CLARISC

CLARISC: A cloud database for cloud amount, cloud optical thickness and cloud top pressure for liquid and ice phase clouds. CLARISC was produced by the synergy of ISCCP-H and CLARA satellite products. CLARISC_v2: ISCCP-H and CLARA A2.1 CLARISC_v3: ISCCP-H and CLARA A3 🌤️ Why use CLARISC? The CLARISC cloud database significantly enhances the performance of radiative transfer model (RTM) simulations of surface solar radiation (SSR) and its long-term changes. By incorporating CLARISC as input, the agreement between modeled and ground-based SSR measurements, both in absolute values and in temporal trends, is notably improved. Structurally, CLARISC extends the CLARA dataset by adopting a framework similar to ISCCP-H, providing a more detailed vertical and microphysical representation of clouds. It separates daytime clouds into low-, mid-, and high-level layers, distinguished by phase (liquid or ice), and provides corresponding parameters for cloud amount, cloud optical thickness, and cloud-top pressure.This level of detail is not available in CLARA alone, making CLARISC a more comprehensive and RTM-ready cloud dataset for surface radiation studies. Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. Which are the main drivers of Global Dimming and Brightening? Atmospheric Research, Volume 322, 2025, 108140, ISSN 0169-8095. https://doi.org/10.1016/j.atmosres.2025.108140 CLARISC was created by Dr. Michael Stamatis during his PhD, and for further details, equations and methodology, please refer to his PhD Thesis: https://www.didaktorika.gr/eadd/handle/10442/59941 and the accompanying CLARISC.pptx file. Description Improved and state-of-the-art databases for cloud and aerosol properties, such as EUMETSAT’s CLARA-A2.1, NASA’s ISCCP-H and MERRA-2, were used as input data to the RTM. The same model was utilized during the framework of this PhD, in the work by Stamatis et al. (2023), but using as input only data from ISCCP-H (for clouds) and MERRA-2 (for aerosols and other meteorological data). However, as described in detail in that work, artificial patterns were detected in the changes of SSR, i.e. GDB, originating from corresponding artificial patterns in the ISSCP-H cloud satellite data, which are associated with changes in satellite viewing geometries. Thus, in order to rectify these artificial patterns, it was decided to prepare a new cloud input dataset to the model, free from these artifacts. To this aim, a new cloud database was created containing the model required low-, medium- and high-level cloud amount and optical thickness for liquid and ice phase clouds, similar to those provided by the ISCCP-H. To achieve this the CLARA-A2.1 satellite database was chosen, because its data do not suffer from changing satellite viewing geometries or artificial patterns due to the applied methodology for cloud screening. This methodology produced enhancements by utilizing high-quality cloud data also from CALIPSO-CALIOP. Additionally, the original AVHRRGAC Level 1b dataset was significantly improved through rigorous quality control measures, including the removal of orbit overlaps (Karlsson and Hakansson 2018). CLARA-A2.1 is built using historical data from the Advanced Very High Resolution