Spinning Enhanced Visible And Infrared Imager Data Research Articles

Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.

A new operational Mediterranean diurnal optimally interpolated sea surface temperature product within the Copernicus Marine Service

Abstract. Within the Copernicus Marine Service, a new operational MEDiterranean diurnal optimally interpolated sea surface temperature (MED DOISST) product has been developed. This product provides hourly mean maps (level 4) of subskin SST at 1/16∘ horizontal resolution over the Mediterranean Sea from January 2019 to the present. Subskin is the temperature at ∼1 mm depth of the ocean surface and then potentially subjected to a large diurnal cycle. The product is built by combining hourly SST data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board the Meteosat Second Generation and model analyses from the Mediterranean Forecasting System (MedFS) through optimal interpolation. SEVIRI and MedFS (first layer) SST data are respectively used as the observation source and first guess. The choice of using a model output as first guess represents an innovative alternative to the commonly adopted climatologies or previous day analyses, providing physically consistent estimates of hourly SSTs. The accuracy of the MED DOISST product is assessed here by comparison against surface drifting buoy measurements covering the years 2019 and 2020. The diurnal cycle reconstructed from DOISST is in good agreement with the one observed by independent drifter data, with a mean bias of 0.041±0.001 K and root mean square difference (RMSD) of 0.412±0.001 K. The new SST product is more accurate than the input MedFS SST during the central warming hours, when the model, on average, underestimates drifter SST by 1/10 of a degree. The capability of DOISST to reconstruct diurnal warming events, which may reach intense amplitudes larger than 5 K in the Mediterranean Sea, is also analyzed. Specifically, a comparison with the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) diurnal skin SST product, SEVIRI, MedFS, and drifter data shows that the DOISST product is able to reproduce more accurately diurnal warming events larger than 1 K. This product can contribute to improving the prediction capability of numerical models that assimilate or correct the heat fluxes starting from level 4 SST data and the monitoring of surface heat budget estimates and temperature extremes which can have significant impacts on the marine ecosystem. The full MED DOISST product (released on 4 May 2021) is available upon free registration at https://doi.org/10.48670/moi-00170 (CNR, 2021). The reduced subset used here for validation and review purposes is openly available at https://doi.org/10.5281/zenodo.5807729 (Pisano, 2021).

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.

Snow and cloud detection using a convolutional neural network and low-resolution data from the Electro-L No. 2 Satellite

We describe an algorithm based on a convolutional neural network that detects cloud formations and snow cover in satellite images using textures. Herein, multispectral satellite images, received from a multizone scanning instrument used for hydrometeorological support and installed on the Russian satellite Electro-L No. 2, are used as input data. The problem of snow and cloud classification in the absence of a spectral channel in the range of 1.4 to 1.8 μm, which is necessary for their accurate separation, is considered. The developed algorithm can produce cloud and snow cover masks for an area limited by the values of the solar zenith angle in the range of 0 deg to 80 deg for daytime. Algorithm accuracy was evaluated using machine learning metrics and comparing its results with ground truth masks segmented manually by an experienced interpreter. In addition, we compared the resulting masks with a similar cloud mask product from the European Organisation for the Exploitation of Meteorological Satellites based on the data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument installed on the Meteosat-8 satellite. According to the results of this comparison, we conclude that the cloud masks produced by the proposed convolutional neural network-based algorithm have a lower probability of false detection than products based on the SEVIRI data. The proposed algorithm is fully automatic, and it works in any season of the year during the daytime.

