Regional Cloud Cover Research Articles

Quantifying the Impact of Crude Oil Spills on the Mangrove Ecosystem in the Niger Delta Using AI and Earth Observation

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km2 region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta.

A Multi-Level SAR-Guided Contextual Attention Network for Satellite Images Cloud Removal

In the field of remote sensing, cloud cover severely reduces the quality of satellite observations of the earth. Due to the complete absence of information in cloud-covered regions, cloud removal with a single optical image is an ill-posed problem. Since the synthetic aperture radar (SAR) can effectively penetrate clouds, fusing SAR and optical remote sensing images will effectively alleviate this problem. However, existing SAR-based optical cloud removal methods fail to effectively leverage the global information provided by the SAR image, resulting in limited performance gains. In this paper, we introduce a novel cloud removal method named the Multi-Level SAR-Guided Contextual Attention Network (MSGCA-Net). MSGCA-Net is designed with a multi-level architecture that integrates a SAR-Guided Contextual Attention (SGCA) module to fuse the dependable global contextual information from SAR images with the local features of optical images effectively. In the module of SGCA, the SAR image provides reliable global contextual information and genuine structure of cloud-covered regions, while the optical image provides the local feature information. The proposed model can efficiently extract and fuse global and local contextual information in SAR and optical images. We trained and evaluated the performance of the model on both simulated and real-world datasets. Both qualitative and quantitative experimental evaluation demonstrated that the proposed method can yield high quality cloud-free images and outperform state-of-the-art cloud removal methods.

Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.

Efficient On-Board Compression for Arbitrary-Shaped Cloud-Covered Remote Sensing Images via Adaptive Filling and Controllable Quantization

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.

A Lightweight Machine-Learning Method for Cloud Removal in Remote Sensing Images Constrained by Conditional Information

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.

Vegetation Influences on Cloud Cover in Typical Plain and Plateau Regions of Eurasia: 2001–2021

Vegetation Influences on Cloud Cover in Typical Plain and Plateau Regions of Eurasia: 2001–2021

Denoising Diffusion Probabilistic Feature-Based Network for Cloud Removal in Sentinel-2 Imagery

Cloud contamination is a common issue that severely reduces the quality of optical satellite images in remote sensing fields. With the rapid development of deep learning technology, cloud contamination is expected to be addressed. In this paper, we propose Denoising Diffusion Probabilistic Model-Cloud Removal (DDPM-CR), a novel cloud removal network that can effectively remove both thin and thick clouds in optical image scenes. Our network leverages the denoising diffusion probabilistic model (DDPM) architecture to integrate both clouded optical and auxiliary SAR images as input to extract DDPM features, providing significant information for missing information retrieval. Additionally, we propose a cloud removal head adopting the DDPM features with an attention mechanism at multiple scales to remove clouds. To achieve better network performance, we propose a cloud-oriented loss that considers both high- and low-frequency image information as well as cloud regions in the training procedure. Our ablation and comparative experiments demonstrate that the DDPM-CR network outperforms other methods under various cloud conditions, achieving better visual effects and accuracy metrics (MAE = 0.0229, RMSE = 0.0268, PSNR = 31.7712, and SSIM = 0.9033). These results suggest that the DDPM-CR network is a promising solution for retrieving missing information in either thin or thick cloud-covered regions, especially when using auxiliary information such as SAR data.

Temporal and spatial variation of cloud cover in arid regions of Central Asia in the last 40 years

Water resources are one of the key factors restricting the development of arid areas, and cloud water resources is an important part of water resources. The arid region of central Asia is the core region of the current national green silk road construction, and is the largest arid region in the world. Based on cloud cover data of ECMWF, the current study analyzed temporal and spatial characteristics of cloud properties in arid regions of Central Asia between 1980 and 2019. Our findings show that: (1) From the point of view of spatial distribution, total cloudiness in arid regions of Central Asia was low in the south and high in the north. The distribution of high cloud frequency and medium cloud frequency was higher in the south and lower in the north, while low cloud frequency distribution was low in the south and high in the north. (2) In terms of time, the variation of cloud cover and cloud type frequency had obvious seasonal characteristics. From winter to spring, cloud cover increased, and the change of cloud type frequency increased. From spring to summer, cloud cover continued to increase and the change of cloud type frequency increased further. Cloud cover began to decrease from summer to autumn, and the change of cloud type frequency also decreased. (3) Generally, average total cloud cover decreased in most of central Asia, and high and medium cloud cover increased while low cloud cover decreased. This study provides a reference for the rational development of cloud resources in the region.

