International Satellite Cloud Climatology Project Research Articles

Lessons Learned from the Updated GEWEX Cloud Assessment Database

AbstractSince the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

Climatology and trends of cloudiness in a productive rice and vegetable region of South‐Eastern South America

AbstractClouds are a key element of the climate system. They play a crucial role in modulating solar radiative flux reaching the Earth's surface and influencing environmental conditions. In particular, total cloud cover (TCC) has both direct and indirect influence on agricultural production. Through dialogues with vegetable and rice producers of Northeastern Argentina (NEA), an important productive region in South‐Eastern South America, the key role of cloud cover on crop yields was brought to the forefront. In this study, we present the climatology and observed long‐term changes in TCC in NEA. The analyses are performed based on two independent datasets: TCC ground‐based (GB) observations and satellite‐based estimates from the International Satellite Cloud Climatology Project (ISCCP). The datasets cover a common period, from December 1983 to November 2016 (Satellite Period), while GB TCC observations extend further, from March 1961 to February 2021 (GB Period). To facilitate a more comprehensive examination of GB TCC, we introduce a novel Cloud Index, expressed in a familiar unit (%), which allows  the study of its temporal variations. TCC exhibits a distinct annual cycle and substantial spatial variability over NEA, consistently evident in both satellite and GB datasets. Over the Satellite Period (GB Period), we observed a decrease in NEA's TCC in both datasets (GB dataset), with seasonal variations and spatial heterogeneity. We ensured the reliability of our results by comparing the two datasets, which showed similar temporal variability, although the ISCCP Cloud Amount values were larger than the GB TCC. The described TCC climatology and observed changes have considerable implications for NEA's agricultural production and provide a robust foundation for future research. This article generates robust methodological basis for the analysis of this complex variable, a necessary step to explore the forcing of climate variability that modulates cloudiness in future research, and that could facilitate future studies in other regions of South America and the world.

Cloud type frequency over Poland (2003–2021) revealed by independent satellite‐based (MODIS) and surface‐based (SYNOP) observations

AbstractRecent studies have identified several statistically significant trends in cloud genera frequency over Poland. Notably, there has been an increase in high and convective clouds, along with a decrease in Stratus, Altostratus and Nimbostratus. These earlier studies were, however, only based on traditional, surface data. As the ability to observe mid and high‐level clouds from the ground is limited, due to overlapping, we address the question of whether these trends are confirmed by other sources of cloud data, such as satellite records. In this paper, we analyse cloud data from two fully independent sources to characterize the cloud type climatology over Poland for the period 2003–2021: cloud optical thickness (COT) and cloud top pressure (CTP) parameters retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS), and surface (SYNOP) observations of cloud genera from the country's 27 ground‐based stations. The International Satellite Cloud Climatology Project (ISCCP) COT–CTP classification was used to define MODIS cloud types. We explore the hypothesis that MODIS data, classified using the ISCCP philosophy, confirms the changes in cloud type frequencies found in SYNOP observations. Our analysis is based on the application of the Spearman rank correlation test to mean monthly SYNOP and MODIS cloud type frequencies, and we compare per decade trends for two time series. Our hypothesis was confirmed, but only for Cirrus, Altostratus + Nimbostratus and Cumulus. We therefore conclude that the two analysed data sources are not fully consistent, and cannot substitute for each other in cloud type climatologies. Hence, at least for Poland, they should be treated as independent rather than complementary. We also demonstrate that the increase in high‐level cloud frequency over Poland, which has been observed by other authors who based their research on synoptic data, is not due to a decrease in the frequency of low‐ and mid‐level clouds. Our findings are confirmed by satellite records.

