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R A Roebeling - One of the best experts on this subject based on the ideXlab platform.
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Rainfall retrievals over West Africa using SEVIRI: evaluation with TRMM-PR and monitoring of the daylight time monsoon progression
2010Co-Authors: E. L. A. Wolters, B. J. J. M. Van Den Hurk, R A RoebelingAbstract:Abstract. This paper describes the application of the KNMI cloud physical properties – precipitation properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) information, retrieved from visible, near-infrared and infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-9 to estimate precipitation occurrence and intensity. It is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence and intensity over West Africa with a sufficient accuracy, using tropical monsoon measurement mission precipitation radar (TRMM-PR) and a small number of rain gauge observations as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring both the seasonal and synoptical evolution of the West African monsoon (WAM). It is shown that the SEVIRI-detected rainfall area agrees well with TRMM-PR, having a correlation coefficient of 0.86, with the areal extent of rainfall by SEVIRI being ~10% larger than TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. The frequency distributions of rain rate reveal that the median rain rates of CPP-PP and TRMM-PR are similar. However, rain rates >7 mm h−1 are retrieved more frequently by SEVIRI than by TRMM-PR, which is partly explained by known biases in TRMM-PR. Finally, it is illustrated that both the seasonal and synoptical time scale of the WAM can be well detected from SEVIRI daytime observations. It was found that the daytime westward MCS travel speed fluctuates between 50 and 60 km h−1. Furthermore, the ratio of MCS precipitation to the total precipitation was estimated to be about 27%. Our results indicate that rainfall retrievals from SEVIRI can be used to monitor the West African monsoon.
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SEVIRI rainfall retrieval and validation using weather radar observations
Journal of Geophysical Research, 2009Co-Authors: R A Roebeling, I. HollemanAbstract:[1] This paper presents and validates a new algorithm to detect precipitating clouds and estimate rain rates from cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The precipitation properties (PP) algorithm uses information on cloud condensed water path (CWP), particle effective radius, and cloud thermodynamic phase to detect precipitating clouds, while information on CWP and cloud top height is used to estimate rain rates. An independent data set of weather radar data is used to determine the optimum settings of the PP algorithm and calibrated it. For a 2-month period, the ability of SEVIRI to discriminate precipitating from nonprecipitating clouds is evaluated using weather radar over the Netherlands. In addition, weather radar and rain gauge observations are used to validate the SEVIRI retrievals of rain rate and accumulated rainfall across the entire study area and period. During the observation period, the spatial extents of precipitation over the study area from SEVIRI and weather radar are highly correlated (correlation ≈ 0.90), while weaker correlations (correlation ≈ 0.63) are found between the spatially mean rain rate retrievals from these instruments. The combined use of information on CWP, cloud thermodynamic phase, and particle size for the detection of precipitation results in an increase in explained variance (∼10%) and decrease in false alarms (∼15%), as compared to detection methods that are solely based on a threshold CWP. At a pixel level, the SEVIRI retrievals have an acceptable accuracy (bias) of about 0.1 mm h−1 and a precision (standard error) of about 0.8 mm h−1. It is argued that parts of the differences are caused by collocation errors and parallax shifts in the SEVIRI data and by irregularities in the weather radar data. In future studies we intend to exploit the observations of the European weather radar network Operational Programme for the Exchange of Weather Radar Information (OPERA) and extend this study to the entirety of Europe.
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evaluation of the daylight cycle of model predicted cloud amount and condensed water path over europe with observations from msg SEVIRI
Journal of Climate, 2009Co-Authors: R A Roebeling, E Van MeijgaardAbstract:Abstract The evaluation of the diurnal cycle of cloud amount (CA) and cloud condensed water path (CWP) as predicted by climate models receives relatively little attention, mostly because of the lack of observational data capturing the diurnal cycle of such quantities. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat-8 satellite is the first instrument able to provide accurate information on diurnal cycles during daylight hours of cloud properties over land and ocean surfaces. This paper evaluates the daylight cycle of CA and CWP as predicted by the Regional Atmospheric Climate Model version 2 (RACMO2), using corresponding SEVIRI retrievals. The study is done for Europe using hourly cloud properties retrievals from SEVIRI during the summer months from May to September 2004. The results of this study show that SEVIRI-retrieved daylight cycles of CA and CWP provide a powerful tool for identifying climate model deficiencies. Over Europe the SEVIRI retrievals of clo...