Cloud Climatologies from Global Climate Models—A Comparison of CMIP5 and CMIP6 Models with Satellite Data

Abstract Simulating clouds with global climate models is challenging as the relevant physics involves many nonlinear processes covering a wide range of spatial and temporal scales. As key components of the hydrological cycle and the climate system, an evaluation of clouds from models used for climate projections is an important prerequisite for assessing the confidence in the results from these models. Here, we compare output from models contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6) with satellite data and with results from their predecessors (CMIP5). We use multiproduct reference datasets to estimate the observational uncertainties associated with different sensors and with internal variability on a per-pixel basis. Selected cloud properties are also analyzed by region and by dynamical regime and thermodynamic conditions. Our results show that for parameters such as total cloud cover, cloud water path, and cloud radiative effect, the CMIP6 multimodel mean performs slightly better than the CMIP5 ensemble mean in terms of mean bias, pattern correlation, and relative root-mean square deviation. The intermodel spread in CMIP6, however, is not reduced compared to CMIP5. Compared with CALIPSO-ICECLOUD data, the CMIP5/6 models overestimate cloud ice, particularly in the lower and middle troposphere, partly due to too high ice fractions for given temperatures. This bias is reduced in the CMIP6 multimodel mean. While many known biases such as an underestimation in cloud cover in stratocumulus regions remain in CMIP6, we find that the CMIP5 problem of too few but too reflective clouds over the Southern Ocean is significantly improved.

Stability in time and consistency between atmospheric corrections: Assessing the reliability of Sentinel-2 products for biodiversity monitoring in tropical forests

Earth observation satellite imagery is increasingly accessible, and has become a key component for vegetation mapping and monitoring. Sentinel-2 satellites acquire optical images with five days’ revisit frequency, which is an important feature to increase the probability of acquisition with reasonable cloud cover in tropical regions. Regular and reliable satellite observations open perspectives for the monitoring of vegetation properties and biodiversity. Atmospheric correction methods (ACMs) producing bottom-of-atmosphere (BOA) reflectance are critical to ensure temporal consistency of higher-level products and optimal sensitivity to changes in vegetation properties. Still their application in tropical regions remains challenging due to complex atmospheric issues. This study aims at performing ACM inter-comparison in the context of tropical forest monitoring. We produced BOA reflectance for a set of Sentinel-2 acquisitions corresponding to a forested area in Cameroon, using four atmospheric correction methods: Sen2cor, MAJA, Overland and LaSRC. We selected five successive acquisitions with moderate to no cloud cover, and computed a set of spectral indices and spectral diversity metrics in order to compare the consistency of these products through time, under the hypothesis that they should remain stable over a short period. We also assessed the agreement between atmospheric correction methods. Two spatial extents were used for the computation of spectral diversity metrics to assess the robustness of the data-driven processes applied to compute spectral diversity. We found that the choice of an ACM did have a significant impact on BOA reflectance and higher-level products. In the visible domain, Overland and LaSRC produced consistent BOA reflectance values, while MAJA and Sen2Cor showed strong variability which could not be explained by changes in surface properties. This directly influenced the temporal consistency of NDVI. Yet, the influence on the temporal consistency for EVI and NDWI was moderate. Spectral diversity metrics were consistent through time for all methods, but to a lesser degree than vegetation indices. When comparing the mean values over the period considered, vegetation indices were stable across methods, but not diversity metrics. Spatial context changes had an impact on the Shannon index, but not on Bray-Curtis dissimilarity. These results suggest that the choice of ACM has major potential implications for tropical forest monitoring.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Abstract. We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

Temporal evolution of the influence of atmospheric circulation on regional cloud cover as revealed by tree-ring δ18O in southwestern China

The hydroclimate of southwestern China has experienced pronounced fluctuations over the past few decades. In this study, the total cloud cover variability of southwestern China during May–October for the past 276 years was reconstructed based on the composite tree-ring δ18O chronology. The record spans the common interval from 1734 to 2009 CE. Moisture-related factors, such as relative humidity, the precipitation, and cloud cover over the region, were the climatic factors controlling tree-ring δ18O. The reconstructed cloud cover exhibited a decreasing trend since 1900, which was linked to the weakening of the Indian summer monsoon. However, the relationship between cloud cover and the monsoon system was temporally unstable, which may be due to El Niño-southern oscillation and the Indian Ocean dipole. Furthermore, the sea surface temperature (SST) over the Indian and East Pacific oceans influenced the delivery of moisture over the study area; however, the influence of SST on the different oceanic regions varied between different periods. During the low-cloud-cover period, the link between SST and regional cloud cover weakened, and local circulation became the driving factor. Additionally, local circulation may disrupt the influence of monsoons on the hydroclimate in southwestern China, which was supported by the backward trajectory results.