Radiometer (AVHRR), which was used aboard NOAA satellites in polar orbit, and the Metop polar orbiters, run by EUMETSAT since 2006. Five spectral channels—two visible and three infrared—with an initial horizontal field-of-view (FOV) size of 1.1 km are measured by AVHRR. The first edition of the data record, that was released in 2012 (Karlsson et al., 2013), is enhanced and expanded upon by CLARA-A2.1, which now spans a 40-year period (1982–2022). Cloud mask/cloud amount, cloud top temperature/pressure/height, cloud thermodynamic phase, cloud optical thickness (for liquid and ice clouds separately), particle effective radius, and cloud water path are among the cloud products provided by CLARA-A2.1. These cloud products can be found as daily and monthly averages in a latitude-longitude grid of 0.25° by 0.25”, as well as daily global products (Level 2b) that have been resampled on a grid of 0.05° by 0.05°. The monthly products are averages of all the daily means. In the present study the following parameters from CLARA-A2.1 database were utilized: (a) the day-time Fractional Cloud Cover (CFC) or cloud amount (CA) in % for the total atmospheric column, (b) the day and night-time CFC in % for the total atmospheric column and for low-, middle- and high level clouds, which used to produce initially the day-time CA for low-, middle- and high level CA (Eq. 2.8), (c) the Cloud Optical Thickness (COT) for liquid and for solid phase clouds and (d) the Cloud Top Pressure (CTP) in hPa. However, CLARA-A2.1 only provides the total daytime cloud amount and optical depth in the entire atmospheric column and not low-, middle- and high-level cloud amounts which are used as input data in our RTM. Thus, the CLARA-A2.1 cloud amount and cloud optical thickness had to be apportioned to low-, middle- and high-level clouds, all separately in both liquid and solid phases. The new cloud input data were produced using the original CLARA-A2.1 data combined with information from ISCCP-H, specifically the ratios of the ISCCP-H cloud amounts of a specific level i (low, middle, high) and phase j (liquid, ice) over the ISCCP-H total cloud amount. The new cloud amounts were estimated using Eqs. 2.8 and 2.9. The cloud optical thickness (COT) wasn’t computed directly by the ISCCP-H COT ratios, but first the cloud transmissivity (tr) was calculated as shown in Eqs. 2.10 – 2.14 (Vardavas and Taylor, 2011) with the help of the corresponding ISCCP-H transmissivity ratios and then the computed transmissivity for low-, middle-, high-level clouds for liquid and ice phase converted to the corresponding COT (Eq. 2.15). Note here that g is the cloud scattering asymmetry factor, μ is the cosine of zenith angle and the k is the ratio of absorbing cloud optical depth to scattering cloud optical depth. Due to the synergy between CLARA and ISCCP, the CLARISC cloud types include one low-, one middle-, and one high-level cloud types, each one in liquid and ice phases, thus the overall number of CLARISC cloud types is 6. According to the new cloud database, produced based on the synergy of CLARA-A2.1 and ISCCP-H and named as CLARISC, the cloud changes, though being partially similar over land to the corresponding ones based on ISCCP-H, do