Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP

The Aqua, SNPP, and JPSS satellites carry a combination of hyperspectral infrared sounders (AIRS, CrIS) and high-spatial-resolution narrowband imagers (MODIS, VIIRS). They provide an opportunity to acquire high-quality long-term cloud data records and are a key component of the existing Program of Record of cloud observations. By matching observations from sounders and imagers across different platforms at pixel scale, this study evaluates the self-consistency and continuity of cloud retrievals from Aqua and SNPP by multiple algorithms, including the AIRS Version-7 retrieval algorithm and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) Version-2 for sounders, and the Standard Aqua-MODIS Collection-6.1 and the NASA MODIS-VIIRS continuity cloud products for imagers. Metrics describing detailed statistical distributions at sounder field of view (FOV) and the joint histograms of cloud properties are evaluated. These products are found highly consistent despite their retrieval from different sensors using different algorithms. Differences between the two sounder cloud products are mainly due to cloud clearing and treatment of clouds in scenes with unsuccessful atmospheric profile retrievals. The sounder subpixel cloud heterogeneity evaluated using the standard deviation of imager retrievals at sounder FOV shows good agreement between the standard and continuity products from different satellites. However, impact of algorithm and instrument differences between MODIS and VIIRS is revealed in cloud top pressure retrievals and in the imager cloud distribution skewness. Our study presents a unique aspect to examine NASA’s progress toward building a continuous cloud data record with sufficient quality to investigate clouds’ role in global environmental change.

Improved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS data

An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS–MODIS data set consists of 162 117 combined scenes sampled on 208 d over 2005–2020. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For previously unseen data, the algorithm successfully detects > 93 % of profiles affected by clouds, up from ∼ 16 % for the Level 2 flagging. At the same time, false positives reported for actually clear profiles are comparable to the Level 2 results. The classification performance is not dependent on geolocation but slightly decreases over low-cloud-cover regions. The new cloudiness flag is applied to determine average global cloud cover maps over 2015–2019, successfully reproducing the spatial patterns of mid-level to high clouds seen in MODIS data. It is also applied to four example cloud fields to illustrate its reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure (pCT) yields reliable predictions with correlation coefficients > 0.82. It is shown that the model can correctly identify > 85 % of profiles with pCT < 400 hPa. Similar to the cloud classification model, global maps and example cloud fields are provided, which reveal good agreement with MODIS results. The combination of the cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.

EVALUATION OF CLOUDS, RADIATION, AND PRECIPITATION IN CMIP6 MODELS USING GLOBAL WEATHER STATES DERIVED FROM ISCCP-H CLOUD PROPERTY DATA

A clustering methodology is applied to cloud optical depth cloud top pressure (TAU-PC) histograms from the new, 1-degree resolution, ISCCP-H dataset, to derive an updated global Weather State (WS) dataset. Then, PC-TAU histograms from current-climate CMIP6 model simulations are assigned to the ISCCP-H WSs along with their concurrent radiation and precipitation properties, to evaluate model cloud, radiation, and precipitation properties in the context of the Weather States. The new ISCCP-H analysis produces WSs that are very similar to those previously found in the lower resolution ISCCP-D dataset. The main difference lies in the splitting of the ISCCP-D thin stratocumulus WS between the ISCCP-H shallow cumulus and stratocumulus WSs, which results in the reduction by one of the total WS number. The evaluation of the CMIP6 models against the ISCCP-H Weather States, shows that, in the ensemble mean, the models are producing an adequate representation of the frequency and geographical distribution of the WSs, with measurable improvements compared to the WSs derived for the CMIP5 ensemble. However, the frequency of shallow cumulus clouds continues to be underestimated, and, in some WSs the good agreement of the ensemble mean with observations comes from averaging models that significantly overpredict and underpredict the ISCCP-H WS frequency. In addition, significant biases exist in the internal cloud properties of the model WSs, such as the model underestimation of cloud fraction in middle-top clouds and secondarily in midlatitude storm and stratocumulus clouds, that result in an underestimation of cloud SW cooling in those regimes.

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.

Improved cloud detection for the Aura Microwave Limb Sounder: Training an artificial neural network on colocated MLS and Aqua-MODIS data

An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1,710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS-MODIS data set consists of 162,117 combined scenes sampled on 208 days over 2005–2020. We show that the algorithm can correctly classify > 96 % of cloudy and clear instances for previously unseen MLS scans. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For all profiles in the colocated MLS-MODIS data set, the algorithm successfully detects 97.8 % of profiles affected by clouds, up from 15.8 % for the Level 2 flagging. Meanwhile, false positives reported for actually clear profiles are reduced to 1.7 %, down from 6.2 % in Level 2. The classification performance is not dependent on geolocation. The new cloudiness flag is applied to determine average global cloud cover between 2015 and 2019, successfully reproducing the spatial patterns of mid-level to high clouds reported in previous studies. It is also applied to four example cloud fields to illustrate the reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure yields reliable predictions with correlation coefficients greater than 0.99. The combination of cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

In order to better understand cloud-precipitation relationships, we extend the concept of cloud regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), to include precipitation information. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, we derive cloud-precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1-degree grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics.In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud-precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies where characterization of the diurnal cycle is essential.