Preliminary Investigation of Total Ozone Column and Its Relationship with Atmospheric Variables

This study attempts to investigate the interaction between lower and upper atmosphere, employing daily data of Total Ozone Column (TOC) and atmospheric parameter (cloud cover) over Nigeria from 1998-2012; in order to study the dynamic effect of ozone on climate and vice versa. This is due to the fact that ozone and climate influence each other and the understanding of the dynamic effect of the interconnectivity is still an open research area. Monthly mean daily TOC and cloud cover data were obtained from the Earth Probe Total Ozone Mass Spectroscopy (EPTOMS) and the International Satellite Cloud Climatology Project (ISCCP)-D2 datasets respectively. Bivariate analysis and Mann Kendall trend tests were used in data analysis. MATLAB and ArcGIS software were employed in analyzing the data. Results reveal that TOC increased spatially from the coastal region to the north eastern region of the country. Seasonally, the highest value of TOC was observed at the peak of rainy season when cloud activity is very high, while the lowest value was recorded in dry season. These variations were attributed to rain producing mechanisms and atmospheric phenomena which influence the transport and distribution of ozone. Furthermore, the statistical analysis reveals significant relationship between TOC and low and middle cloud covers in contrast to high cloud cover. This relationship is consistent with previous studies using other atmospheric variables. This study has given scientific insight which is useful in understanding the coupling of the lower and upper atmosphere.

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

Interdecadal change in autumn rainfall over Southeast China and its association with tropical Pacific SST

In this paper, Japan Meteorological Agency (JRA-55) reanalysis and observational rainfall datasets from the National Climate Center (NCC) of China, as well as satellite datasets from the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Climatology Project (GPCP), and International Satellite Cloud Climatology Project (ISCCP), are used. The correlation coefficient and fast Fourier transform (FFT) low-pass filter are also used, in order to reveal the interdecadal decrease in autumn rainfall in Southeast China (SEC) after 1990. The close and robust relationship between the interdecadal variation in autumn rainfall in SEC and sea surface temperature (SST) in the tropical Pacific is investigated. The most significant and stable region of correlation is located in 10° S–10° N, 160° E–160° W, in which there also exists interdecadal warming after 1990. Furthermore, the interdecadal warming of SST can induce Gill responses of the atmosphere: a cyclone anomaly is produced on each side of the equator in the lower troposphere, with a westerly anomaly to the west of the dateline, and an anticyclone anomaly is produced in the upper troposphere. In particular, the cyclone anomaly on the northern side of the equator is located in the Northwest Pacific (NWP), and its ambient northerly airflow weakens meridional water vapor transport, as well as the local descending motion and low-troposphere divergence, in favor of the interdecadal decrease in SEC rainfall after 1990. In addition, the sensitive experiments with ECHAM-5.4 model also confirm that the interdecadal warming in the region (10° S–10° N, 160° E–160° W) would motivate the atmospheric Gill response and thereby cause the sinking motion in SEC and support the interdecadal decrease in autumn rainfall in SEC.

Cloud Screening Method in Complex Background Areas Containing Snow and Ice Based on Landsat 9 Images.

The first step in the application of Landsat 9 imagery is cloud screening, and the International Satellite Cloud Climatology Project (ISCCP) has made cloud screening an important part of the World Climate Research Program. The accurate identification of clouds in remote sensing satellite images containing snow and ice on the subsurface has been a challenging task in the cloud screening process. It is imperative to fully exploit the characteristic heterogeneous information of the cloud and snow subsurface, to solve the problem of cloud–snow confusion in the snow and ice environment, and to carry out research on cloud screening technology without interference from the snow and ice subsurface. In view of this, this paper will systematically carry out research on cloud screening methods in snow and ice environments. In this paper, we propose the building of a cloud screening algorithm that takes into account the difficulty of the fact that snow and ice on the subsurface can easily interfere with cloud recognition, and the influence of an empirical threshold or statistical threshold that makes its application less effective, and then establish a dynamic threshold cloud screening algorithm that is suitable for snow and ice environments. The research results will provide new ideas and perspectives to solve the problem of surface-type interference that most of the existing cloud screening algorithms contend with.

Cloud Response Is Significantly Biased by CMIP6 Over the Tibetan Plateau

AbstractTo reduce the uncertainty of cloud feedback, models should prioritize the evaluation of cloud response to surface warming. Using International Satellite Cloud Climatology Project‐H data sets, we quantified the cloud fraction response of different cloud types to surface warming in the Tibetan Plateau (TP) and examined the performance of Coupled Model Intercomparison Project Phase 6 models in reproducing cloud response. Results showed multimodel ensemble mean (MEM) exhibited a similar pattern of total cloud fraction response with the observation but severely underestimated it over the western TP. MEM also obviously underestimated cloud fraction of optically thin cirrus (bias >15%) and had a wrong trend of −0.02% 10a−1 against observed +2.44% 10a−1. Moreover, models failed to reproduce extensive negative response of optically thick clouds and exhibited significant underestimation for the negative response of optically thick but lower‐top clouds. Especially, models even simulated an opposite response with International Satellite Cloud Climatology Project for optically thin but lower‐top cloud (bias hit −0.4% K−1).

