scholarly journals Comparison of CALIPSO-Like, LaRC, and MODIS Retrievals of Ice-Cloud Properties over SIRTA in France and Florida during CRYSTAL-FACE

2007 ◽  
Vol 46 (3) ◽  
pp. 249-272 ◽  
Author(s):  
M. Chiriaco ◽  
H. Chepfer ◽  
P. Minnis ◽  
M. Haeffelin ◽  
S. Platnick ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiant Energy ◽  
Energy System ◽  
Effective Radius ◽  
Cloud Detection ◽  
Regional Study ◽  
Crystal Face ◽  
Ice Clouds ◽  
Microphysical Properties ◽  
Cloud Properties

Abstract This study compares cirrus-cloud properties and, in particular, particle effective radius retrieved by a Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-like method with two similar methods using Moderate-Resolution Imaging Spectroradiometer (MODIS), MODIS Airborne Simulator (MAS), and Geostationary Operational Environmental Satellite imagery. The CALIPSO-like method uses lidar measurements coupled with the split-window technique that uses the infrared spectral information contained at the 8.65-, 11.15-, and 12.05-μm bands to infer the microphysical properties of cirrus clouds. The two other methods, using passive remote sensing at visible and infrared wavelengths, are the operational MODIS cloud products (using 20 spectral bands from visible to infrared, referred to by its archival product identifier MOD06 for MODIS Terra) and MODIS retrievals performed by the Clouds and the Earth’s Radiant Energy System (CERES) team at Langley Research Center (LaRC) in support of CERES algorithms (using 0.65-, 3.75-, 10.8-, and 12.05-μm bands); the two algorithms will be referred to as the MOD06 and LaRC methods, respectively. The three techniques are compared at two different latitudes. The midlatitude ice-clouds study uses 16 days of observations at the Palaiseau ground-based site in France [Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA)], including a ground-based 532-nm lidar and the MODIS overpasses on the Terra platform. The tropical ice-clouds study uses 14 different flight legs of observations collected in Florida during the intensive field experiment known as the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL-FACE), including the airborne cloud-physics lidar and the MAS. The comparison of the three methods gives consistent results for the particle effective radius and the optical thickness but discrepancies in cloud detection and altitudes. The study confirms the value of an active remote sensing method (CALIPSO like) for the study of subvisible ice clouds, in both the midlatitudes and Tropics. Nevertheless, this method is not reliable in optically very thick tropical ice clouds, because of their particular microphysical properties.

scholarly journals The variability of tropical ice cloud properties as a function of the large-scale context from ground-based radar-lidar observations over Darwin, Australia

2011 ◽  
Vol 11 (16) ◽  
pp. 8363-8384 ◽  
Author(s):  
A. Protat ◽  
J. Delanoë ◽  
P. T. May ◽  
J. Haynes ◽  
C. Jakob ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Radius ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Ice Water Content ◽  
Mean Differences ◽  
Ice Water ◽  
Lidar Observations

Abstract. The high complexity of cloud parameterizations now held in models puts more pressure on observational studies to provide useful means to evaluate them. One approach to the problem put forth in the modelling community is to evaluate under what atmospheric conditions the parameterizations fail to simulate the cloud properties and under what conditions they do a good job. It is the ambition of this paper to characterize the variability of the statistical properties of tropical ice clouds in different tropical "regimes" recently identified in the literature to aid the development of better process-oriented parameterizations in models. For this purpose, the statistical properties of non-precipitating tropical ice clouds over Darwin, Australia are characterized using ground-based radar-lidar observations from the Atmospheric Radiation Measurement (ARM) Program. The ice cloud properties analysed are the frequency of ice cloud occurrence, the morphological properties (cloud top height and thickness), and the microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration). The variability of these tropical ice cloud properties is then studied as a function of the large-scale cloud regimes derived from the International Satellite Cloud Climatology Project (ISCCP), the amplitude and phase of the Madden-Julian Oscillation (MJO), and the large-scale atmospheric regime as derived from a long-term record of radiosonde observations over Darwin. The vertical variability of ice cloud occurrence and microphysical properties is largest in all regimes (1.5 order of magnitude for ice water content and extinction, a factor 3 in effective radius, and three orders of magnitude in concentration, typically). 98 % of ice clouds in our dataset are characterized by either a small cloud fraction (smaller than 0.3) or a very large cloud fraction (larger than 0.9). In the ice part of the troposphere three distinct layers characterized by different statistically-dominant microphysical processes are identified. The variability of the ice cloud properties as a function of the large-scale atmospheric regime, cloud regime, and MJO phase is large, producing mean differences of up to a factor 8 in the frequency of ice cloud occurrence between large-scale atmospheric regimes and mean differences of a factor 2 typically in all microphysical properties. Finally, the diurnal cycle of the frequency of occurrence of ice clouds is also very different between regimes and MJO phases, with diurnal amplitudes of the vertically-integrated frequency of ice cloud occurrence ranging from as low as 0.2 (weak diurnal amplitude) to values in excess of 2.0 (very large diurnal amplitude). Modellers should now use these results to check if their model cloud parameterizations are capable of translating a given atmospheric forcing into the correct statistical ice cloud properties.

