Meteosat Second Generation Spinning Enhanced Visible And Infrared Imager Research Articles

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Abstract. Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challenge. Most observational data are two-dimensional (2D), offering either cloud top information or vertical profiles. We trained a neural network (Res-UNet) to merge high-resolution satellite images from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) with 2D CloudSat radar reflectivities to generate 3D cloud structures. The Res-UNet extrapolates the 2D reflectivities across the full disk of MSG SEVIRI, enabling a reconstruction of the cloud intensity, height, and shape in three dimensions. The imbalance between cloudy and clear-sky CloudSat profiles results in an overestimation of cloud-free pixels. Our root mean square error (RMSE) accounts for 2.99 dBZ. This corresponds to 6.6 % error on a reflectivity scale between −25 and 20 dBZ. While the model aligns well with CloudSat data, it simplifies multi-level and mesoscale clouds in particular. Despite these limitations, the results can bridge data gaps and support research in climate science such as the analysis of deep convection over time and space.

Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images.

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time.

Toward a Reliable Correction of NOAA AVHRR Orbital Drift

The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radiometer) orbital drift prevents the use of its derived land surface temperature (LST) data for global studies of temperature trends, especially for the 80s and 90s over land. In a previous study, we showed how orbital drift correction methods could be validated by simulating a reference and drifted time series from alternative MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data, thanks to their high (15 min) temporal resolution. In this study, we show how these alternative data allow identifying orbital drift effects on different land covers, and how these effects could be mitigated with novel approaches. We also identify two key statistical parameters to assess orbital drift correction performance: the bias between corrected and drifted time series and the trend of their difference. We present two methods and compare their results with an alternative orbital drift correction, validated against in situ data by their authors. Considering an ideal case where the whole influence of the orbital drift is known, our novel approach allows for an almost complete removal of the orbital drift effect (zero bias and 0.05 K/yr difference trend). However, in real cases, when we have only access to the drifted time series, our approach’s performance decreases slightly, mainly through a larger spread of the retrieved statistics. As for the alternative correction method, its performance is poorer, even if it actually succeeds in removing part of the observed orbital drift. These results, as well as the new insights we provide on the orbital drift effect on LST, pave the way toward a reliable correction of NOAA AVHRR orbital drift. We therefore recommend the use of simulated LST time series such as the ones used in this study for the validation of orbital drift correction methods.

Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

On the geometric accuracy and stability of MSG SEVIRI images

Monitoring the geometric quality and temporal stability of Earth Observation satellite sensors is a major task of satellite vendors. As an owner and operator of widely used meteorological satellites, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) carries out operational activities and scientific studies on the radiometric and geometric quality assessments of geostationary Meteosat satellites among others. As part of Level 1 Product Monitoring - Evolution Studies initiated by EUMETSAT, a new methodology has been developed and implemented as a prototype software, namely the Geometric Quality Assessment (GQA) Tool; which enhances image local texture, extracts prominent features and matches them via area-based least squares, and eliminates possible outliers using a set of rules. This article presents the results of algorithm validation activities carried out by using time series images of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor aboard Meteosat Second Generation (MSG) satellites and MEdium Resolution Imaging Spectrometer (MERIS) global mosaics as reference to evaluate short- and long-term geometric temporal stability. Meteosat-11 SEVIRI time series data from a full-day and one year were employed for this purpose. In addition, the geometric quality of Meteosat-8 over Indian Ocean and Meteosat-10 in rapid scan mode was assessed for single acquisitions. The results show that the geometric accuracy of the evaluated datasets yield to the mean shifts up to one km in East-West (x) and North-South (y) directions, with larger errors in the y direction. The image inner geometric accuracy assessments revealed displacement errors with strip pattern in the three spectral bands (i.e. high-resolution visible, infra-red 10.8 and visible 0.8) evaluated within the framework.

NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset

National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

Toward the development of a multi parametric system for a short-term assessment of the seismic hazard in Italy

With the aim to develop a multi-parametric system devote to improving our present capability to assess seismic hazard particularly in the short-medium term, the preliminary step is to identify those parameters that have demonstrated their non-casual relation with earthquake occurrences and whose anomalous variations can be associated to the complex seismic process. Since 80, fluctuations of Earth thermally emitted radiation, measured by satellite sensors in the Thermal InfraRed (TIR) spectral region, have been reported by several authors in some connection with the occurrence of earthquakes; they and can be considered as one of the possible parameters to include within a multi-parametric system. In this paper, the Robust Satellite Techniques (RST) approach has been exploited to highlight Significant Sequences of TIR Anomalies (SSTAs) possibly related to seismic events happened in Italy in the period June 2004 - December 2014. In particular, we evaluated the level of correlation between occurrence of earthquakes with M≥4 and RST-based TIR anomalies using two different spatial resolutions of MSG-SEVIRI (Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imager) images. Results of the two RST analyses show that the 82% of the identified SSTAs does not disappear when downscaled spatial resolution SEVIRI TIR records were used. For both analyses, more than 60% of SSTAs is apparently in connection with the occurrence of M≥4 earthquakes; more than the 80% of them has tendency to anticipate the occurrence of earthquakes. Although about the 40% of SSTAs is not apparently related to documented seismic activity (false positives), results of random tests (i.e. Molchan’s error diagrams) indicate a non-casual correlation between SSTAs and earthquake occurrences. These results confirm that the parameter “RST-based satellite TIR anomalies” is one of the possible candidates to be included in a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH).

