scholarly journals CLAAS: the CM SAF cloud property dataset using SEVIRI

2013 ◽  
Vol 13 (10) ◽  
pp. 26451-26487 ◽  
Author(s):  
M. Stengel ◽  
A. Kniffka ◽  
J. F. Meirink ◽  
M. Lockhoff ◽  
J. Tan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Sampling Rate ◽  
High Temporal Resolution ◽  
Cloud Detection ◽  
Cloud Property ◽  
Diurnal Cycles ◽  
Temporal Sampling ◽  
Cloud Properties ◽  
The Impact

Abstract. An 8 yr record of satellite based cloud properties named CLAAS (CLoud property dAtAset using SEVIRI) is presented, which was derived within the EUMETSAT Satellite Application Facility on Climate Monitoring. The dataset is based on SEVIRI measurements of the Meteosat Second Generation satellites, of which the visible and near-infrared channels were intercalibrated with MODIS. Including latest development components of the two applied state-of-the-art retrieval schemes ensure high accuracy in cloud detection, cloud vertical placement and microphysical cloud properties. These properties were further processed to provide daily to monthly averaged quantities, mean diurnal cycles and monthly histograms. In particular the collected histogram information enhance the insight in spatio-temporal variability of clouds and their properties. Due to the underlying intercalibrated measurement record, the stability of the derived cloud properties is ensured, which is exemplarily demonstrated for three selected cloud variables for the entire SEVIRI disk and a European subregion. All data products and processing levels are introduced and validation results indicated. The sampling uncertainty of the averaged products in CLAAS is minimized due to the high temporal resolution of SEVIRI. This is emphasized by studying the impact of reduced temporal sampling rates taken at typical overpass times of polar-orbiting instruments. In particular cloud optical thickness and cloud water path are very sensitive to the sampling rate, which in our study amounted to systematic deviations of over 10% if only sampled once a day. The CLAAS dataset facilitates many cloud related applications at small spatial scales of a few kilometres and short temporal scales of a few hours. Beyond this, the spatiotemporal characteristics of clouds on diurnal to seasonal, but also on multi-annual scales, can be studied.

scholarly journals CLAAS: the CM SAF cloud property data set using SEVIRI

2014 ◽  
Vol 14 (8) ◽  
pp. 4297-4311 ◽  
Author(s):  
M. Stengel ◽  
A. Kniffka ◽  
J. F. Meirink ◽  
M. Lockhoff ◽  
J. Tan ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Sampling Rate ◽  
High Temporal Resolution ◽  
Cloud Detection ◽  
Data Set ◽  
Cloud Property ◽  
Temporal Sampling ◽  
Cloud Properties ◽  
The Impact

Abstract. An 8-year record of satellite-based cloud properties named CLAAS (CLoud property dAtAset using SEVIRI) is presented, which was derived within the EUMETSAT Satellite Application Facility on Climate Monitoring. The data set is based on SEVIRI measurements of the Meteosat Second Generation satellites, of which the visible and near-infrared channels were intercalibrated with MODIS. Applying two state-of-the-art retrieval schemes ensures high accuracy in cloud detection, cloud vertical placement and microphysical cloud properties. These properties were further processed to provide daily to monthly averaged quantities, mean diurnal cycles and monthly histograms. In particular, the per-month histogram information enhances the insight in spatio-temporal variability of clouds and their properties. Due to the underlying intercalibrated measurement record, the stability of the derived cloud properties is ensured, which is exemplarily demonstrated for three selected cloud variables for the entire SEVIRI disc and a European subregion. All data products and processing levels are introduced and validation results indicated. The sampling uncertainty of the averaged products in CLAAS is minimized due to the high temporal resolution of SEVIRI. This is emphasized by studying the impact of reduced temporal sampling rates taken at typical overpass times of polar-orbiting instruments. In particular, cloud optical thickness and cloud water path are very sensitive to the sampling rate, which in our study amounted to systematic deviations of over 10% if only sampled once a day. The CLAAS data set facilitates many cloud related applications at small spatial scales of a few kilometres and short temporal scales of a~few hours. Beyond this, the spatiotemporal characteristics of clouds on diurnal to seasonal, but also on multi-annual scales, can be studied.

scholarly journals Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

2017 ◽  
Vol 9 (2) ◽  
pp. 881-904 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Cornelia Schlundt ◽  
Caroline Poulsen ◽  
...  
Keyword(s):  
Individual Characteristics ◽  
Digital Object Identifier ◽  
Water Path ◽  
Cloud Property ◽  
Temporal Sampling ◽  
Cloud Properties ◽  
Retrieval Systems ◽  
Specific Subset ◽  
The Individual ◽  
The Impact

