mask with satellite measurements of active remote sensors of cloud properties (CLOUDSAT/CALIPSO combined in the DARDAR (raDAR–liDAR) product) reveals that the high IWC mask is capable of detecting high IWC values > 1 g m−3 in the DARDAR profiles with a probability of detection of 60–80 %. The best CPP predictors of high IWC were the condensed water path, cloud optical thickness, cloud phase, and cloud top height. The evaluation of the high IWC mask against DARDAR provided indications that the MSG-CPP high IWC mask is more sensitive to cloud ice or cloud water in the upper part of the cloud, which is relevant for aviation purposes. Biases in the CPP results were also identified, in particular a solar zenith angle (SZA) dependence that reduces the performance of the high IWC mask for SZAs > 60°. Verification statistics show that for the detection of high IWC a trade-off has to be made between better detection of high IWC scenes and more false detections, i.e., scenes identified by the high IWC mask that do not contain IWC > 1 g m−3. However, the large majority of these detections still contain IWC values between 0.1 and 1 g m−3. Comparison of the high IWC mask against results from the Rapidly Developing Thunderstorm (RDT) algorithm applied to the same geostationary SEVIRI data showed that there are similarities and differences with the high IWC mask: the RDT algorithm is very capable of detecting young/new convective cells and areas, whereas the high IWC mask appears to be better capable of detecting more mature and ageing convection as well as cirrus remnants. The lack of detailed understanding of what causes aviation hazards related to high IWC, as well as the lack of clearly defined user requirements, hampers further tuning of the high IWC mask. Future evaluation of the high IWC mask against field campaign data, as well as obtaining user feedback and user requirements from the aviation industry, should provide more information on the performance of the MSG-CPP high IWC mask and contribute to improving the practical use of the high IWC mask.

Evaluating the MSG satellite Multi-Sensor Precipitation Estimate for extreme rainfall monitoring over northern Tunisia

Abstract Knowledge and evaluation of extreme precipitation is important for water resources and flood risk management, soil and land degradation, and other environmental issues. Due to the high potential threat to local infrastructure, such as buildings, roads and power supplies, heavy precipitation can have an important social and economic impact on society. At present, satellite derived precipitation estimates are becoming more readily available. This paper aims to investigate the potential use of the Meteosat Second Generation (MSG) Multi-Sensor Precipitation Estimate (MPE) for extreme rainfall assessment in Tunisia. The MSGMPE data combine microwave rain rate estimations with SEVIRI thermal infrared channel data, using an EUMETSAT production chain in near real time mode. The MPE data can therefore be used in a now-casting mode, and are potentially useful for extreme weather early warning and monitoring. Daily precipitation observed across an in situ gauge network in the north of Tunisia were used during the period 2007–2009 for validation of the MPE extreme event data. As a first test of the MSGMPE product's performance, very light to moderate rainfall classes, occurring between January and October 2007, were evaluated. Extreme rainfall events were then selected, using a threshold criterion for large rainfall depth (>50 mm/day) occurring at least at one ground station. Spatial interpolation methods were applied to generate rainfall maps for the drier summer season (from May to October) and the wet winter season (from November to April). Interpolated gauge rainfall maps were then compared to MSGMPE data available from the EUMETSAT UMARF archive or from the GEONETCast direct dissemination system. The summation of the MPE data at 5 and/or 15 min time intervals over a 24 h period, provided a basis for comparison. The MSGMPE product was not very effective in the detection of very light and light rain events. Better results were obtained for the slightly more moderate and moderate rain event classes in terms of percentage of detected events, correlation coefficient, and ratio bias. The results for extreme events were mixed, with high pixel correlations of R=0.75 achieved for some events, while for other events the correlation between satellite and ground observation was rather weak. MPE data for northern Tunisia seem more reliable during the summer season and for larger event scales. The MSGMPE data have demonstrated to be very informative for early warning purposes, but need to be combined with other near real time data or information to give reliable and quantitative estimates of extreme rainfall.

