Spinning Enhanced Visible And Infrared Imager Research Articles

A Lagrangian perspective on the lifecycle and cloud radiative effect of deep convective clouds over Africa

Abstract. The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 W m−2. Despite this, due to the opposite sign of these fluxes, the net average of the anvil cloud radiative effect (CRE) over the tropics is observed to be neutral. Research into the response of the anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of the anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact the anvil CRE. To study the connection between the deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). Retrieved cloud properties and derived broadband radiative fluxes are provided by the Community Cloud retrieval for CLimate (CC4CL) algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of the SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs, while larger, organised systems tend to have a CRE closer to 0. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is close to neutral (−0.94 ± 0.91 W m−2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks.

Understanding the rapid increase in drought stress and its connections with climate desertification since the early 1990s over the Brazilian semi-arid region

The aridity-related drought studies have been carried out extensively in Brazilian semi-arid ecosystems, although there is no report on relating aridity with different drought indices over the recent decades. Due to drought impact on agriculture and natural ecosystems, it has attracted extensive attention in the academic community. In this study, the Standardized Precipitation Evapotranspiration Index (SPEI), the Soil Moisture and Ocean Salinity-derived Soil Water Deficit Index (SWDI), the Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) radiance-derived solar and infrared estimates, and the SEVIRI-derived Normalized Difference Vegetation Index (NDVI) datasets were employed to investigate the spatial and temporal characteristics of the drought episodes and the vegetation dynamic responses. An approach was implemented for identifying drought using a combination of mathematical and statistical features derived from the SPEI. The results showed that: (1) The frequency, duration, and intensity of drought severity identified by the SPEI and SWDI from 1990 to 2022 showed different characteristics in the Brazilian semi-arid region from 1990 to 2022. The frequency of moderate, severe, and extreme drought ranged from 5% to 92%, the duration was mostly concentrated in 5–6 dry months. (2) The whole Brazilian semi-arid region exhibited an overall drying tendency. (3) The NDVI-derived vegetation in the Brazilian semi-arid ecosystems exhibited an overall decreased trend during 2004–2022, indicating a degrading trend of the vegetation cover. The region where degrading trend of NDVI was negatively correlated with SPEI accounted for approximately 13% of the region. (4) The frequency, and average duration of the flash drought were 21% concentrated in 6 pentads, during the region growing season. (5) The spatial and temporal trends with the significance test of the rainfall, air temperature, and SEVIRI radiance-based estimations suited well with those of the SPEI data. These research results were significant for the region to respond to and prevent drought through human-induced land degradation.

Daytime variation in the aerosol indirect effect for warm marine boundary layer clouds in the eastern North Atlantic

Abstract. Warm boundary layer clouds in the eastern North Atlantic region exhibit significant diurnal variations in cloud properties. However, the diurnal cycle of the aerosol indirect effect (AIE) for these clouds remains poorly understood. This study takes advantage of recent advancements in the spatial resolution of geostationary satellites to explore the daytime variation in the AIE by estimating the cloud susceptibilities to changes in cloud droplet number concentration (Nd). Cloud retrievals for the month of July over 4 years (2018–2021) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat-11 over this region are analyzed. Our results reveal a significant “U-shaped” daytime cycle in susceptibilities of the cloud liquid water path (LWP), cloud albedo, and cloud fraction. Clouds are found to be more susceptible to Nd perturbations at noon and less susceptible in the morning and evening. The magnitude and sign of cloud susceptibilities depend heavily on the cloud state defined by cloud LWP and precipitation conditions. Non-precipitating thin clouds account for 44 % of all warm boundary layer clouds in July, and they contribute the most to the observed daytime variation. Non-precipitating thick clouds are the least frequent cloud state (10 %), and they exhibit more negative LWP and albedo susceptibilities compared to thin clouds. Precipitating clouds are the dominant cloud state (46 %), but their cloud susceptibilities show minimal variation throughout the day. We find evidence that the daytime variation in LWP and albedo susceptibilities for non-precipitating clouds is influenced by a combination of the diurnal transition between non-precipitating thick and thin clouds and the “lagged” cloud responses to Nd perturbations. The daytime variation in cloud fraction susceptibility for non-precipitating thick clouds can be attributed to the daytime variation in cloud morphology (e.g., overcast or broken). The dissipation and development of clouds do not adequately explain the observed variation in cloud susceptibilities. Additionally, daytime variation in cloud susceptibility is primarily driven by variation in the intensity of cloud response rather than the frequency of occurrence of cloud states. Our results imply that polar-orbiting satellites with an overpass time at 13:30 LT underestimate daytime mean values of cloud susceptibility, as they observe susceptibility daily minima in the study region.