A fast and accurate radiative transfer model for aerosol remote sensing

After several decades’ development of retrieval techniques in aerosol remote sensing, no fast and accurate analytical Radiative Transfer Model (RTM) has been developed and applied to create global aerosol products for non-polarimetric instruments such as Ocean and Land Colour Instrument/Sentinel-3 (OLCI/Sentinel-3) and Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). Global aerosol retrieval algorithms are typically based on a Look-Up–Table (LUT) technique, requiring high-performance computers. The current eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm also utilizes the LUT method. In order to have a near-real time retrieval and achieve a quick and accurate “FIRST-LOOK” aerosol product without high-demand of computing resource, we have developed a Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance (FASMAR) for aerosol remote sensing. The FASMAR is developed based on a successive order of scattering technique. In FASMAR, the first three orders of scattering are calculated exactly. The contribution of higher orders of scattering is estimated using an extrapolation technique and an additional correction function. The evaluation of FASMAR has been performed by comparing with radiative transfer model SCIATRAN for all typical observation/illumination geometries, surface/aerosol conditions, and wavelengths 412, 550, 670, 870, 1600, 2100 nm used for aerosol remote sensing. The selected observation/illumination conditions are based on the observations from both geostationary satellite (e.g. MSG/SEVIRI) and polar-orbit satellite (e.g. OLCI/Sentinel-3). The percentage error of the top of atmosphere reflectance calculated by FASMAR is within ± 3% for typical polar-orbit/geostationary satellites’ observation/illumination geometries. The accuracy decreases for solar and viewing zenith angles larger than 70∘. However, even in such cases, the error is within the range ± 5%. The evaluation of model performance also shows that FASMAR can be used for all typical surfaces with albedo in the interval [0−1]and aerosol with optical thickness in the range [0.01−1].

Near real-time estimation of Sea and Land surface temperature for MSG SEVIRI sensors

Land and Sea Surface Temperatures (LST and SST) are both recognized as Essential Climate Variables, and are routinely retrieved by a wealth of satellites. However, for validated approaches, the latest data are usually not available to the general public. We offer to bridge this gap, by using Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI), with its 15 min temporal resolution. Here, we present generic algorithms for the retrieval of both LST and SST, valid for the SEVIRI instrument onboard MSG platforms 8–11, which we validate using hourly data of 4 ground stations and 11 buoys in Spain over the years 2015 to 2018. These validations show that in the best conditions of surface homogeneity (cloud-free summer nights), errors in our LST estimation are below 1.5 K for stations with good thermal homogeneity. Comparison with LSA-SAF (Land Surface Analysis - Satellite Application Facility) LST shows differences below 2 K for most of SEVIRI disk, with higher differences in arid areas and during daytime. As for SST retrieval, the average error amount to 0.67 K for cloud-free buoy data. These algorithms have been implemented in a near-real time processing chain, which provide actualized LST and SST maps every 15 min within 5 min of image reception. These maps, along with other products, can be freely consulted from a dedicated webpage (https://www.uv.es/iplsat).

Validation of GOES-16 ABI and MSG SEVIRI active fire products

The recent deployment of the next generation of geostationary weather satellites provides an opportunity for the establishment of a robust global network of geostationary fire data that can greatly complement existing polar-orbiting satellite fire products. Among other benefits, geostationary satellites provide frequent sampling of diurnal variations in fire activity. Building on established satellite active fire data validation protocols, we used Landsat-8 Operational Land Imager (OLI) as reference fire data to validate the fire products derived from two geostationary satellite sensors: the Advanced Baseline Imager (ABI) on board the National Oceanic and Atmospheric Administration (NOAA) GOES-16 satellite (launched November 2016), and the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI), on board the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Second Generation (MSG) satellite series (multiple launch dates). The two primary algorithms associated with these geostationary active fire data sets are the Fire Detection and Characterization (FDC) product based on the heritage Wildfire Automated Biomass-Burning Algorithm (WF-ABBA) for the GOES-series, and the Fire Radiative Power (FRP-PIXEL) product based on the Fire Thermal Anomaly algorithm (FTA) for the MSG series. Our standardized validation method allowed for a direct inter-comparison between the complementary active fire datasets. Specifically, we present an error assessment of the detection probability (omission error) and false alarm rate (commission error) for two periods in 2017 and 2018 that include extensive fire activity in the respective full-disk sectors covered by each product. The results highlight (i) the restrictiveness of the FRP-PIXEL product (98% omission error) compared to the FDC product (84% omission error), and (ii) the elevated false alarm rate of FDC (88% commission error) compared to FRP-PIXEL (8% commission error). These validation results will be used to help support the development of a harmonized global multi-sensor active fire dataset to be integrated into the Global Wildfire Information System (GWIS).