Revealing the potential of spectral and textural predictor variables in a neural network-based rainfall retrieval technique

ABSTRACTEstimating rainfall areas and rates from geostationary satellite images has the opportunity of both, a high spatial and a high temporal resolution which cannot be achieved by other satellite-based systems until now. Most recent retrieval techniques are solely based on spectral channels of the satellites. These retrievals can be classified as ‘purely pixel-based’ because no information about the neighbourhood pixels is included. Assuming that precipitation is highly correlated with cloud processes and therefore with cloud texture, textural information derived from the neighbourhood of a pixel might give valuable information about the cloud type and hence about a respective probability of the rainfall rate. To study the potential of textural variables to improve optical rainfall retrieval techniques, rainfall areas and rainfall rates were estimated over Germany for the year 2010 using a neural network approach. In addition to the spectral predictor variables from Meteosat Second Generation (MSG), different Grey Level Co-occurance Matrix (GLCM) based textural variables were calculated from all MSG channels. Using recursive feature selection, models were trained and their performance was compared to spectral-only models. Contrary to the expectations, the performance of the models did not increase when textural information was included.

Remote sensing and modelling analysis of the extreme dust storm hitting the Middle East and eastern Mediterranean in September 2015

Abstract. The extreme dust storm that affected the Middle East and the eastern Mediterranean in September 2015 resulted in record-breaking dust loads over Cyprus with aerosol optical depth exceeding 5.0 at 550 nm. We analyse this event using profiles from the European Aerosol Research Lidar Network (EARLINET) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), geostationary observations from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and high-resolution simulations from the Regional Atmospheric Modeling System (RAMS). The analysis of modelling and remote sensing data reveals the main mechanisms that resulted in the generation and persistence of the dust cloud over the Middle East and Cyprus. A combination of meteorological and surface processes is found, including (a) the development of a thermal low in the area of Syria that results in unstable atmospheric conditions and dust mobilization in this area, (b) the convective activity over northern Iraq that triggers the formation of westward-moving haboobs that merge with the previously elevated dust layer, and (c) the changes in land use due to war in the areas of northern Iraq and Syria that enhance dust erodibility.

Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method

The new Heliosat-4 method estimates the downwelling shortwave irradiance received at ground level in all sky conditions. It provides the global irradiance and its direct and diffuse components on a horizontal plane and the direct irradiance for a plane normal to sun rays. It is a fully physical model using a fast, but still accurate approximation of radiative transfer modelling and is therefore well suited for geostationary satellite retrievals. It can also be used as a fast radiative transfer model in numerical weather prediction models. It is composed of two models based on abaci, also called look-up tables: the already-published McClear model calculating the irradiance under cloud-free conditions and the new McCloud model calculating the extinction of irradiance due to clouds. Both have been realized by using the libRadtran radiative transfer model. The main inputs to Heliosat-4 are aerosol properties, total column water vapour and ozone content as provided by the Copernicus Atmosphere Monitoring Service (CAMS) every 3 h. Cloud properties are derived from images of the Meteosat Second Generation (MSG) satellites in their 15min temporal resolution using an adapted APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) scheme. The 15min means of irradiance estimated by Heliosat-4 are compared to corresponding measurements made at 13 stations within the Baseline Surface Radiation Network and being located in the field of view ofMSG and in various climates. The bias for global irradiance is comprised between 2 and 32Wm−2. The root mean square error (RMSE) ranges between 74 and 94Wm−2. Relative RMSE values range between 15% and 20% of the mean observed irradiance for stations in desert and Mediterranean climates, and between 26% and 43% for rainy climates with mild winters. Correlation coefficients between 0.91 and 0.97 are found. The bias for the direct irradiance at normal incidence is comprised between −163 and +50Wm−2. The RMSE ranges from 160Wm−2 (29% of the mean observed irradiance) to 288Wm−2 (63%). The correlation coefficient ranges between 0.67 and 0.87.

