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Satellite-Based Cloud Climatologies

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 10048

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Special Issue Editors

Dr. Karl-Göran Karlsson

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Guest Editor

Swedish Meteorological and Hydrological Institute (SMHI), Folkborgsvägen 17, SE-60176 Norrköping, Sweden
Interests: cloud detection in meteorological satellite imagery; cloud climatologies; evaluation of cloud simulations in climate models

Dr. Andrew K. Heidinger

E-Mail Website
Guest Editor

NOAA/NESDIS, Cooperative Institute of Meteorological Satellite Studies (CIMSS), Space Science and Engineering Center (SSEC), University of Wisconsin, 1225 West Dayton, Madison, WI 53706-0000, USA
Interests: cloud remote sensing using imagers; new algorithms and radiative transfer models for operational satellites and sensors (AVHRR, VIIRS and ABI); sensor calibration

Special Issue Information

Dear Colleagues,

The monitoring of global cloud conditions has become increasingly important for two main reasons:

Differences in the way global and regional cloudiness are simulated in climate models explain a large part of the spread in future model-predicted temperature changes until the end of this century (i.e., in the year 2100). Validation of predicted cloud conditions and their changes requires global observations with high temporal and spatial resolution, and this task can only be achieved from space-borne sensors.
Finding the best geographic locations for solar energy production is becoming increasingly crucial because of the urgency of converting energy production from fossil-based to renewable energy sources.

In both cases, high-accuracy cloud climatologies, preferably with well-determined spatial and temporal characteristics (i.e., including trends), are required. While the second application above may primarily be satisfied with access to information on mean cloud conditions and some information on mean cloud transmissivities, the first application field requires a deeper characterisation of various cloud properties in order to fully understand and evaluate the differences seen in climate model simulations.

Satellite-based cloud climatologies have been available for about three decades, starting with the first data records from the International Satellite Cloud Climatology Project (ISCCP), derived from bispectral imagery. This was later extended and complemented with additional data records (e.g., PATMOS-X, CLARA-A2 and ESA-CLOUD-CCI) using sensors with multispectral and sounding capabilities (e.g., AVHRR, AATSR, HIRS, MODIS, SEVIRI). However, the last and the coming decade introduced (or will introduce) sensors with a greatly enhanced capacity for cloud monitoring in terms of the number of available spectral channels and their spatial and temporal measurement resolution. Thus, both the ability to describe various cloud properties as well as actual cloud processes or cloud dynamics will be increased. This means that new approaches for the compilation of cloud climatologies can be introduced.

This Special Issue will address both traditionally compiled cloud climatologies and the foreseen and ongoing changed paradigms in applied cloud climatology approaches. We welcome articles addressing the following topics:

Updates on the latest cloud climatology results from the longest available time series of satellite measurements (i.e., covering more than four decades).
Validation studies of the above-mentioned data records.
Can we trust trends in cloud properties from these long data records?
Aspects to consider when comparing the above-mentioned data records to results from climate model simulations (e.g., is there a need to use satellite-simulator tools like COSP?).
New prototype climatologies of cloud properties enabled by sensors introduced during the last decade and during the next few years (if possible, also related to experiences of the MODIS-based data records).
New cloud detection approaches (being crucial to all derived cloud climatologies): Will ANN-based methods be able to provide both more accurate products and more accurate uncertainty information?
Ways of bridging the gap between new and old types of cloud climatologies based on early sensors and the most recently introduced sensors.

Dr. Karl-Göran Karlsson
Dr. Andrew K. Heidinger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Global and regional cloud climatologies
Cloud properties
Cloud dynamics
Cloud detection
Cloud parameter retrievals
Cloud climate trends

Published Papers (9 papers)

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17 pages, 5130 KiB

Open AccessArticle

Estimation of Top-of-Atmosphere Longwave Cloud Radiative Forcing Using FengYun-4A Geostationary Satellite Data

by Ri Xu, Jun Zhao, Shanhu Bao, Huazhe Shang, Fangling Bao, Gegen Tana and Lesi Wei

