High Cloud Cover Research Articles

Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state

Earth observation coupled with novel image analysis techniques now present a unique and powerful tool for the historical study of shoreline change at local to global scale. However, satellite-derived shoreline (SDS) data is limited in certain areas and is associated with large uncertainties relating to environmental factors, tidal range, and wave action. We use 14 years of monthly topographic surveys at two macrotidal sites in the UK representing end members of beach type (reflective, dissipative) to investigate the influence of tidal elevation and wave action on SDS accuracy. We find that applying appropriate water level corrections can significantly improve SDS accuracy. Results show that a different approach is required for water level definition depending on beach type and reveal that ultimately SDS accuracy is primarily controlled by beach state (beach profile shape). Accounting for tidal elevation led to substantial accuracy improvement at both sites and formed the optimal SDS strategy for the reflective site (Slapton). At the dissipative site (Perranporth) considering wave-induced water level fluctuations (wave setup and/or runup), including wave shoaling, was critical for reducing the tidally corrected SDS RMSE by a third and the mean bias by three quarters. An important realization for areas with high cloud cover such as the UK, and/or low satellite coverage, was that critically low image availability restricts temporally the type of phenomenon that can be detected (e.g., seasonal/interannual variability) and may compromise computed long-term trends. Our results suggest that the optimal approach is site-specific and depends on the shoreline translation method used and is therefore different depending on the application. We propose optimal SDS strategies to increase confidence in SDS extraction in meso-macrotidal environments with potentially low satellite useability (i.e., high cloud cover and/or low satellite coverage) depending on the spatial scale of the intended application. Long-term trends derived using this approach can reproduce trends from ground-based surveys and therefore enable more accurate projections of future shoreline position to be made.

Accounting for cloud cover and circannual variation puts the effect of lunar phase on deer–vehicle collisions into perspective

Abstract Although several studies have focused on the influence of moonlight on deer–vehicle collisions, findings have been inconsistent. This may be due to neglect of the effects of cloud cover, a major impediment to moon illumination and circannual variation in both deer and human activity. We assessed how median cloud cover interacted with the illuminated fraction of the moon in affecting daily roe deer (Capreolus capreolus) roadkill in Slovenia (Central Europe). Data included nationwide roadkill (n = 49,259), collected between 2010 and 2019 by hunters, as required by law. Roadkill peaked under medium to high cloud cover and decreased during nights with low or extremely high cloudiness. This pattern was more pronounced on nights with a full moon. However, the effects of moon illumination and cloud cover had a lower predictive potential than circannual variation, as collisions clearly peaked in April/May, July and August/September. Our results suggest that moonlight could influence roe deer movements through compensatory foraging. However, on nights with a full moon, collisions could also be affected by weather. On bright nights, roe deer might be less active due to increased human presence and sustained vehicular traffic. Then, with medium to high cloud cover and also rainfall, human presence in the environment may be low enough to increase deer movements, but vehicular traffic can still be intermediate, maximizing the risk of collisions. Finally, with overcast skies, widespread rainfall can reduce both traffic volume and human outdoor activity, decreasing the risk of collisions. Moon illumination may indeed affect wildlife–vehicle collisions and roadkill, but its effects should be quantified as a function of cloud cover. Moreover, to make studies truly comparable, research about wildlife–vehicle collisions should also account for time of the year. Policy implications. Because collisions with roe deer peak at particular periods of the year, signs should be installed seasonally. By doing so, they would warn drivers about the risk, improve drivers' awareness and increase their safety. Moreover, as collisions also increase on nights with a full moon and overcast skies, interactive warning signs that are activated by ground illumination should also be useful.

