Spinning Enhanced Visible And Infrared Imager Channel Research Articles

Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: evaluation of candidate approaches with MODIS observations

Abstract. This study presents and evaluates several candidate approaches for downscaling observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) in order to increase the horizontal resolution of subsequent cloud optical thickness (τ) and effective droplet radius (reff) retrievals from the native ≈3km×3km spatial resolution of the narrowband channels to ≈1km×1km. These methods make use of SEVIRI's coincident broadband high-resolution visible (HRV) channel. For four example cloud fields, the reliability of each downscaling algorithm is evaluated by means of collocated 1 km×1 km MODIS radiances, which are reprojected to the horizontal grid of the HRV channel and serve as reference for the evaluation. By using these radiances, smoothed with the modulation transfer function of the native SEVIRI channels, as retrieval input, the accuracy at the SEVIRI standard resolution can be evaluated and an objective comparison of the accuracy of the different downscaling algorithms can be made. For the example scenes considered in this study, it is shown that neglecting high-frequency variations below the SEVIRI standard resolution results in significant random absolute deviations of the retrieved τ and reff of up to ≈14 and ≈6 µm, respectively, as well as biases. By error propagation, this also negatively impacts the reliability of the subsequent calculation of liquid water path (WL) and cloud droplet number concentration (ND), which exhibit deviations of up to ≈89gm-2 and ≈177cm-3, respectively. For τ, these deviations can be almost completely mitigated by the use of the HRV channel as a physical constraint and by applying most of the presented downscaling schemes. Uncertainties in retrieved reff at the native SEVIRI resolution are smaller, and the improvements from downscaling the observations are less obvious than for τ. Nonetheless, the right choice of downscaling scheme yields noticeable improvements in the retrieved reff. Furthermore, the improved reliability in retrieved cloud products results in significantly reduced uncertainties in derived WL and ND. In particular, one downscaling approach provides clear improvements for all cloud products compared to those obtained from SEVIRI's standard resolution and is recommended for future downscaling endeavors. This work advances efforts to mitigate impacts of scale mismatches among channels of multiresolution instruments on cloud retrievals.

The sensitivity of the colour of dust in MSG-SEVIRI Desert Dust infrared composite imagery to surface and atmospheric conditions

Abstract. Infrared “Desert Dust” composite imagery taken by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), onboard the Meteosat Second Generation (MSG) series of satellites above the equatorial East Atlantic, has been widely used for more than a decade to identify and track the presence of dust storms from and over the Sahara Desert, the Middle East, and southern Africa. Dust is characterised by distinctive pink colours in the Desert Dust false-colour imagery; however, the precise colour is influenced by numerous environmental properties, such as the surface thermal emissivity and skin temperature, the atmospheric water vapour content, the quantity and height of dust in the atmosphere, and the infrared optical properties of the dust itself. For this paper, simulations of SEVIRI infrared measurements and imagery have been performed using a modelling system, which combines dust concentrations simulated by the aerosol transport model COSMO-MUSCAT (COSMO: COnsortium for Small-scale MOdelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) with radiative transfer simulations from the RTTOV (Radiative Transfer for TOVS) model. Investigating the sensitivity of the synthetic infrared imagery to the environmental properties over a 6-month summertime period from 2011 to 2013, it is confirmed that water vapour is a major control on the apparent colour of dust, obscuring its presence when the moisture content is high. Of the three SEVIRI channels used in the imagery (8.7, 10.8, and 12.0 µm), the channel at 10.8 µm has the highest atmospheric transmittance and is therefore the most sensitive to the surface skin temperature. A direct consequence of this sensitivity is that the background desert surface exhibits a strong diurnal cycle in colour, with light blue colours possible during the day and purple hues prevalent at night. In dusty scenes, the clearest pink colours arise from high-altitude dust in dry atmospheres. Elevated dust influences the dust colour primarily by reducing the contrast in atmospheric transmittance above the dust layer between the SEVIRI channels at 10.8 and 12.0 µm, thereby boosting red and pink colours in the imagery. Hence, the higher the dust altitude, the higher the threshold column moisture needed for dust to be obscured in the imagery: for a sample of dust simulated to have an aerosol optical depth (AOD) at 550 nm of 2–3 at an altitude of 3–4 km, the characteristic colour of the dust may only be impaired when the total column water vapour is particularly moist (⪆39 mm). Meanwhile, dust close to the surface (altitude <1 km) is only likely to be apparent when the atmosphere is particularly dry and when the surface is particularly hot, requiring column moisture ⪅13 mm and skin temperatures ⪆314 K, and is highly unlikely to be apparent when the skin temperature is ⪅300 K. Such low-altitude dust will regularly be almost invisible within the imagery, since it will usually be beneath much of the atmospheric water vapour column. It is clear that the interpretation of satellite-derived dust imagery is greatly aided by knowledge of the background environment.

