Reliability Evaluation of the Joint Observation of Cloud Top Height by FY-4A and HIMAWARI-8

It is well known that the measurement of cloud top height (CTH) is important, and a geostationary satellite is an important measurement method. However, it is difficult for a single geostationary satellite to observe the global CTH, so joint observation by multiple satellites is imperative. We used both active and passive sensors to evaluate the reliability of joint observation of geostationary satellites, which includes consistency and accuracy. We analyzed the error of CTH of FY-4A and HIMAWARI-8 and the consistency between the two satellites and conducted research on the problem of missing measurement (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has CTH data, but FY-4A/HIMAWARI-8 does not) of the two satellites. The results show that FY-4A and HIMAWARI-8 have good consistency and can be jointly observed, but the measurement of CTH of FY-4A and HIMAWARI-8 has large errors, and the error of FY-4A is greater than that of HIMAWIRI-8. The error of CTH is affected by the CTH, cloud optical thickness (COT) and cloud type, and the consistency between the two satellites is mainly affected by the cloud type. FY-4A and HIMAWARI-8 have the problem of missing measurement. The missing rate of HIMAWARI-8 is greater than that of FY-4A, and the missing rate is not affected by the CTH, COT and surface type. Therefore, although FY-4A and HIMAWARI-8 have good consistency, the error of CTH and the problem of missing measurement still limit the reliability of their joint observation.

Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes

We present an updated Cloud Regime (CR) dataset based on Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically joint histograms that partition cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal area grid and pre-specified 3-hour UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1-degree daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal angle grids. Both editions consist of 11 clusters whose centroids are nearly identical.We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations, and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O₂–O₂, MODIS, and Suomi-NPP VIIRS

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.

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

<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>

Toward Cloud Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in Opaque Convective Clouds

For passive satellite imagers, current retrievals of cloud optical thickness and effective particle size fail for convective clouds with 3D morphology. Indeed, being based on 1D radiative transfer (RT) theory, they work well only for horizontally homogeneous clouds. A promising approach for treating clouds as fully 3D objects is cloud tomography, which has been demonstrated for airborne observations. However, more efficient forward 3D RT solvers are required for cloud tomography from space. Here, we present a path forward by acknowledging that optically thick clouds have “veiled cores” (VCs). Sunlight scattered into and out of this deep region does not contribute significant information about the inner structure of the cloud to the spatially detailed imagery. We investigate the VC location for the MISR and MODIS imagers. While MISR provides multiangle imagery in the visible and near-infrared (IR), MODIS includes channels in the shortwave IR, albeit at a single view angle. This combination will enable future 3D retrievals to disentangle the cloud’s effective particle size and extinction fields. We find that, in practice, the VC is located at an optical distance of ~5, starting from the cloud boundary along the line of sight. For MODIS’s absorbing wavelengths the VC covers a larger volume, starting at smaller optical distances. This concept will not only lead to a reduction in the number of unknowns for the tomographic reconstruction but also significantly increase the speed and efficiency of the 3D RT solver at the heart of the algorithm by applying, say, the photon diffusion approximation inside the VC.

Long-Term Changes in Solar Shortwave Irradiance Due to Different Atmospheric Factors According to Measurements and Reconstruction Model in Northern Eurasia

The temporal variability of solar shortwave radiation (SSR) has been assessed over northern Eurasia (40°–80° N; 10° W–180° E) by using an SSR reconstruction model since the middle of the 20th century. The reconstruction model estimates the year-to-year SSR variability as a sum of variations in SSR due to changes in aerosol, effective cloud amount and cloud optical thickness, which are the most effective factors affecting SSR. The retrievals of year-to-year SSR variations according to different factors were tested against long-term measurements in the Moscow State University Meteorological Observatory from 1968–2016. The reconstructed changes show a good agreement with measurements with determination factor R2 = 0.8. The analysis of SSR trends since 1979 has detected a significant growth of 2.5% per decade, which may be explained by its increase due to the change in cloud amount (+2.4% per decade) and aerosol optical thickness (+0.4% per decade). The trend due to cloud optical thickness was statistically insignificant. Using the SSR reconstruction model, we obtained the long-term SSR variability due to different factors for the territory of northern Eurasia. The increasing SSR trends have been detected on most sites since 1979. The long-term SSR variability over northern Eurasia is effectively explained by changes in cloud amount and, in addition, by changes in aerosol loading over the polluted regions. The retrievals of the SSR variations showed a good agreement with the changes in global radiance measurements from the World Radiation Data Center (WRDC) archive. The work was supported by RFBR grant number 18-05-00700.