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

Abstract. High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

Detecting cloud contamination in passive microwave satellite measurements over land

Abstract. Remotely sensed brightness temperatures from passive observations in the microwave (MW) range are used to retrieve various geophysical parameters, e.g. near-surface temperature. Cloud contamination, although less of an issue at MW than at visible to infrared wavelengths, may adversely affect retrieval quality, particularly in the presence of strong cloud formation (convective towers) or precipitation. To limit errors associated with cloud contamination, we present an index derived from stand-alone MW brightness temperature observations, which measure the probability of residual cloud contamination. The method uses a statistical neural network model trained with the Global Precipitation Microwave Imager (GMI) observations and a cloud classification from Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers. The index confidence increases with the number of available frequencies and performs better over the ocean, as expected. In all cases, even for the more challenging radiometric signatures over land, the model reaches an accuracy of ≥70 % in detecting contaminated observations. Finally an application of this index is shown that eliminates grid cells unsuitable for land surface temperature estimation.

Novel WkNN-based technique to improve instantaneous rainfall estimation over the north of Algeria using the multispectral MSG SEVIRI imagery

For the estimation of rainfall in northern Algeria, a new method is proposed in this study. It based on the k nearest weighted neighbours (WkNN) classification algorithm using the dataset from the radiometer-imager SEVIRI (Spinning Enhanced Visible and Infra-Red Imager) boarded on the satellite MSG (Meteosat Second Generation) to determine the pixel rainfall rate among the 16 predefined intensity levels observed in the Sétif meteorological radar.The implementation of the classification consists in using the spectral characteristics of a new sample (pixel) as input variables of the WkNN classifier to predict his class of membership based on the weighted distances separating him from the samples of the set of learning. The results thus obtained are validated with respect to the rainfall intensity classes observed and co-located by ground radar. The results showed a significant improvement for instantaneous estimate of precipitation during the day, with a correlation coefficient r=0.87 and statistical parameters RMSE=2.29mm/h,Bias=0.45mm/h,MAE=0.35mm/h, and we have obtained the results similar overnight.

Downscaling of Satellite OPEMW Surface Rain Intensity Data

This paper presents a geostatistical downscaling procedure to improve the spatial resolution of precipitation data. The kriging method with external drift has been applied to surface rain intensity (SRI) data obtained through the Operative Precipitation Estimation at Microwave Frequencies (OPEMW), which is an algorithm for rain rate retrieval based on Advanced Microwave Sounding Units (AMSU) and Microwave Humidity Sounder (MHS) observations. SRI data have been downscaled from coarse initial resolution of AMSU-B/MHS radiometers to the fine resolution of Spinning Enhanced Visible and InfraRed Imager (SEVIRI) flying on board the Meteosat Second Generation (MSG) satellite. Orographic variables, such as slope, aspect and elevation, are used as auxiliary data in kriging with external drift, together with observations from Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager (MSG-SEVIRI) in the water vapor band (6.2 µm and 7.3 µm) and in thermal-infrared (10.8 µm and 8.7 µm). The validation is performed against measurements from a network of ground-based rain gauges in Southern Italy. It is shown that the approach provides higher accuracy with respect to ordinary kriging, given a choice of auxiliary variables that depends on precipitation type, here classified as convective or stratiform. Mean values of correlation (0.52), bias (0.91 mm/h) and root mean square error (2.38 mm/h) demonstrate an improvement by +13%, −37%, and −8%, respectively, for estimates derived by kriging with external drift with respect to the ordinary kriging.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

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.