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.

High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites

Abstract. Surface net radiation (SNR) is a vital input for many land surface and hydrological models. However, most of the current remote sensing datasets of SNR come mostly at coarse resolutions or have large gaps due to cloud cover that hinder their use as input in models. Here, we present a downscaled and continuous daily SNR product across Europe for 2018–2019. Long-wave outgoing radiation is computed from a merged land surface temperature (LST) product in combination with Meteosat Second Generation emissivity data. The merged LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel-3A satellite. This approach makes use of the medium spatial (approx. 5–7 km) but high temporal (30 min) resolution, gap-free data from MSG along with the low temporal (2–3 d) but high spatial (1 km) resolution of the Sentinel-3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias correction and Kalman filter assimilation. Short-wave outgoing radiation is computed from the incoming short-wave radiation from MSG and the downscaled albedo using 1 km PROBA-V data. MSG incoming short-wave and long-wave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the mean squared error by ca. 7 % with an increase in spatial detail compared to the original MSG product. The resulting pan-European SNR dataset, as well as the merged LST product, can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.8332222 (Rains, 2023a) and https://doi.org/10.5281/zenodo.8332128 (Rains, 2023b).

Comprehensive accuracy assessment of long-term geostationary SEVIRI-MSG evapotranspiration estimates across Europe

This study quantifies the accuracy of evapotranspiration (ET) estimates from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) geostationary sensor onboard the Meteosat Second Generation (MSG) satellites, along seven key dimensions, i.e., diurnal cycle, daily, intra-annual, inter-annual, ecosystem, climate zone, and products intercomparison. In situ measurements were collected at 54 eddy covariance (EC) sites to evaluate the accuracy of SEVIRI actual ET products (diurnal and daily SEVIRI-ETa) as well as reference ET (daily SEVIRI-ET0) covering the period from 2004 to 2018 across Europe. SEVIRI-ETa is produced by the Tiled ECMWF Surface Scheme of Exchange processes at the Land surface (TESSEL) model, while SEVIRI-ET0 is estimated by a combination of a thermodynamically-based and an atmospheric boundary layer model. This evaluation is further separated according to the land cover heterogeneity of the SEVIRI pixels across all 54 EC sites, using MODIS land cover data. The Root Mean Squared Error (RMSE), along with the Kling-Gupta efficiency (KGE) and their respective decompositions, were employed to quantify the errors.For diurnal SEVIRI-ETa estimates, we found that the KGE (RMSE [mm hour−1]) varied between −1.6 (0.04) to 0.8 (0.14), with a median value of 0.26 (0.07). Higher accuracy for diurnal SEVIRI-ETa was obtained in the summer and during the mid-day time. For daily SEVIRI-ETa, the KGE (RMSE [mm day−1]) varied between −0.88 (0.43) to 0.93 (1.79), with a median value of 0.6 (0.77) and for daily SEVIRI-ET0 the KGE (RMSE [mm day−1]) varied between 0.51 (0.40) to 0.94 (1.50), with a median value of 0.77 (0.57). For daily SEVIRI-ETa, intra-annual accuracy was low from January to March, increased in the mid-year, and then began to decline from November to December. Although accuracy remained relatively stable during the middle of the year, it varied considerably in the winter period. In the inter-annual dimension, the mid-year positive KGE values and distributions changed over time from 2004 to 2018. In spatial dimensions, the highest accuracy was in peat and grassland ecosystems, and the lowest in cropland ecosystem, with similar patterns observed in the boreal snow fully humid warm summer and warm temperate fully humid hot summer climate zones. Regarding SEVIRI-ET0 results, similar to SEVIRI-ETa, intra-annual accuracy was low in the first quarter of the year and the last one but high in the mid-year. In the inter-annual dimension, unlike SEVIRI-ETa, almost an identical pattern was observed for the mid-year positive KGE values, demonstrating only a slight change in SEVIRI-ET0 accuracy during 2004–2018. However, the highest accuracy was found in crop ecosystem, while the lowest was in forest ecosystem, reflecting similar trends in the warm temperate fully humid hot summer and warm temperate summer dry hot summer climate zones. The observed range of median RMSE changed between 0.4 and 1.5 mm day−1, also suggests a reasonable accuracy for SEVIRI-ET estimates in all spatial domains.Our results showed that the main trends in the accuracies (median KGEs) of SEVIRI-ET (both ETa and ET0) remained similar in separated homogeneous and heterogeneous sites and were comparable to combined sites among the dimensions. Through error decomposition, we discerned that SEVIRI-ET estimates performed particularly well in explaining inter-annual and spatial variabilities. Furthermore, the intercomparison of ET products revealed that SEVIRI satellite-derived ETa exhibited the strongest correlation with in situ ET measurements across all ecosystem types and climate zones, outperforming other products (such as MODIS, PML, GLEAM, and BESS). The ET estimates from other products exhibited lower standard deviation errors and were in closer agreement with the in situ measurements. This study provides the first comprehensive evaluation of the accuracy of SEVIRI diurnal and daily ET products across Europe, which may serve as a stimulus for further optimized selection of these products by potential users for various applications.

CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations

Abstract. CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).

Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals

Abstract. The formation of ice in clouds is an important process in mixed-phase clouds, and the radiative properties and dynamical developments of clouds strongly depend on their partitioning between the liquid and ice phases. In this study, we investigated the sensitivities of the cloud phase to the ice-nucleating particle (INP) concentration and thermodynamics. Moreover, passive satellite retrieval algorithms and cloud products were evaluated to identify whether they could detect cloud microphysical and thermodynamical perturbations. Experiments were conducted using the ICOsahedral Nonhydrostatic (ICON) model at the convection-permitting resolution of about 1.2 km on a domain covering significant parts of central Europe, and they were compared to two different retrieval products based on Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements. We selected a day with multiple isolated deep convective clouds, reaching a homogeneous freezing temperature at the cloud top. The simulated cloud liquid pixel fractions were found to decrease with increasing INP concentration, both within clouds and at the cloud top. The decrease in the cloud liquid pixel fraction was not monotonic and was stronger in high-INP cases. Cloud-top glaciation temperatures shifted toward warmer temperatures with an increasing INP concentration by as much as 8 ∘C. Moreover, the impact of the INP concentration on cloud-phase partitioning was more pronounced at the cloud top than within the cloud. Furthermore, initial and lateral boundary temperature fields were perturbed with increasing and decreasing temperature increments from 0 to ±3 and ±5 K between 3 and 12 km, respectively. Perturbing the initial thermodynamic state was also found to systematically affect the cloud-phase distribution. However, the simulated cloud-top liquid pixel fraction, diagnosed using radiative transfer simulations as input to a satellite forward operator and two different satellite remote-sensing retrieval algorithms, deviated from one of the satellite products regardless of perturbations in the INP concentration or the initial thermodynamic state for warmer subzero temperatures while agreeing with the other retrieval scheme much better, in particular for the high-INP and high-CAPE (convective available potential energy) scenarios. Perturbing the initial thermodynamic state, which artificially increases the instability of the mid- and upper-troposphere, brought the simulated cloud-top liquid pixel fraction closer to the satellite observations, especially in the warmer mixed-phase temperature range.

Flash Drought and Its Characteristics in Northeastern South America during 2004–2022 Using Satellite-Based Products

The term flash drought describes a special category of drought with rapid onset and strong intensity over the course of days or weeks. To characterize the impact of flash droughts on vegetation coverage, this study assessed the influence of soil water deficits on vegetation dynamics in the northeastern South America region by combining time series of vegetation index, rainfall, and soil moisture based on satellite products at a daily time scale. An 18-year analysis, from 2004 to 2022, of the Normalized Difference Vegetation Index (NDVI), Standard Precipitation Index (SPI), and surface soil moisture (SSM) was performed based on three different satellite remote sensing estimates: the spinning enhanced visible and infrared imager (SEVIRI) and the integrated multi-satellite retrievals algorithm (IMERG), and the soil moisture and ocean salinity (SMOS). The results revealed that flash drought events exerted dramatic impacts on terrestrial ecosystems in the study region during the first two decades of the 2000s, with changes in seasonal and regional vegetation dynamics. Further, the fixed-threshold values to characterize flash drought events were suggested as the timing when the water deficit was less than −1.0 units and vegetation index reached the value equal to +0.3 during five consecutive weeks or more, coupled with soil moisture rates below 40% percentile, leading to a strong region-wide drought throughout the entire region. Additionally, the results of linear least squares trend analyses revealed a negative trend in the pentad-SEVIRI radiance for the solar channel 1 within the semiarid ecosystems of the study region (i.e., the Caatinga biome) that was suggested as a reduction in clouds in the 18 years of the study. Developing combined threshold measures of flash drought based on satellite remote sensing may lead to an accurate assessment of flash drought mitigation.