Cloud and DNI nowcasting with MSG/SEVIRI for the optimized operation of concentrating solar power plants

Abstract. A novel approach for the nowcasting of clouds and direct normal irradiance (DNI) based on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellite is presented for a forecast horizon up to 120 min. The basis of the algorithm is an optical flow method to derive cloud motion vectors for all cloudy pixels. To facilitate forecasts over a relevant time period, a classification of clouds into objects and a weighted triangular interpolation of clear-sky regions are used. Low and high level clouds are forecasted separately because they show different velocities and motion directions. Additionally a distinction in advective and convective clouds together with an intensity correction for quickly thinning convective clouds is integrated. The DNI is calculated from the forecasted optical thickness of the low and high level clouds. In order to quantitatively assess the performance of the algorithm, a forecast validation against MSG/SEVIRI observations is performed for a period of 2 months. Error rates and Hanssen–Kuiper skill scores are derived for forecasted cloud masks. For a forecast of 5 min for most cloud situations more than 95 % of all pixels are predicted correctly cloudy or clear. This number decreases to 80–95 % for a forecast of 2 h depending on cloud type and vertical cloud level. Hanssen–Kuiper skill scores for cloud mask go down to 0.6–0.7 for a 2 h forecast. Compared to persistence an improvement of forecast horizon by a factor of 2 is reached for all forecasts up to 2 h. A comparison of forecasted optical thickness distributions and DNI against observations yields correlation coefficients larger than 0.9 for 15 min forecasts and around 0.65 for 2 h forecasts.

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In the present study, two currently available coarse-scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time-invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well-organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset-calibrated time-invariable coefficients to retrieve SSM, with coefficients of determination (R2) varying from 0.304 to 0.534 and root mean square errors (RMSE) ranging from 0.020 m3/m3 to 0.029 m3/m3. Further evaluation with different land use types results in acceptable debiased RMSE between 0.021 m3/m3 and 0.048 m3/m3 when comparing the estimated MSG pixel-scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time-invariable coefficients when using publicly accessed coarse-scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.

Novel SVM-based technique to improve rainfall estimation over the Mediterranean region (north of Algeria) using the multispectral MSG SEVIRI imagery

In this work, a new rainfall estimation technique based on the high spatial and temporal resolution of the Spinning Enhanced Visible and Infra Red Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) is presented. This work proposes efficient scheme rainfall estimation based on two multiclass support vector machine (SVM) algorithms: SVM_D for daytime and SVM_N for night time rainfall estimations.Both SVM models are trained using relevant rainfall parameters based on optical, microphysical and textural cloud proprieties. The cloud parameters are derived from the Spectral channels of the SEVIRI MSG radiometer. The 3-hourly and daily accumulated rainfall are derived from the 15 min-rainfall estimation given by the SVM classifiers for each MSG observation image pixel. The SVMs were trained with ground meteorological radar precipitation scenes recorded from November 2006 to March 2007 over the north of Algeria located in the Mediterranean region.Further, the SVM_D and SVM_N models were used to estimate 3-hourly and daily rainfall using data set gathered from November 2010 to March 2011 over north Algeria. The results were validated against collocated rainfall observed by rain gauge network. Indeed, the statistical scores given by correlation coefficient, bias, root mean square error and mean absolute error, showed good accuracy of rainfall estimates by the present technique.Moreover, rainfall estimates of our technique were compared with two high accuracy rainfall estimates methods based on MSG SEVIRI imagery namely: random forests (RF) based approach and an artificial neural network (ANN) based technique. The findings of the present technique indicate higher correlation coefficient (3-hourly: 0.78; daily: 0.94), and lower mean absolute error and root mean square error values. The results show that the new technique assign 3-hourly and daily rainfall with good and better accuracy than ANN technique and (RF) model.