Elevation dependency of temperature trend over the Qinghai-Tibetan Plateau during 1901–2015

The Qinghai-Tibetan Plateau (Q-TP hereafter) has experienced dramatic warming in recent decades, resulting in severe effects on the ecosystems and downstream. However, none of previous studies investigated elevation-dependency temperature trend with the high resolution over the long-term period. Based on monthly temperature dataset with 0.1° developed by generative adversarial network, elevation-dependency temperature trend over the Q-TP and 5 climate zones (humid, humid-semihumid, semihumid, semiarid, arid region) during 1901–1946, 1946–1965, 1965–1997 and 1997–2015 are investigated. Snow cover (SNC), high cloud cover (HCC), middle cloud cover (MCC), specific humidity (SHUM) and soil moisture content (SOILM) are introduced to analyze possible mechanism. There are 4 cases of elevation-dependency temperature trend, which are positive/negative elevation-dependency warming (EDW+/EDW–) and cooling (EDC+/EDC–). These patterns (EDW+, EDW–, EDC+ and EDC–) are identified as warming/cooling trends that become stronger/weaker with increasing elevation. EDW– signal is found during 1901–1946 due to the influence of SOILM. The most prominent EDW– signal occurs over the arid region. EDC+/− is presented during 1946–1965 under the dominance of SHUM and SOILM. The stronger EDC signal is shown over the arid and semiarid region than over the humid-semihumid region. The subtle EDW+ signal is shown over the semihumid, semiarid and arid regions during 1965–1997 when SOILM has a relatively large contribution to temperature trend. The robust EDW+ signal is exhibited from 1997 to 2015 when SNC plays a vital role in regulating the temperature change. There is a more significant EDW+ over the humid, humid-semihumid, and semihumid regions than that over the semiarid and arid regions during this period. Above all, SNC, SHUM and SOILM are found to be the primary contributors to elevation-dependency temperature trend. SOILM and SHUM are associated with hydrological effects and control temperature variations over the Q-TP during 1901–1997. SNC is related to snow/ice-albedo feedback and dominates temperature variations over the Q-TP during 1997–2015.

Past climates and plant migration – the significance of the fossil record

This article is a Commentary on Denk et al. (2023), 238: 2668–2684.

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.

Decomposing Fast and Slow Responses of Global Cloud Cover to Quadrupled CO2 Forcing in CMIP6 Models

Cloud changes and their attribution under global warming still remains a challenge in climatic change studies, especially in decomposing the fast and slow cloud responses to anthropogenic forcing. In this study, the responses of global cloud cover to the quadrupled CO2 forcing are investigated quantitatively by decomposing the total response into fast and slow ones using the multi-model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). During the quasi-equilibrium period after the quadrupling of CO2 forcing, the global mean changes of simulated total cloud cover (TCC) in the total, fast, and slow responses are −2.42%, −0.64%, and −1.78%, respectively. Overall, the slow response dominates the total response in most regions over the globe with similar spatial patterns. TCC decreases at middle and low latitudes but increases at high latitudes in the total and slow responses. Whereas, it mainly decreases in the middle and low latitudes of the southern hemisphere as well as in the middle and high latitudes of the northern hemisphere in the fast response. A change in vertical motion is the major contributor to the cloud cover change at middle and low latitudes, while the decrease in upper atmospheric temperature leads to an increase in high cloud cover at high latitudes. In addition, the anomaly in water vapor convergence/diffusion also contributes to the cloud cover increase/decrease at low latitudes.

Assessment of solar energy potential in China using an ensemble of photovoltaic power models