Cloudy Region Drought Index (CRDI) Based on Long-Time-Series Cloud Optical Thickness (COT) and Vegetation Conditions Index (VCI): A Case Study in Guangdong, South Eastern China

Widespread and long-lasting drought disasters can aggravate environmental degradation. They can lead to significant economic losses and even affect social stability. The existing drought index mostly chose arid and semi-arid regions as study areas, because cloudy weather in humid and semi-humid regions hindered the satellite in its attempts to obtain the surface reflectivity. In order to solve this problem, a cloudy region drought index (CRDI) is proposed to estimate the drought of the clouded pixels. Due to the cumulative effect of drought, the antecedent drought index (ADI) has a certain impact on the calculation of the current drought. Furthermore, cloud is the only source of natural precipitation, and it also affects the evaporation and emission process on the ground. Therefore, based on the remote sensing drought index, ADI and cloud optical thickness (COT) are used to estimate the drought of pixels with missing data due to cloud occlusion. In this paper, a case study of the cloudy Guangdong, which is located in a humid area, is presented. First, we calculated the CRDI using Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2003 to 2017, and then discussed the effect of CRDI with the data from 2016 as examples. Through the analysis of the parameters of regression equation, filling efficiency, rationality of the estimated value, the continuity of CRDI and the rationality of CRDI spatial distribution results, it is concluded that CRDI can effectively estimate the drought severity of the cloud-covered pixels, and more comprehensive drought data can be obtained by using CRDI. The successful application of CRDI in Guangdong shows it is robust and flexible, suggesting high efficiency and great potential for further utilization.

Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O₂-O₂, MODIS and Suomi-NPP VIIRS

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 derived from Copernicus Sentinel-5 Precursor (S5P) TROPOMI radiance measurements: cloud top height and cloud optical thickness (retrieved with the S5P OCRA/ROCINN_CAL algorithm), cloud height (S5P OCRA/ROCINN_CRB and S5P FRESCO) 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 (2018-04-30 to 2020-02-27) from the CLOUDNET network of ceilometers, lidars and radars. Peculiar geographical patterns were identified, and will be mitigated in future releases of the cloud data products. Zonal mean latitudinal variation of S5P cloud properties are 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 an 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.

Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm

In this study we developed a neural network (NN) that can be used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. This effort builds upon our previous work, which explored the sensitivity of neural network input, architecture, and other design requirements for this type of remote sensing problem. In particular this work introduces a framework for appropriately weighting total and polarized reflectances, which have vastly different magnitudes and measurement uncertainties. The NN is trained using an artificial training set and applied to research scanning polarimeter (RSP) data obtained during the ORACLES field campaign (ObseRvations of Aerosols above CLouds and their intEractionS). The polarimetric RSP observations are unique in that they observe the same cloud from a very large number of angles within a variety of spectral bands, resulting in a large dataset that can be explored rapidly with a NN approach. The usefulness of applying a NN to a dataset such as this one stems from the possibility of rapidly obtaining a retrieval that could be subsequently applied as a first guess for slower but more rigorous physical-based retrieval algorithms. This approach could be particularly advantageous for more complicated atmospheric retrievals – such as when an aerosol layer lies above clouds like in ORACLES. For RSP observations obtained during ORACLES 2016, comparisons between the NN and standard parametric polarimetric (PP) cloud retrieval give reasonable results for droplet effective radius (re: R=0.756, RMSE=1.74 µm) and cloud optical thickness (τ: R=0.950, RMSE=1.82). This level of statistical agreement is shown to be similar to comparisons between the two most well-established cloud retrievals, namely, the polarimetric and the bispectral total reflectance cloud retrievals. The NN retrievals from the ORACLES 2017 dataset result in retrievals of re (R=0.54, RMSE=4.77 µm) and τ (R=0.785, RMSE=5.61) that behave much more poorly. In particular we found that our NN retrieval approach does not perform well for thin (τ<3), inhomogeneous, or broken clouds. We also found that correction for above-cloud atmospheric absorption improved the NN retrievals moderately – but retrievals without this correction still behaved similarly to existing cloud retrievals with a slight systematic offset.