SURFACE ENERGY BALANCE SYSTEM (SEBS) AND SATELLITE DATA FOR MONITORING WATER CONSUMPTION OF IRRIGATED SUGARCANE

The objective of this research was to evaluate the water consumption of irrigated sugarcane areas using the Surface Energy Balance System model (SEBS) driven by products derived from Meteosat Second Generation (MSG), SPOT/VEGETATION, Terra/MODIS satellite data and meteorological observations data from Minas Gerais State, Brazil. The actual evapotranspiration from SEBS model (AET-SEBS) was compared against crop evapotranspiration under standard conditions (ETc), obtained from weather data based on reference evapotranspiration (ET0) and the crop coefficient (Kc) values from FAO recommends. Results showed that there was a good agreement between AET-SEBS and ETc when the sugarcane was at the maximum development stage under center pivot irrigation and the Kc value corresponded to 1.25. The AET-SEBS values seem to overestimate water use during the sugarcane late stage in areas which the Kc value was 0.7. Increasing the Kc value equal to 1.25 for sugarcane late stage decreased the differences between ETc and AET-SEBS. In conclusion, the estimation of evapotranspiration using satellite data and SEBS model approach was satisfactory to monitor water usage of large sugarcane areas irrigated by center pivots in the state of Minas Gerais, Brazil.

Nowcasting of deep convective clouds and heavy precipitation: Comparison study between NWP model simulation and extrapolation

An evaluation of convective cloud forecasts performed with the numerical weather prediction (NWP) model COSMO and extrapolation of cloud fields is presented using observed data derived from the geostationary satellite Meteosat Second Generation (MSG). The present study focuses on the nowcasting range (1–5h) for five severe convective storms in their developing stage that occurred during the warm season in the years 2012–2013. Radar reflectivity and extrapolated radar reflectivity data were assimilated for at least 6h depending on the time of occurrence of convection. Synthetic satellite imageries were calculated using radiative transfer model RTTOV v10.2, which was implemented into the COSMO model. NWP model simulations of IR10.8μm and WV06.2μm brightness temperatures (BTs) with a horizontal resolution of 2.8km were interpolated into the satellite projection and objectively verified against observations using Root Mean Square Error (RMSE), correlation coefficient (CORR) and Fractions Skill Score (FSS) values. Naturally, the extrapolation of cloud fields yielded an approximately 25% lower RMSE, 20% higher CORR and 15% higher FSS at the beginning of the second forecasted hour compared to the NWP model forecasts. On the other hand, comparable scores were observed for the third hour, whereas the NWP forecasts outperformed the extrapolation by 10% for RMSE, 15% for CORR and up to 15% for FSS during the fourth forecasted hour and 15% for RMSE, 27% for CORR and up to 15% for FSS during the fifth forecasted hour. The analysis was completed by a verification of the precipitation forecasts yielding approximately 8% higher RMSE, 15% higher CORR and up to 45% higher FSS when the NWP model simulation is used compared to the extrapolation for the first hour. Both the methods yielded unsatisfactory level of precipitation forecast accuracy from the fourth forecasted hour onward.

A method for convective storm detection using satellite data

Moisture and instability, along with a triggering mechanism, are the main keys of deep convective storms initiation and evolution. Satellite data can provide indirect measurements of instability and moisture of a wide area in short periods of time. This paper studies the use of an objective method based on a blended use of multiple satellite-based convection estimation techniques. This method is based on different techniques arranged in a several layers approach of different convective features, aiming to stratify a cloud shield. Meteosat Second Generation (MSG) infrared (IR) 10.8μm and water vapor (WV) 6.2μm channels are explored together with tropopause temperature information provided by a numerical model. Threshold, brightness temperature differences (BTD), and time trends are applied to the information available resulting in a five layers product, highlighting areas of different convective activities. This cloud shield stratification method showed a great ability to better evaluate strong convection when compared with simpler techniques such as IR false color, and was especially useful to better identify the strongest convective cell in a large area with several convective outbreaks. A validation analysis was conducted using radar and lightning data, showing that this approach is very helpful in distinguishing very strong cases from weaker ones by pointing out subtle convective patterns only present in severe storms. Also, small changes in storm evolution were more pronounced in the method output. Besides some uncertainties that were observed, likely due to the large viewing angle, techniques derived from MSG spectral bands displayed good accuracy in studying large convective systems in the South America southern region.