not show artificial patterns (Figure AD1). For example, as it is shown in Figure AD1ia-d, the artificial pattern over the Atlantic Ocean is linked to the actual coverage from the geostationary satellite METEOSAT (Karlsson and Devasthale 2018), while the pattern over the Indian Ocean is linked to the region where geostationary data were unavailable until 1997 and AVHRR polar-orbiting satellite data were used instead (Evan et al., 2007). The removal of ISCCP-H artificial patterns in the CLARISC cloud amount changes resulted (as it will be shown in the next section) in a similar absence in the associated model computed SSR changes. CLARA A3: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLARA_AVHRR_V003 CLARA A2.1: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLARA_AVHRR_V002_01 ISCCP-H: https://www.ncei.noaa.gov/products/climate-data-records/cloud-properties-isccp Data structure: Coordinates: time x lon x lat x cloud_type Data Variables: cldamt_types: cloud amount tau_types: cloud optical thickness pc_types: cloud top pressure time range: January 1984 - December 2018 cloud_types: 1: low-level liquid clouds 2: low-level ice clouds 3: mid-level liquid clouds 4: mid-level ice clouds 5: high-level liquid clouds 6: high-level ice clouds <xarray.Dataset> Size: 6GBDimensions: (time: 420, lon: 576, lat: 361, cloud_type: 6)Coordinates: * time (time) datetime64[ns] 3kB 1984-01-01 1984-02-01 ... 2018-12-01 * lon (lon) float32 2kB -180.0 -179.4 -178.8 ... 178.1 178.8 179.4 * lat (lat) float32 1kB -90.0 -89.5 -89.0 -88.5 ... 89.0 89.5 90.0 * cloud_type (cloud_type) int64 48B 1 2 3 4 5 6Data variables: cldamt_types (cloud_type, time, lat, lon) float32 2GB ... tau_types (cloud_type, time, lat, lon) float32 2GB ... pc_types (cloud_type, time, lat, lon) float32 2GB ... The CLARISC_v2 cloud database has also been used as input to the Radiative Transfer Model in these 2 works of my PhD: Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. Which are the main drivers of Global Dimming and Brightening? Atmospheric Research, Volume 322, 2025, 108140, ISSN 0169-8095. https://doi.org/10.1016/j.atmosres.2025.108140 Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. How strong are the links between global warming and surface solar radiation changes? Climatic Change 177, 156 (2024). https://doi.org/10.1007/s10584-024-03810-6CLARISC：面向液态与冰相云的云量、云光学厚度及云顶气压云数据库。该数据集由ISCCP-H与CLARA卫星产品融合生成。 CLARISC_v2：基于ISCCP-H与CLARA A2.1构建 CLARISC_v3：基于ISCCP-H与CLARA A3构建 🌤️ 为何选用CLARISC？ CLARISC云数据库可显著提升地表太阳辐射（Surface Solar Radiation, SSR）及其长期变化的辐射传输模型（Radiative Transfer Model, RTM）模拟性能。将CLARISC作为模型输入后，模式模拟与地面观测的SSR在绝对数值与时间趋势上的一致性均得到显著提升。 从结构上看，CLARISC借鉴ISCCP-H框架对CLARA数据集进行了拓展，实现了对云更精细的垂直与微物理表征。该数据集将日间云按相态（液态/冰相）划分为低云、中云与高云三层，并提供对应云量、云光学厚度及云顶气压参数。CLARA单一数据集无法提供此类细节，因此CLARISC是面向地表辐射研究的更全面、适配辐射传输模型的云数据库。 Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. Which are the main drivers of Global Dimming and Brightening? Atmospheric Research, Volume 322, 2025, 108140, ISSN 0169-8095. https://doi.org/10.1016/j.atmosres.2025.108140 CLARISC由Michael Stamatis博士在其博士研究期间构建，如需获取详细公式与方法学信息，请参阅其博士论文：https://www.didaktorika.gr/eadd/handle/10442/59941 及配套的CLARISC.pptx文件。 ## 数据集描述 本研究将云与气溶胶属性的先进改进数据集（如欧洲气象卫星应用组织（EUMETSAT）的CLARA-A2.1、美国国家航空航天局（NASA）的ISCCP-H与MERRA-2）作为辐射传输模型（RTM）的输入数据。