Spinning Enhanced Visible And Infrared Imager Research Articles

Meteosat-Based Characterization of the Spatiotemporal Evolution of Warm Convective Cloud Fields over Central Europe

AbstractThe spatiotemporal evolution of warm convective cloud fields over central Europe is investigated on the basis of 30 cases using observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary Meteosat platforms. Cloud fields are tracked in successive satellite images using cloud motion vectors. The time-lagged autocorrelation is calculated for spectral reflectance and cloud property fields using boxes of 16 × 16 pixels and adopting both Lagrangian and Eulerian perspectives. The 0.6-μm reflectance, cloud optical depth, and water path show a similar characteristic Lagrangian decorrelation time of about 30 min. In contrast, significantly lower decorrelation times are observed for the cloud effective radius and droplet density. It is shown that the Eulerian decorrelation time can be decomposed into an advective component and a convective component using the spatial autocorrelation function. In an Eulerian frame cloud fields generally decorrelate faster than in a Lagrangian one. The Eulerian decorrelation time contains contributions from the spatial decorrelation of the cloud field advected by the horizontal wind. A typical spatial decorrelation length of 7 km is observed, which suggests that sampling of SEVIRI observations is better in the temporal domain than in the spatial domain when investigating small-scale convective clouds. An along-track time series of box-averaged cloud liquid water path is derived and compared with the time series that would be measured at a fixed location. Supported by previous results, it is argued that this makes it possible to discriminate between local changes such as condensation and evaporation on the one hand and advective changes on the other hand.

Satellite observations of surface temperature during the March 2015 total solar eclipse.

The behaviour of remotely sensed land surface temperatures (LSTs) from the spinning-enhanced visible and infrared imager (SEVIRI) during the total solar eclipse of 20 March 2015 is analysed over Europe. LST is found to drop by up to several degrees Celcius during the eclipse, with the minimum LST occurring just after the eclipse mid-point (median=+1.5 min). The drop in LST is typically larger than the drop in near-surface air temperatures reported elsewhere, and correlates with solar obscuration (r=-0.47; larger obscuration = larger LST drop), eclipse duration (r=-0.62; longer duration = larger LST drop) and time (r=+0.37; earlier eclipse = larger LST drop). Locally, the LST drop is also correlated with vegetation (up to r=+0.6), with smaller LST drops occurring over more vegetated surfaces. The LSTs at locations near the coast and at higher elevation are also less affected by the eclipse. This study covers the largest area and uses the most observations of eclipse-induced surface temperature drops to date, and is the first full characterization of satellite LST during an eclipse (known to the author). The methods described could be applied to Geostationary Operational Environmental Satellite (GOES) LST data over North America during the August 2017 total solar eclipse.This article is part of the themed issue 'Atmospheric effects of solar eclipses stimulated by the 2015 UK eclipse'.

Influences of cloud heterogeneity on cirrus optical properties retrieved from the visible and near-infrared channels of MODIS/SEVIRI for flat and optically thick cirrus clouds

11Nonstandard abbreviations: Cloud Optical Thickness (COT), Effective Diameter (De), Independent Pixel Approximation (IPA), Ice Water Content (IWC), Near-infrared (NIR), Plane Parallel Approximation (PPA), Severely Roughened Aggregated Columns (SRAC), Thermal infrared (TIR), Three-dimensional (3-D) ,Visible (VIS). The influences of three-dimensional radiative effects and horizontal heterogeneity effects on the retrieval of cloud optical thickness (COT) and effective diameter (De) for cirrus clouds are explored by the SHDOM radiative transfer model. The stochastic cirrus clouds are generated by the Cloudgen model based on the Atmospheric Radiation Measurement program data. Incorporating a new ice cloud spectral model, we evaluate the retrieval errors for two solar zenith angles (SZAs) (30° and 60°), four solar azimuth angles (0°, 45°, 90°, and 180°), and two sensor settings (Moderate Resolution Imaging Spectrometer (MODIS) onboard Aqua and Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8). The domain-averaged relative error of COT (μ) ranges from −24.1 % to -1.0 % (SZA = 30°) and from −11.6 % to 3.3 % (SZA = 60°), with the uncertainty within 7.5 % to –12.5 % (SZA = 30°) and 20.0 % - 27.5 % (SZA = 60°). For the SZA of 60° only, the relative error and uncertainty are parameterized by the retrieved COT by linear functions, providing bases to correct the retrieved COT and estimate their uncertainties. Besides, De is overestimated by 0.7–15.0μm on the domain average, with the corresponding uncertainty within 6.7–26.5μm. The retrieval errors show no discernible dependence on solar azimuth angle due to the flat tops and full coverage of the cirrus samples. The results are valid only for the two samples and for the specific spatial resolution of the radiative transfer simulations.