An Assessment of SEVIRI Imagery at Various Temporal Resolutions and the Effect on Accurate Dust Emission Mapping

This paper evaluates the use of the ‘Dust red/green/blue (RGB)’ product derived from Spinning Enhanced Visible and Infrared Imager (SEVIRI) data at 15-min, 30-min, and 60-min temporal resolutions, for monitoring dust emissions in the Middle East. From January 2006 to December 2006, observations of dust emission point sources were recorded at each temporal resolution across the Middle East. Previous work has demonstrated that using SEVIRI data is a major improvement on other remote sensing methods for mapping dust sources in the Sahara, by enabling dust-storm observations through sequential images, back to the point of first emission. However, the highest temporal resolution available (15-min observations) produces 96 images per day, resulting in significantly higher data management requirements than data provided at 30-min and 60-min intervals. To optimize future research workflows, this paper investigates the effect of lowering the temporal resolution on the number and spatial distribution of observed dust emission events in the Middle East. The results show that the number of events observed reduced by 17% for 30-min resolution and 50% for 60-min resolution. These differences change seasonally, with the highest reduction observed in summer (34% and 64% reduction, respectively).

SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval.

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

Investigating Volcanic Plumes from Mt. Etna Eruptions of December 2015 by Means of AVHRR and SEVIRI Data.

In early December 2015, a rapid sequence of strong paroxysmal events took place at the Mt. Etna crater area (Sicily, Italy). Intense paroxysms from the Voragine crater (VOR) generated an eruptive column extending up to an altitude of about 15 km above sea level. In the following days, other minor ash emissions occurred from summit craters. In this study, we present results achieved by monitoring Mt. Etna plumes by means of RSTASH (Robust Satellite Techniques-Ash) algorithm, running operationally at the Institute of Methodologies for Environmental Analysis (IMAA) on Advanced Very High Resolution Radiometer (AVHRR) data. Results showed that RSTASH detected an ash plume dispersing from Mt. Etna towards Ionian Sea starting from 3 December at 08:40 UTC, whereas it did not identify ash pixels on satellite data of same day at 04:20 UTC and 04:40 UTC (acquired soon after the end of first paroxysm from VOR), due to a mixed cloud containing SO2 and ice. During 8–10 December, the continuity of RSTASH detections allowed us to estimate the mass eruption rate (an average value of about 1.5 × 103 kg/s was retrieved here), quantitatively characterizing the eruptive activity from North East Crater (NEC). The work, exploiting information provided also by Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, confirms the important contribution offered by RSTASH in identifying and tracking ash plumes emitted from Mt. Etna, despite some operational limitations (e.g., cloud coverage). Moreover, it shows that an experimental RST product, tailored to SEVIRI data, for the first time used and preliminarily assessed here, may complement RSTASH detections providing information about areas mostly affected by volcanic SO2.

Spatial analysis of aerosol optical depth obtained by air quality modelling and SEVIRI satellite observations over Portugal

The aim of this work is to explore the aerosol optical depth (AOD) data provided by different sources, namely satellite observations, air quality modelling and Aerosol Robotic Network (AERONET) observations, in order to improve characterisation of aerosol spatial distribution and to assess the contribution of the dust events on AOD over Portugal. Thus, the spatial and temporal variations of the AOD data were analysed for May 2011 taking into account the occurrence of mineral dust events. Satellite data obtained by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument and air quality modelling results from the Comprehensive Air Quality Model (CAMx) over Portugal were explored. In this work, AOD values with a temporal resolution of 15 min were obtained from SEVIRI. Additionally, 3D data with 23 vertical layers for each aerosol constituent, size distribution and chemical composition were obtained from the modelling approach and used to compute the AOD at 550 nm by integrating of aerosol extinction coefficient (Qext) over the total atmospheric column. Despite the outbreaks of mineral dust occurring during this period, the overall contribution of organic carbon (OC), black carbon (BC) and sulphate (SO 4 ) compounds to the monthly average AOD values estimated by the model is very important achieving 95% mainly located in the northern part of the studied region. The model outputs and SEVIRI data were validated with AERONET observations presenting correlation coefficients of 0.71 and 0.68 respectively. Three different modes of aerosol were classified based on Ångström coefficient versus AOD and distinguishing between the dominance of fine and coarse aerosol fractions. The analysis implemented reveal that intercomparison of satellite observations with air quality modelling contribute to better understanding of the discrepancies presented in spatial pattern of AOD. • AOD data from satellite observations and air quality modelling were used. • Spatial and temporal variations of the AOD data were investigated. • Better correlation between modelling results and AERONET observations was obtained. • Satellite data and modelling results are in agreement but with different absolute values. • Analysis for observation points and for all domain provided different conclusions.