A study on the anomalous TOA net radiative warming by clouds in a sub-region within the Indian summer monsoon region

Indian summer monsoon clouds generally exert a net radiative cooling at top of atmosphere. In contrast, clouds over a sub-region comprising parts of South peninsular India, Sri Lanka and adjoining Bay of Bengal (SISB) inside the Indian monsoon region exert a net radiative warming. In this work, an attempt is made to understand the reasons behind the anomalous radiative warming found over SISB. Ten-year (2000–2009) top of atmosphere radiative flux data from Clouds and the Earth’s Radiant Energy System payloads onboard Aqua and Terra satellites and cloud data from International Satellite Cloud Climatology Project during the peak Indian summer monsoon season of July–August are analyzed to understand the underlying causes. Top of Atmosphere net radiative forcing is positive and as high as ∼15 Wm−2 over SISB during the peak Indian summer monsoon season.Cloud cover amounts over SISB with net radiative warming and north Bay of Bengal with net radiative cooling are compared. Cloud cover amounts of high-level cirrostratus and deep convective cumulonimbus clouds are less by 65% and 87% respectively over SISB when compared to north Bay of Bengal. SISB is located on the leeward side of the Western Ghats mountain chain with descending motion. Adiabatic compression and associated warming of descending air leads to dehydration of air parcel. The upper tropospheric tropical easterly jet sweeps the deep convective cloud tops of the Indian monsoon and advect them to large distance as upper level thin cirrus family of clouds. When these clouds reach SISB with descending motion, they thin out by melting and evaporation or sublimation as the air mass is compressed and heated adiabatically. Over SISB, pressure vertical velocity at 500 hPa is moderately inversely correlated to cloud cover amount of cirrostratus and cumulonimbus clouds. Cloud cover amount of these two clouds is significantly correlated to shortwave cloud radiative forcing and longwave cloud radiative forcing over SISB. When cloud cover amount of cirrostratus and cumulonimbus clouds are low (<∼15% and <∼5% respectively), magnitude of longwave cloud radiative forcing is mostly higher than the magnitude of shortwave cloud radiative forcing which results in net radiative warming. Reduction in cloud cover amount of cirrostratus and cumulonimbus clouds by subsidence appears to be the cause behind the observed warming over SISB. When the magnitude of shortwave cloud radiative forcing is below (above) ∼55 Wm−2, longwave cloud radiative forcing is more (less) than the magnitude of shortwave cloud radiative forcing which results in net warming (cooling). Net radiative warming is found in 65% of grid points over SISB.

The Interaction between African Easterly Waves and Different Types of Deep Convection and Its Influence on Atlantic Tropical Cyclones

This study revisited the association of African easterly waves (AEWs) to Atlantic tropical cyclone (TC) development using weather states (WSs) from the International Satellite Cloud Climatology Project, National Hurricane Center best track hurricane data (HURDAT2), and reanalysis products. The WS data are used as a proxy for two different types of deep convection. This study covers July–October 1984–2009. Statistical analysis based on HURDAT2 and objectively tracked AEWs has shown that a small fraction (~20%) of the AEWs that propagate from Africa serve as TC precursors. About 80% of the AEWs from the continent were non-developing. As in the past work, our study showed an important difference between developing and non-developing AEWs. Composites based on developing AEWs revealed well-organized large scale deep convection (one type, composed of mesoscale systems and thick anvil clouds) is tightly coupled to the AEW trough, while scattered, less well-organized deep convection (second type, isolated cumulonimbus and cumulus congestus clouds) dominated a large area downstream of the developing AEW trough. Developing AEWs propagate westwards while strengthening. In contrast, non-developing AEWs showed that the peak well-organized deep convection is located either behind (to the east of) or far ahead (to the west) of the AEW trough (peaks values are not in close proximity). Moreover, well-organized deep convections associated with non-developing AEWs were weaker than those associated with developing AEWs. The results indicated that convective activity ahead of the non-developing AEWs is weak. Positive relative humidity (RH) anomalies dominate the area around AEWs and downstream over the main TC development region. In contrast, negative RH dominated the main TC development region ahead of non-developing AEWs, suggesting an unfavorable environment downstream of the AEWs. The results also showed that developing AEWs maintained stronger features in the lower and middle troposphere, while non-developing AEWs exhibited weaker structures, in agreement with past work.