Development of solar energy is one of the key solutions towards carbon neutrality in China. The output of solar energy is dependent on weather conditions and shows distinct spatiotemporal characteristics. Previous studies have explored the photovoltaic (PV) power potential in China but with single models and low-resolution radiation data. Here, we estimated the PV power potential in China for 2016–2019 using an ensemble of 11 PV models based on hourly solar radiation at the resolution of 5 km retrieved by the Himawari-8 geostationary satellite. On the national scale, the ensemble method revealed an annual average PV power potential of 242.79 kWh m−2 with the maximum in the west (especially the Tibetan Plateau) and the minimum in the southeast (especially the Sichuan Basin). The multi-model approach shows inter-model spreads of 6 %–7 % distributed uniformly in China, suggesting a robust spatial pattern predicted by these models. The seasonal variation in general shows the largest PV power generation in summer months except for Tibetan Plateau, where the peak value appears in spring because the high cloud coverage dampens the regional solar radiation in summer. On the national scale, the deseasonalized PV power potential shows a high correlation with cloud coverage (R2 = 0.71, p < 0.01) but a low correlation with aerosol optical depth (R2 = 0.08, p < 0.05). Sensitivity experiments show that national PV power potential increases by 0.55 % per 1 W m−2 increase of radiation and 0.79 % per 1 m s−1 increase of wind speed, but decreases by 0.46 % per 1 °C increase of air temperature. These sensitivities provide a solid foundation for the future projection of PV power potential in China under climate change.

Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat

Burn-area products from remote sensing provide the backbone for research in fire ecology, management, and modelling. Landsat imagery could be used to create an accurate burn-area map time series at ecologically relevant spatial resolutions. However, the low temporal resolution of Landsat has limited its development in wet tropical and subtropical regions due to high cloud cover and rapid burn-area revegetation. Here, we describe a 34-year Landsat-based burn-area product for wet, subtropical Hong Kong. We overcame technical obstacles by adopting a new LTS fire burn-area detection pipeline that (1) Automatically uniformized Landsat scenes by weighted histogram matching; (2) Estimated pixel resemblance to burn areas based on a random forest model trained on the number of days between the fire event and the date of burn-area detection; (3) Iteratively merged features created by thresholding burn-area resemblance to generate burn-area polygons with detection dates; and (4) Estimated the burn severity of burn-area pixels using a time-series compatible approach. When validated with government fire records, we found that the LTS fire product carried a low area of omission (11%) compared with existing burn-area products, such as GABAM (49%), MCD64A1 (72%), and FireCCI51 (96%) while effectively controlling commission errors. Temporally, the LTS fire pipeline dated 76.9% of burn-area polygons within two months of the actual fire event. The product represents the first Landsat-based burn-area product in wet tropical and subtropical Asia that covers the entire time series. We believe that burn-area products generated from algorithms like LTS fire will effectively bridge the gap between remote sensing and field-based studies on wet tropical and subtropical fire ecology.

Former-CR: A Transformer-Based Thick Cloud Removal Method with Optical and SAR Imagery

In the field of remote sensing, cloud and cloud shadow will result in optical remote sensing image contamination, particularly high cloud cover, which will result in the complete loss of certain ground object information. The presence of thick cloud severely limits the use of optical images in production and scientific research, so it is critical to conduct further research into removing the thick cloud occlusion in optical images to improve the utilization rate of optical images. The state-of-the-art cloud removal methods proposed are largely based on convolutional neural network (CNN). However, due to CNN’s inability to gather global content information, those cloud removal approaches cannot be improved further. Inspired by the transformer and multisource image fusion cloud removal method, we propose a transformer-based cloud removal method (Former-CR), which directly reconstructs cloudless images from SAR images and cloudy optical images. The transformer-based model can efficiently extract and fuse global and local context information in SAR and optical images, generating high-quality cloudless images with higher global consistency. In order to enhance the global structure, local details, and visual effect of the reconstructed image, we design a new loss function to guide the image reconstruction. A comparison with several SAR-based cloud removal methods through qualitative and quantitative experimental evaluation on the SEN12MS-CR dataset demonstrates that our proposed method is effective and superior.