Remote Sens. 2024, 16(8), 1415; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16081415 - 17 Apr 2024

Viewed by 314

Abstract

The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRF_TOA) has drawn a significant amount of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatiotemporal resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLR_clear). Consequently, this study addresses the challenge of constructing LCRF_TOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLR_clear based on the SBDART radiation transfer model (RTM), we developed a model for estimating OLR_clear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLR_clear results showed an RMSE of 5.05 W/m² and MBE of 1.59 W/m² compared to ERA5. The corresponding RMSE and MBE value compared to CERES was 6.52 W/m² and 2.39 W/m². Additionally, the calculated LCRF_TOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRF_TOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRF_TOA due to varied cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation. The results suggest that high-height clouds exert a greater degree of radiative forcing more frequently, while low-height clouds are more frequently found in the lower forcing range. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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21 pages, 20756 KiB

Open AccessArticle

A Novel Method for Cloud and Cloud Shadow Detection Based on the Maximum and Minimum Values of Sentinel-2 Time Series Images

by Kewen Liang, Gang Yang, Yangyan Zuo, Jiahui Chen, Weiwei Sun, Xiangchao Meng and Binjie Chen

Remote Sens. 2024, 16(8), 1392; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16081392 - 15 Apr 2024

Viewed by 412

Abstract

Automatic and accurate detection of clouds and cloud shadows is a critical aspect of optical remote sensing image preprocessing. This paper provides a time series maximum and minimum mask method (TSMM) for cloud and cloud shadow detection. Firstly, the Cloud Score+S2_HARMONIZED (CS+S2) is employed as a preliminary mask for clouds and cloud shadows. Secondly, we calculate the ratio of the maximum and sub-maximum values of the blue band in the time series, as well as the ratio of the minimum and sub-minimum values of the near-infrared band in the time series, to eliminate noise from the time series data. Finally, the maximum value of the clear blue band and the minimum value of the near-infrared band after noise removal are employed for cloud and cloud shadow detection, respectively. A national and a global dataset were used to validate the TSMM, and it was quantitatively compared against five other advanced methods or products. When clouds and cloud shadows are detected simultaneously, in the S2ccs dataset, the overall accuracy (OA) reaches 0.93 and the F1 score reaches 0.85. Compared with the most advanced CS+S2, there are increases of 3% and 9%, respectively. In the CloudSEN12 dataset, compared with CS+S2, the producer’s accuracy (PA) and F1 score show increases of 10% and 4%, respectively. Additionally, when applied to Landsat-8 images, TSMM outperforms Fmask, demonstrating its strong generalization capability. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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15 pages, 5075 KiB

Open AccessArticle

Decadal Stability and Trends in the Global Cloud Amount and Cloud Top Temperature in the Satellite-Based Climate Data Records

by Abhay Devasthale and Karl-Göran Karlsson

Remote Sens. 2023, 15(15), 3819; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15153819 - 31 Jul 2023

Cited by 2 | Viewed by 887

Abstract

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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17 pages, 4711 KiB

Open AccessArticle

Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020

by Karl-Göran Karlsson, Abhay Devasthale and Salomon Eliasson

Remote Sens. 2023, 15(12), 3044; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15123044 - 10 Jun 2023

Cited by 4 | Viewed by 1249

Abstract

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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17 pages, 8551 KiB

Open AccessArticle

Estimating Layered Cloud Cover from Geostationary Satellite Radiometric Measurements: A Novel Method and Its Application

by Zhonghui Tan, Shuo Ma, Xin Wang, Yudi Liu, Weihua Ai and Wei Yan

Remote Sens. 2022, 14(22), 5693; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225693 - 11 Nov 2022