Arabian Red Sea coastal soils as potential mineral dust sources

Abstract. Both Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite observations suggest that the narrow heterogeneous Red Sea coastal region is a frequent source of airborne dust that, because of its proximity, directly affects the Red Sea and coastal urban centers. The potential of soils to be suspended as airborne mineral dust depends largely on soil texture, moisture content and particle size distributions. Airborne dust inevitably carries the mineralogical and chemical signature of a parent soil. The existing soil databases are too coarse to resolve the small but important coastal region. The purpose of this study is to better characterize the mineralogical, chemical and physical properties of soils from the Arabian Red Sea coastal plain, which in turn will help to improve assessment of dust effects on the Red Sea, land environmental systems and urban centers. Thirteen surface soils from the hot-spot areas of windblown mineral dust along the Red Sea coastal plain were sampled for analysis. Analytical methods included optical microscopy, X-ray diffraction (XRD), inductively coupled plasma optical emission spectrometry (ICP-OES), ion chromatography (IC), scanning electron microscopy (SEM) and laser particle size analysis (LPSA). We found that the Red Sea coastal soils contain major components of quartz and feldspar, as well as lesser but variable amounts of amphibole, pyroxene, carbonate, clays and micas, with traces of gypsum, halite, chlorite, epidote and oxides. The range of minerals in the soil samples was ascribed to the variety of igneous and metamorphic provenance rocks of the Arabian Shield forming the escarpment to the east of the Red Sea coastal plain. The analysis revealed that the samples contain compounds of nitrogen, phosphorus and iron that are essential nutrients to marine life. The analytical results from this study will provide a valuable input into dust emission models used in climate, marine ecology and air quality studies.

Cloud ice caused by atmospheric mineral dust – Part 1: Parameterization of ice nuclei concentration in the NMME-DREAM model

Abstract. Dust aerosols are very efficient ice nuclei, important for heterogeneous cloud glaciation even in regions distant from desert sources. A new generation of ice nucleation parameterizations, including dust as an ice nucleation agent, opens the way towards a more accurate treatment of cold cloud formation in atmospheric models. Using such parameterizations, we have developed a regional dust-atmospheric modelling system capable of predicting, in real time, dust-induced ice nucleation. We executed the model with the added ice nucleation component over the Mediterranean region, exposed to moderate Saharan dust transport, over two periods lasting 15 and 9 days, respectively. The model results were compared against satellite and ground-based cloud-ice-related measurements, provided by SEVIRI (Spinning Enhanced Visible and InfraRed Imager) and the CNR-IMAA Atmospheric Observatory (CIAO) in Potenza, southern Italy. The predicted ice nuclei concentration showed a reasonable level of agreement when compared against the observed spatial and temporal patterns of cloud ice water. The developed methodology permits the use of ice nuclei as input into the cloud microphysics schemes of atmospheric models, assuming that this approach could improve the predictions of cloud formation and associated precipitation.

Enhancing weak transient signals in SEVIRI false color imagery: Application to dust source detection in southern Africa

A method is described to significantly enhance the signature of dust events using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The approach involves the derivation of a composite clear-sky signal for selected channels on an individual time step and pixel basis. These composite signals are subtracted from each observation in the relevant channels to enhance weak transient signals associated with either (a) low levels of dust emission or (b) dust emissions with high salt or low quartz content. Different channel combinations, of the differenced data from the steps above, are then rendered in false color imagery for the purpose of improved identification of dust source locations and activity. We have applied this clear-sky difference (CSD) algorithm over three (globally significant) source regions in southern Africa: the Makgadikgadi Basin, Etosha Pan, and the Namibian and western South African coast. Case study analyses indicate three notable advantages associated with the CSD approach over established image rendering methods: (i) an improved ability to detect dust plumes, (ii) the observation of source activation earlier in the diurnal cycle, and (iii) an improved ability to resolve and pinpoint dust plume source locations.

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.

