Meteosat Second Generation Spinning Enhanced Visible And Infrared Imager Research Articles

Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance

AbstractThis study is a companion research effort to “Part I,” which emphasized use of infrared data for understanding various aspects of growing convective clouds in the Meteosat Second Generation (MSG) satellite’s Spinning Enhanced Visible and Infrared Imager (SEVIRI) imagery. Reflectance and derived brightness variability (BV) fields from MSG SEVIRI are used here to understand relationships between cloud-top signatures and physical processes for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a ≥35-dBZ echo from a new convective cloud. This study uses daytime SEVIRI visible (VIS) and near-infrared (NIR) reflectances from 0.6 to 3.9 μm (3-km sampling distance), as well as high-resolution visible (1-km sampling distance) fields. Data from 123 CI events observed during the 2007 Convection and Orographically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, so to meet this study’s objectives. These data are those used in Part I. A total of 27 VIS–NIR and BV “interest fields” are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the fields that contain the most unique information for describing principally cloud-top glaciation, as well as the presence of vigorous updrafts. Time changes in 1.6- and 3.9-μm reflectances, as well as BV in advance of CI, are shown to contain the most unique information related to the formation and increase in size of ice hydrometeors. Several methods are proposed on how results from this analysis may be used to monitor growing convective clouds per MSG pixel or per cumulus cloud “object” over 1-h time frames.

Overshooting cloud top detections using MSG SEVIRI Infrared brightness temperatures and their relationship to severe weather over Europe

This purposes of this paper are to: 1) demonstrate an objective overshooting top (OT) detection method using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared data, 2) produce an OT database for all operational SEVIRI data over Europe and north Africa for the six Northern Hemisphere warm seasons observed by SEVIRI to date, and 3) determine the frequency of OT detections in the vicinity of confirmed severe weather reports recorded within the European Severe Weather Database (ESWD). Algorithm performance is demonstrated for a case where numerous severe storms were present. Qualitative comparisons indicate that most OT detections correspond with the characteristic OT signature in SEVIRI 1 km resolution visible channel imagery, but some OTs were left undetected due in part to relatively coarse SEVIRI spatial resolution over the European domain. The SEVIRI OT database shows a strong relationship between OT maxima and regions with high terrain. OTs are found to occur more frequently during the day over land and during the night over water. Inter- and intra-seasonal variability in OT frequency and location are also shown. An OT was found near 47% of the confirmed ESWD events. The OT–severe weather relationship is strong for large hail (53%) and severe wind (52%) events but relatively weak for tornado events (14%). The weak OT–tornado relationship may be related two factors: 1) low-level wind shear is found to be of greater importance than large CAPE and strong updrafts (i.e. OTs) in tornadic storm environments across Europe and 2) a weakening of the storm updraft and collapse of the OT region has been documented prior to tornado formation. The relatively strong overall OT–severe weather relationship suggests that OT detections can be used to increase forecaster confidence that a given storm is severe, especially in regions where frequent, ground based Doppler weather radar data is unavailable.

Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 2. Implementation and evaluation

An original method, based on optimal estimation, was presented in a part one of this paper for the joint retrieval of the mean daily total column aerosol optical depth and the surface Bidirectional Reflectance Factor (BRF) from the daily accumulated Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The objective of this paper is to evaluate the benefits of the proposed approach and to document the limits of the algorithm assumptions in the context of its implementation in an operational ground segment. A twofold approach is followed. In a first step, by looking at the posterior correlation error matrix the capability of the so‐called Land Daily Aerosol (LDA) algorithm to decouple the surface‐atmosphere signal is analyzed. In particular, the impact of the prior information is investigated in detail. In a second step, the results of the algorithm are compared with independent data sets of aerosol optical depth and surface reflectance. In this phase, the accuracy of the algorithm is evaluated against ground observations from the AERONET network. LDA is shown to be in good agreement with these data, especially when the prior update mechanism is activated. Comparisons with the MODIS surface product showed that the bihemispherical reflectance derived from the LDA products is consistent with the equivalent MODIS white‐sky albedo. Aerosol spatial distributions are comparable in terms of geographical location and intensity, in particular for aerosol episodes with a limited daily variation.

Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory

An original method is presented in this paper for the joint retrieval of the mean daily total column aerosol optical depth and surface BRF from the daily accumulated Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The proposed algorithm is based on the optimal estimation (OE) theory, a one‐dimensional variational retrieval scheme that seeks an optimal balance between information that can be derived from the observations, and the one that is derived from prior knowledge of the system. The forward radiative transfer model explicitly accounts for the surface anisotropy and its coupling with the atmosphere. The low rate of change in the surface reflectance is used to derive the prior information on the surface state variables. The reliable estimation of the measurement system error is one of the most critical aspects of the OE method as it strongly determines the likelihood of the solution. An important effort in the proposed method has thus been dedicated to this issue, where the actual radiometric performances of SEVIRI are dynamically taken into account.

RST analysis of MSG-SEVIRI TIR radiances at the time of the Abruzzo 6 April 2009 earthquake

Abstract. Space-time fluctuations of Earth's emitted Thermal Infrared (TIR) radiation have been observed from satellite months to weeks before earthquakes occurrence. The general RST approach has been proposed in order to discriminate normal (i.e. related to the change of natural factor and/or observation conditions) TIR signal fluctuations from anomalous signal transient possibly associated to earthquake occurrence. In this work RST approach is applied to the Abruzzo 6 April 2009 event (ML=5.8) by using for the first time MSG-SEVIRI (Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imager) thermal infrared observations. A validation/confutation analysis has been performed in order to verify the presence/absence of anomalous space-time TIR transients in the presence/absence of significant seismic activity. March–April 2009 has been analyzed for validation purposes. Relatively unperturbed periods (no earthquakes with ML≥5) have been taken for confutation. A specific TIR anomalies space-time persistence analysis as well as a cloud coverage distribution test have been introduced in order to eliminate artifacts and outliers both in the validation and confutation phases. Preliminary results show clear differences in TIR anomalies occurrence during the periods used for validation and confutation purposes. Quite clear TIR anomalies appear also to mark main tectonic lines related to the preparatory phases of others, low magnitude (ML~4) earthquakes, occurred in the area.

Impact of point spread function of MSG-SEVIRI on active fire detection

The spatial resolution of sensors is a concept frequently described in an inappropriate way, usually identified by the sampling distance in the image capturing process. The shape of the modulation transfer function (MTF) has no influence on the results in applications based on homogeneous distribution of radiance. However, in the case of high-temperature events (HTEs), the spatial location of the burning area inside the pixel is a key issue to solve, in order to quantify the radiance. The point spread function (PSF) should be considered both in fire detection-oriented algorithms and in the application of bispectral processes. This paper analyses the impact of the PSF of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) sensor on the determination of thermal fire parameters. The PSF influence on the brightness temperature (BT), in the mid-infrared (MIR) 3.9 μm spectral band, on detection algorithms is analysed. Errors in the fire temperature retrieved by the bispectral technique, due to non-coincidence in the PSF involved, are also analysed. The results obtained show a difference of around 20 K in the BT in the 3.9 μm spectral band, depending on the fire location inside the pixel. Finally, the probability detection of the minimal size of the burning area was analysed, and revealed that there is a 90% probability of detecting a fire with a burning area of 10 ha whereas an area of 4 ha is detected with a probability of 50%.

Modeling the Error Statistics in Support Vector Regression of Surface Temperature From Infrared Data

Land and sea surface temperatures are important input parameters for many hydrological and meteorological models. Satellite infrared remote sensing is an effective tool for mapping these variables on regional and global scales. A supervised approach, based on support vector machines (SVMs), has recently been developed to estimate surface temperature from satellite radiometry. However, in order to integrate temperature estimates into hydrological or meteorological data-assimilation schemes (e.g., in flood-prevention applications), a further critical input is often required in the form of pixelwise error statistics. This information is important because it quantifies inaccuracies in the temperature estimate computed for each pixel. This letter proposes two novel methods to model the statistics of the SVM regression error on a pixelwise basis. Both approaches take into account the nonstationary behavior of the error itself. This problem has been only recently explored in the SVM literature through the use of Bayesian reformulations of SVM regressions. The methods proposed in this letter extend this approach by integrating it with either maximum-likelihood or confidence-interval supervised estimators. In both cases, the goal is improved modeling of the error contribution due to intrinsic random variability in the data (e.g., noise). The methods are experimentally validated on Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) images.