Investigation of isolated thunderstorms lightning activity over eastern Mediterranean using Meteosat rapid scan infrared imagery

ABSTRACTAn analysis of selected spatiotemporal characteristics of isolated thunderstorms in relation to cloud-to-ground (CG) lightning over part of the eastern Mediterranean is performed. The purpose of the study is twofold: to better understand and improve the basic knowledge of the physical mechanisms of the phenomenon and to offer new means of nowcasting the lightning activity in such thunderstorms. Meteosat Second Generation (MSG) Rapid Scan Service (RSS) infrared imagery which offers the option of tracking the examined storms with a time frequency of 5 minutes is one of the two utilized datasets, the other being ZEUS very low frequency (VLF) lightning detection system’s CG discharge data. It was shown that a cloud top temperature of about −20°C is required for the onset of lightning activity. A rapid drop of the cloud-top temperature of about 11°C in 5 minutes on average is observed a few minutes before or during lightning initiation. The maximization of the activity is usually quite close to the overall minimum cloud-top temperature of the cell. A temperature increase of 3.5°C from this overall minimum can mark the end of the activity, which is also associated to the time evolution of the cell’s horizontal extent. In fact, after the cell’s horizontal area stops to increase and/or starts to gradually diminish, CG lightning activity is expected to stop.

RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor

Abstract Wildfires are a worldwide phenomenon with local and global effects. Many satellite-based methods for fire detection and monitoring have been developed to exploit data acquired by sensors onboard polar orbiting platforms. Their relatively low temporal resolution (hours) is, however, decidedly not adequate for detecting short-living events or fires characterised by a marked diurnal cycle and rapid evolution times. Geostationary satellites have very high temporal resolution of 30 to 2.5 min and could, in principle, be more suitable for providing timely alerts and facilitating possible mitigation actions. However, such short revisit times are coupled with spatial resolutions of 3–5 km, which are coarser than those of the polar orbiting sensors generally having 1 km capability in the infrared region. This could represent a significant limitation for small fire detection and precise localization. However, unlike polar orbiting systems, the geostationary attitude assures very stable observational conditions at the pixel level, including fixed view angles, same time of day, and same ground resolution cell dimensions. This can be crucial for fire-detection methods based on multi-temporal analyses. This work describes in detail the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES), a multi-temporal change-detection technique, and its application to the data of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation (MSG) platform. Its performance in terms of reliability and sensitivity was verified by > 20,000 SEVIRI images collected throughout the day during a four-year-collaboration with the regional Civil Protection Departments and other Local Authorities of two Italian regions which provided about 950 near real-time ground and aerial checks of the RST-FIRES detections. The results indicate a mean commission error rate of 18%, ranging from 3% to 30% according to the specific experimental campaign, with an average omission error of 44%, ranging from 3% to 66% depending on the campaign and the validation source used. This study fully demonstrates the added value of the RST-FIRES technique for the early warning of fire events. During the considered validation campaigns, RST-FIRES provided the sole fire alert in 348 cases; in further 227 cases, its warnings were given > 1 h before any other source of information was provided. Finally, this paper presents and discusses a comparison among RST-FIRES and other SEVIRI-based fire detection products. On the whole, RST-FIRES is shown to be 3 to 70 times more sensitive than all of the other considered SEVIRI-based products. This satisfactory result was not completely unexpected, considering the quite high false positive rate exhibited by RST-FIRES and the fact that other algorithms were designed to work in a wider area, from continental to the SEVIRI full disk.