Evaluation of land surface temperature by comparing FY-3C/VIRR with Terra/MODIS and MSG/SEVIRI data

ABSTRACTLand surface temperature (LST) is a key parameter in the physics of land surface processes on a regional and global scale, as it combines the results of all surface–atmospheric interactions and energy fluxes between the atmosphere and the ground. Due to discrepancies in retrieval methods, input parameters, and sensor characteristics, various LST products are different from each other. As one of the important LST contributors, the operational LST product from the visible and infrared radiometer (VIRR) onboard Feng Yun 3C satellite (denoted as VIRR LST1) has not been comprehensively assessed, and the detailed LST retrieval accuracy is still unknown to the public. Taking this into consideration, a new group of VIRR LST (denoted as VIRR LST2) was derived using a generalized split-window method for operational use. Therefore, this study aimed to evaluate the consistency between VIRR LST1 and VIRR LST2 in detail using the LSTs derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) data over the study area (from 11° W to 2° E and 32° N to 44° N). Several LST images in different seasons were used and registered in space, viewing time, and viewing geometry. After that, the LST differences in day-variation, land-cover types, and viewing zenith angles (VZAs) were also investigated. The results indicate that the differences between VIRR LST1 (or VIRR LST2) and MODIS LST were within 2.80 K, and the corresponding values between VIRR LST1 (or VIRR LST2) and SEVIRI LST were within 3.30 K. The VIRR LST1 was always underestimated, and VIRR LST2 was overestimated during the exuberant vegetation day. Furthermore, the mean differences between VIRR LST1 (or VIRR LST2) and MODIS LST for bare soil were larger than those for vegetation since the land surface emissivity for bare soil was often not well produced. On the other hand, the variation was not pronounced when comparing VIRR LST1 (or VIRR LST2) and SEVIRI LST, which could be caused by many more mixed pixels in the SEVIRI data set. In addition, the LST differences seemed to VZA dependent.

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

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

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

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

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

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

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.

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.

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.

LSA SAF Meteosat FRP products – Part 1: Algorithms, product contents, and analysis

Abstract. Characterizing changes in landscape fire activity at better than hourly temporal resolution is achievable using thermal observations of actively burning fires made from geostationary Earth Observation (EO) satellites. Over the last decade or more, a series of research and/or operational "active fire" products have been developed from geostationary EO data, often with the aim of supporting biomass burning fuel consumption and trace gas and aerosol emission calculations. Such Fire Radiative Power (FRP) products are generated operationally from Meteosat by the Land Surface Analysis Satellite Applications Facility (LSA SAF) and are available freely every 15 min in both near-real-time and archived form. These products map the location of actively burning fires and characterize their rates of thermal radiative energy release (FRP), which is believed proportional to rates of biomass consumption and smoke emission. The FRP-PIXEL product contains the full spatio-temporal resolution FRP data set derivable from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) imager onboard Meteosat at a 3 km spatial sampling distance (decreasing away from the west African sub-satellite point), whilst the FRP-GRID product is an hourly summary at 5° grid resolution that includes simple bias adjustments for meteorological cloud cover and regional underestimation of FRP caused primarily by underdetection of low FRP fires. Here we describe the enhanced geostationary Fire Thermal Anomaly (FTA) detection algorithm used to deliver these products and detail the methods used to generate the atmospherically corrected FRP and per-pixel uncertainty metrics. Using SEVIRI scene simulations and real SEVIRI data, including from a period of Meteosat-8 "special operations", we describe certain sensor and data pre-processing characteristics that influence SEVIRI's active fire detection and FRP measurement capability, and use these to specify parameters in the FTA algorithm and to make recommendations for the forthcoming Meteosat Third Generation operations in relation to active fire measures. We show that the current SEVIRI FTA algorithm is able to discriminate actively burning fires covering down to 10−4 of a pixel and that it appears more sensitive to fire than other algorithms used to generate many widely exploited active fire products. Finally, we briefly illustrate the information contained within the current Meteosat FRP-PIXEL and FRP-GRID products, providing example analyses for both individual fires and multi-year regional-scale fire activity; the companion paper (Roberts et al., 2015) provides a full product performance evaluation and a demonstration of product use within components of the Copernicus Atmosphere Monitoring Service (CAMS).