Water and energy budgets simulation over the AMMA-Niger super-site spatially constrained with remote sensing data

The SEtHyS_Savannah model [Saux-Picart et al., submitted for publication. SEtHyS_Savannah: a multiple source land surface model applied to sahelian landscapes. Agricultural and Forest Meteorology] was developed as an extension of the SEtHyS land surface model to simulate the water and energy fluxes over dry savannah landscapes. The vegetation cover is represented by a two layer model and a mulch approach is used for the soil description. The SEtHyS_Savannah model was regionalized over the AMMA-Niger super-site (about 50 km by 40 km), with the help of remote sensing data. The model uses a regular 1km grid and each cell is divided in sub-grid patches in order to represent land cover and soil heterogeneities (tile approach). The vegetation cover parameters were prescribed according to the land cover map and the seasonal evolution of the Leaf Area Index (LAI), both derived from SPOT-HRV (Satellite Pour l’Observation de la Terre – High Resolution Visible) data imagery. The atmospheric forcing was assumed homogeneous over the area and provided by a meteorological station installed at the Fakara experimental site. The surface water and energy budgets were simulated over a one-year period (2005) at a 5-min time step and validated against MSG-SEVIRI (Meteosat Second Generation – Spinning Enhanced Visible and Infra-red Imager) land surface temperature and ENVISAT-ASAR (ENVIronnement SATellite – Advanced Synthetic Aperture Radar) soil humidity products. The results show realistic surface fluxes and good agreement with the MSG-SEVIRI temperature observations. The soil moisture comparison presents significant correlation but large root mean square errors. These discrepancies are the consequence of both the use of a non-spatialized atmospheric forcing and to residual vegetation effects on the radar signal. Despite these uncertainties, the results increase confidence in the model representation of Sahelian soil–vegetation processes and open new perspectives to quantify the effects of vegetation changes on evapotranspiration and runoff over the region.

Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data

In this paper a cloud detection algorithm applied to the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) data is described. In order to obtain a good performance in cloud detection, physical, statistical and temporal approaches have been used. In the statistical algorithm, the spectral and textural features of the MSG-SEVIRI images have been used as input, while, in the physical tests, a set of dynamic thresholds has been used. The physical algorithm does not use real time ancillary data— such as sea surface temperature map and NWP temperature and humidity profiles. A further test is applied to that pixels having low confidence to be clear or cloudy. This test takes advantage of the best MSG-SEVIRI temporal resolution and it applies the K-Nearest Neighbour classifier to the spectral and textural features calculated in “temporal” boxes 3 × 3 pixels, defined “temporal” because their elements belong to three subsequent MSG-SEVIRI images. The MACSP (cloud MAsk Coupling of Statistical and Physical methods) algorithm has been validated against the MODIS cloud mask and compared with CPR (Cloud Profiling Radar) and SAFNWC cloud masks. The outcomes show that the MACSP detects 91.8% of the total number of the pixels used for validation against MODIS cloud mask correctly, while the SAFNWC cloud mask detects 89.2% of them correctly. In particular, the MACSP classifies as cloudy 8.8% of the pixels classified by the MODIS cloud mask as clear, while the SAFNWC cloud mask classifies as cloudy 12.1% of them. The MACSP detects 91.2% of the cloudy CPR pixels and 90.8% of the cloud-free CPR pixels, considered for comparison, correctly. On the other hand, the SAFNWC and CPR cloud masks agree in the detection of 90.7% of the cloudy pixels and of 90.2% of the cloud-free pixels.

Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures

A physically based algorithm for the retrieval of total water vapor column (TWC) over cloud-free land surfaces proposed by Kleespies and McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] is evaluated and extended for use in atmospheric correction and surface irradiance calculation schemes. Thermal infrared split window channels at 10.8 and 12.0 μm of the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) instrument are used. The proposed algorithm takes advantage of the improved measurement capabilities of the MSG-SEVIRI instrument with its 15 min temporal resolution and its radiometric accuracy of 0.25 K and 0.37 K in the 10.8 and 12.0 μm channels. The temporal resolution allows exploitation of the daily land surface temperature variation. There is no further need for explicit auxiliary information on air and land surface temperatures, which is difficult to obtain on an operational basis. Updated coefficients for the split window parameterization are derived based on simulations of ‘top-of-atmosphere’ SEVIRI brightness temperatures for the globally representative Thermodynamic Initial Guess Retrieval (TIGR3) set of radiosonde profiles. It turns out that the linear dependency on the transmission ratio in both split window channels as originally proposed by Kleespies & McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] has to be extended towards a non-linear approach in order to make it applicable to the full range of global atmospheric conditions. Sensitivity studies reveal that the parameterization relies on the availability of input brightness temperatures with a variation larger than approximately 5 K during the daily cycle. The new TWC algorithm was tested with MSG-SEVIRI data for European and African regions for the period March–August 2004 and compared with radiosonde data. The results show that the algorithm is capable of producing TWC values with a mean bias of − 0.2 mm and an RMSE of 6.8 mm. From the total amount of 2583 coincidences for all viewing zenith angles both for winter and summer conditions, 82% were within a ± 5 mm and 94% were within a ± 10 mm difference interval between MSG-based and radiosonde-based TWC. A second comparison to European GPS measurements for the same period from March to August 2004 reveals a bias of − 3.0 mm and an RMSE of 6.0 mm. This result is obtained for 11 UTC GPS measurements which proved to match best the MSG-TWC values. Comparing MSG-TWC to daily cloud-free mean GPS values shows a lower bias of − 2.56 mm and an increased RMSE of 6.6 mm. These findings support the usefulness of the new MSG-based algorithm for surface irradiance calculations and atmospheric correction purposes.

Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor

This study presents first results on Normalized Difference Vegetation Index (NDVI), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the geostationary satellite Meteosat Second Generation (MSG) covering the African continent. With a temporal resolution of 15 min MSG offers complementary information for NDVI monitoring compared to vegetation monitoring based on polar orbiting satellites. The improved temporal resolution has potential implications for accurate NDVI assessment of the African continent; e.g. the increased amount of available scenes are expected to help overcome problems related to cloud cover which makes the MSG data particularly well suited for early warning systems. Time series of 2004 MSG NDVI was compared to MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua NDVI for the Dahra site in the Senegalese Sahel, West Africa. It was found that NDVI was available for 82 days with multiple cloud free acquisitions per day during the growing season as compared to 47 days with information from either MODIS Terra or Aqua for that particular site. Differences in MSG SEVIRI and MODIS BRDF on a seasonal scale were found to influence the time series of NDVI for the test site; MSG NDVI being higher than MODIS in July–August and lower in October–November. Preliminary composite analysis suggests that the period of compositing to produce continent scale cloud free products can be reduced to ∼5 days using MSG NDVI as compared to polar orbiting data. With the availability of diurnal reflectance information the significance of differences between the red and near-infrared wavelengths due to anisotropy become evident, causing diurnal variations in observed NDVI. Diurnal MSG NDVI was compared to in situ measured MSG NDVI at the test site in Senegal and the same “bowl-shaped” diurnal curve was found for a medium dense cover of annual grasses. The range in observed NDVI and time of diurnal minimum was different due to different viewing geometry. Daily minimum of in situ measured NDVI was around solar noon whereas minimum MSG NDVI occurs one hour prior to noon due to the test site location 12° west of the satellite sensor. Diurnal variation in observed NDVI was studied for a number of pixels characterized by different sensor view zenith angles and vegetation types. This analysis illustrated the diurnal NDVI dependency of illumination conditions, view angle and vegetation intensity and pinpoints the importance of proper BRDF modeling to produce daily values of MSG NDVI normalized for acquisition time, which will be the subject of a forthcoming paper.

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.