Abstract. New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.For each dataset a digital object identifier has been issued:Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

scholarly journals Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

2010 ◽  
Vol 49 (11) ◽  
pp. 2315-2333 ◽  
Author(s):  
Galina Wind ◽  
Steven Platnick ◽  
Michael D. King ◽  
Paul A. Hubanks ◽  
Michael J. Pavolonis ◽  
...  
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Single Layer ◽  
Precipitable Water ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Water Vapor Absorption ◽  
Cloud Properties ◽  
Cloud Models ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.

scholarly journals Evolution of Severe and Nonsevere Convection Inferred from GOES-Derived Cloud Properties

2013 ◽  
Vol 52 (9) ◽  
pp. 2009-2023 ◽  
Author(s):  
John L. Cintineo ◽  
Michael J. Pavolonis ◽  
Justin M. Sieglaff ◽  
Andrew K. Heidinger
Keyword(s):  
Temporal Trends ◽  
Weather Radar ◽  
Severe Weather ◽  
False Alarms ◽  
High Temporal Resolution ◽  
Convective Initiation ◽  
Temporal Sampling ◽  
Cloud Properties ◽  
Storm Evolution ◽  
Echo Threshold

AbstractGeostationary satellites [e.g., the Geostationary Operational Environmental Satellite (GOES)] provide high temporal resolution of cloud development and motion, which is essential to the study of many mesoscale phenomena, including thunderstorms. Initial research on thunderstorm growth with geostationary imagery focused on the mature stages of storm evolution, whereas more recent research on satellite-observed storm growth has concentrated on convective initiation, often defined arbitrarily as the presence of a given radar echo threshold. This paper seeks to link the temporal trends in robust GOES-derived cloud properties with the future occurrence of severe-weather radar signatures during the development phase of thunderstorm evolution, which includes convective initiation. Two classes of storms (severe and nonsevere) are identified and tracked over time in satellite imagery, providing distributions of satellite growth rates for each class. The relationship between the temporal trends in satellite-derived cloud properties and Next Generation Weather Radar (NEXRAD)-derived storm attributes is used to show that this satellite-based approach can potentially be used to extend severe-weather-warning lead times (with respect to radar-derived signatures), without a substantial increase in false alarms. In addition, the effect of varying temporal sampling is investigated on several storms during a period of GOES super-rapid-scan operations (SRSOR). It is found that, from a satellite perspective, storms evolve significantly on time scales shorter than the current GOES operational scan strategies.

scholarly journals Will a perfect model agree with perfect observations? The impact of spatial sampling

2016 ◽  
Author(s):  
N. A. J. Schutgens ◽  
E. Gryspeerdt ◽  
N. Weigum ◽  
S. Tsyro ◽  
D. Goto ◽  
...  
Keyword(s):  
Black Carbon ◽  
Measurement Errors ◽  
Spatial Scales ◽  
Global Model ◽  
Spatial Sampling ◽  
Model Data ◽  
Temporal Sampling ◽  
Fold Reduction ◽  
Perfect Model ◽  
The Impact

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid-spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or air-borne observations concern even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid-spacing) over either the large areas of global model grid-boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field-of-view of ground-sites ("perfect" observations). Our analysis suggests that instantaneous RMS differences between these perfect observations and perfect global models can easily amount to 30–160%, for a range of observables like AOT (Aerosol Optical Thickness), extinction, black carbon mass concentrations, PM2.5, number densities and CCN (Cloud Condensation Nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a three-fold reduction i.c. AOT), than for others (e.g. a two-fold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75%). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

scholarly journals Will a perfect model agree with perfect observations? The impact of spatial sampling

2016 ◽  
Vol 16 (10) ◽  
pp. 6335-6353 ◽  
Author(s):  
Nick A. J. Schutgens ◽  
Edward Gryspeerdt ◽  
Natalie Weigum ◽  
Svetlana Tsyro ◽  
Daisuke Goto ◽  
...  
Keyword(s):  
Black Carbon ◽  
Measurement Errors ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Global Model ◽  
Spatial Sampling ◽  
Model Data ◽  
Temporal Sampling ◽  
Perfect Model ◽  
The Impact

Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of  ∼ 200 km, while satellite observations of aerosol use so-called pixels of  ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous root-mean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30–160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM2.5, number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10–75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

Impact of Cloud Model Microphysics on Passive Microwave Retrievals of Cloud Properties. Part II: Uncertainty in Rain, Hydrometeor Structure, and Latent Heating Retrievals

2006 ◽  
Vol 45 (7) ◽  
pp. 955-972 ◽  
Author(s):  
Eun-Kyoung Seo ◽  
Michael I. Biggerstaff
Keyword(s):  
Cloud Model ◽  
Mesoscale Convective System ◽  
Cloud Water ◽  
Passive Microwave ◽  
Convective System ◽  
Latent Heating ◽  
Cloud Property ◽  
Property Relations ◽  
Cloud Properties ◽  
The Impact

