Cloud Cover Research Articles

Lake total suspended matter retrieval by wind speed: A machine learning model trained by time-series satellite imagery

This study aims to develop an affordable and continuous method for monitoring water quality in arid, remote areas with high erosion rates. It presents a hypothesis that establishes a link between the concentration of Total Suspended Matter (TSM) and wind speed, emphasizing its ecological importance in lakes with dry conditions and high erosion rates. Building upon this hypothesis, the study introduces Wind2TSM-Net, a machine learning model that effectively bridges between different regression and neural network algorithms. This model connects on-site wind speed measurements with TSM data obtained through a physically remote sensing approach. It accurately predicts TSM concentration values, overcoming challenges such as cloud interference and reducing reliance on satellite imagery. The model was applied to Iran's Chah-Nimeh Reservoirs (CNRs) as a case study in an arid and remote area. The results revealed a significant correlation between TSM concentration and wind speed measurements, with impressive performance metrics (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 1.97 g/m3, mean absolute error (MAE) = 1.33). These findings highlight the effectiveness of Wind2TSM-Net in monitoring TSM values in remote and dry regions, particularly during extreme weather conditions when on-site measurements are impractical or cloud cover obstructs satellite observations.

Detection of long-term slope displacement using time-series DInSAR and geological factor analysis for susceptibility assessment of landslides in northwestern Kyushu Island

Ongoing climate change has increased the impact of landslides and related slope disasters on infrastructure and human lives. Microwave satellite remote sensing, particularly interferometric analysis of synthetic aperture radar (InSAR) data, is a powerful tool for routine monitoring of the displacement of slopes on small wavelength scales, independently from solar illumination and cloud coverage. Although various sophisticated techniques for time-series InSAR have been developed, practical application in conjunction with geological analysis to reveal intrinsic and triggering factors of slope displacement is still limited. In this paper, we describe a practical case of time-series InSAR analysis with a special focus on its geological implications in northwestern Kyushu, a high-risk area for slope-related disasters in Japan. The extracted susceptible polygons from the InSAR results show accuracy comparable with that of other recent research on landslide mapping. Intrinsic factor analysis based on a geographic information system reveals that the displacement occurs on relatively gentle slopes instead of steep areas, corresponding to the transient zone of Paleogene-Neogene sedimentary rocks and basalt. Triggering factor analysis based on correlation coefficients reveals a significant link between some displacement areas with mean and/or maximum precipitation for each observation duration. Those findings confirm the importance of carefully accounting for the geological background for landslide susceptibility assessment and policy making, apart from topographic and meteorological conditions.

Exploring optimal integration schemes for Sentinel-1 SAR and Sentinel-2 multispectral data in land cover mapping across different atmospheric conditions

Integrating remote sensing data, such as Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 multispectral data, can provide valuable insights for this task. However, in tropical regions, such as Yogyakarta, Indonesia, atmospheric interference from cloud cover and haze can result in spectral confusion and decreased usability of multispectral data, leading to reduced classifier accuracy. This study investigates the impact of integrating Sentinel-1 SAR and Sentinel-2 multispectral data and their derivatives on land cover mapping in part of Yogyakarta under hazy and clear atmospheric conditions. This study employed four Schemes: (1) using only SAR data and its derivatives, (2) using only multispectral data and its derivatives, (3) combining SAR, multispectral, and their derivatives, and (4) combining SAR, multispectral, and their derivatives with feature selection using Recursive Feature Elimination. The XGBoost classification algorithm tuned using Bayesian optimization was used for classification, and the accuracy and efficiency (processing time) of each Scheme were evaluated. The results showed that integrating SAR data and optical data with clear and hazy condition increased the accuracy by 4 % (clear) to 14.58 % (hazy). The integration of SAR and optical data had the highest accuracy (79.58% on hazy imagery and 84.58% on clear imagery) but longer processing time. However, the feature selection conducted in decreased model complexity and processing time, with a reduction in processing time of 78.74% on hazy imagery and 49.89% as compared to utilizing the full SAR and optical data variables. Lastly, the McNemar test concluded the significance of integrating SAR data for mapping land cover, especially with hazy optical data.

