Meteosat Second Generation Satellite Research Articles

Evapotranspiration and surface energy fluxes across Europe, Africa and Eastern South America throughout the operational life of the Meteosat second generation satellite

AbstractThe exchange of energy and water fluxes between the Earth's surface and the atmosphere is crucial to a series of processes that impact human life. Noteworthy examples are agriculture yields, water availability, intensity and extent of droughts and the ability of ecosystems to provide services to society. The relevance of these processes has motivated the Satellite Application Facility on Land Surface Analysis (LSA SAF) programme to set up an operational framework to estimate—among other variables—evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the Meteosat Second Generation (MSG) satellite. The LSA SAF programme has recently launched the reprocessing of the ET and SEF datasets on the basis of the most recent version of the algorithm and homogenous forcing datasets. This article features the resulting ET/SEF dataset, a Data Record that encompasses the period from the start of the operational life of the MSG satellite (2004) till 2020 and covers the field of view of the MSG satellite (i.e. Europe, Africa and Eastern South America). Details on the algorithm and the datasets driving the ET/SEF estimates are also provided as well as a quality assessment.

Quantitative Estimation of Rainfall from Remote Sensing Data Using Machine Learning Regression Models

To estimate rainfall from remote sensing data, three machine learning-based regression models, K-Nearest Neighbors Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were implemented using MSG (Meteosat Second Generation) satellite data. Daytime and nighttime data from a rain gauge are used for model training and validation. To optimize the results, the outputs of the three models are combined using the weighted average. The combination of the three models (hereafter called Com-RSK) markedly improved the predictions. Indeed, the MAE, MBE, RMSE and correlation coefficient went from 23.6 mm, 10.0 mm, 40.6 mm and 89% for the SVR to 20.7 mm, 5.5 mm, 37.4 mm, and 94% when the models were combined, respectively. The Com-RSK is also compared to a few methods using the classification in the estimation, such as the ECST Enhanced Convective Stratiform Technique (ECST), the MMultic technique, and the Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The Com-RSK show superior performance compared to ECST, MMultic and CS-RADT methods.The Com-RSK is also compared to the two products of satellite estimates, namely CMORPH and CHIRPS. The results indicate that Com-RSK performs better than CMORPH and CHIRPS according to MBE, RMSE and CC (coefficient correlation). A comparison with three types of satellite precipitation estimation products, such as global product, regional product, and near real-time product, is performed. Overall, the methodology developed here shows almost the same results as regional product methods and exhibits better results than near real-time and global product methods.

Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data

In this paper, we implement the AdaBoost algorithm to optimize the classifications results of precipitations intensities carried out by One versus All strategy using Support Vector Machine (OvA-SVM). The model developed which combines the AdaBoost algorithm with a multiclass SVM is applied to images from the MSG (Meteosat Second Generation) satellite. Other variants to build multiclass SVMs, such as the OvO-SVM (One versus One SVM), SBT-SVM (Slant Binary Tree SVM) and DDAG-SVM (Decision Directed Acyclic Graph) are also implemented on which we tested the AdaBoost algorithm. The study showed that the AdaBoost algorithm performed better in the case of the OvA-SVM variant compared to the other variants.In order to evaluate the elaborated model, some classification techniques, such as the ECST Enhanced Convective Stratiform Technique (ECST), the SART where the Support vector machine, Artificial neural network and Random forest classifiers are combined, the Convective/Stratiform Rain Area Delineation Technique (CS-RADT) and the Random Forest technique (RFT) are applied. The classification results obtained show that AdaBoost with OvA-SVM (AdaOvA-SVM) presents very interesting performances where the evaluation parameters POD, POFD, FAR, BIAS, CSI and PC indicate the values 95.2%, 12.4%, 14.7%, 0.9, 88.1% and 96.5% respectively. Indeed, the AdaOvA-SVM technique has surpassed the CS-RADT, ECST and RFT techniques. As for the comparison with the SART, we noted that OvA-SVM presents very close results. The same trend was also observed when estimating precipitation. At the end of this study, it is shown that the AdaBoost algorithm performs better on a weak classifier or on a strong classifier operating in an unfavorable environment.