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Validation of liquid cloud property retrievals from SEVIRI using ground-based observations
Geophysical Research Letters, 2008Co-Authors: R A Roebeling, S. Placidi, D. P. Donovan, Herman Russchenberg, A.j. FeijtAbstract:Partly due to aerosol effects stratocumulus clouds vary considerably in liquid water path (LWP), geometrical thickness (h) and droplet number concentration (Nc). Cloud models have been developed to simulate h and Nc using satellite retrieved cloud optical thickness (?) and effective radius (re) values. In this paper we examine the consistency between LWP and h values inferred from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard METEOSAT-8. The use of METEOSAT-8 data means that time series of LWP and h can be validated at a 15-minute resolution, and used for examining the first indirect aerosol effect. For single-layered stratocumulus clouds the LWP and h retrievals from SEVIRI are compared to corresponding ground-based observations at two Cloudnet sites. A study on the sensitivity of the cloud model to the uncertainties in SEVIRI retrievals of ? and re reveals that h and Nc simulations are only accurate for clouds with effective radii larger than 5 ?m. The SEVIRI and ground-based retrievals of LWP and h show very good agreement, with accuracies of about 15 g m?2 and 20 m, respectively. This agreement could only be achieved by assuming sub-adiabatic profiles of droplet concentration and liquid water path in the cloud model. The degree of adiabaticity for single-layered stratocumulus clouds could be quantified by simultaneous analysis of SEVIRI and ground-based LWP and h values, which suggests that stratocumulus clouds over North Western Europe deviate, on average, from adiabatic clouds.
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Validation of Cloud Liquid Water Path Retrievals from SEVIRI Using One Year of CloudNET Observations
Journal of Applied Meteorology and Climatology, 2008Co-Authors: R A Roebeling, Hartwig Manfred Deneke, A.j. FeijtAbstract:Abstract The accuracy and precision are determined of cloud liquid water path (LWP) retrievals from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8 using 1 yr of LWP retrievals from microwave radiometer (MWR) measurements of two CloudNET stations in northern Europe. The MWR retrievals of LWP have a precision that is superior to current satellite remote sensing techniques, which justifies their use as validation data. The Cloud Physical Properties (CPP) algorithm of the Satellite Application Facility on Climate Monitoring (CM-SAF) is used to retrieve LWP from SEVIRI reflectances at 0.6 and 1.6 μm. The results show large differences in the accuracy and precision of LWP retrievals from SEVIRI between summer and winter. During summer, the instantaneous LWP retrievals from SEVIRI agree well with those from the MWRs. The accuracy is better than 5 g m−2 and the precision is better than 30 g m−2, which is similar to the precision of LWP retrievals from MWR. The added value of the 15...
Iphigenia Keramitsoglou - One of the best experts on this subject based on the ideXlab platform.
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Wildfire Detection and Tracking over Greece Using MSG‑SEVIRI Satellite Data
Remote Sensing, 2011Co-Authors: Nicolaos I. Sifakis, Christos Iossifidis, Charalampos KONTOES, Iphigenia KeramitsoglouAbstract:Abstract: Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real-time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT’s Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade’s archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.
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Wildfire detection and tracking over Greece using MSG-SEVIRI satellite data
Remote Sensing, 2011Co-Authors: Nicolaos I. Sifakis, Christos Iossifidis, Charalampos KONTOES, Iphigenia KeramitsoglouAbstract:Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real‑time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT’s Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade’s archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.
Kevin Ruddick - One of the best experts on this subject based on the ideXlab platform.