U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal

ABSTRACT Optical remotely sensed time series data have various key applications in Earth surface dynamics. However, cloud cover significantly hampers data analysis and interpretation. Despite synthetic aperture radar (SAR)-to-optical image translation techniques emerging as a promising solution, their effectiveness is diminished by their inability to adequately account for the intertwined nature of temporal and spatial dimensions. This study introduces U-SeqNet, an innovative model that integrates U-Net and Sequence-to-Sequence (Seq2Seq) architectures. Leveraging a pioneering spatiotemporal teacher forcing strategy, U-SeqNet excels in adapting and reconstructing data, capitalizing on available cloud-free observations to improve accuracy. Rigorous assessments through No Reference and Full Reference Image Quality Assessments (NR – IQA and FR – IQA) affirm U-SeqNet’s exceptional performance, marked by a Natural Image Quality Evaluator (NIQE) score of 5.85 and Mean Absolute Error (MAE) of 0.039. These results underline U-SeqNet’s exceptional capabilities in image reconstruction and its potential to improve remote sensing analysis by enabling more accurate and efficient multimodal and multitemporal cloud removal techniques.

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

Cloud and snow neural network segmentation using the Electro-L No. 2 satellite low-resolution data

This paper is devoted to cloud and snow semantic segmentation using multispectral satellite imagery, received from a multizone scanning instrument (MSU-GS) installed on the Russian satellite Electro-L No. 2. For segmentation, some additional geographic information is used such as latitude, longitude, and altitude. The main difficulty of evaluating snow and cloud masks from MSU-GS data is absence of shortwave infrared (SWIR) spectral channels in the 1.3-1.6μm range, which are necessary for accurate snow and cloud distinction. This fact makes it impossible to apply any of existing algorithms for cloud and snow destinction used for other multispectral satellite imagery. The results of the presented work include a self-collected dataset, consisting of cloud masks from the meteorological satellites GOES-16 and Meteosat-10 L2 products, snow masks from Terra/MODIS products, considered to be the ground truth for multispectral imagery from Electro-L No. 2 with geographical information for each sample, as well as a trained Multi-Scale Attention Network (MANet), able to perform snow and cloud segmentation for multispectral data from Electro-L No. 2. The purpose of this study is to create a stable algorithm for MSU-GS imagery to conduct an operational snow and cloud coverage monitoring of the full Earth disk, that can not be evaluated by using multispectral data from other Russian satellites. The novelty of the work is the combining geographical and multispectral information with weighted sampling approach for hard samples, as well as using other satellite products for creating ground truth masks for needed satellite imagery. The developed algorithm was tested on various scenes during all seasons with F1 and IoU metrics. We reach segmentation quality equal to F1=0.7454,F1=0.8773 and IoU=0.7398,IoU=0.7976 for snow and clouds respectively. The proposed neural network can be used at any time of year during daytime for processing Electro-L No. 2 multispectral data in real time.

Using remote sensing to map degraded mountain peatlands with high climate mitigation potential in Colombia's Central Cordillera