Kalman filter physical retrieval of surface emissivity and temperature from SEVIRI infrared channels: a validation and intercomparison study

Abstract. A Kalman filter-based approach for the physical retrieval of surface temperature and emissivity from SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared observations has been developed and validated against in situ and satellite observations. Validation for land has been provided based on in situ observations from the two permanent stations at Evora and Gobabeb operated by Karlsruhe Institute of Technology (KIT) within the framework of EUMETSAT's Satellite Application Facility on Land Surface Analysis (LSA SAF). Sea surface retrievals have been intercompared on a broad spatial scale with equivalent satellite products (MODIS, Moderate Resolution Imaging Spectroradiometer, and AVHRR, Advanced Very High Resolution Radiometer) and ECMWF (European Centre for Medium-Range Weather Forecasts) analyses. For surface temperature, the Kalman filter yields a root mean square accuracy of ≈ ±1.5 °C for the two land sites considered and ≈ ±1.0 °C for the sea. Comparisons with polar satellite instruments over the sea surface show nearly zero temperature bias. Over the land surface the retrieved emissivity follows the seasonal vegetation cycle and permits identification of desert sand regions using the SEVIRI channel at 8.7 μm due to the strong quartz reststrahlen bands around 8–9 μm. Considering the two validation stations, we have found that emissivity retrieved in SEVIRI channel 10.8 μm over the gravel plains of the Namibian desert is in excellent agreement with in situ observations. Over Evora, the seasonal variation of emissivity with vegetation is successfully retrieved and yields emissivity values for green and dry vegetation that are in good agreement with spectral library data. The algorithm has been applied to the SEVIRI full disk, and emissivity maps on that global scale have been physically retrieved for the first time.

An Improved Algorithm for Retrieving Land Surface Emissivity and Temperature From MSG-2/SEVIRI Data

This paper presents an improved algorithm for simultaneously retrieving both land surface emissivity (LSE) and land surface temperature (LST) using data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the MSG-2 satellite. First, the temperature-independent spectral index-based method for LSE retrieval is reviewed and improved in terms of three aspects: atmospheric correction, fitting of the bidirectional reflectivity model, and retrieval of the LSE in SEVIRI channel 10. Then, the generalized split-window method with seven unknown coefficients is used to derive the LST. Finally, this improved algorithm is applied to several MSG-2/SEVIRI data sets over a study area with geospatial coverage of latitude 30 ° N-45 ° N and longitude 15 ° W-15 ° E, and using detailed cases, the modifications to the original LSE/LST retrieval methods are shown to be effective and reasonable. In addition, the SEVIRI-derived LSTs are cross-validated primarily using the Moderate Resolution Imaging Spectroradiometer-derived validated LST data extracted from the MOD11B1 product on two clear-sky days (August 22, 2009 and July 3, 2008). The validation results indicate that more than 70% of the differences are within 2.5 K and that the LST differences tend to be lower at night than in the day, which may result from the homogeneous thermal conditions at night.

Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI

The present study aims to investigate the potential of the random forests ensemble classification and regression technique to improve rainfall rate assignment during day, night and twilight (resulting in 24-hour precipitation estimates) based on cloud physical properties retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data.Random forests (RF) models contain a combination of characteristics that make them well suited for its application in precipitation remote sensing. One of the key advantages is the ability to capture non-linear association of patterns between predictors and response which becomes important when dealing with complex non-linear events like precipitation. Due to the deficiencies of existing optical rainfall retrievals, the focus of this study is on assigning rainfall rates to precipitating cloud areas in connection with extra-tropical cyclones in mid-latitudes including both convective and advective-stratiform precipitating cloud areas. Hence, the rainfall rates are assigned to rain areas previously identified and classified according to the precipitation formation processes. As predictor variables water vapor-IR differences and IR cloud top temperature are used to incorporate information on cloud top height. ΔT8.7–10.8 and ΔT10.8–12.1 are considered to supply information about the cloud phase. Furthermore, spectral SEVIRI channels (VIS0.6, VIS0.8, NIR1.6) and cloud properties (cloud effective radius, cloud optical thickness) are used to include information about the cloud water path during daytime, while suitable combinations of temperature differences (ΔT3.9–10.8, ΔT3.9–7.3) are considered during night-time.The development of the rainfall rate retrieval technique is realised in three steps. First, an extensive tuning study is carried out to customise each of the RF models. The daytime, night-time and twilight precipitation events have to be treated separately due to differing information content about the cloud properties between the different times of day. Secondly, the RF models are trained using the optimum values for the number of trees and number of randomly chosen predictor variables found in the tuning study. Finally, the final RF models are used to predict rainfall rates using an independent validation data set and the results are validated against co-located rainfall rates observed by a ground radar network. To train and validate the model, the radar-based RADOLAN RW product from the German Weather Service (DWD) is used which provides area-wide gauge-adjusted hourly precipitation information.Regarding the overall performance, as indicated by the coefficient of determination (Rsq), hourly rainfall rates show already a good correlation with Rsq=0.5 (day and night) and Rsq=0.48 (twilight) between the satellite and radar based observations. Higher temporal aggregation leads to better agreement. Rsq rises to 0.78 (day), 0.77 (night) and 0.75 (twilight) for 8-h interval. By comparing day, night and twilight performance it becomes evident that daytime precipitation is generally predicted best by the model. Twilight and night-time predictions are generally less accurate but only by a small margin. This may due to the smaller number of predictor variables during twilight and night-time conditions as well as less favourable radiative transfer conditions to obtain the cloud parameters during these periods.However, the results show that with the newly developed method it is possible to assign rainfall rates with good accuracy even on an hourly basis. Furthermore, the rainfall rates can be assigned during day, night and twilight conditions which enables the estimation of rainfall rates 24hday.

The application of Rapid Scan data to the Convective Rainfall Rate algorithm from SAF NWC for the area of the Czech Republic

The goal of this paper is to calculate new calibration matrices using Rapid Scan (RSS) Meteosat Second Generation (MSG) measurements and to evaluate their impact on precipitation estimates for a territory of the Czech Republic. The calibration matrices are the most important part of the Convective Rainfall Rate algorithm, which uses the IR 10.8μm, WV 6.2μm and VIS 0.6μm spectral SEVIRI (Spinning Enhanced Visible and Infrared Imager) channels of the MSG to assess satellite precipitation estimates (SPEs). The calibration matrices were calculated using Czech radar data from 21 summer days during which severe convection and heavy precipitation were observed. The resultant matrices were compared with those obtained using conventional 15min MSG scans. The comparison showed significant differences in the calibration matrices, which resulted in differences in the estimated precipitation. The application of RSS data significantly increased the rain rates and improved the structure of the matrices; however, the matrices were subjectively modified to increase the accuracy of the resulting SPEs. The calibration matrices were also calibrated by shifting the radar data forward 5, 10, 15 and 20min with respect to the MSG measurement, because some delay between the information obtained by the MSG and the radars was expected. The impact of the matrices was evaluated by verifying the SPEs with the radar-derived precipitation estimates merged with the rain gauge observations as the ground truth. The results showed that the calibration matrices that were based on the RSS data improved the categorical skill scores and reduced the mean error (ME), the mean absolute error (MAE), and root mean square error (RMSE) of SPEs. Subjectively, the estimated precipitation values are more realistic, the estimated maximum is closer to observations and convective cores are more pronounced and easily identifiable. The difference between the matrices calibrated by shifting the radar data in time was quite small, and the impact on the SPEs was not readily apparent. However, the time shifts of 15 and 20min slightly improved the categorical skill scores and reduced the ME, MAE and RMSE.

An Advanced Model for the Estimation of the Surface Solar Irradiance Under All Atmospheric Conditions Using MSG/SEVIRI Data

A new advanced model for estimation of surface solar irradiance from satellite (AMESIS), designed to estimate with better accuracy the incident solar radiation at the surface from the spinning enhanced visible and infrared imager (SEVIRI) satellite measurements, has been developed. The new generations of sensors such as SEVIRI payload on board the geostationary meteosat second generation gives an opportunity to improve the solar irradiance estimation at surface with accuracy as well as the high spatial and time resolution for a large geographical area according to the needs of solar energy applications. The model developed takes into account the effect of aerosol, the overcast and partially cloudy coverage, and provides irradiance solar maps every 15 min both for monitoring purposes and for monthly, annual averages of surface solar irradiance. Cloud and aerosol microphysical parameters are retrieved by using VIS and IR SEVIRI channels, while surface solar irradiance is retrieved through the high-resolution broadband visible channel. Comparisons with the Global Atmosphere Watch station ground-based measurements of incoming solar radiation agree with the values retrieved with AMESIS model. The results show a very good correlation of about 0.99, a root mean square and a bias ranging, respectively, between 1 and 2.7 J/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and -0.6 and 0.4 J/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> depending on the station.