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Synergy between polar-orbiting and geostationary sensors: Remote sensing of the ocean at high spatial and high temporal resolution☆
Remote Sensing of Environment, 2014Co-Authors: Quinten Vanhellemont, Griet Neukermans, Kevin RuddickAbstract:Abstract Ocean colour sensors have been capturing the state of the world's oceans for over a decade. They are typically installed on polar-orbiting satellites and cover the entire earth every 1 to 2 days. This temporal resolution is insufficient to observe oceanic processes occurring at a higher frequency, especially when taking cloud cover into account. Data from geostationary platforms can be obtained with a much better temporal resolution (images every 15 or 60 min), and thus are useful to study those processes. We show that by synergistically combining marine reflectance data from SEVIRI, a geostationary sensor, and MODIS Aqua, a polar orbiter, the resulting product is an improvement over both data sources. The synergy approach takes the reflectance from MODIS, with high quality and high spatial resolution, and modulates this over the day by the temporal variability of the SEVIRI reflectance, normalized to the SEVIRI reflectance at the time of MODIS overpass. The temporal frequency of the synergy product is much better than that of MODIS, and by using the latter's high quality data, the limited spatial and radiometric resolution of SEVIRI is enhanced. As the SEVIRI data is limited to a single broad red band (560–710 nm), the applications of the synergy product are limited to parameters that can be derived from this band, such as suspended particulate matter (SPM), turbidity (T) and the diffuse attenuation of photosynthetically available radiation (Kpar) in turbid waters. A geostationary ocean colour sensor over Europe will provide invaluable data concerning our marine environment. The cost of increasing the spatial resolution of a geostationary sensor is very high, and this study illustrates that a lower resolution geostationary ocean colour sensor combined with a high resolution polar orbiting sensor, can provide a high frequency synergetic product with high spatial resolution.
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Diurnal variability of turbidity and light attenuation in the southern North Sea from the SEVIRI geostationary sensor
Remote Sensing of Environment, 2012Co-Authors: Griet Neukermans, Kevin Ruddick, Naomi GreenwoodAbstract:Abstract This study follows up on the successful feasibility study of Neukermans et al. (2009) for mapping suspended matter in turbid waters from the SEVIRI sensor on board the METEOSAT geostationary weather satellite platform. Previous methodology is extended to the mapping of turbidity, T , and vertical attenuation of photosynthetically active radiation (PAR), K PAR . The spatial resolution of the SEVIRI products is improved from 3 km × 6.5 km to 1 km × 2 km using the broad high resolution visual band. The previous atmospheric correction is further improved and the uncertainties on marine reflectance due to digitization are considered. Based on a two year archive of SEVIRI imagery, available every 15 min, the diurnal variability of T and K PAR is investigated during cloud free periods and validated using half-hourly T and K PAR data obtained from a system of moored buoys (SmartBuoys) in the southern North Sea. Based on numerous match-ups, 80% of SEVIRI derived T and K PAR are within 53% and 39% of SmartBuoy T and K PAR , respectively. Results further show that on cloud free days, the SEVIRI T and K PAR signals are in phase with the SmartBuoy data, with an average difference in the timing of the maximum T and K PAR of 11 min and 23 min, respectively. It is concluded that diurnal variability of T and K PAR can now be mapped by remote sensing offering new opportunities for improving ecosystem models and monitoring of turbidity. Limitations of the current SEVIRI sensor and perspectives for design of future geostationary sensors and synergy with polar orbiting satellites are discussed.