Near real-time estimation of high spatiotemporal resolution rainfall from cloud top properties of the MSG satellite and commercial microwave link rainfall intensities

High spatiotemporal resolution rainfall is needed in predicting flash floods, local climate impact studies and agriculture management. Rainfall estimation techniques like satellites and the commercial microwave links (MWL) rainfall estimation have independently made significant advancements in high spatiotemporal resolution rainfall estimation. However, their combination for rainfall estimation has received little attention, while it could benefit many applications in ungauged areas. This study investigated the usability of the random forest (RF) algorithm trained with MWL rainfall and Meteosat Second Generation (MSG) based cloud top properties for estimating high spatiotemporal resolution rainfall in the sparsely gauged Kenyan Rift Valley. Our approach retrieved cloud top properties for use as predictor variables from rain areas estimated from the MSG data and estimated path average rainfall intensities from the MWL to serve as the target variable. We trained and validated the RF algorithm using parameters derived through optimal parameter tuning. The RF rainfall intensity estimates were compared with gauge, MWL, Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Multisensor Precipitation Estimate (MPE) to evaluate its rainfall intensities from point and spatial perspectives. The results can be described as good, considering they were achieved in near real-time, pointing towards a promising rainfall estimation alternative based on the RF algorithm applied to MWL and MSG data. The applicative benefits of this technique could be huge, considering that many ungauged areas have a growing MWL network and MSG and, in the future, Meteosat Third Generation (MTG) coverage.

Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation

Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.

Improving SEVIRI-Based Hotspots Detection by Using Multiple Simultaneous Observations

Geostationary satellites like meteosat second generation (MSG) allow the detection and monitoring of thermal anomalies (wild fires and volcanic eruptions) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in monitoring and containing the fire events and could provide information on the temporal behavior of the fire (through fire radiative power) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation of the operational applicability of this tool is currently represented by the low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. The limitations related to the sensitivity of the geostationary sensor to fire sizes have been, at least in part, overcome by introducing specific algorithms. However, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km2 (at Mediterranean latitudes), makes this information not very interesting for the institutions involved in firefighting. This paper analyzes the feasibility of improving the localization of the thermal anomalies (hotspots) by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 located at long. 9.0°, MSG-10 located at 0.0° and MSG-8 located at long. 41.5°. The results confirm the possibility of improving the accuracy of the detection by exploiting the observation of the events from different positions in space.

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.

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.

Artificial intelligence systems for rainy areas detection and convective cells' delineation for the south shore of Mediterranean Sea during day and nighttime using MSG satellite images

The aim of this study is to investigate the potential of cloud classification by means of support vector machines using high resolution images from northern Algeria. The images were taken from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. An automatic system was developed to operate during both day and nighttime by following two steps of data processing. The first aims to detect rainy areas in cloud systems, whereas the second delineates convective cells from stratiform ones. A set of 12 spectral parameters was selected to extract information about cloud properties, which are different from day to night. The training and validation steps of this study were performed by in-situ rainfall measurement data, collected during the rainy season of years 2011 and 2012 via automatic rain gauge stations distributed in northern Algeria. Artificial neural networks (ANNs) and support vector machine (SVM) were explored, by combining spectral parameters derived from MSG images. Better performances were obtained by the SVM classifier, in terms of Critical Success Index and Probability of Detection for rainy areas detection (CSI=0.81, POD=91%), and also for convective/stratiform delineation (CSI=0.55, POD=74%).

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

Abstract. Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m−2) compared to the negative biases found for SAFRAN (−10 W m−2) and the MSG satellite (−12 W m−2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by −12 and −16 W m−2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Retrieval of surface solar irradiance, based on satellite-derived cloud information, in Greece

Cloud properties derived from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument, on board the MSG (Meteosat Second Generation) satellite, have been used to retrieve the surface global solar irradiance incident on a horizontal surface GHI (global horizontal irradiance) and the DNI (direct normal irradiance) with a temporal resolution of 15 min. The daily amount of solar energy as well as monthly and annual sums, are estimated. Based on a 6-year study period (2008–2013), a monthly climatology is derived. Results are compared with ground-based measurements in Greece. Comparison shows a general good agreement between satellite and ground data, with the highest differences occurring in cases of broken or very thick cloudiness. The highest collected monthly solar energy values are found during summer months, in Southern Peloponnese, Crete and Cyclades islands, and exceed 250 kWh m−2. The annual average energy for GHI is 1400–1500 kWh m−2 in Northern Greece and up to 1900 kWh m−2 in Southern Peloponnese, Crete and the islands. For DNI, values increase to about 9% in Northern Greece and around 15% in Southern Greece.