A critical evaluation of the ability of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) thermal infrared red‐green‐blue rendering to identify dust events: Theoretical analysis

Using a combination of idealized radiative transfer simulations and a case study from the first field campaign of the Saharan Mineral Dust Experiment (SAMUM) in southern Morocco, this paper provides a systematic assessment of the limitations of the widely used Spinning Enhanced Visible and Infrared Imager (SEVIRI) red‐green‐blue (RGB) thermal infrared dust product. Both analyses indicate that the ability of the product to identify dust, via its characteristic pink coloring, is strongly dependent on the column water vapor, the lower tropospheric lapse rate, and dust altitude. In particular, when column water vapor exceeds ∼20–25 mm, dust presence, even for visible optical depths of the order 0.8, is effectively masked. Variability in dust optical properties also has a marked impact on the imagery, primarily as a result of variability in dust composition. There is a moderate sensitivity to the satellite viewing geometry, particularly in moist conditions. The underlying surface can act to confound the signal seen through variations in spectral emissivity, which are predominantly manifested in the 8.7μm SEVIRI channel. In addition, if a temperature inversion is present, typical of early morning conditions over the Sahara and Sahel, an increased dust loading can actually reduce the pink coloring of the RGB image compared to pristine conditions. Attempts to match specific SEVIRI observations to simulations using SAMUM measurements are challenging because of high uncertainties in surface skin temperature and emissivity. Recommendations concerning the use and interpretation of the SEVIRI RGB imagery are provided on the basis of these findings.

Numerical simulation of the 7 to 9 September 2006 AMMA mesoscale convective system: Evaluation of the dynamics and cloud microphysics using synthetic observations

AbstractThis paper presents a numerical simulation of a Mesoscale Convective System (MCS) observed during the AMMA (African Monsoon Multidisciplinary Analysis) experiment with the BRAMS model (Brazilian Regional Atmospheric Modelling System). The aim is to document the life cycle of the MCS and to identify key cloud microphysical processes and their signatures by making use of synthetic observations calculated from the simulated fields. These observations: ARM (Atmospheric Radiation Measurement) 95 GHz equivalent radar reflectivity factor and Doppler velocity and infrared brightness temperatures in three SEVIRI (Spinning Enhanced Visible and InfraRed Imager) channels centred at 8.7, 10.6 and 12 µm are simulated using respectively Mie scattering theory and FASDOM (Fast Discrete Ordinate Method), a fast radiative transfer code. Synthetic observations and model variables are compared to various measurements from several platforms (W‐band and Massachusetts Institute of Technology (MIT) ground‐based Doppler radars, soundings, aircraft measurements, and Meteosat Second Generation) to evaluate the model at different scales and to identify the signatures of microphysical properties with a focus on the anvil part of the MCS. A method using both the ARM and the MIT radar data is used to identify the different regimes within the MCS. A relatively good agreement with direct comparisons is found, as well as discrepancies in the microphysical scheme parametrization that clearly need improvements (using in situ measurements). Microphysical signatures are also studied using joint radar reflectivity/Doppler‐height histograms. Their analysis shows that the model tends to overplay the role of the riming processes, even in the anvil part of the MCS. Comparisons of the Particle Size Distributions (simulated and measured in situ) show the model's ability to reproduce complex PSDs (e.g. a multimodal behaviour). Copyright © 2010 Royal Meteorological Society

Dust Aerosol Optical Depth Retrieval over a Desert Surface Using the SEVIRI Window Channels