High Spatiotemporal Flood Monitoring Associated with Rapid Lake Shrinkage Using Planet Smallsat and Sentinel-1 Data

High spatiotemporal flood monitoring is critical for flood control, mitigation, and management purposes in areas where tectonic and geological events significantly exacerbate flood disasters. For example, the rapid lake shrinkage resulting from the transformations of enclosed seas into lakes by the rapid land movement in the collision zone dramatically increases the flood risks in Indonesia, which requires frequent and detailed monitoring and assessment. This study primarily quantified the detailed flood disasters associated with the rapid lake shrinkage in Gorontalo Regency in Gorontalo Province, Indonesia using high spatiotemporal monitoring with a combination of PlanetScope smallsat constellations, Sentinel-1, and surface water datasets. Based on the findings that indicated its volume, distribution, pace, and pattern, the flood event that occurred in Gorontalo in November 2022 was demonstrated within a short interval of 2–12 days. The results also indicate both direct and indirect floodwater overflow from different water resources. Combining these results with the surface water occurrences from 1984 to 2021, our findings reveal the historical major flood-prone areas associated with the rapid lake shrinkage. These findings are expected to aid in the timely high spatiotemporal monitoring of rapid environmental change-induced flood disasters, even in tropical regions with high cloud coverage. Furthermore, these are also expected to be integrated into the flood hazard mitigation and management strategies associated with local-specific tectonic and geological systems.

Assessment of using spaceborne LiDAR to monitor the particulate backscatter coefficient on large, freshwater lakes: A test using CALIPSO on Lake Michigan

The Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite was launched in 2006 with the primary goal of measuring the properties of clouds and aerosols in Earth’s atmosphere using LiDAR. Since then, numerous studies have shown the viability of using CALIPSO to observe day/night differences in subsurface optical properties of oceans and large seas from space. To date no studies have been done on using CALIPSO to monitor the subsurface optical properties of large, freshwater-lakes. This is likely due to the limited spatial resolution of CALIPSO, which makes the mapping of subsurface properties of regions smaller than large seas impractical. Still, CALIPSO does pass over some of the world’s largest, freshwater-lakes, yielding important information about the water. Here we use the entire CALIPSO data record (approximately 15 years) to measure the particulate backscatter coefficient (bbp, m−1) across Lake Michigan. We then compare the LiDAR derived values of bbp to optical imagery values obtained from MODIS and to in situ measurements. Critically, we find that the LiDAR derived bbp aligns better in non-summer months with in situ values when compared to the optically imagery. However, due to both high cloud coverage and high wind speeds on Lake Michigan, this comes with the caveat that the CALIPSO product is limited in its usability. We close by speculating on the roll that spaceborne LiDAR, including CALIPSO and other satitlites, have on the future of monitoring the Great Lakes and other large bodies of fresh water.

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.

The use of sentinel-2 imagery to generate vegetations maps for the Northern Antarctic peninsula and offshore islands.

We used Sentinel-2 imagery time series to generate a vegetation map for the Northern part of the Antarctica Peninsula and offshore islands, including the South Shetlands. The vegetation cover was identified in the NDVI maximum value composite image. The NDVI values were associated with the occurrence of algae (0.15 - 0.20), lichens (0.20 - 0.50), and mosses (0.50 - 0.80). The vegetation cover distribution map was validated using the literature information. Generating a vegetation map distribution on an annual basis was not possible due to high cloud cover in the Antarctic region, especially in coastal áreas, so optical images from 2016 to 2021 were necessary to map the vegetation distribution in the entire study área. The final map analyzed in association with the weather data shows the occurrence of a microenvironment over the western islands of the Antarctic Peninsula that provided vegetation growth conditions. The Sentinel-2 images with 10m spatial resolution allow the assembly of accurate vegetation distribution maps for the Antarctica Peninsula and Islands, the Google Earth Engine cloud computing being essential to process a large amount of the satellite images necessary for processing these maps.