Climate impact of volcanic eruptions: the sensitivity to eruption season and latitude in MPI-ESM ensemble experiments

Abstract. Explosive volcanic eruptions influence near-surface temperature and precipitation especially in the monsoon regions, but the impact varies with different eruption seasons and latitudes. To study this variability, two groups of ensemble simulations are performed with volcanic eruptions in June and December at 0∘ representing an equatorial eruption (EQ) and at 30∘ N and 30∘ S representing Northern and Southern Hemisphere eruptions (NH and SH). Results show significant cooling especially in areas with enhanced volcanic aerosol content. Compared to the EQ eruption, stronger cooling emerges in the Northern Hemisphere after the NH eruption and in the Southern Hemisphere after the SH eruption. Stronger precipitation variations occur in the tropics than in the high latitudes. Summer and winter eruptions lead to similar hydrological impacts. The NH and the SH eruptions have reversed climate impacts, especially in the regions of the South Asian summer monsoon (SASM). After the NH eruption, direct radiative effects of volcanic aerosols induce changes in the interhemispheric and land–sea thermal contrasts, which move the intertropical convergence zone (ITCZ) southward and weaken the SASM. This reduces the moisture transport from the ocean and reduces cloud formation and precipitation in India. The subsequent radiative feedbacks due to regional cloud cover lead to warming in India. After the SH eruption, vice versa, a northward movement of the ITCZ and strengthening of the SASM, along with enhanced cloud formation, lead to enhanced precipitation and cooling in India. This emphasizes the sensitivity of regional climate impacts of volcanic eruptions to eruption latitude, which relates to the dynamical response of the climate system to radiative effects of volcanic aerosols and the subsequent regional physical feedbacks. Our results indicate the importance of considering dynamical and physical feedbacks to understand the mechanism behind regional climate responses to volcanic eruptions and may also shed light on the climate impact and potential mechanisms of stratospheric aerosol engineering.

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

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.

Temporal instability of lake charr phenotypes: Synchronicity of growth rates and morphology linked to environmental variables?

Pathways through which phenotypic variation among individuals arise can be complex. One assumption often made in relation to intraspecific diversity is that the stability or predictability of the environment will interact with expression of the underlying phenotypic variation. To address biological complexity below the species level, we investigated variability across years in morphology and annual growth increments between and within two sympatric lake charr Salvelinus namaycush ecotypes in Rush Lake, USA. A rapid phenotypic shift in body and head shape was found within a decade. The magnitude and direction of the observed phenotypic change were consistent in both ecotypes, which suggests similar pathways caused the variation over time. Over the same time period, annual growth increments declined for both lake charr ecotypes and corresponded with a consistent phenotypic shift of each ecotype. Despite ecotype‐specific annual growth changes in response to winter conditions, the observed annual growth shift for both ecotypes was linked, to some degree, with variation in the environment. Particularly, a declining trend in regional cloud cover was associated with an increase of early‐stage (ages 1–3) annual growth for lake charr of Rush Lake. Underlying mechanisms causing changes in growth rates and constrained morphological modulation are not fully understood. An improved knowledge of the biology hidden within the expression of phenotypic variation promises to clarify our understanding of temporal morphological diversity and instability.

Mapping mangrove forest using Landsat 8 to support estimation of land-based emissions in Kenya

The Kenyan coast is constantly under persistent cloud cover which hinders mapping using optical images. Up-to-date land-cover information in such areas is sometimes missing from national mapping initiatives. This study uses a computed composite image based on a mean of cloud and shadow free Function of Mask masked multi-temporal Landsat 8 images acquired during long-dry season in a pilot area. We test the effectiveness of the composite to map mangrove forest using random forest (RF) and support vector machines (SVM) machine learning algorithms integrated with context from Markov random fields (MRF(s)). MRFs was chosen because it is computationally efficient hence can be scaled out nationally. The MRF frameworks are compared to pixel-based classification using threefold independent validation samples. SVM–MRFs and RF–MRFs methods have the highest overall accuracy compared to pixel-based classification. However, visual assessment of predicted land-cover using aerial photograghs established that SVM–MRFs framework corresponded well to land-cover in the study area. This framework also managed to map classes with limited ground reference data better than RF–MRFs. Generally, context in both techniques played a discriminative role especially in heterogeneous regions. Therefore, scaling out this approaches would go a long way in generating mangrove forest map inventory in persistent cloud cover regions which is useful for land-based emission estimation.