A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin

The seasonal and interannual dynamics of tropical rainforests play a critical role in the global carbon cycle and climate change. This paper retrieved and compared land surface phenology from observations acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard geostationary satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) on polar-orbiting satellites over the Congo Basin. To achieve this, we first retrieved canopy greenness cycles (CGCs) and their transition timing from two-band enhanced vegetation index (EVI2) derived from SEVIRI and MODIS data between 2006 and 2013. We then assessed the influences of SEVIRI and MODIS data quality on the reconstruction of the EVI2 temporal trajectory, the detection of the CGC onset and end timing, and the total number of successful CGC retrievals. The significance of influences was determined using the one-tailed two-sample Kolmogorov–Smirnov test. The results indicate that diurnal SEVIRI observations greatly increased the probability of capturing cloud-free daily EVI2 in the rainforest-dominated region of the Congo Basin, where the proportion of good quality (PGQ) observations during a CGC was up to 80% higher than that from MODIS. As a result, the double annual CGCs of the Congo Basin rainforests were well identified from SEVIRI but sparsely detected from MODIS, whereas the single annual CGC in the savanna-dominated northern and southern Congo Basin was successfully retrieved from both SEVIRI and MODIS. Moreover, the decreases of PGQ in an EVI2 time series were found to significantly increase the uncertainties of retrieved phenological timings and increase the probabilities of CGC retrieval failures.

Analysis of ocean diurnal variations from the Korean Geostationary Ocean Color Imager measurements using the DINEOF method

High-frequency images of the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)) derived from the Korean Geostationary Ocean Color Imager (GOCI) provide a unique opportunity to study diurnal variation of water turbidity in coastal regions of the Bohai Sea, Yellow Sea, and East China Sea. However, there are many missing pixels in the original GOCI-derived Kd(490) images due to clouds and various other reasons. Data Interpolating Empirical Orthogonal Function (DINEOF) is a method to reconstruct missing data in geophysical datasets based on the Empirical Orthogonal Function (EOF). It utilizes both temporal and spatial coherencies of data to infer a solution at the missing locations. In this study, the DINEOF is applied to GOCI-derived Kd(490) data in the Yangtze River mouth and the Yellow River mouth regions, and the DINEOF reconstructed Kd(490) data are used to fill in the missing pixels. In fact, DINEOF has been used to fill in gaps in ocean color chlorophyll-a and turbidity data from the Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) in previous studies. Our GOCI validation results show that the bias between the reconstructed data and the original Kd(490) value is quite small (<∼5%). The standard deviation of the reconstructed/original ratio is ∼0.25 and ∼0.30 for the mouths in the Yangtze River and Yellow River, respectively. In addition, GOCI high temporal resolution measurements in Kd(490) can capture sub-diurnal variation due to the tidal forcing. The spatial patterns and temporal functions of the first three EOF modes are also examined. The first EOF mode characterizes the general mean spatial distribution of the region, while the second and third EOF modes represent the variations due to the tidal forcing in the region.

Diagnosing Horizontal and Inter-Channel Observation Error Correlations for SEVIRI Observations Using Observation-Minus-Background and Observation-Minus-Analysis Statistics

It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error correlations for Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations that are assimilated into the Met Office high-resolution model. The errors are calculated using a diagnostic that calculates statistical averages of observation-minus-background and observation-minus-analysis residuals. This diagnostic is sensitive to the background and observation error statistics used in the assimilation, although, with careful interpretation of the results, it can still provide useful information. We find that the diagnosed SEVIRI error variances are as low as one-tenth of those currently used in the operational system. The water vapour channels have significantly correlated inter-channel errors, as do the surface channels. The surface channels have larger observation error variances and inter-channel correlations in coastal areas of the domain; this is the result of assimilating mixed pixel (land-sea) observations. The horizontal observation error correlations range between 30 km and 80 km, which is larger than the operational thinning distance of 24 km. We also find that estimates from the diagnostics are unaffected by biased observations, provided that the observation-minus-background and observation-minus-analysis residual means are subtracted.

Using naive Bayes classifier for classification of convective rainfall intensities based on spectral characteristics retrieved from SEVIRI

This paper presents a new algorithm to classify convective clouds and determine their intensity, based on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The convective rainfall events at 15 min, 4 × 5 km spatial resolution from 2006 to 2012 are analysed over northern Algeria. The convective rain classification methodology makes use of the relationship between cloud spectral characteristics and cloud physical properties such as cloud water path (CWP), cloud phase (CP) and cloud top height (CTH). For this classification, a statistical method based on ‘naive Bayes classifier’ is applied. This is a simple probabilistic classifier based on applying ‘Bayes’ theorem with strong (naive) independent assumptions. For a 9-month period, the ability of SEVIRI to classify the rainfall intensity in the convective clouds is evaluated using weather radar over the northern Algeria. The results indicate an encouraging performance of the new algorithm for intensity differentiation of convective clouds using SEVIRI data.

Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite

Abstract. Landscape fires occur on a large scale in (sub)tropical savannas and grasslands, affecting ecosystem dynamics, regional air quality and concentrations of atmospheric trace gasses. Fuel consumption per unit of area burned is an important but poorly constrained parameter in fire emission modelling. We combined satellite-derived burned area with fire radiative power (FRP) data to derive fuel consumption estimates for land cover types with low tree cover in South America, Sub-Saharan Africa, and Australia. We developed a new approach to estimate fuel consumption, based on FRP data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) in combination with MODIS burned-area estimates. The fuel consumption estimates based on the geostationary and polar-orbiting instruments showed good agreement in terms of spatial patterns. We used field measurements of fuel consumption to constrain our results, but the large variation in fuel consumption in both space and time complicated this comparison and absolute fuel consumption estimates remained more uncertain. Spatial patterns in fuel consumption could be partly explained by vegetation productivity and fire return periods. In South America, most fires occurred in savannas with relatively long fire return periods, resulting in comparatively high fuel consumption as opposed to the more frequently burning savannas in Sub-Saharan Africa. Strikingly, we found the infrequently burning interior of Australia to have higher fuel consumption than the more productive but frequently burning savannas in northern Australia. Vegetation type also played an important role in explaining the distribution of fuel consumption, by affecting both fuel build-up rates and fire return periods. Hummock grasslands, which were responsible for a large share of Australian biomass burning, showed larger fuel build-up rates than equally productive grasslands in Africa, although this effect might have been partially driven by the presence of grazers in Africa or differences in landscape management. Finally, land management in the form of deforestation and agriculture also considerably affected fuel consumption regionally. We conclude that combining FRP and burned-area estimates, calibrated against field measurements, is a promising approach in deriving quantitative estimates of fuel consumption. Satellite-derived fuel consumption estimates may both challenge our current understanding of spatiotemporal fuel consumption dynamics and serve as reference datasets to improve biogeochemical modelling approaches. Future field studies especially designed to validate satellite-based products, or airborne remote sensing, may further improve confidence in the absolute fuel consumption estimates which are quickly becoming the weakest link in fire emission estimates.

Synergistic use of Lagrangian dispersion and radiative transfer modelling with satellite and surface remote sensing measurements for the investigation of volcanic plumes: the Mount Etna eruption of 25–27 October 2013

Abstract. In this paper we combine SO2 and ash plume dispersion modelling with satellite and surface remote sensing observations to study the regional influence of a relatively weak volcanic eruption from Mount Etna on the optical and micro-physical properties of Mediterranean aerosols. We analyse the Mount Etna eruption episode of 25–27 October 2013. The evolution of the plume along the trajectory is investigated by means of the FLEXible PARTicle Lagrangian dispersion (FLEXPART) model. The satellite data set includes true colour images, retrieved values of volcanic SO2 and ash, estimates of SO2 and ash emission rates derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations and estimates of cloud top pressure from SEVIRI (Spinning Enhanced Visible and InfraRed Imager). Surface remote sensing measurements of aerosol and SO2 made at the ENEA Station for Climate Observations (35.52° N, 12.63° E; 50 m a.s.l.) on the island of Lampedusa are used in the analysis. The combination of these different data sets suggests that SO2 and ash, despite the initial injection at about 7.0 km altitude, reached altitudes around 10–12 km and influenced the column average aerosol particle size distribution at a distance of more than 350 km downwind. This study indicates that even a relatively weak volcanic eruption may produce an observable effect on the aerosol properties at the regional scale. The impact of secondary sulfate particles on the aerosol size distribution at Lampedusa is discussed and estimates of the clear-sky direct aerosol radiative forcing are derived. Daily shortwave radiative forcing efficiencies, i.e. radiative forcing per unit AOD (aerosol optical depth), are calculated with the LibRadtran model. They are estimated between −39 and −48 W m−2 AOD−1 at the top of the atmosphere and between −66 and −49 W m−2 AOD−1 at the surface, with the variability in the estimates mainly depending on the aerosol single scattering albedo. These results suggest that sulfate particles played a large role in the transported plume composition and radiative forcing, while the contribution by ash particles was small in the volcanic plume arriving at Lampedusa during this event.