The Biophysical Impacts of Idealized Afforestation on Surface Temperature in China: Local and Nonlocal Effects

Abstract Afforestation can impact surface temperature through local and nonlocal biophysical effects. However, the local and nonlocal effects of afforestation in China have rarely been explicitly investigated. In this study, we separate the local and nonlocal effects of idealized afforestation in China based on a checkerboard method and the regional Weather Research and Forecasting (WRF) Model. Two checkerboard pattern–like afforestation simulations (AFF1/4 and AFF3/4) with regularly spaced afforested and unaltered grid cells are performed; afforestation is implemented in one out of every four grid cells in AFF1/4 and in three out of every four grid cells in AFF3/4. The mechanisms for the local and nonlocal effects are examined through the decomposition of the surface energy balance. The results show that the local effects dominate surface temperature responses to afforestation in China, with a cooling effect of approximately −1.00°C for AFF1/4 and AFF3/4. In contrast, the nonlocal effects warm the land surface by 0.14°C for AFF1/4 and 0.41°C for AFF3/4. The local cooling effects mainly result from 1) enhanced sensible and latent heat fluxes and 2) decreases in downward shortwave radiation due to increased low cloud cover fractions. The nonlocal warming effects mainly result from atmospheric feedbacks, including 1) increases in downward shortwave radiation due to decreased low cloud cover fractions and 2) increases in downward longwave radiation due to increased middle and high cloud cover fractions. This study highlights that, despite the unexpected nonlocal warming effect, afforestation in China still has great potential in mitigating climate warming through biophysical processes.

Convection Self‐Aggregation in CNRM‐CM6‐1: Equilibrium and Transition Sensitivity to Surface Temperature

AbstractThis study investigates the spontaneous self‐aggregation of convection in non‐rotating Radiative‐Convective Equilibrium (RCE) simulations performed by the CNRM‐CM6‐1 General Circulation Model within the framework of the RCE Model Intercomparison Project (RCEMIP). In this model, the level of convection self‐aggregation at equilibrium, as quantified by metrics based on moisture or moist static energy, strongly increases with sea surface temperature (SST). As it gets warmer, the troposphere gets drier, high cloud cover diminishes in dry regions, the top of high cloud rises and their thickness increases in moist regions, and low‐cloud cover increases. At high SSTs, the large‐scale circulation exhibits a shallow component, stronger than its deep counterpart. The transition toward self‐aggregation has a similar first 20‐day phase for all SSTs within the 295–305 K range. It primarily involves radiative positive feedback processes. Then, for SSTs above approximately 298 K, a new, slower, transition toward higher levels of self‐aggregation occurs. It is concomitant with a shift from a top‐heavy to a more bottom‐heavy large‐scale circulation, a strengthening of the shallow circulation and a reduced mobility of convective aggregates. This second transition is mostly driven by the dry regions, still involves longwave radiative positive feedbacks, but also advective positive feedbacks in the driest regions. It is argued that boundary‐layer radiative cooling difference between moist and dry regions, which is stronger at high SSTs, is instrumental in this second phase of self‐aggregation. The sensitivity of deep convection to environmental dry air also likely acts as a positive feedback on the system.

Diverse cooling effects of green space on urban heat island in tropical megacities

Cities in tropical regions are experiencing high heat risks by overlaying the urban heat island (UHI) effect. Urban green space (UGS) can provide local cooling effect and reduce UHI. However, there still lack a comprehensive exploration of the characteristics of UHI and cooling effect of UGS due to high cloud coverage and limited number of available remote sensing observations. In this study, the enhanced spatial and temporal adaptive reflectance data fusion method was employed to develop an enhanced land surface temperature data in winter seasons in three tropical megacities, Dhaka, Kolkata, and Bangkok. The spatiotemporal variations of surface urban heat island (SUHI) were explored from 2000 to 2020 with a 5-years interval. The optimal size of UGS associated with its cooling effects was assessed by using the threshold value of efficiency (TVoE). The relationship between the intensity and range of urban cooling island (UCI) and four landscape metrics of green space patches, total area (P_Area), shape index (P_SI), normalized difference vegetation index (P_NDVI), and land surface temperature (P_LST), were analyzed. The results show that the average SUHI intensity increased by 0.98°C, 1.42°C, and 0.73°C in Dhaka, Kolkata, and Bangkok, respectively, from 2000 to 2020. The maximum intensity of UCI ranges from 4.83°C in Bangkok to 8.07°C in Kolkata, and the maximum range of UCI varies from 300 m in Bangkok to 420 m in Kolkata. The optimal size of green space is 0.37 ha, 0.77 ha, and 0.42 ha in Dhaka, Kolkata, and Bangkok, respectively. The P_NDVI and P_Area had significant positive effects on UCI intensity and range, while the background temperature had significant negative effects. With higher background temperature, the optimal patch size of UGS is larger. This study provides useful information for developing effective heat mitigation and adaptation strategies to enhance climate resilience in tropical cities.