Cloud Cover over the Arabian Peninsula from Global Remote Sensing and Reanalysis Products

Abstract Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future climate prediction. Despite the crucial role of desert clouds in the distribution of water and energy budgets, their climatology is still largely incomplete. With arid regions projected to become dryer under global warming conditions, understanding the characteristics of their cloud cover can provide critical insights. In this work, cloud coverage was investigated over one of the Earth's most arid regions – the Arabian Peninsula. Four total cloud cover (TCC) products, namely the International Satellite Cloud Climatology Project H (ISCCP), the CM SAF Cloud, Albedo and Surface Radiation AVHRR 2 (CLARA) satellite datasets, the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) Reanalysis (R-2) (NCEP) and the ECMWF Interim Re-Analysis (ERA) reanalyses, were used to construct a climatology of desert clouds over the peninsula between 1984 and 2009, accounting for the different products' uncertainties and limitations. Satellite retrievals and reanalysis fields were first validated against ground observations from the United Arab Emirates, for which homogeneity assessments were conducted. The validation was done using statistical indicators, including Normalized Root Mean Square Errors (nRMSE), relative biases (rBIAS), and correlation coefficients, on monthly and seasonal scales. The ISCCP dataset resulted in the highest correlations with the ground observations (overall 0.38) and the lowest nRMSE (overall 0.54), while CLARA had the lowest rBIAS (overall 0.02). All products showed discrepancies when compared to the ground observations, both annually and on a seasonal basis. When extended to the entire Arabian Peninsula, the satellite and reanalysis products showed decreasing spring and increasing summer (except for ISCCP) TCC evolution across the region. At inter-annual scales, the TCC over the peninsula showed a significant discontinuity in 1998. This shift could be linked to the forcing of the El Nino Southern Oscillation on water vapor transport over the region, as well as to documented artifacts in satellite retrievals and model outputs. These discrepancies are indicative of a need for detailed assessments to be made of the uncertainties existing in TCC data for the Arabian Peninsula. Plain Language Summary Clouds represent a primary source of uncertainty in future climate predictions. This is particularly pronounced in dryland clouds, given their sporadic and intermittent nature. With global warming predicted to enhance aridification trends in terrestrial arid zones, historical cloudiness trends over arid and hyper arid regions are very important – yet their climatology to date is still largely incomplete. The quality of observations and/or model outputs are an important consideration, making validation and intercomparison assessments imperative. This work investigated cloud cover over the Arabian Peninsula, one of the most arid regions on the Earth. Four cloud cover datasets were used, derived from satellite measurements and atmospheric reanalysis model outputs. The selected study period was between 1984 and 2009. The four datasets were initially compared with observations taken at ground stations in the United Arab Emirates. The four products were then studied over the entire Arabian Peninsula. Results showed that cloud cover displayed marked seasonal characteristics and large inter-annual temporal evolution. An abrupt change in regional cloud cover time series was found to occur in 1998. This could be attributed to the forcing of the El Nino Southern Oscillation, although significant documented uncertainties in satellite products over this time span call for deeper investigations of this causal relation.

Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

Indian monsoon precipitation isotopes linked with high level cloud cover at local and regional scales

Precipitation stable isotopes preserve historic changes of evaporation in the source regions and precipitation processes, therefore, they can be used to reveal regional hydrological cycle dynamics and paleoclimate reconstructions. In monsoon regions, strong inverse impacts of convection on precipitation isotope ratios, have created a debate regarding the interpretation of isotope records as local climate proxies. The proportions of stratiform to convective precipitation on water isotopes, together with the influence mechanisms on seasonal and interannual scales remain highly uncertain. To further address the influence of precipitation patterns on water isotopes, we used 10 yrs of precipitation isotope data from the southern Tibetan Plateau (TP) to explore the effects of large-scale cloud cover and local climate on precipitation isotopes. Correlation analysis performed between local precipitation δ18O values and different level cloud data, indicated significant negative correlations between precipitation isotopes and high level cloud cover on both seasonal and interannual time scales. This result suggests that high-level convection in the upper moisture transport stream is a main control on precipitation isotopes in the southern TP. The clear and coherent variations of precipitation isotopes with the Southern Oscillation Index and outgoing longwave radiation confirmed that strong convection activity in the moisture source region and during transport significantly depleted heavy isotopes in vapor, producing substantially decreased precipitation δ18O in the study region. These results agree with earlier findings of tree ring cellulose isotope records that correlate with cloud cover, but we emphasized the important role of larger-scale regional cloud cover. We also delineated different maximum correlation zones for seasonal and interannual time scales, likely due to different mechanisms. These findings further improve the interpretation of paleoisotope records from the Indian summer monsoon region.