Abstract The impact of model microphysics on the retrieval of cloud properties based on passive microwave observations was examined using a three-dimensional, nonhydrostatic, adaptive-grid cloud model to simulate a mesoscale convective system over ocean. Two microphysical schemes, based on similar bulk two-class liquid and three-class ice parameterizations, were used to simulate storms with differing amounts of supercooled cloud water typical of both the tropical oceanic environment, in which there is little supercooled cloud water, and midlatitude continental environments in which supercooled cloud water is more plentiful. For convective surface-level rain rates, the uncertainty varied between 20% and 60% depending on which combination of passive and active microwave observations was used in the retrieval. The uncertainty in surface rain rate did not depend on the microphysical scheme or the parameter settings except for retrievals over stratiform regions based on 85-GHz brightness temperatures TB alone or 85-GHz TB and radar reflectivity combined. In contrast, systematic differences in the treatment of the production of cloud water, cloud ice, and snow between the parameterization schemes coupled with the low correlation between those properties and the passive microwave TB examined here led to significant differences in the uncertainty in retrievals of those cloud properties and latent heating. The variability in uncertainty of hydrometeor structure and latent heating associated with the different microphysical parameterizations exceeded the inherent variability in TB–cloud property relations. This was true at the finescales of the cloud model as well as at scales consistent with satellite footprints in which the inherent variability in TB–cloud property relations are reduced by area averaging.

scholarly journals Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

2017 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Cornelia Schlundt ◽  
Caroline Poulsen ◽  
...  
Keyword(s):  
Near Infrared ◽  
Individual Characteristics ◽  
Digital Object Identifier ◽  
Passive Imaging ◽  
Cloud Particle ◽  
Water Path ◽  
Cloud Property ◽  
Cloud Properties ◽  
Retrieval Systems ◽  
Specific Subset

Abstract. New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. Together with the ensured spectral consistency and rigorous uncertainty propagation though all processing levels, the Cloud_cci datasets approach new benchmarks for climate data records of cloud properties based on passive imaging sensors. For each dataset a Digital Object Identifier has been issued: Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

scholarly journals Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI

2011 ◽  
Vol 11 (12) ◽  
pp. 5603-5624 ◽  
Author(s):  
L. Bugliaro ◽  
T. Zinner ◽  
C. Keil ◽  
B. Mayer ◽  
R. Hollmann ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Sets ◽  
Cloud Detection ◽  
Cloud Property ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Weather Model

Abstract. Validation of cloud properties retrieved from passive spaceborne imagers is essential for cloud and climate applications but complicated due to the large differences in scale and observation geometry between the satellite footprint and the independent ground based or airborne observations. Here we illustrate and demonstrate an alternative approach: starting from the output of the COSMO-EU weather model of the German Weather Service realistic three-dimensional cloud structures at a spatial scale of 2.33 km are produced by statistical downscaling and microphysical properties are associated to them. The resulting data sets are used as input to the one-dimensional radiative transfer model libRadtran to simulate radiance observations for all eleven low resolution channels of MET-8/SEVIRI. At this point, both cloud properties and satellite radiances are known such that cloud property retrieval results can be tested and tuned against the objective input "truth". As an example, we validate a cloud property retrieval of the Institute of Atmospheric Physics of DLR and that of EUMETSAT's Climate Monitoring Science Application Facility CMSAF. Cloud detection and cloud phase assignment perform well. By both retrievals 88% of the pixels are correctly classified as clear or cloudy. The DLR algorithm assigns the correct thermodynamic phase to 95% of the cloudy pixels and the CMSAF retrieval to 84%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and to a very low extent by the CMSAF code (−0.12 K with a standard deviation of 7.6 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation. Cloud effective radii are most difficult to evaluate but the APICS algorithm shows that realistic histograms of occurrences can be derived (CMSAF was not evaluated in this context). Cloud water path, which is a combination of the last two quantities, is slightly underestimated by APICS, while CMSAF shows a larger scattering.

scholarly journals MIMO-SAR Interferometric Measurements for Structural Monitoring: Accuracy and Limitations

2021 ◽  
Vol 13 (21) ◽  
pp. 4290
Author(s):  
Andreas Baumann-Ouyang ◽  
Jemil Avers Butt ◽  
David Salido-Monzú ◽  
Andreas Wieser
Keyword(s):  
Multiple Input Multiple Output ◽  
Sampling Rate ◽  
Cost Effective ◽  
Meteorological Conditions ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
High Temporal Resolution ◽  
Practical Applications ◽  
Temporal Sampling ◽  
Input Multiple Output

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25m can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

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