Multi-channel Imager Algorithm (MIA): A novel cloud top phase classification algorithm applied to Himawari-8 Geostationary Satellite

2021 ◽  
Author(s):  
Yi Zeng ◽  
Yannian Zhu ◽  
Jiaxi Hu ◽  
Minghuai Wang ◽  
Daniel Rosenfeld
Keyword(s):  
Classification Algorithms ◽  
Hit Rate ◽  
Cloud Properties ◽  
Phase Classification ◽  
Multiple Thresholds ◽  
Phase Liquid ◽  
Rgb Images ◽  
Cloud Optical Thickness ◽  
Universal Applicability ◽  
Ice Phase

<p>Cloud top thermodynamic phase (liquid, or ice) classification is critical for the retrieval of cloud properties such as cloud top particle effective radius, cloud optical thickness and cloud water path. The physical basis for phase classification is the different absorption and scattering properties between water droplets and ice crystals over different wavelengths. Passive sensors always use the hand-tuned phase classification algorithms such as decision trees or voting schemes involving multiple thresholds. In order to improve the accuracy and universal applicability of phase classification algorithms, this study uses unsupervised K-means clustering method to classify phase using Himawari-8 (H8) multi-channel RGB images (multi-channel image algorithm, MIA). In order to evaluate the phase classification obtained by MIA, H8-CLP (H8 official product), we use CALIOP phase product as a benchmark. Through the evaluation of cloud top phase of cases from April to October in 2017, the hit rate of liquid and ice phase from H8-MIA is 88% and 65% respectively, and the total hit rate of H8-MIA algorithm is 72%. The hit rate of liquid and ice phase from H8-CLP is 81% and 62% respectively, and the total hit rate of H8-CLP algorithm is 68%. The hit rate of H8-MIA is higher than that of H8-CLP in both liquid and ice phases. It shows that the application of MIA algorithm to H8 satellite can provide more accurate and continuous cloud top phase information with high spatial and temporal resolution.</p>

scholarly journals Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers

2019 ◽  
Vol 12 (5) ◽  
pp. 2863-2879 ◽  
Author(s):  
Nikos Benas ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
Piet Stammes
Keyword(s):  
Size Distribution ◽  
Optical Thickness ◽  
Droplet Size Distribution ◽  
Effective Radius ◽  
High Temporal Resolution ◽  
Climate Data ◽  
Cloud Properties ◽  
Data Record ◽  
Stratocumulus Clouds ◽  
Cloud Optical Thickness

Abstract. Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the observation of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals are analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis is based on the use of Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectances and products from Meteosat-8 and Meteosat-10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals from two full days, over ocean and land, and using different spectral combinations of visible and shortwave-infrared channels, are performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud-bow- and cloud-glory-related irregularities to the width of the assumed droplet size distribution is analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For the case with continental clouds a broader size distribution (effective variance around 0.15) is obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

scholarly journals MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP

2015 ◽  
Vol 8 (11) ◽  
pp. 11893-11924 ◽  
Author(s):  
B. Marchant ◽  
S. Platnick ◽  
K. Meyer ◽  
G. T. Arnold ◽  
J. Riedi
Keyword(s):  
Hydrological Cycle ◽  
Temporal Distribution ◽  
Phase Discrimination ◽  
Ice Surface ◽  
Phase Classification ◽  
Microphysical Property ◽  
Moderate Resolution ◽  
Cloud Optical Thickness ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Thermodynamic Phase

Abstract. Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.

scholarly journals Seven years of global retrieval of cloud properties using space-borne data of GOME

2012 ◽  
Vol 5 (7) ◽  
pp. 1551-1570 ◽  
Author(s):  
L. Lelli ◽  
A. A. Kokhanovsky ◽  
V. V. Rozanov ◽  
M. Vountas ◽  
A. M. Sayer ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Trace Gases ◽  
Retrieval Algorithm ◽  
Cloud Properties ◽  
The Pacific ◽  
Asymptotic Equations ◽  
Spherical Cloud ◽  
The Pacific Ocean ◽  
Cloud Optical Thickness

Abstract. We present a global and regional multi-annual (June 1996–May 2003) analysis of cloud properties (spherical cloud albedo – CA, cloud optical thickness – COT and cloud top height – CTH) of optically thick (COT > 5) clouds, derived using measurements from the GOME instrument on board the ESA ERS-2 space platform. We focus on cloud top height, which is obtained from top-of-atmosphere backscattered solar light measurements in the O2 A-band using the Semi-Analytical CloUd Retrieval Algorithm SACURA. The physical framework relies on the asymptotic equations of radiative transfer. The dataset has been validated against independent ground- and satellite-based retrievals and is aimed to support trace-gases retrievals as well as to create a robust long-term climatology together with SCIAMACHY and GOME-2 ensuing retrievals. We observed the El Niño-Southern Oscillation anomaly in the 1997–1998 record through CTH values over the Pacific Ocean. The global average CTH as derived from GOME is 5.6 ± 3.2 km, for a corresponding average COT of 19.1 ± 13.9.