Cited by 2 | Viewed by 1570

Abstract

Layered cloud cover (LCC), that is, cloud cover at different levels, is crucial for estimating cloud radiative effects and modeling climate change. However, accurate LCC characterization using passive satellite measurements is challenging because of the difficulties in resolving cloud vertical structures. In this study, we developed a novel method to estimate LCC from geostationary satellite radiometric measurements. The proposed method resolves cloud vertical structures by retrieving cloud-top and cloud-base heights for both single- and multi-layer clouds; thus, better estimating LCC. Our results agreed well with active satellite measurements, showing identification accuracies of 86%, 90%, and 91% for high, medium, and low clouds, respectively. Additionally, our LCC estimates derived from satellite measurements were used to evaluate those from atmospheric reanalysis. The annual averaged total, high, medium, and low cloud covers given by our methods were 0.681, 0.393, 0.356, and 0.455, respectively, while those from ERA-5 were 0.623, 0.415, 0.274, and 0.392, respectively. These results indicate that the total cloud cover determined by ERA-5 was lower than that derived from satellite measurements, potentially as a result of medium and low-level clouds. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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25 pages, 7333 KiB

Open AccessArticle

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

by Vasileios Tzallas, Anja Hünerbein, Martin Stengel, Jan Fokke Meirink, Nikos Benas, Jörg Trentmann and Andreas Macke

Remote Sens. 2022, 14(21), 5548; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215548 - 03 Nov 2022

Cited by 1 | Viewed by 1732

Abstract

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively). Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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0 pages, 4694 KiB

Open AccessArticle

Warming Trend and Cloud Responses over the Indochina Peninsula during Monsoon Transition

by Mien-Tze Kueh and Chuan-Yao Lin

Remote Sens. 2022, 14(16), 4077; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164077 - 20 Aug 2022

Cited by 1 | Viewed by 1188 | Correction

Abstract

An exacerbated precipitation–temperature relationship can lead to compound extremes. The role of clouds in such a relationship is relatively uncertain. Here, we investigate the cloud–precipitation–temperature relationships over the Indochina Peninsula during the summer monsoon transition. The negative correlation between cloudiness/precipitation and surface maximum temperature is valid on seasonal and interannual timescales. The near-surface temperature exhibits interdecadal variability and a long-term warming trend. The warming trend has accelerated in the past two decades. In the anomalous warm years, the remarkably strong western Pacific subtropical high inhibits the development of clouds, especially the middle and high cloud-top regimes, leading to the suppression of deep convection and precipitation. There are more optically thin (moderate to thick) clouds with smaller (larger) effective radii in the high cloud-top regime for the warm (cold) years. The dominance of shallow cumulus is a distinct feature in the warm years. The daytime heating of enhanced surface insolation due to decreased cloudiness is worsened by the dry condition of the precipitation deficit. The water vapor warming effect can prevent an efficient drop in nighttime temperature, thereby exacerbating the warm condition under the warming trend. The cloud–precipitation–temperature relationships coupling with the monsoon development can be used to diagnose the regional scale cloud–climate interactions in climate models. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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16 pages, 4887 KiB

Open AccessArticle

Using GOES-R ABI Full-Disk Reflectance as a Calibration Source for the GOES Imager Visible Channels

by Andrew K. Heidinger, Michael J. Foster, Kenneth R. Knapp and Timothy J. Schmit

Remote Sens. 2022, 14(15), 3630; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153630 - 29 Jul 2022

Cited by 1 | Viewed by 1306

Abstract

The availability of onboard calibration for solar reflectance channels on recently launched advanced geostationary imagers provides an opportunity to revisit the calibration of the visible channels on past geostationary imagers, which lacked onboard calibration systems. This study used the data from the Advanced Baseline Imager (ABI) on GOES-16 and GOES-17 to calibrate the visible channels on the GOES-IP (GOES-8, -9, -10, -11, -12, -13, and -15) sensors (1994–2021). The visible channels are dominant sources of information for many of the essential climate variables from these sensors. The technique developed uses the stability of the integrated full-disk reflectance to define a calibration target that is applied to past sensors to generate new calibration equations. These equations are found to be stable and agree well with other established techniques. Given the lack of assumptions and ease of application, this technique offers a new calibration method that can be used to complement existing techniques used by the operational space agencies with the GSICS Project. In addition, its simplicity allows for its application to data that existed prior to many of the reference data employed in current calibration methods. Full article

(This article belongs to the Special Issue Satellite-Based Cloud Climatologies)

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