New fire diurnal cycle characterizations to improve fire radiative energy assessments made from MODIS observations

Abstract. Accurate near real time fire emissions estimates are required for air quality forecasts. To date, most approaches are based on satellite-derived estimates of fire radiative power (FRP), which can be converted to fire radiative energy (FRE) which is directly related to fire emissions. Uncertainties in these FRE estimates are often substantial. This is for a large part because the most often used low-Earth orbit satellite-based instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) have a relatively poor sampling of the usually pronounced fire diurnal cycle. In this paper we explore the spatial variation of this fire diurnal cycle and its drivers using data from the geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). In addition, we sampled data from the SEVIRI instrument at MODIS detection opportunities to develop two approaches to estimate hourly FRE based on MODIS active fire detections. The first approach ignored the fire diurnal cycle, assuming persistent fire activity between two MODIS observations, while the second approach combined knowledge on the climatology of the fire diurnal cycle with active fire detections to estimate hourly FRE. The full SEVIRI time series, providing full coverage of the fire diurnal cycle, were used to evaluate the results. Our study period comprised of 3 years (2010–2012), and we focused on Africa and the Mediterranean basin to avoid the use of potentially lower quality SEVIRI data obtained at very far off-nadir view angles. We found that the fire diurnal cycle varies substantially over the study region, and depends on both fuel and weather conditions. For example, more "intense" fires characterized by a fire diurnal cycle with high peak fire activity, long duration over the day, and with nighttime fire activity are most common in areas of large fire size (i.e., large burned area per fire event). These areas are most prevalent in relatively arid regions. Ignoring the fire diurnal cycle generally resulted in an overestimation of FRE, while including information on the climatology of the fire diurnal cycle improved FRE estimates. The approach based on knowledge of the climatology of the fire diurnal cycle also improved distribution of FRE over the day, although only when aggregating model results to coarser spatial and/or temporal scale good correlation was found with the full SEVIRI hourly reference data set. We recommend the use of regionally varying fire diurnal cycle information within the Global Fire Assimilation System (GFAS) used in the Copernicus Atmosphere Monitoring Services, which will improve FRE estimates and may allow for further reconciliation of biomass burning emission estimates from different inventories.

Hyper fast radiative transfer for the physical retrieval of surface parameters from SEVIRI observations

This paper describes the theoretical aspects of a fast scheme for the physical retrieval of surface temperature and emissivity from SEVIRI data, their implementation and some sample results obtained. The scheme is based on a Kalman Filter approach, which effectively exploits the temporal continuity in the observations of the geostationary Meteosat Second Generation (MSG) platform, on which SEVIRI (Spinning Enhanced Visible and InfraRed Imager) operates. Such scheme embodies in its core a physical retrieval algorithm, which employs an hyper fast radiative transfer code highly customized for this retrieval task. Radiative transfer and its customizations are described in detail. Fastness, accuracy and stability of the code are fully documented for a variety of surface features, showing a peculiar application to the massive Greek forest fires in August 2007.

Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests

AbstractA new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3–8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data.