Quantitative Differences between Lightning and Nonlightning Convective Rainfall Events as Observed with Polarimetric Radar and MSG Satellite Data

Abstract To increase understanding of the relationships between lightning and nonlightning convective storms, lightning observations from the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) campaign were analyzed with Meteosat Second Generation (MSG) geostationary satellite and S-band NASA Polarimetric Doppler Weather Radar (NPOL) data. The study’s goal was to analyze the time evolution of infrared satellite fields and ground-based polarimetric radar during NAMMA to quantify relationships between satellite and radar observations for lightning and nonlightning convective clouds over equatorial Africa. Using NPOL data, very low-frequency arrival time difference lightning data, and MSG Spinning Enhanced Visible and Infrared Imager observations, the physical attributes of growing cumulus clouds, including ice mass production, updraft strength, cloud depth, and cloud-top glaciation were examined. It was found that, on average, the lightning storms had stronger updrafts (seen in the satellite and radar fields), which lead to the formation of deeper clouds (seen in the satellite and radar fields) and subsequently much more ice in the mixed-phase region (as confirmed in radar observations), as well as much more nonprecipitating ice in the top 1 km of the cloud (as quantified in both satellite and radar fields) than the nonlightning storms. Computed radar-derived ice masses in cumulus clouds verifies the traditional MSG indicators of cloud-top glaciation, while NPOL verifies internal structures (i.e., large amounts of graupel) where satellite and radar show strong updrafts.

Rainfall detection over northern Algeria by combining MSG and TRMM data

In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

Infrared and Microwave Image Fusion for Rainfall Detection over Northern Algeria

Rain areas delineation proposed in this paper is based on the image fusion from geostationary Meteosat Second Generation (MSG) satellite, with the low-earth orbiting passive Tropical Rainfall Measuring Mission (TRMM) satellite. The fusion technique described in this work used an artificial neural network (ANN). It's has been developed to detect instantaneous rainfall by using information from the IR images of MSG satellite and from TRMM Microwave Imager (TMI). The study is carried out over north of Algeria. Seven spectral parameters are used as input data of ANN to identify raining or non - raining pixels. Corresponding data of raining /non-raining pixels are taken from a PR (precipitation radar) issued from TRMM. Results from the developed scheme are compared with the results of SI method (Scattering Index) taken as reference method. The results show that the developed model performs very well and overcomes the deficiencies of use a single satellite.

A STEP BEYOND VISUALIZATION: HOW TO INGEST METEOSAT SECOND GENERATION SATELLITE DATA AND PRODUCTS INTO McIDAS-V, ILWIS AND TerraMA2

The Laboratory for Analyzing and Processing Satellite Images (LAPIS) of the University of Alagoas (UFAL) has been very active in the usage of Meteosat Second Generation (MSG) satellite data and products since 2007. These data and products are received in near-real time using simple and low cost ground reception infrastructure. Several examples of the ingest and display process for MSG satellite data and products are presented. The ingest of these satellite data and products is accomplished using a McIDAS-V, ILWIS and TerraMA2 tools. It is also shown how these MSG satellite-based products can be combined with other data. Results show that McIDAS-V, ILWIS and TerraMA2 are very useful tools to researchers and forecasters in the input and display process for MSG satellite data and products.

New Retrieval Algorithm for Deriving Land Surface Temperature From Geostationary Orbiting Satellite Observations

Accurate derivations of land surface temperature (LST) and land surface emissivity (LSE) from satellite measurements are difficult because the two variables are closely coupled. Features of significant/insignificant temporal variations in LST/LSE are recognized to de-couple both values using multiple-temporal satellite observations over the same geolocation. In this paper, a new approach is presented for deriving LST and LSE simultaneously by using multiple-temporal satellite observations. Two split-window regression formulas are carefully selected for the approach, and two satellite observations over the same geolocation within a certain time interval are utilized. The method is particularly applicable to geostationary satellite missions from which qualified multiple-temporal observations are available. The approach is designed and implemented for generating the LST and LSE values from the U.S. geostationary operational environmental satellite (GOES) eight imager data and the european meteosat second generation (MSG) mission spinning enhanced visible and infrared imager (SEVIRI) data. The performance of the algorithm is evaluated in terms of both accuracy and sensitivity. The retrieval results are compared against ground-truth observations from the U.S. Atmospheric radiation measurement facility and six surface radiation budget network (SURFRAD) stations. The validation results show the LST retrieval accuracy is around 1.95 K with good correlations of up to 0.9038. The method is applicable to the future U.S. GOES-R mission as well as the MSG mission considering that the advanced baseline imager (ABI) onboard the GOES-R satellites and the SEVIRI onboard the MSG satellite have similar split-window bands.