The Top‐of‐Atmosphere, Surface and Atmospheric Cloud Radiative Kernels Based on ISCCP‐H Datasets: Method and Evaluation

AbstractThis study aims to create observation‐based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK‐derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP‐FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave (SW), longwave (LW), and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). With cloud fraction change (CFC) datasets from doubled‐CO2 simulation and short‐term observational anomalies, we derive all the TOA, SFC and ATM cloud feedback for SW, LW and Net using our CRKs.The direct comparison with modeled and observed CRKs (or cloud radiative effects), cloud feedback from previous model results and the Clouds and the Earth's Radiant Energy System products show that our CRKs and CRK‐derived cloud feedback are reasonably well validated. We estimate the uncertainty for the CRK‐derived cloud feedback and show that the CFC‐associated uncertainty contributes &gt;98.5% of the total cloud feedback uncertainty while CRK's is very small. Our preliminary evaluation also shows that some near‐zero/small cloud feedback in the TOA‐alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback and reveals some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA‐alone feedback. In addition, the atmospheric longwave cloud feedback seems to play a role in enhancing meridional atmospheric energy transport.

The Southern Ocean Radiative Bias, Cloud Compensating Errors, and Equilibrium Climate Sensitivity in CMIP6 Models

AbstractCoupled Model Intercomparison Project Phase 6 (CMIP6) models are analyzed using an established cloud clustering methodology. This enables a comparison of cloud representation in models and observations. The simulation of stratocumulus clouds over the Southern Ocean is shown to have changed substantially from earlier generation models. The CMIP6 models analyzed show stratocumulus clouds now occur more often in simulations than in International Satellite Cloud Climatology Project (ISCCP) observations, but are not bright enough compared to Clouds and the Earth's Radiant Energy System (CERES) data. This is in contrast to the “too few, too bright” problem, which has characterized prior model simulations of stratocumulus clouds, particularly over the Southern Ocean. The cloud clusters also enable the calculation of mean and compensating shortwave cloud radiative effect (SW CRE) errors from model data. The compensating errors are shown to be much larger than mean errors suggesting the CMIP6 models still have much to improve in their cloud representation. A statistically significant negative relationship between the mean and compensating errors in SW CRE over the Southern Ocean is identified. This relationship is observed elsewhere, but is only significant over the Southern Ocean. This implies model tuning efforts are hiding biases in the representation of clouds in this region. CMIP6 models have been shown to have a higher equilibrium climate sensitivity (ECS) relative to CMIP5 simulations. The link between ECS and SW CRE mean and compensating errors is investigated but no evidence of a relationship between these variables was found.

Cloud, Aerosol, and Radiative Properties Over the Western North Atlantic Ocean.

This study examines the atmospheric properties of weather states (WSs) derived from the International Satellite Cloud Climatology Project over the Western North Atlantic Ocean. In particular, radiation and aerosol data corresponding to two sites in the study domain, Pennsylvania State University and Bermuda, were examined to characterize the atmospheric properties of the various satellite-derived WSs. At both sites, the fair weather WS was most prevalent, followed by the cirrus WS. Differences in the seasonality of the various WSs were observed at the two sites. Fractional sky cover and effective shortwave cloud transmissivity derived from ground-based radiation measurements were able to capture differences among the satellite-derived WSs. Speciated aerosol optical thicknesses (AOT) from the Modern-Era Retrospective Analysis for Research and Applications, version 2 were used to investigate potential differences in aerosol properties among the WSs. The clear sky WS exhibited below-average seasonal values of AOT at both sites year-round, as well as relatively high rates of occurrence with low AOT events. In addition, the clear sky WS showed above-average contributions from dust and black carbon to the total AOT year-round. Finally, transitions between various WSs were examined under low, high, and midrange AOT conditions. The most common pathway was for the WSs to remain in the same state after a 3 h interval. Some WSs, such as mid latitude storms, deep convection, middle top, and shallow cumulus, were more prevalent as ending states under high AOT conditions. This work motivates examining differences in aerosol properties between WSs in other regions.

Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes

AbstractWe 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.

Observed trends in clouds and precipitation (1983–2009): implications for their cause(s)

Abstract. Satellite observations (International Satellite Cloud Climatology Project (ISCCP), 1983–2009) of linear trends in cloud cover are compared to those in global precipitation (Global Precipitation Climatology Project (GPCP) pentad V2.2, 1983–2009), to investigate possible cause(s) of the linear trends in both cloud cover and precipitation. The spatial distributions of the linear trends in total cloud cover and precipitation are both characterized primarily by a broadening of the major ascending zone of Hadley circulation. Our correlation studies suggest that global warming, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) can explain 67 %, 49 % and 38 %, respectively, of the spatial variabilities in the linear trends in cloud cover, but causality is harder to establish. Further analysis of the broadening of the major ascending zone of Hadley circulation shows that the trend in global temperature, rather than those in AMO and PDO, is the primary contributor to the observed linear trends in total cloud cover and precipitation in 1983–2009. The underlying mechanism driving this broadening is proposed to be the moisture–convection–latent-heat feedback cycle under global warming conditions. The global analysis is extended by investigating connections between clouds and precipitation in China, based on a large number of long-running, high-quality surface weather stations in 1957–2005. This reveals a quantitative matching relationship between the reduction in light precipitation and the reduction in total cloud cover. Furthermore, our study suggests that the reduction in cloud cover in China is primarily driven by global temperature; PDO plays a secondary role, while the contribution from AMO and Niño3.4 is insignificant, consistent with the global analysis.

Merging High-Resolution Satellite Surface Radiation Data with Meteorological Sunshine Duration Observations over China from 1983 to 2017

Surface solar radiation (Rs) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR), the recent release of International Satellite Cloud Climatology Project (ISCCP) HXG cloud products provide a promising opportunity for building long-term Rs data with high resolutions (3 h and 10 km). In this study, we compare three satellite Rs products based on AVHRR cloud products over China from 1983 to 2017 with direct observations of Rs and sunshine duration (SunDu)-derived Rs. The results show that SunDu-derived Rs have higher accuracy than the direct observed Rs at time scales of a month or longer by comparing with the satellite Rs products. SunDu-derived Rs is available from the 1960s at more than 2000 stations over China, which provides reliable decadal estimations of Rs. However, the three AVHRR-based satellite Rs products have significant biases in quantifying the trend of Rs from 1983 to 2016 (−4.28 W/m2/decade to 2.56 W/m2/decade) due to inhomogeneity in satellite cloud products and the lack of information on atmospheric aerosol optical depth. To adjust the inhomogeneity of the satellite Rs products, we propose a geographically weighted regression fusion method (HGWR) to merge ISCCP-HXG Rs with SunDu-derived Rs. The merged Rs product over China from 1983 to 2017 with a spatial resolution of 10 km produces nearly the same trend as that of the SunDu-derived Rs. This study makes a first attempt to adjust the inhomogeneity of satellite Rs products and provides the merged high-resolution Rs product from 1983 to 2017 over China, which can be downloaded freely.

Adjusting ISCCP Cloud Detection to Increase Consistency of Cloud Amount and Reduce Artifacts

AbstractThe International Satellite Cloud Climatology Project (ISCCP) began collecting data in the 1980s to help understand the distribution of clouds. Since then, it has provided important information on clouds in time and space and their radiative characteristics. However, it was apparent from some long-term time series of the data that there are some latent artifacts related to the changing satellite coverage over the more than 30 years of the record. Changes in satellite coverage effectively create secular changes in the time series of view zenith angle (VZA) for a given location. There is an inconsistency in the current ISCCP cloud detection algorithm related to VZA: two satellites viewing the same location from different VZAs can produce vastly different estimates of cloud amount. Research is presented that shows that a simple change to the cloud detection algorithm can vastly increase the consistency. This is accomplished by making the cloud–no cloud threshold VZA dependent. The resulting cloud amounts are more consistent between different satellites and the distributions are shown to be more spatially homogenous. Likewise, the more consistent spatial data lead to more consistent temporal statistics.

