DOI: 10.5281/zenodo.17386247
title: CLARISC

description:  

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/s10584-024-03810-6