Improving Typical Urban Land-Use Classification with Active-Passive Remote Sensing and Multi-Attention Modules Hybrid Network: A Case Study of Qibin District, Henan, China

The study of high-precision land-use classification is essential for the sustainable development of land resources. This study addresses the problem of classification errors in optical remote-sensing images under high surface humidity, cloud cover, and hazy weather. The synthetic aperture radar (SAR) images are sensitive to soil moisture, and the microwave can penetrate clouds, haze, and smoke. By using both the active and passive remote-sensing data, the Sentinel-1A SAR and Sentinel-2B multispectral (MS) images are combined synergistically. The full-band data combining the SAR + MS + spectral indexes is thus constructed. Based on the high dimensionality and heterogeneity of this data set, a new framework (MAM-HybridNet) based on two-dimensional (2D) and three-dimensional (3D) hybrid convolutional neural networks combined with multi-attention modules (MAMs) is proposed for improving the accuracy of land-use classification in cities with high surface humidity. In addition, the same training samples supported by All bands data (SAR + MS + spectral index) are selected and compared with k-Nearest Neighbors (KNN), support vector machine (SVM), 2D convolutional neural networks, 3D convolutional neural networks, and hybridSN classification models to verify the accuracy of the proposed classification model. The results show that (1) fusion classification based on Sentinel-2B MSI and Sentinel-1A SAR data produce an overall accuracy (OA) of 95.10%, a kappa coefficient (KC) of 0.93, and an average accuracy (AA) of 92.86%, which is better than the classification results using Sentinel-2B MSI and Sentinel-1A SAR images separately. (2) The classification accuracy improves upon adding the spectral index, and the OA, KC, and AA improve by 3.77%, 0.05, and 5.5%, respectively. (3) With the support of full-band data, the algorithm proposed herein produces better results than other classification algorithms, with an OA of 98.87%, a KC of 0.98, and an AA of 98.36%. These results indicate that the synergistic effect of active-passive remote-sensing data improves land-use classification. Additionally, the results verify the effectiveness of the proposed deep-learning classification model for land-use classification.

Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods

Amazonas is a mountain region in Peru with high cloud cover, so using optical data in the analysis of surface changes of water bodies (such as the Burlan and Pomacochas lakes in Peru) is difficult, on the other hand, SAR images are suitable for the extraction of water bodies and delineation of contours. Therefore, in this research, to determine the surface changes of Burlan and Pomacochas lakes, we used Sentinel-1 A/B products to analyse the dynamics from 2014 to 2020, in addition to evaluating the procedure we performed a photogrammetric flight and compared the shapes and geometric attributes from each lake. For this, in Google Earth Engine (GEE), we processed 517 SAR images for each lake using the following algorithms: a classification and regression tree (CART), Random Forest (RF) and support vector machine (SVM).) 2021-02-10, then; the same value was validated by comparing the area and perimeter values obtained from a photogrammetric flight, and the classification of a SAR image of the same date. During the first months of the year, there were slight increases in the area and perimeter of each lake, influenced by the increase in rainfall in the area. CART and Random Forest obtained better results for image classification, and for regression analysis, Support Vector Regression (SVR) and Random Forest Regression (RFR) were a better fit to the data (higher R2), for Burlan and Pomacochas lakes, respectively. The shape of the lakes obtained by classification was similar to that of the photogrammetric flight. For 2021-02-10, for Burlan Lake, all 3 classifiers had area values between 42.48 and 43.53, RFR 44.47 and RPAS 45.63 hectares. For Pomacohas Lake, the 3 classifiers had area values between 414.23 and 434.89, SVR 411.89 and RPAS 429.09 hectares. Ultimately, we seek to provide a rapid methodology to classify SAR images into two categories and thus obtain the shape of water bodies and analyze their changes over short periods. A methodological scheme is also provided to perform a regression analysis in GC using five methods that can be replicated in different thematic areas.

Variation and comparison of cloud cover in MODIS and four reanalysis datasets of ERA-interim, ERA5, MERRA-2 and NCEP

Cloud plays an important role in climate change and radiative forcing. Atmospheric reanalysis datasets provide an economical and efficient way to explore the worldwide cloud variation. This study compares the monthly total cloud cover (TCC) and high cloud cover (HCC) of the four commonly used atmospheric reanalysis datasets, including European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), ECMWF's Fifth-generation Reanalysis (ERA5), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and National Centers for Environmental Prediction (NCEP: only TCC), with MODIS retrievals during 2001–2018. The spatial and temporal distributions of TCC for the four reanalyses are generally consistent with MODIS results, while showing better consistency over land than over ocean. The largest TCC discrepancy between MODIS and the reanalysis appears over the polar regions. Among the four reanalysis datasets, ERA5 presents the highest correlation and the smallest mean bias compared with MODIS, while NCEP shows the lowest and the largest. Although all the three reanalyses (ERA-Interim, ERA5 and MERRA-2) generally give an overestimation of HCC compared with MODIS, the ERA-Interim shows the closest to MODIS. The largest difference of HCCs between MODIS and the reanalysis is located over the Tibet Plateau. To the annual change of global means, ERA-Interim and MERRA-2 appear a significant increasing trend of TCC over both land and ocean. ERA5 and MERRA-2 show a significant decreasing trend of HCC over ocean, and MODIS gives a reduce over land. Furthermore, During the ENSO events, a positive anomaly of TCC, which is positively correlated with the ENSO intensity over the eastern equatorial Pacific, is captured by all the four reanalyses, among which ERA5 and ERA-Interim appear more distinct.

Meteorological factor contributions to the seesaw concentration pattern between PM2.5 and O3 in Shanghai

With the implementation of various stringent emission reduction measures since 2013 in China, significant declines in fine particle (PM2.5) concentrations have occurred nationwide. However, China has suffered from increasing levels of ozone pollution in eastern urban areas. Many studies focus on the chemical interaction between PM2.5 and O3, but the meteorological mechanisms of the seesaw variation pattern between them are still unclear. Taking the megacity Shanghai (SH) as an example, we explored the meteorological causes of two types of PM2.5-O3 concentration variation seesaw events, i.e., high PM2.5 concentration with low O3 concentration (Type-one) events and low-PM-high-O3 (Type-two) seesaw events. The backward trajectories of the 144 Type-one events are divided into three clusters. Among the three clusters of Type-one seesaw events, the boundary layer height decreases by 20.53%–53.58%, and the wind speed decreases by 17.99%–28.29%, which is unfavorable for the diffusion of local air pollutants and contributes to the accumulation of PM2.5. Additionally, a backward air mass with a high content of PM2.5 plays an important role in the Type-one events, especially in the case of cluster one. In terms of cluster two, the increase in cloud cover, decrease in solar radiation and increase in relative humidity also promote the hygroscopic growth of aerosols and suppress the production of O3. As for cluster three, higher cloud cover and relative humidity contribute to the seesaw pattern of PM and O3. The 64 Type-two seesaw events are divided into two clusters. O3-rich air masses from the nearby east sea surface and remote northeast China increase the local O3 of SH. Moreover, in cluster one, high boundary layer depth and wet deposition contribute to the decrease in PM2.5 concentration. In cluster two, a obvious decrease in cloud cover and increase in solar radiation are also favorable for the photochemical production of ozone. The results will provide suggestions for the government to use to take measures to improve the air quality of SHs.