How Accurate Are Satellite-Derived Surface Solar Radiation Products over Tropical Oceans?

AbstractSurface solar radiation (SSR) over the ocean is essential for studies of ocean–atmosphere interactions and marine ecology, and satellite remote sensing is a major way to obtain the SSR over ocean. A new high-resolution (10 km; 3 h) SSR product has recently been developed, mainly based on the newly released cloud product of the International Satellite Cloud Climatology Project H series (ISCCP-HXG), and is available for the period from July 1983 to December 2018. In this study, we compared this SSR product with in situ observations from 70 buoy sites in the Global Tropical Moored Buoy Array (GTMBA) and also compared it with another well-known satellite-derived SSR product from the Clouds and the Earth’s Radiant Energy System (CERES; edition 4.1), which has a spatial resolution of approximately 100 km. The results show that the ISCCP-HXG SSR product is generally more accurate than the CERES SSR product for both ocean and land surfaces. We also found that the accuracy of both satellite-derived SSR products (ISCCP-HXG and CRERS) was higher over ocean than over land and that the accuracy of ISCCP-HXG SSR improves greatly when the spatial resolution of the product is coarsened to ≥ 30 km.

The Impact of MJO, Kelvin, and Equatorial Rossby Waves on the Diurnal Cycle over the Maritime Continent

The impacts of the Madden–Julian Oscillation (MJO), Kelvin waves, and Equatorial Rossby (ER) waves on the diurnal cycle of rainfall and types of deep convection over the Maritime Continent are investigated using rainfall from the Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis and Infrared Weather States (IR–WS) data from the International Satellite Cloud Climatology Project. In an absolute sense, the MJO produced its strongest modulations of rainfall and organized deep convection over the islands, when and where convection is already strongest. The MJO actually has a greater percentage modulation over the coasts and seas, but it does not affect weaker diurnal cycle there. Isolated deep convection was also more prevalent over land during the suppressed phase, while organized deep convection dominated the enhanced phase, consistent with past work. This study uniquely examined the effects of Kelvin and ER waves on rainfall, convection, and their diurnal cycles over the Maritime Continent. The modulation of convection by Kelvin waves closely mirrored that by the MJO, although the Kelvin wave convection continued farther into the decreasing phase. The signals for ER waves were also similar but less distinct. An improved understanding of how these waves interact with convection could lead to improved subseasonal forecast skill.

Cloud Characteristics and Radiation Forcing in the Global Land Monsoon Region From Multisource Satellite Data Sets

AbstractThe global land monsoon region has the highest land cloud amount in the world affecting two thirds of the world's population. Understanding the characteristics of cloud‐radiation relies heavily on satellite data set, while few studies have addressed the advantages and weaknesses of current existing satellite data sets in estimating the cloud‐radiation characteristics over global land monsoon regions. Multisource satellite data sets are used in this study to show the cloud characteristics in different monsoon regions. We find that all satellite data sets consistently show a peak of cloud fraction, cloud top height and cloud radiation forcing during summer over the global land monsoon regions. A regional difference in cloud characteristics is observed from multisource data sets. The seasonal cycle of cloud amount in the North American monsoon region is relatively smaller than that of the other monsoon regions. High‐level clouds dominate the North African monsoon, while Low‐level clouds dominate the Asian monsoon. The cloud properties and their radiative forcings revealed by four cloud‐parameter data sets with multispectral imagers, that is, International Satellite Cloud Climatology Project (ISCCP)‐D2, ISCCP‐H, Moderate Resolution Imaging Spectroradiometer (MODIS)‐MYD, and MODIS‐MOD, are similar to one another, except stronger short‐wave cloud radiative forcing in ISCCP‐FD. Multidata comparison confirmed the climate and seasonal cycles of cloud characteristics in this study, demonstrating a better representation of cloud vertical structure in CloudSat over global land monsoon region.