scholarly journals Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach

2016 ◽  
Vol 9 (3) ◽  
pp. 1135-1152 ◽  
Author(s):  
Hans Martin Schulz ◽  
Boris Thies ◽  
Shih-Chieh Chang ◽  
Jörg Bendix
Keyword(s):  
Optical Thickness ◽  
Cloud Forest ◽  
New Technique ◽  
Remotely Sensed Data ◽  
Detection Scheme ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
Frequency Map ◽  
Fog Detection

Abstract. The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of  ≈ 40, classification errors significantly increase.

scholarly journals Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator

2018 ◽  
Author(s):  
Martin Stengel ◽  
Cornelia Schlundt ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Salomon Eliasson ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Reanalysis Data ◽  
Cloud Fraction ◽  
Satellite Observations ◽  
Vertical Position ◽  
Polar Regions ◽  
Cloud Properties ◽  
Low Clouds ◽  
Low Cloud ◽  
Cloud Optical Thickness

Abstract. An evaluation of the ERA-Interim clouds using satellite observations is presented. To facilitate such an evaluation in a proper way, a simplified satellite simulator has been developed and applied to six-hourly ERA-Interim reanalysis data covering the period 1982 to 2014. The simulator converts modelled cloud fields, for example those of the ERA-Interim reanalysis, to simulated cloud fields by accounting for specific characteristics of passive imaging satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which form the basis of many long-term observational datasets of cloud properties. It is attempted to keep the simulated cloud fields close to the original modelled cloud fields to allow a quality assessment of the latter based on comparisons of the simulated clouds fields with the observations. Applying the simulator to ERA-Interim data, this study firstly focuses on spatial distribution and frequency of clouds (total cloud fraction) and on their vertical position, using cloud top pressure to express the cloud fraction of high, mid-level and low clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic uncertainties in the satellite observations and are further stratified by accounting for the limited sensitivity of the observations to clouds with very low cloud optical thickness (COT). The comparisons show that ERA-Interim has generally too low cloud fraction – nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low clouds – for which the comparisons only show a minor sensitivity to cloud optical thickness thresholds applied. The amount of ERA-Interim high clouds, being higher than in the observations, agrees to the observations within their estimated uncertainties. Removing the optically very thin clouds (COT 

scholarly journals Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers

2019 ◽  
Author(s):  
Nikos Benas ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
Piet Stammes
Keyword(s):  
Size Distribution ◽  
Optical Thickness ◽  
Droplet Size Distribution ◽  
Effective Radius ◽  
High Temporal Resolution ◽  
Climate Data ◽  
Cloud Properties ◽  
Data Record ◽  
Stratocumulus Clouds ◽  
Cloud Optical Thickness

Abstract. Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the detection of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals were analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis was based on the use of SEVIRI reflectances and products from Meteosat-8 and -10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals over different underlying surfaces (ocean/land) and using different spectral combinations of visible and shortwave-infrared channels were also performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud bow and glory related irregularities to the width of the assumed droplet size distribution was analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For a case with continental clouds a broader size distribution (effective variance around 0.15) was obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

scholarly journals Retrieval of cloud properties from sky radiometer observed spectral zenith radiances

2019 ◽  
Author(s):  
Pradeep Khatri ◽  
Hironobu Iwabuchi ◽  
Tadahiro Hayasaka ◽  
Hitoshi Irie ◽  
Tamio Takamura ◽  
...  
Keyword(s):  
Standard Procedure ◽  
Optimal Estimation ◽  
Calibration Procedure ◽  
Evaluation Study ◽  
Vapor Concentration ◽  
Ancillary Data ◽  
Cloud Particle ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
Long Term Observation

Abstract. An optimal-estimation based algorithm to retrieve cloud optical thickness (COD) and cloud-particle effective radius (CER) from spectral zenith radiances observed by a narrow field of view (FOV) ground-based sky radiometer is developed. To further address the filter-degradation problem while analyzing data of long-term observation, an on-site calibration procedure is proposed, which is found to have a very good accuracy with respect to a standard procedure, i.e., a procedure of deriving calibration constants using a master instrument. An error evaluation study conducted by assuming errors in observation-based transmittances and ancillary data of water vapor concentration and surface albedo suggests that the errors in input data can influence retrieved CER more effectively than COD. Except for some narrow domains that fall within COD 

scholarly journals Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O<sub>2</sub>–O<sub>2</sub>, MODIS, and Suomi-NPP VIIRS

2021 ◽  
Vol 14 (3) ◽  
pp. 2451-2476
Author(s):  
Steven Compernolle ◽  
Athina Argyrouli ◽  
Ronny Lutz ◽  
Maarten Sneep ◽  
Jean-Christopher Lambert ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Recognition Algorithm ◽  
Atmospheric Composition ◽  
Oxygen Absorption ◽  
Absorption Bands ◽  
Cloud Data ◽  
Geographical Patterns ◽  
Cloud Properties ◽  
Cloud Height ◽  
Cloud Optical Thickness

Abstract. Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

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