The Portuguese Large Wildfire Spread database (PT-FireSprd)

Abstract. Wildfire behaviour depends on complex interactions between fuels, topography, and weather over a wide range of scales, being important for fire research and management applications. To allow for significant progress towards better fire management, the operational and research communities require detailed open data on observed wildfire behaviour. Here, we present the Portuguese Large Wildfire Spread database (PT-FireSprd) that includes the reconstruction of the spread of 80 large wildfires that occurred in Portugal between 2015 and 2021. It includes a detailed set of fire behaviour descriptors, such as rate of spread (ROS), fire growth rate (FGR), and fire radiative energy (FRE). The wildfires were reconstructed by converging evidence from complementary data sources, such as satellite imagery and products, airborne and ground data collected by fire personnel, and official fire data and information in external reports. We then implemented a digraph-based algorithm to estimate the fire behaviour descriptors and combined it with the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) fire radiative power estimates. A total of 1197 ROS and FGR estimates were calculated along with 609 FRE estimates. The extreme fires of 2017 were responsible for the maximum observed values of ROS (8900 m h−1) and FGR (4400 ha h−1). Combining both descriptors, we describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. Analysis of the database showed that burned extent is mostly determined by FGR rather than by ROS. Finally, we explored a practical example to show how the PT-FireSprd database can be used to study the dynamics of individual wildfires and to build robust case studies for training and capacity building. The PT-FireSprd is the first open-access fire progression and behaviour database in Mediterranean Europe, dramatically expanding the extant information. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel. PT-FireSprd data are publicly available through https://doi.org/10.5281/zenodo.7495506 (Benali et al., 2022) and have large potential to improve current knowledge on wildfire behaviour and to support better decision making.

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.

Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic

Abstract. Contrail cirrus constitute the largest radiative forcing (RF) component to the total aviation effect on climate. However, the microphysical properties and radiative effects of contrail cirrus and natural cirrus clouds in the same meteorological conditions are still not completely resolved. Motivated by these uncertainties, we investigate an extended cirrus region perturbed by aviation in the North Atlantic region (NAR) on 26 March 2014 during the Midlatitude Cirrus (ML-CIRRUS) experiment. On that day, high air traffic density in the NAR combined with large scale cold and humid ambient conditions favored the formation of a contrail cirrus outbreak situation. In addition, low coverage by low-level water clouds and the homogeneous oceanic albedo increased the sensitivity for retrieving cirrus properties and their radiative effect from satellite remote sensing. This allowed us to extend the current knowledge on contrail cirrus by combining airborne in situ, lidar and satellite observations. In the synoptic context of a ridge cirrus, an extended thin ice cloud with many persistent contrails and contrail cirrus has been observed for many hours with the geostationary Meteosat Second Generation (MSG)/Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from the early morning hours until dissipation after noon. Airborne lidar observations aboard the German High Altitude and LOng Range Research Aircraft (HALO) suggest that this cirrus has a significant anthropogenic contribution from aviation. A new method based on in situ measurements was used to distinguish between contrails, contrail cirrus and natural cirrus based on ice number and gas phase NO concentrations. Results show that contrail effective radii (Reff) reach at most 11 µm, while contrail cirrus Reff can be as large as 51 µm. Contrail and contrail cirrus mean Reff is 18 % smaller than that of natural cirrus. We find that a difference in Reff between contrail cirrus and natural cirrus survives in this contrail cirrus outbreak event. As for radiative effects, a new method to estimate top-of-atmosphere instantaneous RF in the solar and thermal range is developed based on radiative transfer model simulations exploiting in situ and lidar measurements, satellite observations and ERA5 reanalysis data for both cirrus and cirrus-free regions. Broadband irradiances estimated from our simulations compare well with satellite observations from MSG, indicating that our method provides a good representation of the real atmosphere and can thus be used to determine the RF of ice clouds. For a larger spatial area around the flight path, we find that the contrail cirrus outbreak is warming in the early morning and cooling during the day. The methods presented here and the results will be valuable for future research to constrain uncertainties in the assessment of radiative impacts of contrail cirrus and natural cirrus and for the formulation and evaluation of contrail mitigation options.