Cloud regime analysis over Central Europe based on 14 years of satellite data

&lt;p&gt;&lt;span&gt;The improvement of our understanding of the spatiotemporal variability of cloud properties and their governing processes is of high importance, given the crucial role of clouds in the climate system. The availability of long-term and high-quality satellite observations together with mature remote sensing techniques has made feasible the creation of multi-decadal climate data records for this purpose.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Various cloud classification techniques have been developed and applied in the past, each with distinct advantages and disadvantages, allowing studying clouds from different perspectives. One of these techniques is the creation of cloud regimes which provides information on the prevalence of simultaneously occurring cloud types over a region. This study uses the k-means clustering method, applied to 2-dimensional histograms of cloud top pressure and optical thickness, in order to derive and analyze cloud regimes over Europe during the last decade. Europe is selected for this work because it is an appropriate region for studying cloud regimes since the prevailing atmospheric circulation patterns and its diverse geomorphology, result in a mixture of diverse cloud types. In order to achieve that, the CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), is used as basis for the derivation of the cloud regimes. In particular, pixel-level Cloud Optical Thickness (COT) and Cloud Top Pressure (CTP) products of CLAAS-2.1, from 2004 to 2017, are used in order to compute 2D histograms on a 1&amp;#176;&amp;#215;1&amp;#176; spatial resolution. Then the k-means clustering algorithm is applied, treating each 2D COT-CTP histogram of each grid point and time step as an individual data point. Various sensitivity studies on the subsampling of the data and the selection of the cloud regimes were carried out, in order to test the robustness of the method and of the results.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In contrast to the previous studies and taking advantage of the geostationary orbit of Meteosat Second Generation (MSG), on which SEVIRI is aboard, a better sampling of the diurnal cycle of clouds is thus included in the derivation process of cloud regimes. Furthermore, the annual cycle of the produced cloud regimes is examined. In addition, for each regime, the time step with its highest spatial frequency of occurrence is selected for a visual comparison with the corresponding RGB image. Finally, a comparison of the cloud regimes against the synoptic large scale weather pattern classification is investigated. The weather pattern classification consists of 29 typical defined patterns of the daily synoptic circulation and it is produced by the German Weather Service (DWD).&lt;/span&gt;&lt;/p&gt;

Changes in HIRS Detection of Cloud over Australia from 1985 to 2001

A long-term archive of cloud properties (cloud top pressure, CTP; and cloud effective emissivity, ε) determined from High-resolution Infrared Radiation Sounder (HIRS) data is investigated for evidence of regional cloud cover change. In the 17 years between 1985 and 2001, different cloud types are analysed over the Australian region (10° S–45° S, 105° E–160° E) and areas of change in total cloud frequency examined. Total cloud frequency change over the Australian region between two adjacent eight-year time periods (1994 to 2001 minus 1985 to 1992) shows the largest increases (ranges between 6% and 12%) of average HIRS total cloud cover occurring over the offshore regions to the northwest and northeast of the continent. Over land, the largest reduction of average HIRS total cloud frequency is in the southwestern region of Australia (between 2% and 8%). Through central Australia, there is a 2% to 7% increase in average HIRS total cloud frequency when comparing these eight-year periods. This paper examines the regional cloud changes in 17 years over Australia that are embedded in global cloud statistics. Examining total HIRS cloud cover frequency over Australia and comparing two different eight-year time periods, has highlighted notable areas of average change. Preliminary reporting of satellite-derived HIRS cloud products and Global Precipitation Climatology Project (GPCP) rainfall products during La Niña seasons between 1985 and 2001 has also been undertaken.

A Fast Retrieval of Cloud Parameters Using a Triplet of Wavelengths of Oxygen Dimer Band around 477 nm

Clouds act as a major reflector that changes the amount of sunlight reflected to space. Change in radiance intensity due to the presence of clouds interrupts the retrieval of trace gas or aerosol properties from satellite data. In this paper, we developed a fast and robust algorithm, named the fast cloud retrieval algorithm, using a triplet of wavelengths (469, 477, and 485 nm) of the O2–O2 absorption band around 477 nm (CLDTO4) to derive the cloud information such as cloud top pressure (CTP) and cloud fraction (CF) for the Geostationary Environment Monitoring Spectrometer (GEMS). The novel algorithm is based on the fact that the difference in the optical path through which light passes with regard to the altitude of clouds causes a change in radiance due to the absorption of O2–O2 at the three selected wavelengths. To reduce the time required for algorithm calculations, the look-up table (LUT) method was applied. The LUT was pre-constructed for various conditions of geometry using Vectorized Linearized Discrete Ordinate Radiative Transfer (VLIDORT) to consider the polarization of the scattered light. The GEMS was launched in February 2020, but the observed data of GEMS have not yet been widely released. To evaluate the performance of the algorithm, the retrieved CTP and CF using observational data from the Global Ozone Monitoring Experiment-2 (GOME-2), which cover the spectral range of GEMS, were compared with the results of the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm, which is based on the O2 A-band. There was good agreement between the results, despite small discrepancies for low clouds.

Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.