Abstract The authors present a new algorithm to retrieve aerosol optical depth (AOD) over a desert using the window channels centered at 8.7, 10.8, and 12.0 μm of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the Meteosat Second Generation satellite. The presence of dust aerosols impacts the longwave outgoing radiation, allowing the aerosols over the desert surfaces to be detected in the thermal infrared (IR) wavelengths. To retrieve the aerosol properties over land, the surface contribution to the satellite radiance measured at the top of the atmosphere has to be taken into account. The surface radiation depends on the surface temperature, which is characterized by a strong diurnal variation over the desert, and the surface emissivity, which is assumed to be constant over a time span of 24 h. The surface emissivity is based on clear-sky observations that are corrected for atmospheric absorption and emission. The clear-sky image is a composite of pixels that is characterized by the highest brightness temperature (BT) of the SEVIRI channel at 10.8 μm, and by a negative BT difference between the channels at 8.7 and 10.8 μm. Because of the lower temperatures of clouds and aerosols compared to clear-sky conditions, the authors assume that the selected pixel values are obtained for a clear-sky day. A forward model is used to simulate the thermal IR radiation transfer in the dust layer. The apparent surface radiation for the three window channels in the presence of aerosols is calculated as a function of the surface emissivity and the surface temperature, the aerosol layer temperature, and the AOD for different aerosol loadings. From these simulations two emissivity ratios, which are stored in lookup tables (LUT), are calculated. The retrieval algorithm consists of processing the clear-sky image and computing the surface emissivity, processing the instantaneous image, and computing the apparent surface radiation for the three window channels. The two emissivity ratios are computed using the radiances at 8.7 and 10.8 μm and at 8.7 and 12.0 μm, respectively. The SEVIRI AOD is obtained by the inversion of these emissivity ratios using the corresponding LUT. The algorithm is applied to a minor dust event over the Sahara between 19 and 22 June 2007. For the validation the SEVIRI AOD is compared with the AOD from the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) along the satellite track.

Intercomparison of Two BRDF Models in the Estimation of the Directional Emissivity in MIR Channel From MSG1-SEVIRI Data

This work intercompared two Bi-directional Reflectance Distribution Function (BRDF) models, the modified Minnaert's model and the RossThick-LiSparse-R model, in the estimation of the directional emissivity in Middle Infra-Red (MIR) channel from the data acquired by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard the first Meteosat Second Generation (MSG1). The bi-directional reflectances in SEVIRI channel 4 (3.9 microm) were estimated from the combined MIR and Thermal Infra-Red (TIR) data and then were used to estimate the directional emissivity in this channel with aid of the BRDF models. The results show that: (1) Both models can relatively well describe the non-Lambertian reflective behavior of land surfaces in SEVIRI channel 4; (2) The RossThick-LiSparse-R model is better than the modified Minnaert's model in modeling the bi-directional reflectances, and the directional emissivities modeled by the modified Minnaert's model are always lower than the ones obtained by the RossThick-LiSparse-R model with averaged emissivity differences of approximately 0.01 and approximately 0.04 over the vegetated and bare areas, respectively. The use of the RossThick-LiSparse-R model in the estimation of the directional emissivity in MIR channel is recommended.

Improving GERB scene identification using SEVIRI: Infrared dust detection strategy

The combination of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Geostationary Earth Radiation Budget (GERB) instruments on Meteosat-8 provides a powerful new tool for detecting aerosols and estimating their radiative effect at high temporal and spatial resolution. However, at present no specific aerosol treatment is performed in the GERB processing chain, severely limiting the use of the data for aerosol studies. A particular problem relates to the misidentification of Saharan dust outbreaks as cloud which can bias the shortwave and longwave fluxes. In this paper an algorithm is developed which employs multiple-linear regression, using information from selected thermal infrared SEVIRI channels, to detect dust aerosol over ocean and provide an estimate of the optical depth at 0.55 μm ( τ 055). To test the performance of the algorithm, it has been applied to a number of dust events observed by SEVIRI during March and June 2004. The results are compared to co-located MODIS observations taken from the Terra and Aqua platforms, and ground based observations from the Cape Verde AERONET site. In terms of detection capability, employing the algorithm results in a notable improvement in the routine GERB scene identification. Locations identified by MODIS as being likely to be dust contaminated were originally classified as cloud in over 99.5% of the cases studied. With the application of the detection algorithm approximately 60–70% of these points are identified as dusty depending on the dust model employed. The algorithm is also capable of detecting dust in regions and at times which would be excluded when using shortwave observations, due for example to the presence of sun-glint, or through the night. We further investigate whether the algorithm is capable of generating useful information concerning the aerosol loading. Comparisons with co-located retrievals from the SEVIRI 0.6 μm solar reflectance band observations show a level of agreement consistent with that expected from the simulations, with rms differences of between 0.5 and 0.8, and a mean bias ranging from − 0.5 to 0.3 dependent on the dust representation employed in the algorithm. Temporally resolved comparisons with observations from the Capo Verde AERONET site through the months of March and June reinforce these findings, but also indicate that the algorithm is capable of discerning the diurnal pattern in aerosol loading. The algorithm has now been incorporated within the routine GERB processing in detection mode, and will be used to provide an experimental aerosol product for assessment by the scientific community.

Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI

Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 /spl mu/m) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from /spl plusmn/0.6% to /spl plusmn/24% and /spl plusmn/0.1% to /spl plusmn/33%, respectively, whereas the broadband spectrum errors lie between /spl plusmn/0.3% and /spl plusmn/7%. In the case of channels IR10.8 and IR12.0, 73% of the land surfaces within the MSG disk present relative errors less than /spl plusmn/1.5%, and almost all (26%) of the remaining areas have relative errors of /spl plusmn/2.0%. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required.

Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data

A feasibility study of land surface temperature (LST) and land surface emissivity (LSE) estimation is presented using METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, based on the two-temperature method (TTM). The performance of TTM was assessed by taking into consideration the noise in SEVIRI channels IR 10.8 and IR 12.0 as well as the errors in the atmospheric profiles. Imposed errors due to uncertainties on atmospheric information were generated based on the background error covariance matrix used in the assimilation scheme of the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Circulation Model. TTM has provided LST values with averaged error bias [root-mean square error (RMSE)] ranging from 0.0 to 0.5 K (from 0.8 to 2.5 K) for geographical–seasonal model atmospheres stored in MODTRAN4 with water vapor content varying between 0.85 and 4.11 g cm −2. Obtained results suggest that TTM may be used as a complementary method to split-window (SW) algorithms over areas where LSE is not well known a priori.

Emissivity and temperature estimation from MSG SEVIRI data; method validation with simulated and NOAA-14 AVHRR data

Land surface temperature (LST), i.e. the radiometric surface temperature corresponding to the field-of-view of a sensor, is an important input for weather forecasting and climate models. Only satellite-based measurements provide the intrinsic spatial averaging required for LST determination on the scale of these models; therefore, LST also provides the best approximation to the thermodynamic temperature at pixel scale. Meteosat Second Generation (MSG) (launched on 28.08.2002), provides data with increased spatial, spectral, and temporal resolutions compared to earlier Meteosats. Among various applications involving data from MSG's primary payload, i.e. the Spinning Enhanced Visible and Infrared Imager (SEVIRI), emissivity and LST estimation on an operational basis is of a major concern. Here, issues related to emissivity retrieval from infrared radiances are addressed and the suitability of various algorithms with respect to SEVIRI are discussed. A scheme to estimate SEVIRI channel emissivity based on thermal infrared spectral indices (TISI) is adopted and issues concerning its operational use are addressed. A method validation using synthetic at-ground radiances shows that the achievable accuracy independent of surface type is about 0.005–0.009 for emissivity (in a 11–12 μm channel) and 1.5 K for LST. The method is also applied to NOAA-14 AVHRR data over central Europe. Atmospheric variables were calculated using MODTRAN and ECMWF (European Centre for Medium-range Weather Forecasts) reanalyses.

Radiative effects of simulated cirrus clouds on top of a deep convective storm in METEOSAT second generation SEVIRI channels

¶Cirrus clouds often form above intense convective storms due to several different mechanisms and affect the radiation field at the top of the atmosphere. Radiative transfer computations are performed to characterize these effects within the spectral bands of METEOSAT Second Generation’s (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Computations refer to five visible, near infrared and infrared MSG SEVIRI channels centered at 0.8, 1.6, 3.9, 10.8, and 12.0 µm. Reflectances and brightness temperatures are computed using the 1-D radiative transfer model STREAMER adopting simple parameterizations of the cloud layers and associated microphysical properties for the determination of the necessary optical properties. A sensitivity study is carried out by varying the cirrus ice crystal size and optical depth. The 1.6 and 3.9 µm channels reveal instrumental for the simultaneous detection of optical depth and crystal size of the cirrus layer. In particular, the results of the 3.9 µm channel show that the smaller the crystal size the higher the reflectance values. The computations provide interpretation clues on the phenomenon of ice crystal plumes on top of deep convective clouds, which are known to produce enhanced reflectivity signatures in the 3.7 µm channel of the Advanced Very High Resolution Radiometer (AVHRR). The sensitivity of the IR channels to cirrus cloud optical depth and ice crystal size is examined and the brightness temperature differences evaluated. Satellite observations and radiative transfer computations are at present the only way of studying such cloud features due to the unavailability of in situ aircraft measurements.