scholarly journals Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

2019 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Stephan Finkensieper ◽  
Benjamin Würzler ◽  
...  
Keyword(s):  
Radiant Energy ◽  
Energy System ◽  
Global Scale ◽  
Radiative Flux ◽  
Surface Radiation ◽  
Digital Object Identifier ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Global Quality ◽  
Ice Water

Abstract. We present version 3 of the Cloud_cci AVHRR-PM dataset which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from Advanced Very High Resolution Radiometer (AVHRR) measurements recorded by the afternoon (post meridiem, PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling, shortwave and longwave broadband fluxes at the surface (bottom-of-atmosphere - BOA) and top-of-atmosphere (TOA). All fluxes were determined at AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) show a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at BOA and TOA. The Cloud_cci AVHRR-PM version 3 dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a Digital Object Identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

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

2010 ◽  
Vol 10 (9) ◽  
pp. 21931-21988 ◽  
Author(s):  
L. Bugliaro ◽  
T. Zinner ◽  
C. Keil ◽  
B. Mayer ◽  
R. Hollmann ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Ice Clouds ◽  
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 79%. Cloud top temperature is slightly overestimated by the DLR code (+3.1 K mean difference with a standard deviation of 10.6 K) and underestimated by the CMSAF code (−16.4 K with a standard deviation of 37.3 K). Both retrievals account reasonably well for the distribution of optical thickness for both water and ice clouds, with a tendency to underestimation for the DLR and to overestimation for the CMSAF algorithm. Cloud effective radii are most difficult to evaluate and not always the algorithms are able to produce realistic values. The CMSAF cloud water path, which is a combination of the last two quantities, is particularly accurate for ice clouds, while water clouds are overestimated, mainly because of the effective radius overestimation for water clouds.

scholarly journals Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties

2020 ◽  
Vol 12 (1) ◽  
pp. 41-60 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Stephan Finkensieper ◽  
Benjamin Würzler ◽  
...  
Keyword(s):  
High Resolution ◽  
Radiant Energy ◽  
Energy System ◽  
Global Scale ◽  
Radiative Flux ◽  
Surface Radiation ◽  
Digital Object Identifier ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Very High

Abstract. We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

scholarly journals Improvements to the Geostationary Visible Imager Ray-Matching Calibration Algorithm for CERES Edition 4

2016 ◽  
Vol 33 (12) ◽  
pp. 2679-2698 ◽  
Author(s):  
David R. Doelling ◽  
Conor O. Haney ◽  
Benjamin R. Scarino ◽  
Arun Gopalan ◽  
Rajendra Bhatt
Keyword(s):  
Dynamic Range ◽  
Spectral Band ◽  
Radiant Energy ◽  
Convective Cloud ◽  
Energy System ◽  
Processing Scheme ◽  
Matching Algorithm ◽  
Cloud Properties ◽  
Calibration Algorithm ◽  
The Difference

AbstractThe Clouds and the Earth’s Radiant Energy System (CERES) project relies on geostationary imager–derived TOA broadband fluxes and cloud properties to account for the regional diurnal fluctuations between the Terra and Aqua CERES and MODIS measurements. The CERES project employs a ray-matching calibration algorithm in order to transfer the Aqua MODIS calibration to the geostationary (GEO) imagers, thereby allowing the derivation of consistent fluxes and cloud retrievals across the 16 GEO imagers utilized in the CERES record. The CERES Edition 4 processing scheme grants the opportunity to recalibrate the GEO record using an improved GEO/MODIS all-sky ocean ray-matching algorithm. Using a graduated angle matching method, which is most restrictive for anisotropic clear-sky ocean radiances and least restrictive for isotropic bright cloud radiances, reduces the bidirectional bias while preserving the dynamic range. Furthermore, SCIAMACHY hyperspectral radiances are used to account for both the solar incoming and Earth-reflected spectra in order to correct spectral band differences. As a result, the difference between the linear regression offset and the maintained GEO space count was reduced, and the calibration slopes computed from the linear fit and the regression through the space count agreed to within 0.4%. A deep convective cloud (DCC) ray-matching algorithm is also presented. The all-sky ocean and DCC ray-matching timeline gains are within 0.7% of one another. Because DCC are isotropic and the brightest, Earth targets with near-uniform visible spectra, the temporal standard error of GEO imager gains, are reduced by up to 60% from that of all-sky ocean targets.