Satellite remote sensing of hailstorms in France

Hailstorms are meteorological phenomena of great interest to the scientific community, owing to their socioeconomic impact, which is mainly on agricultural production. With its global coverage and high spatial and temporal resolution, satellite remote sensing can contribute to monitoring of such events through the development of appropriate techniques. This paper presents an extensive validation in the south of France of a hail detection tool (HDT) developed for the Middle Ebro Valley (MEV). The HDT is based on consecutive application of two filters, a convection mask (CM) and hail mask (HM), using spectral channels of the Meteosat Second Generation (MSG) satellite. The south of France is an ideal area for studying hailstorms, because there is a robust database of hail falls recorded by an extensive network of hailpads managed by the Association Nationale d'Etude et de Lutte contre les Fleáux Atmosphériques (ANELFA). The results show noticeably poorer performance of the HDT in France relative to that in the MEV, with probability of detection (POD) 60.4% and false alarm rate (FAR) 26.6%. For this reason, a new tool to suit the characteristics of hailstorms in France has been developed. The France Hail Detection Tool (FHDT) was developed using logistic regression from channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor of the MSG. The FHDT was validated, resulting in POD 69.3% and FAR 15.4%, thus improving hail detection in the study area as compared with the previous tool. The new tool was tested in a case study with satisfactory results, supporting its future practical application.

Validation of the new HelioClim-3 version 4 real-time and short-term forecast service using 14 BSRN stations

Abstract. Meteosat Second Generation (MSG) satellite images acquired every 15 min during daytime are currently processed by the Heliosat-2 method every night to generate the HelioClim-3 (HC3) database of the surface solar irradiation for the day before. A new service is proposed based on version 4 of HC3 (HC3v4) that offers real-time and forecasted irradiation for horizons up to a few hours. The service is based on a local persistence of the clear-sky index. Its results were compared to coincident high quality 15 min global irradiations measured in fourteen stations belonging to the Baseline Surface Radiation Network (BSRN). For forecasts for a temporal horizon of 15 min ahead, the relative bias and root mean square error (RMSE) range respectively from 0 to 2 %, and 20 to 23 % for most stations. The correlation coefficient ranges from 0.94 to 0.95. These performances are similar to HC3v4 for the same stations. Expectedly, the quality of the forecasts degrades as the temporal horizon increases. For 1 h ahead forecasts of 15 min irradiation, the relative bias, root mean square error (RMSE) and correlation coefficient range respectively from −3 to 1 %, 30 to 37 %, and 0.90 to 0.91.

Validation of three satellite-derived databases of surface solar radiation using measurements performed at 42 stations in Brazil

Abstract. The SoDa website (www.soda-pro.com) is populated with numerous solar-related Web services. Among them, three satellite-derived irradiation databases can be manually or automatically accessed to retrieve radiation values within the geographical coverage of the Meteosat Second Generation (MSG) satellite: the two most advanced versions of the HelioClim-3 database (versions 4 and 5, respectively HC3v4 and HC3v5), and the CAMS radiation service. So far, these databases have been validated against measurements of several stations in Europe and North Africa only. As the quality of such databases depends on the geographical regions and the climates, this paper extends this validation campaign and proposes an extensive comparison on Brazil and global irradiation received on a horizontal surface. Eleven stations from the Brazilian Institute of Space Research (INPE) network offer 1 min observations, and thirty-one stations from the Instituto Nacional de Meteorologia (INMET) network offer hourly observations. The satellite-derived estimates have been compared to the corresponding observations on hourly, daily and monthly basis. The bias relative to the mean of the measurements for HC3v5 is mostly comprised between 1 and 3 %, and that for HC3v4 between 2 and 5 %. These are very satisfactory results and they demonstrate that HC3v5, and to a lesser extent HC3v4, may be used in studies of long-term changes in SSI in Brazil. The situation is not so good with CAMS radiation service for which the relative bias is mostly comprised between 5 and 10 %. For hourly irradiation, the relative RMSE ranges from 15 to 33 %. The correlation coefficient is very large for all stations and the three databases, with an average of 0.96. The three databases reproduce well the hour from hour changes in SSI. The errors show a tendency to increase with the viewing angle of the MSG satellite. They are greater in tropical areas where the relative humidity in the atmosphere is important. It is concluded that except for the overestimation by CAMS radiation service, the three databases are suitable for studies of the solar resources in Brazil.