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Optical remote sensing of coastal waters from geostationary platforms: a feasibility study - Mapping Total Suspended Matter with SEVIRI
2008Co-Authors: Griet Neukermans, Bouchra Nechad, Kevin RuddickAbstract:Geostationary ocean colour sensors do not yet exist, but are under consideration by a number of space agencies. This study tests the feasibility and assesses the potential for optical remote sensing of coastal waters from geostationary platforms, with the existing SEVIRI (Spinning Enhanced Visible and InfraRed Imager) meteorological sensor on the METOSAT Second Generation platform. Data are available in near real time every 15 minutes. SEVIRI lacks sufficient bands for chlorophyll remote sensing but its spectral resolution is sufficient for quantification of Total Suspended Matter (TSM) in turbid waters, using a single broad red band, combined with a suitable near infrared band. A test data set for mapping of TSM was obtained from the SEVIRI Archive of the Royal Meteorological Institute of Belgium (RMIB), covering 15 consecutive days in September 2006 for the Southern North Sea. Atmospheric correction of SEVIRI images included corrections for Rayleigh and aerosol scattering, ozone absorption and atmospheric transmittances. A one-band TSM retrieval algorithm, calibrated by non-linear regression of seaborne measurements of TSM and water-leaving reflectance was applied. Results show that (1) mapping of TSM in the Southern North Sea is feasible with SEVIRI and that TSM maps are well correlated with TSM maps obtained from MODIS (2) during cloud-free days, high frequency dynamics of TSM are detected and (3) daily composites of TSM could be generated in partially cloudy weather.
Ulrich Hamann - One of the best experts on this subject based on the ideXlab platform.
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Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms
Atmospheric Measurement Techniques, 2014Co-Authors: Ulrich Hamann, Peter N. Francis, Andi Walther, Bryan A. Baum, Ralf Bennartz, Luca Bugliaro, M. Derrien, Andrew K. Heidinger, S. Joro, Anke KniffkaAbstract:Abstract. The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) – a crucial parameter to estimate the thermal cloud radiative forcing – can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.
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Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms
2014Co-Authors: Ulrich Hamann, Peter N. Francis, Andi Walther, Bryan A. Baum, Ralf Bennartz, Luca Bugliaro, M. Derrien, Andrew K. Heidinger, S. Joro, Anke KniffkaAbstract:The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from 0.8 kilometers to 0.6 kilometers. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.
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remote sensing of cloud top pressure height from SEVIRI analysis of ten current retrieval algorithms
Atmospheric Measurement Techniques, 2014Co-Authors: Peter N. Francis, Ulrich Hamann, Andi Walther, Bryan A. Baum, Ralf Bennartz, Luca Bugliaro, M. Derrien, Andrew K. HeidingerAbstract:Abstract. The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) – a crucial parameter to estimate the thermal cloud radiative forcing – can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud–Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 km lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 km to 0.6 km. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.
Nicolaos I. Sifakis - One of the best experts on this subject based on the ideXlab platform.
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Wildfire Detection and Tracking over Greece Using MSG‑SEVIRI Satellite Data
Remote Sensing, 2011Co-Authors: Nicolaos I. Sifakis, Christos Iossifidis, Charalampos KONTOES, Iphigenia KeramitsoglouAbstract:Abstract: Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real-time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT’s Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade’s archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.
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Wildfire detection and tracking over Greece using MSG-SEVIRI satellite data
Remote Sensing, 2011Co-Authors: Nicolaos I. Sifakis, Christos Iossifidis, Charalampos KONTOES, Iphigenia KeramitsoglouAbstract:Greece is a high risk Mediterranean country with respect to wildfires. This risk has been increasing under the impact of climate change, and in summer 2007 approximately 200,000 ha of vegetated land were burnt. The SEVIRI sensor, on board the Meteosat Second Generation (MSG) geostationary satellite, is the only spaceborne sensor providing five and 15-minute observations of Europe in 12 spectral channels, including a short-wave infrared band sensitive to fire radiative temperature. In August 2007, when the bulk of the destructive wildfires started in Greece, the receiving station, operated by the Institute for Space Applications and Remote Sensing, provided us with a time series of MSG-SEVIRI images. These images were processed in order to test the reliability of a real‑time detection and tracking system and its complementarity to conventional means provided by the Fire Brigade. EUMETSAT’s Active Fire Monitoring (FIR) image processing algorithm for fire detection and monitoring was applied to SEVIRI data, then fine-tuned according to Greek conditions, and evaluated. Alarm announcements from the Fire Brigade’s archives were used as ground truthing data in order to assess detection reliability and system performance. During the examined period, MSG-SEVIRI data successfully detected 82% of the fire events in Greek territory with less than 1% false alarms.