scholarly journals The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

2020 ◽  
Vol 12 (6) ◽  
pp. 929 ◽  
Author(s):  
Nicolas Clerbaux ◽  
Tom Akkermans ◽  
Edward Baudrez ◽  
Almudena Velazquez Blazquez ◽  
William Moutier ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Radiant Energy ◽  
Longwave Radiation ◽  
Energy System ◽  
Radiation Budget ◽  
Climate Data ◽  
Night Time ◽  
Cloud Properties ◽  
Climate Monitoring ◽  
Orbital Drift

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

scholarly journals Thermodynamic phase retrieval of convective clouds: impact of sensor viewing geometry and vertical distribution of cloud properties

2013 ◽  
Vol 6 (3) ◽  
pp. 539-547 ◽  
Author(s):  
E. Jäkel ◽  
J. Walter ◽  
M. Wendisch
Keyword(s):  
Phase Retrieval ◽  
Near Infrared ◽  
Three Dimensional ◽  
Ice Clouds ◽  
Convective Clouds ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Reflectivity Spectra ◽  
Vertical Distributions ◽  
Thermodynamic Phase

Abstract. The sensitivity of passive remote sensing measurements to retrieve microphysical parameters of convective clouds, in particular their thermodynamic phase, is investigated by three-dimensional (3-D) radiative transfer simulations. The effects of different viewing geometries and vertical distributions of the cloud microphysical properties are investigated. Measurement examples of spectral solar radiance reflected by cloud sides (passive) in the near-infrared (NIR) spectral range are performed together with collocated lidar observations (active). The retrieval method to distinguish the cloud thermodynamic phase (liquid water or ice) exploits different slopes of cloud side reflectivity spectra of water and ice clouds in the NIR. The concurrent depolarization backscattering lidar provides geometry information about the cloud distance and height as well as the depolarization.

scholarly journals Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes?

2019 ◽  
Vol 19 (13) ◽  
pp. 8879-8896 ◽  
Author(s):  
Hailing Jia ◽  
Xiaoyan Ma ◽  
Johannes Quaas ◽  
Yan Yin ◽  
Tom Qiu
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiant Energy ◽  
Single Layer ◽  
Cloud Droplet ◽  
Energy System ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Anthropogenic Aerosol ◽  
Aerosol Cloud

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3, Clouds and the Earth's Radiant Energy System (CERES) Edition-4 L3 products, and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data are employed to systematically study aerosol–cloud correlations over three anthropogenic aerosol regions and their adjacent oceans, as well as explore the effect of retrieval artifacts and underlying physical mechanisms. This study is confined to warm phase and single-layer clouds without precipitation during the summertime (June, July, and August). Our analysis suggests that cloud effective radius (CER) is positively correlated with aerosol optical depth (AOD) over land (positive slopes), but negatively correlated with aerosol index (AI) over oceans (negative slopes) even with small ranges of liquid water path (quasi-constant). The changes in albedo at the top of the atmosphere (TOA) corresponding to aerosol-induced changes in CER also lend credence to the authenticity of this opposite aerosol–cloud correlation between land and ocean. It is noted that potential artifacts, such as the retrieval biases of both cloud (partially cloudy and 3-D-shaped clouds) and aerosol, can result in a serious overestimation of the slope of CER–AOD/AI. Our results show that collision–coalescence seems not to be the dominant cause for positive slope over land, but the increased CER caused by increased aerosol might further increase CER by initializing collision–coalescence, generating a positive feedback. By stratifying data according to the lower tropospheric stability and relative humidity near cloud top, it is found that the positive correlations more likely occur in the case of drier cloud top and stronger turbulence in clouds, while negative correlations occur in the case of moister cloud top and weaker turbulence in clouds, which implies entrainment mixing might be a possible physical interpretation for such a positive CER–AOD slope.

scholarly journals Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

2017 ◽  
Vol 17 (7) ◽  
pp. 4731-4749 ◽  
Author(s):  
Chenglai Wu ◽  
Xiaohong Liu ◽  
Minghui Diao ◽  
Kai Zhang ◽  
Andrew Gettelman ◽  
...  
Keyword(s):  
Nucleation And Growth ◽  
Ice Nucleation ◽  
Ice Crystal ◽  
Ice Clouds ◽  
Model Version ◽  
Microphysical Properties ◽  
Cloud Properties ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Ice Water

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

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