Peatlands are the most carbon-dense ecosystems on earth. In tropical mountains, peatlands are numerous and susceptible to rapid degradation and carbon loss after human disturbances. Quantifying where peatlands are located and how they are affected by land use is key in creating a baseline of carbon stocks and greenhouse gas fluxes from tropical mountain peatlands. However, mapping peatlands in the páramo of the Northern Andes is difficult because they are in a topographically complex environment with nearly continuous cloud cover and frequent conversion to pastures or cropland. The goal of this effort was to identify the different types of páramo peatlands and their degradation patterns in the Colombian Central Cordillera. Moderate resolution cloud-free composites of optical imagery, temporal variance in ALOS- PALSAR L-band SAR, Sentinel-1 C-band SAR, and topography data were used as inputs in a machine learning classifier to identify was used to map 12 land cover classes including peatlands with natural vegetation and peatlands converted to pasture. Field data from 507 control points collected across the study area, including information on the vegetation and carbon content on the top 20 cm of the soil, were used to train and validate the classifier. Results show that the use of multiple platforms and image dates, including variance of the radar returns, is necessary for a clear separation of disturbed and undisturbed peatland classes. Peatland area varied across the study region, covering 7% and 20% of the landscape in the northern and southern portions of the study area, respectively. Disturbed peatlands with exotic grasses cover nearly 2% of the area. The overall accuracy of the peatland classes was 82.6%. Disturbed peatlands with exotic grasses had less carbon in the top 20 cm than undisturbed peatlands with natural vegetation. These results highlight the prevalence of peatlands in the tropical Andes and a promising approach to detecting peatlands converted to agriculture. Understanding the distribution and extent of these carbon dense ecosystems can facilitate the restoration and protection of peatlands in the northern Andes, with implications for the future trajectories of the national greenhouse gas inventory.

Observations and modeling of areal surface albedo and surface types in the Arctic

Abstract. An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and measured net irradiances (median: −6.4 W m−2) for optically thin clouds, while the median value of only 0.1 W m−2 was determined for optically thicker clouds.

Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive models demonstrably improve when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, ‘C-Dax’ reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed “SPECTRA-FOR” (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML.

Implementation and evaluation of a spectral cumulus parametrization for simulating tropical cyclones in JMA‐GSM

AbstractA spectral cumulus parametrization (spectral scheme) developed using a cloud‐resolving model simulation was implemented in Global Spectral Model of the Japan Meteorological Agency (JMA‐GSM, GSM hereafter). The performance of the spectral scheme in terms of mean state, variability, and tropical cyclone (TC) properties was evaluated using atmosphere‐only model experiments by comparing results of the original convection scheme (based on Arakawa–Schubert scheme, AS scheme) and the spectral scheme. Parameter tunings were first conducted for the spectral scheme, through which the climatological errors of the cloud cover in the Southern Ocean and temperature were greatly improved. The spectral scheme showed comparable climatological errors to the AS scheme in the increased resolutions. The spectral scheme greatly improved tropical variability, that is, response to El Niño/Southern Oscillation and Madden–Julian Oscillation. Analyses on TC statistical data revealed that the spectral scheme improved TC properties in terms of genesis frequency, intensity, and the response of TC to tropical variability. Specifically, significant improvements were seen in the Northern Hemisphere. Further analyses on the TC structure revealed that higher TC intensity was sustained for longer times by the spectral scheme than by the AS scheme. The reason for this is that the spectral scheme better simulates convective heating by considering coexistence of different cloud types with parametrized vertically varying entrainment rates, especially from low to mid‐altitudes. The convective heating leads to a sharper vertical‐radial circulation in the TC structure, causing stronger incoming moisture flux toward the TC centre, and resulting in a stronger TC intensity. Consequently, the spectral scheme can simulate a comparable mean state, better variability, and better TC properties compared with the original scheme by simulating a more detailed convective structure. Therefore, it has a potential to increase the TC forecast skill for operational GSM.

Divergent Responses of Summer Terrestrial Evapotranspiration to Cloud Increase in East Asia

AbstractCloud impact on terrestrial evapotranspiration (ET) can be significant, while a fundamental understanding of ET response to cloud change has not been addressed in regional aspects. In this study, seven ET datasets from satellite and land surface models are used to analyze the summer ET in response to low cloud cover (LCC) over East Asia, in combination with satellite microwave and optical vegetation data, as well as ERA5 climatic and surface parameters. In general, all ET datasets present a strong negative relationship with LCC over dense vegetation (e.g., forests), which leads to the ET reduction of more than −0.5 mmday−1 under the large LCC increase, compared with the least cloudy sky conditions. Such reduction is stronger under more humid and denser forests (aridity index AI > 0.5 and enhanced vegetation index EVI > 0.6). In contrast, a positive relationship between ET and LCC presents over arid and sparse lands (AI < 0.2 and EVI < 0.2), accompanied by enhanced ET (>0.2 mmday−1). Analysis shows that under increased LCC conditions, substantial reduction in radiation availability results in the decline in both plant transpiration (ETveg) and soil evaporation (ETsoil) over humid dense vegetation, dominating the overall negative response. The increase of near‐surface relative humidity over arid sparse vegetation may indicate a stronger moisture availability, which promotes ETsoil and partly offsets the negative effect of radiation reduction. The change of vegetation water content, indicated by a satellite microwave vegetation index, is important for ETveg over arid mountainous lands with cloud cover. These findings contribute new insights to the understanding of vegetation‐atmosphere interactions.

A flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches

Orthoimage mosaicking plays an important role in remote sensing applications, such as environment monitoring and protection. However, the cloud coverage in remote sensing images often results in substantial loss of information and degradation of data quality, bringing lots of challenges to orthoimage mosaicking. Therefore, this paper proposes a flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches. Different from most existing pixel-based mosaicking methods, the proposed method defines patch as the minimal processing unit for orthoimage mosaicking, and the size and position of each patch are dynamic variable. Patches from different images are rearranged under the image texture consistency constraint to generate the mosaic image. In the proposed method, the initial positions of available patches of all images are determined first, then the initial size of each patch is determined by the cloud coverage and the effective area of image. Subsequently, considering the inevitable misalignments between different orthoimages, the texture similarity between patches is utilized to select the most suitable patch, and the size of the patch is also dynamically adjusted to make the misalignments within each patch controllable. Finally, considering the color inconsistency in the overlapping areas of neighboring images, an area-weighted image blending algorithm is also presented to achieve invisible seams and smooth transitions in the final mosaic. Not only does the proposed method produce mosaic images with minimal cloud coverage areas by excluding cloud pixels as much as possible, but it is also flexible for the mosaicking of images with varying degrees of misalignments, which is achieved by dynamically adjusting the size and position of each patch while maintaining texture consistency constraint. Both simulated experiments and real data experiments are carried out to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can generate the mosaic image with higher spatial continuity and radiometric consistency.

How well do multi-fire danger rating indices represent China forest fire variations across multi-time scales?

To better support wildfire predictions and risk assessment, multiple fire danger rating indices (FDRIs) have been developed but their credibility in China remains obscure. Compared with the satellite fire observations, 13 FDRIs are evaluated for the historical (2003–2021) forest fire frequency in China from four different time scales: active seasons, trends, interannual variations (IAVs) and discrimination of fire/non-fire days (DFDs). Most FDRIs effectively capture the double active seasons over Southwest China and the dominant active season over Northeast and South China but fail over the other regions. FDRIs with cloud cover perform better in capturing climatological fire seasonality. All FDRIs fail to reproduce the significant decreasing trend of forest fires speculatively due to local fire management and discordant changes in meteorological elements. Most FDRIs have the advantages of the IAVs and DFDs over Southwest China but exhibit deficiencies over the other regions. FDRIs incorporating wind speed perform best in representing both IAVs and DFDs, indicating the indispensable effect of surface wind on the interannual/daily variation of fire danger. This study provides a credible reference for utilizing FDRIs in China, as well as offers insights for developing better regional FDRIs to represent different time-scale variations.

Prediksi Kenaikan Awan Di Wisata Lolai Berbasis Machine Learning

The increase in cloud cover is an important indicator in predicting upcoming weather. However, manual observations of cloud cover are still limited and time-consuming. Therefore, this research aims to develop a cloud cover classification model based on measurement data in Lolai using the Naive Bayes machine learning method. In this study, data on cloud cover, temperature, and humidity measurements were collected directly in Lolai for 30 days and using online BMKG data. Then, the data was processed and divided into training and testing datasets. The Naive Bayes model was applied to the training data and its accuracy was tested on the testing data. The research results show that the cloud cover classification model based on Naive Bayes has varying accuracy levels depending on the data source. For direct measurement data, the model achieved an accuracy rate of 63%, while for online BMKG data, the model achieved an accuracy rate of 80%. In testing on the testing data, the model successfully classified cloud cover based on temperature and humidity data. This research contributes to identifying the relationship between temperature, humidity, and cloud conditions and evaluates the performance of the Naive Bayes model in determining the influence of air temperature and humidity on cloud conditions. It is expected that this research can serve as a basis for the development of weather prediction systems in the future.

Assessment of the Sensitivity of the Mean Climate Simulation over West Africa to Planetary Boundary Layer Parameterization Using RegCM5 Regional Climate Model

This study evaluates the performance of two planetary boundary parameterizations in simulating the mean climate of West Africa using the Regional Climate Model version 5 (RegCM5). These planetary boundary parameterizations are the Holtslag scheme and the University of Washington scheme. Two sets of three one-year simulations were carried out at 25 km horizontal resolution with three different initial conditions. The first set of simulations used the Holtslag scheme (hereafter referred to as Hol), while the second used the University of Washington (UW) scheme (hereafter referred to as UW). The results displayed in this study are an average of the three simulations. During the JJAS rainy season, with respect to GPCP, both models overestimated total rainfall in the orographic regions. The UW experiment represented total rainfall fairly well compared to its counterpart, Hol. Both models reproduced convective rainfall well, with a relatively weak dry bias over the Guinean coast subregion. Globally, UW performed better than Hol in simulating precipitation. The pattern of near-surface temperature in both models was well reproduced with a higher bias with Hol than with UW. Indeed, the UW scheme led to a cooling effect owing to the reduction in eddy heat diffusivity in the lower troposphere contributing to reduce the bias. As a consequence, the height of the planetary boundary layer (PBL) was best simulated using the UW scheme but was underestimated compared to ERA5, while using the Hol scheme failed to capture the height of the PBL. This is coherent with the distribution of total cloud cover, which was better simulated with the UW scheme compared to the Hol scheme. This study shows that use of both planetary boundary parameterizations leads to a good simulation of most of the climatological characteristics of the West African region. Nevertheless, use of the UW scheme contributes to a better performance than use of the Hol scheme, and the differentiation between the two schemes is significant along the Guinea Coast and in orographic regions. In these topographically complex regions, UW appears to be more appropriate than Hol. This study emphasizes the importance of planetary boundary parameterizations for accurately simulating climate variables and for improving climate forecasts and projections in West Africa.

Lottery demand, weather and the cross-section of stock returns

We investigate the role that weather-induced mood plays in underperformance of lottery stocks. We hypothesize that during pleasant weather conditions, investors are more likely to become risk-taking and optimistic, and invest more in lottery stocks. This results in overpricing and lower expected return for these stocks. We use sky cloud cover as a proxy for the weather condition and show that the MAX effect mainly exists following sunny weather; however, there is less evidence for the MAX anomaly following cloudy weather. Our finding is robust to using alternative constructions of MAX and skewness measures as well as other proxies for weather condition such as rain, wind and temperature.

AstroSA: An astronomical observation scheduler assessment framework in python

Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (AstroSA), implemented as a Python package. The AstroSA offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, AstroSA includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.

Feasibility study on retrieving exoplanetary cloud cover distributions using polarimetry

As a new growing field, exocartography aims to map the surface features of exoplanets that are beyond the resolution of traditional observing techniques. While photometric approaches have been discussed extensively, polarimetry has received less attention despite its promising prospects. We demonstrate that the limb polarization of an exoplanetary atmosphere offers valuable insights into its cloud cover distribution. Specifically, we determine an upper limit for the polarimetric precision, which is required to extract information about the latitudinal cloud cover of temperate Jovian planets for scenarios of observations with and without host stars. To compute the scattered stellar radiation of an exoplanetary atmosphere and to study the polarization at various planetary phase angles, we used the three-dimensional Monte Carlo radiative transfer code POLARIS. When the planetary signal can be measured separately from the stellar radiation, information about the latitudinal cloud cover for polar cap models is accessible at polarimetric sensitivities of percent . In contrast, a precision of about e-3 ppm is required when the stellar flux is included to gain this information.

Characteristics and mechanisms of near-surface negative atmospheric electric field anomalies preceding the 5 September 2022, Ms 6.8 Luding earthquake in China

Abstract. A magnitude 6.8 strike-slip earthquake (EQ) struck Luding, Sichuan Province, China, on 5 September 2022, resulting in significant damage to nearby Ganzi Prefecture and the city of Ya'an. In this research, the near-surface atmospheric electric field (AEF) recorded at four sites 15 d before the Luding EQ was analyzed and differentiated, and multisource auxiliary data including precipitation, cloud base height, and low cloud cover were used at the same time. Nine possible seismic AEF anomalies at four sites were obtained preliminarily. Accordingly, microwave brightness temperature (MBT) data, which are very sensitive to the surface dielectrics and are closely related to the air ionization, together with surface soil moisture, lithology, and a 3D-simulated crustal stress field, were jointly analyzed to confirm the seismic relations of the obtained negative AEF anomalies. The geophysical environment for crustal high-stress concentration, positive charge carrier transfer, and surface accumulation was demonstrated to exist and to meet the conditions necessary to generate local negative AEF anomalies. Furthermore, to deal with the spatial disparities in sites and regions with potential atmospheric ionization, near-surface wind field data were employed to scrutinize the reliability of the AEF anomalies by comprehensively analyzing the spatial relationships among surface charges accumulation areas, wind direction and speed, and the AEF sites. Finally, four negative AEF anomalies were deemed to be closely related to the Luding EQ, and the remaining five possible anomalies were ruled out. A possible mechanism of negative AEF anomalies before the Luding EQ is proposed: positive charge carriers were generated from the underground high-stress concentration areas and then transferred to and accumulated on the ground surface to ionize the surface air, thus disturbing the AEF above the ground. This study presents a method for identifying and analyzing seismic AEF anomalies and is also beneficial for the examination of the pre-earthquake coupling process between the coversphere and the atmosphere.

Growth-climate relationships of four tree species in the subtropical evergreen broad-leaved forests in Southwest China

Subtropical forests are an important carbon sink, yet our understanding on tree growth response to inter-annual climate variability remains limited. This study investigated the growth-climate relationships of four evergreen broad-leaved tree species (Stewartia pteropetiolata W. C. Cheng, Schima noronhae Reinw. ex Bl. Bijdr, Machilus gamblei King ex J. D. Hooker, and Machilus yunnanensis Lec) in the subtropical evergreen broad-leaved forests (EBFs) in the Ailao Mountains of Yunnan province, southwest China. We constructed tree ring-width residual chronologies for each species and correlated them with daily and monthly climate data. Ring-width chronologies of S. pteropetiolata and S. noronhae were positively correlated with precipitation, relative humidity, and self-calibrated Palmer drought index (scPDSI) during autumn of the previous year and spring-to-early summer of the current year, indicating that these two Theaceae species were sensitive to moisture availability. Tree radial growth of two Machilus species was primarily limited by excessive precipitation and dense cloud cover during the rainy season in this middle montane cloud forest. Moving correlations between climate variables and radial growth of all species were temporally unstable, with a weakening response to climate variability in recent decades. We highlight the dendroclimatic potential of evergreen broad-leaved tree species in moist subtropical forests. The findings offer a pertinent perspective, emphasizing the necessity of separately considering distinct ecological indicators for trees in order to obtain a comprehensive understanding of climate-growth responses within highly diverse subtropical forests.