A STEP BEYOND VISUALIZATION: HOW TO INGEST METEOSAT SECOND GENERATION SATELLITE DATA AND PRODUCTS INTO McIDAS-V, ILWIS AND TerraMA2

The Laboratory for Analyzing and Processing Satellite Images (LAPIS) of the University of Alagoas (UFAL) has been very active in the usage of Meteosat Second Generation (MSG) satellite data and products since 2007. These data and products are received in near-real time using simple and low cost ground reception infrastructure. Several examples of the ingest and display process for MSG satellite data and products are presented. The ingest of these satellite data and products is accomplished using a McIDAS-V, ILWIS and TerraMA2 tools. It is also shown how these MSG satellite-based products can be combined with other data. Results show that McIDAS-V, ILWIS and TerraMA2 are very useful tools to researchers and forecasters in the input and display process for MSG satellite data and products.

Calibration of the Fire Weather Index over Mediterranean Europe based on fire activity retrieved from MSG satellite imagery

Here we present a procedure that allows the operational generation of daily maps of fire danger over Mediterranean Europe. These are based on integrated use of vegetation cover maps, weather data and fire activity as detected by remote sensing from space. The study covers the period of July–August 2007 to 2009. It is demonstrated that statistical models based on two-parameter generalised Pareto (GP) distributions adequately fit the observed samples of fire duration and that these models are significantly improved when the Fire Weather Index (FWI), which rates fire danger, is integrated as a covariate of scale parameters of GP distributions. Probabilities of fire duration exceeding specified thresholds are then used to calibrate FWI leading to the definition of five classes of fire danger. Fire duration is estimated on the basis of 15-min data provided by Meteosat Second Generation (MSG) satellites and corresponds to the total number of hours in which fire activity is detected in a single MSG pixel during one day. Considering all observed fire events with duration above 1h, the relative number of events steeply increases with classes of increasing fire danger and no fire activity was recorded in the class of low danger. Defined classes of fire danger provide useful information for wildfire management and are based on the Fire Risk Mapping product that is being disseminated on a daily basis by the EUMETSAT Satellite Application Facility on Land Surface Analysis.

Information content of MPEF DIVergence product in diagnosing the environment of deep convection

The use of divergence fields retrieved by tracking cloud and humidity features in satellite water vapour imagery to characterise preconvective environments in cases of strong upper-level dynamics is explored. An hourly upper-troposphere DIVergence (DIV) product is inferred from Atmospheric Motion Vectors (AMVs) tracked in the 6.2μm channel of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). This is an advanced satellite product operationally available since 2008.A specific colour palette is introduced to highlight the information content of the DIV product in mid latitudes. To make this product suitable for using in support to deep convection forecasting, an interpretation scheme of DIV product overlaid by the cloud top brightness temperature lower than −40°C as derived from the corresponding 10.8μm infrared (IR) image is proposed.The potential for operational applications is considered, taking into account that the divergent/convergent upper-level flow at the onset of deep moist convection is a sign of the presence of positive/negative factors, respectively, for the further development of the storm. In this study, the deep convective cells considered are limited to those, reaching the middle troposphere or above. For that purpose, 1166 convective cells with cloud top temperature lower than −50°C that developed over Europe and the Mediterranean coast of Africa from 16 June to 22 July 2010 were investigated. The results show that 76% of all studied deep convective cells developed in areas of divergence (as seen by MPEF DIV product). In 92% of the cases of deep convection appearance (at either divergent or convergent upper-level flow) the convective cells further developed in areas of divergence (as derived by the MPEF DIV product).This relation can be useful for the purposes of convection nowcasting by considering three types of observational information derived by MSG satellite data: composite images of the divergence fields, IR channel 10.8μm images and instability indices, as well as NWP parameters of tropospheric instability and upper-level dynamics.

Application of Meteosat second generation data towards improving the nowcasting of convective initiation

AbstractThe prediction of convective initiation (CI) from a satellite perspective provides forecasters with a constant relatively high temporal and convective scale spatial resolution tool to help protect life and property. By monitoring infrared (IR) channel brightness temperatures, their trends and multi‐spectral channel differences, the prediction of CI can be accomplished on the 0–1 h timescale. These methods, currently employed on the Geostationary Operational Environmental Satellite (GOES) system, have only recently been explored on the Meteosat Second Generation (MSG) satellite system. The additional channels and derived instability indices available on the MSG satellites may provide additional information useful to the prediction of CI. In this paper a concept is developed and discussed towards the advantages and possible applications of the inclusion of MSG specific IR spectral channels and instability information through the analysis of several convective case events over Central Europe and South Africa. Copyright © 2010 Royal Meteorological Society