本博士研究的前期工作（Stamatis等，2023）也曾使用同一模型，但仅以ISCCP-H（云数据）与MERRA-2（气溶胶及其他气象数据）作为输入。然而，正如该工作中详细描述的，在地表太阳辐射（SSR，即GDB）的变化中检测到了人工伪迹，这些伪迹源自ISCCP-H云卫星数据中的对应人工模式，与卫星观测几何的变化相关。因此，为校正这些人工伪迹，研究团队决定构建一个不含此类伪迹的新型云输入数据集。为此，研究人员创建了新的云数据库，包含辐射传输模型所需的、按液态与冰相划分的低、中、高云云量与光学厚度，格式与ISCCP-H提供的数据一致。 本研究选用CLARA-A2.1卫星数据库，因其数据不会因卫星观测几何变化或云筛查方法导致的人工伪迹而失真。该云筛查方法通过结合CALIPSO-CALIOP的高质量云数据实现了数据优化。此外，原始AVHRRGAC Level 1b数据集通过严格的质量控制措施（包括移除轨道重叠数据，Karlsson与Hakansson，2018）得到了显著优化。 CLARA-A2.1基于极轨NOAA卫星与欧洲气象卫星应用组织（EUMETSAT）自2006年起运行的Metop极轨卫星搭载的先进甚高分辨率辐射计（Advanced Very High Resolution Radiometer, AVHRR）的历史数据构建。AVHRR可采集5个光谱通道（2个可见光通道与3个红外通道），初始水平视场（FOV）尺寸为1.1 km。CLARA-A2.1对2012年发布的初代数据记录（Karlsson等，2013）进行了优化与扩展，数据时间跨度现已覆盖1982–2022年共40年。 CLARA-A2.1提供的云产品包括：云掩码/云量、云顶温度/气压/高度、云热力学相态、云光学厚度（分别针对液态与冰相云）、粒子有效半径及云水路径。上述云产品以日平均与月平均形式提供，空间分辨率为0.25°×0.25°经纬度网格；同时提供重采样至0.05°×0.05°网格的日尺度全球产品（Level 2b）。月均产品为所有日均值的平均。 本研究使用了CLARA-A2.1数据库中的以下参数：(a) 整层大气日间云量分数（CFC）或云量（CA，以百分比计）；(b) 整层大气及低、中、高云的昼夜云量分数（CFC，百分比），用于初始计算低、中、高云的日间云量（公式2.8）；(c) 液态与固态相态云的云光学厚度（COT）；(d) 云顶气压（CTP，单位为百帕hPa）。 但CLARA-A2.1仅提供整层大气的日间总云量与光学厚度，并未提供辐射传输模型所需的低、中、高云云量。因此，研究团队需要将CLARA-A2.1的云量与云光学厚度按液态与固态相态分别分配至低、中、高云。新型云输入数据通过结合原始CLARA-A2.1数据与ISCCP-H的信息生成，具体利用ISCCP-H中特定层级i（低、中、高）与相态j（液态、冰相）的云量占总云量的比例。新云量通过公式2.8与2.9估算得到。 云光学厚度（COT）并非直接通过ISCCP-H的COT比例计算，而是先借助ISCCP-H的透射率比例，通过公式2.10–2.14（Vardavas与Taylor，2011）计算云透射率（tr），再将液态与冰相的低、中、高云透射率转换为对应云光学厚度（公式2.15）。此处需说明：g为云散射不对称因子，μ为天顶角余弦，k为吸收云光学厚度与散射云光学厚度的比值。 由于CLARA与ISCCP的融合，CLARISC的云类型包括低、中、高云各一层，每层分别对应液态与冰相，因此CLARISC共包含6种云类型。基于CLARA-A2.1与ISCCP-H融合构建的新型云数据库CLARISC，其云量变化虽在陆地上与ISCCP-H的结果部分相似，但未出现人工伪迹（图AD1）。例如，图AD1ia-d显示，大西洋上空的人工伪迹与对地静止卫星METEOSAT的实际覆盖范围相关（Karlsson与Devasthale，2018）；而印度洋上空的伪迹则与1997年前缺乏对地静止卫星数据、转而使用AVHRR极轨卫星数据的区域相关（Evan等，2007）。CLARISC移除了ISCCP-H云量变化中的人工伪迹，后续章节将证明，模型计算的地表太阳辐射变化也同样不存在此类伪迹。 CLARA A3: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLARA_AVHRR_V003 CLARA A2.1: https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLARA_AVHRR_V002_01 ISCCP-H: https://www.ncei.noaa.gov/products/climate-data-records/cloud-properties-isccp ## 数据结构 坐标维度：时间 × 经度 × 纬度 × 云类型 数据变量： cldamt_types：云量 tau_types：云光学厚度 pc_types：云顶气压 时间范围：1984年1月至2018年12月 云类型： 1: 低云（液态） 2: 低云（冰相） 3: 中云（液态） 4: 中云（冰相） 5: 高云（液态） 6: 高云（冰相） <xarray.Dataset> 数据量：6 GB 维度：(time: 420, lon: 576, lat: 361, cloud_type: 6) 坐标： * time (time) datetime64[ns] 3kB 1984-01-01 1984-02-01 ... 2018-12-01 * lon (lon) float32 2kB -180.0 -179.4 -178.8 ... 178.1 178.8 179.4 * lat (lat) float32 1kB -90.0 -89.5 -89.0 -88.5 ... 89.0 89.5 90.0 * cloud_type (cloud_type) int64 48B 1 2 3 4 5 6 数据变量： cldamt_types (cloud_type, time, lat, lon) float32 2GB ... tau_types (cloud_type, time, lat, lon) float32 2GB ... pc_types (cloud_type, time, lat, lon) float32 2GB ... 本博士研究的两项工作也将CLARISC_v2云数据库作为辐射传输模型的输入： 1. Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. Which are the main drivers of Global Dimming and Brightening? Atmospheric Research, Volume 322, 2025, 108140, ISSN 0169-8095. https://doi.org/10.1016/j.atmosres.2025.108140 2. Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I. How strong are the links between global warming and surface solar radiation changes? Climatic Change 177, 156 (2024). https://doi.org/10.1007/s10584-024-03810-6