Identifying Absorbing Aerosols Above Clouds From the Spinning Enhanced Visible and Infrared Imager Coupled With NASA A-Train Multiple Sensors

Geostationary satellite data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in conjunction with A-Train data are used to develop an algorithm for detecting biomass-burning smoke aerosols above closed-cell stratocumulus (Sc) clouds. The detection relies on spectral signatures, textural characteristics, and time-dependent spectral variation of SEVIRI data. A-Train data including the Ozone Monitoring Instrument (OMI) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used as reference data for the SEVIRI algorithm development. The 15-min repeat cycle of SEVIRI provides the capability for identifying smoke above closed-cell Sc with an OMI aerosol index value exceeding 0.5 and a cloud optical thickness greater than 6 at 0.81 $\mu\mbox{m}$ . The user accuracy of this algorithm is ∼49% when using only spectral signature and textural tests. When incorporating the “temporal consistency” tests into the algorithm, the user accuracy increases to ∼65%. The producer accuracy is over ∼77%, implying that the SEVIRI algorithm generally identifies smoke above clouds when CALIOP also identifies the same feature at the collocated pixel. However, CALIOP has the tendency to underestimate the presence of thin smoke aerosols above liquid clouds during daytime. This algorithm can be used to detect and study the daytime variation of smoke above liquid clouds.

SEVIRI PrePro: A novel software tool for the pre-processing of SEVIRI geostationary orbit EO data products

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) is a geostationary orbit multispectral sensor on-board the Meteosat second Generation (MSG) platform, acquiring Earth Observation (EO) data over Earth's land surface from the optical to infrared parts of electromagnetic spectrum every 15 min. From the sensor a series of operational products are also provided to the user's community at no cost via EUMETSAT or LSA SAF portals.Herein, an open access stand-alone software product developed in Java programming language is presented for automating key pre-processing steps to all the SEVIRI operationally distributed products. The software tool, named Seviri PrePro, makes use of present day multi-core processors and is able to process very large datasets in a short time period, making it appropriate as well for use in a High Performance Computing (HPC) environment. The practical usefulness of the toolkit is also demonstrated herein using as a case study the SEVIRI evapotranspiration (ET) product.The development of SEVIRI PrePro is of significant importance to the SEVIRI users' community and is also very timely given that, to our knowledge, no similar software tool is freely distributed at present. Its use is anticipated to make a significant contribution to a large number of practical applications requiring use of SEVIRI data, including but not limited, weather forecasting and global climate monitoring at a range of geographical scales.

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.

Retrieving Land Surface Temperature from Hyperspectral Thermal Infrared Data Using a Multi-Channel Method.

Land Surface Temperature (LST) is a key parameter in climate systems. The methods for retrieving LST from hyperspectral thermal infrared data either require accurate atmospheric profile data or require thousands of continuous channels. We aim to retrieve LST for natural land surfaces from hyperspectral thermal infrared data using an adapted multi-channel method taking Land Surface Emissivity (LSE) properly into consideration. In the adapted method, LST can be retrieved by a linear function of 36 brightness temperatures at Top of Atmosphere (TOA) using channels where LSE has high values. We evaluated the adapted method using simulation data at nadir and satellite data near nadir. The Root Mean Square Error (RMSE) of the LST retrieved from the simulation data is 0.90 K. Compared with an LST product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat, the error in the LST retrieved from the Infared Atmospheric Sounding Interferometer (IASI) is approximately 1.6 K. The adapted method can be used for the near-real-time production of an LST product and to provide the physical method to simultaneously retrieve atmospheric profiles, LST, and LSE with a first-guess LST value. The limitations of the adapted method are that it requires the minimum LSE in the spectral interval of 800–950 cm−1 larger than 0.95 and it has not been extended for off-nadir measurements.

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%).

Error model for the assimilation of cloud‐affected infrared satellite observations in an ensemble data assimilation system

Infrared satellite observations are strongly affected by clouds, which complicates their effective use in data assimilation. While observation minus first‐guess (FG departure) statistics for cloud‐free data are close to a normal (Gaussian) distribution, the occurrence of clouds leads to strongly increased uncertainty, systematic differences between observations and model forecasts and subsequently a clear deviation of the FG departures from the Gaussianity that is usually assumed in data assimilation. This study aims to classify the cloud impact on Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared brightness temperature observations and model equivalents to mitigate the issues of non‐Gaussian FG departure statistics for data assimilation. A threshold brightness temperature is introduced that allows us to quantify the cloud impact and to derive an error estimate for FG departures as a function of the cloud impact. The use of the dynamic error estimate leads to substantially more Gaussian FG departure statistics. Based on the dynamic error estimate, an observation error model is derived for the assimilation of infrared brightness temperature observations in an all‐sky approach. The proposed method allows us to treat cloud‐free and cloud‐affected observations in a uniform way, without the need for cloud screening.