Fire Characterization by Using an Original RST-Based Approach for Fire Radiative Power (FRP) Computation

Fire radiative power (FRP) is a basic parameter for fire characterization since it represents the heat emission rate of fires. Moreover, its temporal integration (fire radiative energy, FRE) is used as a proxy for estimating biomass burning and emissions. From satellite, FRP is generally computed by comparing the Medium InfraRed (MIR) signal of the fire pixel with the background value on the event image. Such an approach is possibly affected by some issues due to fire extent, clouds and smoke over the event area. The enlargement of the background window is the commonly used gimmick to face these issues. However, it may include unrepresentative signals of the fire pixel because of very different land use/cover. In this paper, the alternative Background Radiance Estimator by a Multi-temporal Approach (BREMA), based on the Robust Satellite Technique (RST), is proposed to characterize background and compute FRP. The approach is presented using data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) platform. Moreover, BREMA is here combined with the RST-FIRES (RST for FIRES detection) technique for fire pixel identification and the σ-SEVIRI retrieval algorithm for transmittance evaluation. Results compared to the operational SEVIRI-based FRP-PIXEL product, although highly correlated in terms of background radiance (r2 = 0.95) and FRP values (r2 = 0.96), demonstrated a major capability of BREMA to estimate background radiances regardless of cloudiness or smoke presence during the event and independently on fire extent. The possible impact of the proposed approach on the estimates of CO2 emissions was also evaluated for comparison with the Global Fire Emissions Database (GFED4s).

Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms

There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval.

Application of Dempster-Shafer theory for optimization of precipitation classification and estimation results from remote sensing data using machine learning

In this work, a technique based on Dempster-Shafer Theory (DST) is proposed to combine three classifiers, namely Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) in order to improve the precipitation estimation over the North of Algeria. Data from SEVIRI (Spinning Enhanced Visible and Infrared Imager) radiometer on board MSG (Meteosat Second Generation) is used. First, the three classifiers were applied for the classification of instantaneous precipitation scenes into three classes (stratiform class, convective class or no-rain class). Secondly, to apply the DST and to calculate the mass functions, normalized probabilities from the classifications are determined. The mass functions of the different classifiers were combined using DST. Maximum plausibility or maximum belief are calculated and used for the final assignment decision.The DST-ANN/SVM/RF method shows interesting performances compared to the separate use of the three classifiers. The proposed method has also been compared to some techniques combining two, three and six classifiers based on machine learning. The results show that Dempster-Shafer Theory performs better than other combination techniques. The estimation results are well correlated with the reference radar measurements, with a correlation coefficient of 0.96 for the developped technique against 0.94 for the technique combining six classifiers, 0.90 for the technique combining three classifiers, 0 0.87 for the technique combining two classifiers and 0.85 for the technique with a single classifier.

Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semi-automated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Ndsat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece from March to May 2020, shows that droplet closure can be achieved to within 30%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km × 4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies.

Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units

Nowcasting of clouds is a challenging spatiotemporal task due to the dynamic nature of the atmosphere. In this study, the use of convolutional gated recurrent unit networks (ConvGRUs) to produce short-term cloudiness forecasts for the next 3 h over Europe is proposed, along with an optimisation criterion able to preserve image structure across the predicted sequences. This approach is compared against state-of-the-art optical flow algorithms using over two and a half years of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation satellite. We show that the ConvGRU trained using our structure-preserving loss function significantly outperforms the optical flow algorithms with an average change in R2, mean absolute error and structural similarity of 12.43%, −8.75% and 9.68%, respectively, across all time steps. We also confirm that merging multiple optical flow algorithms into an ensemble yields significant short-term performance increases (<1 h), and that nowcast skill can vary significantly across different European regions. Furthermore, our results show that blurry images resulting from using globally oriented loss functions can be avoided by optimising for structural similarity when producing nowcasts. We thus showcase that deep-learning-based models using locally oriented loss functions present a powerful new way to produce accurate cloud nowcasts, with important applications to be found in solar power forecasting.

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).

CNES-ESA satellite contribution to the operational monitoring of volcanic activity: The 2021 Icelandic eruption of Mt. Fagradalsfjall

Within the framework of the CIEST2 (Cellule d'Intervention d'Expertise Scientifique et Technique new generation) and thanks to the support of CNES, the French space agency, the first phase of the Fagradalsfjall eruption was exceptionally well covered by high resolution optical satellite data, through daily acquisitions of Pléiades images in stereo mode. In this study, we show how Pléiades data provided real-time information useful for the operational monitoring of the ongoing eruption. An estimation of the volume of lava emitted as well as the corresponding effusion rate could be derived and delivered to the civil protection less than 6 h after the data acquisition. This information is complementary to and consistent with estimates obtained through the HOTVOLC service using SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor on-board Meteosat Second Generation (MGS) geostationary satellites, operated by the European Space Agency (ESA), characterized by a lower spatial resolution and a higher temporal one. In addition to the information provided on the lava emission, Pléiades data also helped characterize the intensity of the eruption by providing insight into the elevation and the velocity of the volcanic plume. The survey of this effusive eruption, well anticipated by a series of precursors, is a proof of concept of the efficiency of optical/thermal satellite data for volcanic crisis real-time monitoring.

Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG

The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe.