Ground Observations Research Articles

Photometry, rotation period determination and dust coma numerical study of comet C/2017 K2 (PanStarrs)

C/2017 K2 (Pan-STARRS) is an Oort cloud comet discovered in May 2017. Ground observations have revealed that this long-period comet was active at a heliocentric distance beyond 20 au. Several studies observed this object when it was far from the Sun and proposed that, at such a distance, its activity is primarily driven by the sublimation of super-volatile ices such as CO and CO2.The aim of this paper is to analyze the comet’s dust behavior when it was much closer to the Sun. A series of images were taken on different observation routines between July and August 2022 with the 0.6 m Helen Sawyer Hogg (HSH) telescope at the Complejo Astronómico El Leoncito (CASLEO), using broadband V and R filters. The objective was to conduct a morphological, photometric, and numerical analysis of this comet.Digital filters were applied to enhance contrast in the cometary images, revealing an active region embedded within an isotropic coma. Analysis of the magnitudes and dust production rate from the A(0°)fρ parameter suggests a steady-state dust behavior, consistent with findings from other authors.Moreover, thanks to the images covering a significant time span, it was possible to determine the comet’s rotation period using a periodogram analysis with the Phase Dispersion Minimization method (PDM) and to calculate the coordinates of the rotation axis, yielding a rotation period of 14.24 h with a pole situated at the ecliptic longitude and latitude of (244°,−20°).Finally, to gain deeper insights into C/2017 K2’s dust behavior, observations were fitted to a newly developed theoretical model for studying dust comas. The analysis suggests that the comet’s dust coma was principally formed by large dust particles emitted at a velocity of 180ms−1.

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

Long-Term Wind and Air Temperature Patterns in the Southeastern Region of Iran through Model Simulation and Ground Observations

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city’s weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model’s output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period.

Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations

During the Lunar New Year of 2020, the Chinese government implemented a strict nationwide lockdown to prevent the spread of the COVID-19, which may have led to changes in the distribution of pollutants. To enhance our understanding of the three-dimensional evolution of pollutants under special control scenarios, this study used vehicle lidar to obtain vertical profiles of aerosols and ozone (O3) in Wuhan during the pandemic. Combined with ground observations and WRF-Chem model simulation results, we explored the evolution characteristics of pollutants. The results showed that the fine particulate matter (PM2.5) concentration near the ground in Wuhan City was significantly reduced (40%) during the emission control period. The aerosol concentration decreased rapidly with an increase in height, and the maximum loading height was consistent with the atmospheric boundary layer height. Conversely, due to factors such as the reduction of the precursor nitrogen dioxide (NO2) and rising temperatures, the near-ground O3 concentration increased by 59%. Despite the significant reduction in emission sources, lidar observations still captured distinct aerosol transport layers on March 7 and March 10, 2020. These transport layers gradually descended, affecting the ground-level aerosol concentration. Additionally, one typical process of O3 external transport was observed in the vertical direction. Our study provides a theoretical basis for a deeper understanding of the spatiotemporal distribution characteristics of pollutants during special control scenarios, aiding policymakers in formulating effective air pollution control strategies.

Bias correction of the hourly satellite precipitation product using machine learning methods enhanced with high-resolution WRF meteorological simulations

Accurate precipitation data are crucial in atmospheric and hydrological studies, especially for water resource management and disaster early warning. Satellite precipitation product (SPP) with high spatiotemporal resolution has been regarded as a valuable alternative precipitation source to ground observations. However, the hourly SPP generally performs poorly compared to daily SPP, thereby bias correction is urgently required. This study investigates the viability of utilizing machine learning methods to correct the bias of the hourly Integrated Multi-satellitE Retrievals for Global Precipitation Measurement-Early (IMERG-E) product. Meanwhile, the Weather Research and Forecasting (WRF) model is utilized to generate high-resolution fields of four hourly meteorological variables, namely, temperature at 2 m (TEMP2), specific humidity at 2 m (Q2), wind direction at 10 m (WDIR10), and wind speed at 10 m (WSPD10), which further serve as covariates in machine learning models to enhance the correction process. Four machine learning models were developed, i.e., Random Forest (RF) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM) without WRF-simulated covariates, and RF-WRF and Bi-LSTM-WRF with meteorological covariates. The results demonstrated that incorporating WRF-simulated meteorological covariates improved model performance. Specifically, correlation coefficient (CC) values increased from 0.47 (RF) to 0.51 (RF-WRF) and rose from 0.55 (Bi-LSTM) to 0.60 (Bi-LSTM-WRF), along with reduced root mean square error (RMSE) and increased critical success index (CSI) values. Furthermore, two Bi-LSTM models consistently outperformed two RF models. Overall, the Bi-LSTM-WRF model emerged as the most effective correction method, which increased CC from 0.43 (IMERG-E) to 0.60, reduced RMSE from 1.91 mm to 1.08 mm, and enhanced CSI from 0.34 to 0.41. This study underscores the potential of integrating high-resolution WRF meteorological outputs into machine learning frameworks for correcting hourly SPPs, contributing significantly to the advancement of precipitation estimation in meteorological and hydrological applications.

Increasing drought sensitivity of plant photosynthetic phenology and physiology

Understanding the impacts of drought on plant photosynthetic phenology and physiology is essential for accurately predicting annual gross primary productivity (GPP) and global carbon cycles. While droughts in different seasons can have different effects on plant growth and photosynthesis, a comprehensive understanding of how plant photosynthetic phenology and physiology respond to different seasons’ droughts and their temporal changes is lacking. Here, we comprehensively evaluated the drought sensitivities of plant photosynthetic phenology and physiology and their temporal trends in the past two decades, using the start/end of the photosynthetic seasons (SOS/EOS) and peak GPP (GPPmax) derived from FLUXNET ground observations since the 1990s and remote-sensing data (2001–2020) over the globe. Our results show that spring droughts leads to delayed SOS in arid ecosystems and advanced SOS in humid ecosystems. In contrast, droughts in summer and autumn generally advance EOS. In addition, spring droughts decrease GPPmax in arid regions but increase it in humid regions, whereas summer droughts lead to significant GPPmax reductions. More importantly, we observed significant increases in the drought sensitivity of SOS, EOS, and GPPmax over the last two decades, which is driven by long-term decreases in background soil moisture and increases in atmospheric water deficit under climate change. Furthermore, these increasing trends were projected to persist in the 21st century under Shared Socioeconomic Pathways 8.5 and 4.0. Overall, our findings underscore the critical role of drought timing in shaping ecosystem responses and highlight the growing vulnerability of ecosystem phenology and physiology to drought, which may restrict the increases in terrestrial carbon uptake under warming.

Ground‐Observed Snow Albedo Changes During Rain‐On‐Snow Events in Northern Alaska

AbstractRain‐on‐snow (ROS) events occur when rain falls on snowpack and can have substantial ecological and social impacts. During ROS events, liquid water in the snowpack can decrease the surface albedo, which contributes to the positive snow‐albedo feedback and further accelerates snowmelt. In a warming climate, the frequency and spatial coverage of ROS events are projected to increase in the high‐latitude regions, especially in northern Alaska. Multi‐year ground observations at two northern Alaska sites are utilized to evaluate 59 ROS events from 2012 to 2022. Results show that ROS events lead to dramatic snow albedo changes with a mean decline of −0.04 per day, which is considerably larger than the multi‐year mean of −0.005 in May and −0.008 in June. A snow albedo model is used to simulate the daily snow albedo changes due to snowpack liquid water content. The simulated impact of liquid water content accounts for only 10% of the observed snow albedo changes. In addition, composite synoptic conditions from reanalysis products reveal different moisture sources for ROS events. ROS events in May are associated with anomalous high pressure systems over the site and meridional transport of warm and moist air from lower latitudes. While the June synoptic conditions for ROS events show little deviation from the climatological mean and suggest local moisture contributions. ROS events in June show comparable snow albedo changes as in May despite the difference in moisture sources, which implies a prolonged impact of ROS events on rapid snow deterioration during late spring.

Tiny rain makers: How aerosols shape extreme rainfall simulation accuracy

Given the rapid spatiotemporal variability of aerosols and their complex impacts on the Earth system, investigating the role of aerosols in modelling extreme weather events, particularly various extreme rainfalls, remains limited. This study explored extreme rainfall simulation sensitivity to aerosol properties, transport and seasonality over the UK and Ireland during four seasons in 2020 by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. Two sets of high-resolution simulations, one including aerosol direct and indirect effects and the other one without any aerosol effects, were conducted to investigate the improvements due to aerosol inputs. Meteorological results were verified using ground and satellite observations to examine the reliability of simulations. The 12 extreme rainfall events during the study months were classified by their backward trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, and their simulation accuracies were quantified through six spatiotemporal metrics and one overall score. By comprehensive analyses of the simulation performances under different conditions, it is found that the model performs better in simulating rainfall events driven by sea salt aerosols (SSA) compared to those promoted by anthropogenic aerosols. Air masses transported from the Arctic and North Atlantic Ocean also help achieve higher rainfall simulation accuracy than European air masses. Rainfall simulations for winter and autumn events outperform those for spring and summer events. When considering aerosol effects in simulations, almost half of the events showed improved performance, while an equal number experienced a decrease and a small proportion remained unchanged. The most remarkable improvements are observed in rainfall simulations with higher concentrations of SSA and the participation of anthropogenic aerosols, while simulations promoted only by anthropogenic aerosols all experience performance reductions. The aerosol effects also led to significant increases in monthly rainfalls, accompanied by great reductions of Cl in larger particle size aerosols and NO3 in smaller particle size aerosols.

A Study of Precipitation Inversion in Xinjiang Region Based on FY-4A and Machine Learning Models

To compensate for the fact meteorological observation stations in Xinjiang are sparse, and spatial and temporal resolution of precipitation monitoring is insufficient in existing studies, in this study the authors proposed a precipitation inversion model that is based on infrared observation data from the Fengyun-4A satellite and the machine learning method. By combining multichannel satellite remote sensing data with ground meteorological observations, they constructed various machine learning models, such as deep forest, random forest, LightGBMs (light gradient-boosting machines), and XGBoost (extreme gradient boosting), using root-mean-square error, mean absolute error, and the coefficient of determination to evaluate and compare the model performance. The trained deep forest model was used to invert the precipitation in Xinjiang from June to August2023. The results show that the machine learning method is effective in exploiting the nonlinear relationship between the satellite observation features and the ground precipitation, and the inversion results are in good agreement with the ground observation data. Among these models, the deep forest model performs best in daytime conditions, and LightGBM is slightly better in nighttime conditions.

Downscaling soil moisture in regions with high soil heterogeneity: the solution based on ensemble learning with sequential and parallel learner

Soil moisture plays an important role in the water and heat exchanges between the land surface and atmosphere, and it has great importance for agricultural production, ecological planning, and water resources management. Although microwave remote sensing has been widely used in large-scale soil moisture monitoring, the accuracy of the downscaled retrieval results cannot be guaranteed for regions with high vegetation coverage and high soil heterogeneity. To address these challenges, this study built soil moisture indice set based on MODIS and elevation data by calculating the Pearson correlation coefficient (R) and Maximum Information Coefficient (MIC), then constructed decision tree models (Gradient Boosting Decision Tree and Random Forest) about the indice set and low-resolution Soil Moisture Active Passive (SMAP) by using two ensemble learning methods (Bagging and Boosting). The models were applied to the high-resolution soil moisture indices in Jilin Province for the years 2017 to 2020 to generate 1 km-resolution products. In the validation process, Triple Collocation Analysis (TCA), comparison of soil moisture maps with coarse and fine resolution, and in-situ measurements in Lishu County, Tongyu County, and Jilin City were used to evaluate the differences between downscaling soil moisture results and ground observations at network, seasonal and point scales. The results were as follows: (1) The correlation coefficient (R2) calculated by the TCA method was 0.733 (GBDT_36km) > 0.649 (RF_36km), and the error variance was 0.0004 (GBDT_36km) < 0.00058 (RF_36km). (2) R at network scale was 0.798 (GBDT_SM) > 0.662 (RF_SM), RMSE was 0.040 (GBDT_SM) < 0.044 (RF_SM), the point scale R was 0.864 (GBDT_SM) > 0.833 (RF_SM), RMSE was 0.029 (GBDT_SM) < 0.039 (RF_SM). The R in four stages of the growth period was GBDT_SM > RF_SM, RMSE was GBDT_SM < RF_SM. In conclusion, the GBDT and RF models can reliably downscale soil moisture in Jilin Province, and the Boosting ensemble learning method represented by GBDT had a better estimation performance.

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.

Cross-Validation Methods for Multisource Precipitation Datasets over the Sparse-Gauge Region: Applicability and Uncertainty

Abstract Evaluating the accuracy of various precipitation datasets over ungauged or even sparse-gauge areas is a challenging task. Cross-validation methods can evaluate three or more datasets based on the error independence from input data, without relying on ground observations. Here, the triple collocation (TC) method is employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the Tibetan Plateau (TP). TC-based results show that ERA5 has better performances than satellite-only precipitation products over mountainous regions with complex terrains. For purely satellite-derived products, IMERG products outperform GSMaP products. Considering the potential existence of error dependencies among input datasets, caution should be exercised. Thus, it is necessary to introduce an alternative cross-validation method (generalized three-cornered hat) and explore the applicability of cross validation from the perspective of error independence. We found that cross-validation methods have high applicability in most TP regions with sparse-gauge density (accounting for about 80.1% of the total area). Additionally, we conducted simulation experiments to discuss the applicability and robustness of TC. The simulation results substantiated that cross validation can serve as a robust evaluation method over sparse-gauge regions. Although it is generally known that the cross-validation methods can be served in sparse-gauge regions, the application condition of different evaluation methods for precipitation products is identified quantitatively in TP now. Significance Statement Cross validation is a powerful assessment method for multisource precipitation datasets. This method is widely used by hydrologists in sparse-gauge or ungauged regions, like the African continent and the Tibetan Plateau (TP). However, as an indirect assessment method, the inherent uncertainty in cross validation warrants emphasis. Here, two cross-validation methods (the triple collocation method and the generalized three-cornered hat method) are employed to evaluate multisource precipitation datasets: China Gauge-based Daily Precipitation Analysis (CGDPA), model-based ERA5, and satellite-derived IMERG-Early, IMERG-Late, GSMaP in near–real time (GSMaP-NRT), and GSMaP moving vector with Kalman filter (GSMaP-MVK) over the TP. In this study, we not only assessed the current mainstream six precipitation datasets but also analyzed their uncertainties by combining these two cross-validation methods.

The frequency of transiting planetary systems around polluted white dwarfs

ABSTRACT This paper investigates the frequency of transiting planetary systems around metal-polluted white dwarfs using high-cadence photometry from ULTRACAM and ULTRASPEC on the ground and space-based observations with TESS. Within a sample of 313 metal-polluted white dwarfs with available TESS light curves, two systems known to have irregular transits are blindly recovered by box-least-squares and Lomb–Scargle analyses, with no new detections, yielding a transit fraction of $0.8_{-0.4}^{+0.6}$ per cent. Planet detection sensitivities are determined using simulated transit injection and recovery for all light curves, producing upper limit occurrences over radii from dwarf to Kronian planets, with periods from 1 h to 27 d. The dearth of short-period, transiting planets orbiting polluted white dwarfs is consistent with engulfment during the giant phases of stellar evolution, and modestly constrains dynamical re-injection of planets to the shortest orbital periods. Based on simple predictions of transit probability, where $(R_* + R_{\rm p})/a\simeq 0.01$, the findings here are nominally consistent with a model where 100 per cent of polluted white dwarfs have circumstellar debris near the Roche limit; however, the small sample size precludes statistical confidence in this result. Single transits are also ruled out in all light curves using a search for correlated outliers, providing weak constraints on the role of Oort-like comet clouds in white dwarf pollution.

Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A

In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ.

Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data

To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.

Analyses of groundwater level in a data-scarce region based on assessed precipitation products and machine learning

Groundwater, a vital natural resource globally, faces challenges related to data scarcity, particularly in data-limited regions. To address this issue in the region of Bahira in Morocco, we developed an innovative approach for estimating groundwater levels by utilizing precipitation products and employed a random forest (RF) machine learning (ML) model to fill data gaps. Our study involved a comprehensive assessment of ten precipitation products, comprising six Reanalysis Precipitation Products (RPPs) and four Satellite-based Precipitation Products (SPPs). We evaluated their performance across various time scales, including daily, monthly, and seasonal, across different topographical classes. The outcomes highlighted the consistency of ERA5-based datasets, boasting daily correlation to values higher than 0.6, whereas monthly and seasonal correlations exceed 0.8, except during summer. GPM-IMERG, MERRA2, and CPC-UPP also demonstrated commendable accuracy, particularly in plain and mountainous areas. Nonetheless, CFSR, CHIRPS, PERSIANN CDR, and TRMM datasets exhibited limitations, particularly in high mountain areas. To address data gaps, we initially explored correlations between RPPs, SPPs, and groundwater data. However, these correlations failed to meet the accuracy standards required for precise predictions. Notably, the strongest correlations were observed in monitoring stations located in mountainous regions, indicating significant aquifer recharge activities in these areas. In the subsequent phase, the Multiple Imputation by Chained Equations (MICE) machine learning-based imputation method served as a valuable tool for estimating groundwater levels in regions where ground observations were insufficient. Our trend analysis yielded significant insights, with approximately 95% of groundwater points displaying negative trends, with a maximum rate of −0.91 m. In contrast, 69% of precipitation stations exhibited negative trends, with a maximum rate of −0.06 mm. Our approach offers a promising potential to address the challenges associated with the scarcity of groundwater and precipitation data, making it a valuable tool for the assessment, monitoring, and management of groundwater resources.

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

Machine learning potentials for global multi-timescale diffuse irradiance estimation: Synthesizing ground observations, time-series, and environmental features

Separating diffuse horizontal irradiance (DHI) from ground-based global horizontal irradiance observations is critical owing to lack of direct DHI observations. Existing models are often site-specific and time-bound, thereby limiting their universal applicability. To address this, this study thoroughly explores machine learning (ML) for constructing global separation models. We develop 36 models using three ML algorithms—light gradient boosting machine (LightGBM), generalized additive model (GAM), and geographically weighted artificial neural network (GWANN)—alongside three data splitting strategies for minutely, 10-minutely, hourly, and daily timescales. LightGBM and GAM outperform GWANN in accuracy, with LightGBM excelling in interpretability, efficiency, and handling missing values, while GWANN exhibits superior stability. Compared with completely random splitting, model accuracy decreases by 2.26 % and 2.16 % for station- and date-based splitting, respectively. Prediction accuracy varies across timescales, with minutely models typically considered the best. Key predictors are Kt, solar zenith angle, and altitude, complemented by the significant influence of time-series Kt, aerosol, and cloud features. The influence of various factors on model accuracy differs and is scale-dependent. Among them, time-series Kt variability factors notably enhance ML-based predictions, especially at minutely scales. The LightGBM model outperforms classic models in overall and site-specific accuracy and adaptability, but its computational efficiency declines, especially with time-series variability factors. The findings highlight that ML-based separation models necessitate careful selection of algorithm, data splitting strategy, and input variables, fully considering study region, data situation, and temporal and spatial scales. This research offers a potential solution for constructing global separation models and insights for model improvement.