A satellite rainfall retrieval technique over northern Algeria based on the probability of rainfall intensities classification from MSG-SEVIRI

In this paper, an algorithm based on the probability of rainfall intensities classification for rainfall estimation from Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) has been developed. The classification scheme uses various spectral parameters of SEVIRI that provide information about cloud top temperature and optical and microphysical cloud properties. The presented method is developed and trained for the north of Algeria. The calibration of the method is carried out using as a reference rain classification fields derived from radar for rainy season from November 2006 to March 2007. Rainfall rates are assigned to rain areas previously identified and classified according to the precipitation formation processes. The comparisons between satellite-derived precipitation estimates and validation data show that the developed scheme performs reasonably well. Indeed, the correlation coefficient presents a significant level (r:0.87). The values of POD, POFD and FAR are 80%, 13% and 25%, respectively. Also, for a rainfall estimation of about 614mm, the RMSD, Bias, MAD and PD indicate 102.06(mm), 2.18(mm), 68.07(mm) and 12.58, respectively.

An Online System for Nowcasting Satellite Derived Temperatures for Urban Areas

The Urban Heat Island (UHI) is an adverse environmental effect of urbanization that increases the energy demand of cities and impacts human health. The study of this effect for monitoring and mitigation purposes is crucial, but it is hampered by the lack of high spatiotemporal temperature data. This article presents the work undertaken for the implementation of an operational real-time module for monitoring 2 m air temperature (TA) at a spatial resolution of 1 km based on the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This new module has been developed in the context of an operational system for monitoring the urban thermal environment. The initial evaluation of TA products against meteorological in situ data from 15 cities in Europe and North Africa yields that its accuracy in terms of Root Mean Square Error (RMSE) is 2.3 °C and Pearson’s correlation coefficient (Rho) is 0.95. The temperature information made available at and around cities can facilitate the assessment of the UHIs in real time but also the timely generation of relevant higher value products and services for energy demand and human health studies. The service is available at http://snf-652558.vm.okeanos.grnet.gr/treasure/portal/info.html.

Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations

Abstract Earth Observation (EO) technology is today at a maturity level that allows deriving operational estimates of Surface Soil Moisture (SSM) from a variety of sensors; yet, such products are provided at present at a coarse spatial and/or temporal resolution, which restricts their use in local or regional scale studies and practical applications. Herein, a methodology to derive SSM estimates from space at previously unattained spatio-temporal resolutions is proposed. The method is based on a variant of the “triangle” inversion technique leveraging on the strengths and synergies of SMOS (Soil Moisture and Ocean Salinity mission) microwave observations and geostationary optical/infrared data. The SSM retrieval technique allows for: i) enhancing the spatial resolution of SMOS SSM product estimates to 3 km spatial resolution, and, ii) providing a temporal average daytime SM product from the instantaneous fine-scale SSM estimates acquired every 15 min; the latter is allowing higher coverage in presence of clouds and representativeness (up to 96 estimates per day) in comparison to the instantaneous estimate at the time of satellite overpass. The proposed technique has been implemented to SMOS and MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) observations acquired over the Iberian Peninsula and Southern France during year 2011. SSM instantaneous estimates at the time of SMOS overpass and daytime-averaged SSM estimates have been obtained and evaluated separately against collocated in-situ measurements acquired from a total of 40 stations belonging to the REMEDHUS, VAS and SMOSMANIA permanent soil moisture measurement networks. Statistical agreement between compared datasets has been evaluated both at individual stations and considering the network average on the basis of several statistical terms computed including correlation, bias, root-mean-squared errors, slope and intercept of linear regression. Results showed that the proposed method not only preserves the quality of SMOS SSM at finer spatial scales, but also allows achieving higher temporal coverage and representativeness in daytime averages. The synergy of SMOS and SEVIRI provides a pathway to enhance water cycle EO capabilities taking full advantage of the new observational records of SSM and operational geostationary information.

Intercomparison of Operational Land Surface Temperature Products Derived From MSG-SEVIRI and Terra/Aqua-MODIS Data

Accuracy assessment of land surface temperature (LST) products is critical to facilitate their use in various studies. As an alternative method for assessing the accuracy of LST products, a new satellite LST product is compared with a heritage LST product to validate and determine the uncertainties in the satellite-derived LST approach. In this study, we propose a method for the intercomparison of the Meteosat second generation-spinning enhanced visible and infrared imager (MSG-SEVIRI) and Terra/Aqua-moderate resolution imaging spectroradiometer (MODIS) LST products. The intercomparison was performed by verifying the collocation in space, temporal concurrence, viewing geometry alignment, and spatial homogeneity between the two LST products. The discrepancies between the SEVIRI and MODIS LST products were investigated over different seasons, times of day, and surface types. SEVIRI LST values are generally higher than MODIS LST values, with positive biases during daytime (approximately 2–4 K) and nighttime (approximately 1–2 K). Significant variability of the daytime LST discrepancies with season, time of day, and surface type is observed. Compared with the daytime LST discrepancies, the nighttime LST discrepancies are less dependent on season, time of day, and surface type.