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Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method. Plain Language Summary: In GNSS relative positioning, the positioning performance of user largely depends on the extent to which atmospheric deviations (ionospheric delay or tropospheric delay) are eliminated or weakened. Compared with the ionosphere, the temporal and spatial characteristics of troposphere are significantly different. It is part of the lower atmosphere, extends from the Earth's surface to an altitude of about 20 km, and contains most of the atmospheric water vapor. The delay of satellite signals through the troposphere is affected by both horizontal and elevation. The traditional tropospheric regional modeling strategy only uses plane coordinates for interpolation, ignoring the influence of elevation factors or the consideration of height‐deviation is not perfect. When the height difference between the reference station and the rover station is large, the modeling accuracy will obviously be reduced. The large modeling residual may bring systematic deviation to the user coordinates, especially the elevation direction component. In this paper, we propose a regional tropospheric linear interpolation method (HCLIM) considering height deviation compensation. This method is simple and effective, The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height difference region are significantly improved. Key Points: A simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposedThe HCLIM method greatly improves the tropospheric modeling accuracy of each elevation angle range in the large height difference site areaThe HCLIM method significantly improves the systematic bias in elevation direction of user RTK positioning due to modeling residuals [ABSTRACT FROM AUTHOR]

In the search for precursors to earthquakes, correlation has been found between geochemical characteristics of soil gases and seismic activity. In this paper we present evidence that seismic waves can trigger emission of soil radon (Rn) and carbon dioxide (CO2). An active experiment was performed in two fault zones in China, the Annighe fault in Sichuan province and the Xiadian fault in Heibei province. An active seismic source was used to generate seismic waves at 10 m depth in wells within bedrock. Rn and CO2 detectors were placed around the wells at a distance of ∼1 m for observing the effects of the seismic waves on the emission of the gases. The observations confirm that the seismic waves have a significant and direct effect on the concentration and flux of soil radon and carbon dioxide. When the seismic events were triggered, the observed concentrations of Rn and CO2 immediately increased and reached peak values within 5–50 min and 30–60 min, with corresponding increases of Rn and CO2 concentrations by 10.5%–238.7% and 3.1%–54.1%, respectively. The measured concentrations and flux of CO2 and Rn after the passage of the seismic waves showed strong correlation, confirming the suggestion that CO2 is the carrier gas for Rn. To the best of our knowledge this is the first direct, in‐situ measurement of gas emission caused by the passage of seismic waves and provides important constraints for better understanding of geochemical earthquake precursors. Plain Language Summary: Observed geochemical properties of soil gases migrated from the deep Earth can be used to survey seismicity, volcanic activity and emission of greenhouse gases. However, due to difficulty in natural earthquake prediction, most observations on the geochemical effects linked to seismic activity are conducted after or away from naturally occurring events, with the effects normally inferred from seismic parameters. In this study, an active seismic source based on methane gaseous detonation was employed to artificially produce seismic events along two fault zones, and in‐situ measurements of concentrations and flux of radon (Rn) and carbon dioxide (CO2) were conducted. These observations showed strong correlation between concentrations and flux of CO2 and Rn after the events, in agreement with the hypothesis that CO2 is the carrier gas for Rn in tectonically active settings. Key Points: Soil CO2 and Rn emission triggered by a new type active source that excited at 10 m deep well in bedrock are observed at first timeQuantitative effect of seismicity on the concentration and flux of soil CO2 and Rn are presentedEmission of CO2 and Rn after the seismicity show strong correlation and proved that CO2 is the carrier gas for Rn [ABSTRACT FROM AUTHOR]

Both long‐term but small number of national stations and short‐term but large number of regional stations have been frequently used to study the extreme hourly precipitation (EXHP) in China. However, few studies focus on the differences of the two for revealing the features of EXHP. In this study, the characteristics of EXHP in Zhejiang Province are investigated using three rainfall data sets at three threshold criteria. The comparison between different data sets shows that increasing the station density can better reflect the climatic spatial distribution of EXHP thresholds if long‐term data is absent. The majority of EXHP can be classified into four weather types: the southwesterly wind type (30.7%–48.5%), the trough type (12.2%–23.6%), the tropical cyclone (TC) type (11.4%–17.5%) and the easterly wind type (4.9%–17.9%). The selection of stations is more sensitive to the proportions of the four weather types than the statistical years and threshold criteria. The monthly and diurnal variations of EXHP, as well as their differences revealed by the three data sets, are varied by weather type. Only using national stations cannot distinguish the spatial differences between the TC type and the easterly wind type, and there is an underestimation for southwesterly wind type and trough type in the mountainous area of southwest Zhejiang. The statistical year and station height are the main reasons for the differences in the duration of EXHP events calculated by different data sets, with the TC type having the largest effect and the southwesterly wind type the smallest. Plain Language Summary: Station data is the most common and reliable data for studying the characteristics of precipitation in China. There are two sources of station data available, that is, national station data with long time series but sparse and regional stations with short time series but numerous. However, few studies have compared the differences between these two sources of data for revealing the characteristics of extreme hourly precipitation (EXHP). In this study, the possible impact of different data sets for revealing the characteristics of EXHP under four weather types are investigated in Zhejiang Province. Zhejiang is located on the southeast coast of China with a long coastline and many mountains. Results suggest that increasing the station density can better reflect the spatial distribution of the EXHP thresholds if long‐term data is absent. EXHP events with strong synoptic‐scale forcing are more sensitive to the results obtained by different data sets. As the national stations are mostly located in the plains, the high frequency centers of EXHP are biased in each weather type if only using national stations, especially in southwest Zhejiang and near the coast. In summary, this paper provides additional perspectives for understanding the characteristics of regional EXHP by using the most common observations. Key Points: The selection of stations is sensitive to the proportions of the four main weather types in Zhejiang where extreme hourly rainfall occursExtreme hourly precipitation events with strong synoptic‐scale forcing are more sensitive to results obtained using different data setsThe sparse national stations located on the plains could hardly reflect the spatial distribution of extreme rainfall in mountainous Zhejiang [ABSTRACT FROM AUTHOR]

Historically, the long wavelength side (6–7.5 μm) of the mid‐infrared water vapor absorption band has been used for imaging from the geostationary perspective. This began with the 6.4 μm band on Europe's Meteosat‐1. While geostationary sounders for moisture profiling, including China's hyperspectral resolution infrared sounder and a planned sounder from Europe, are or will be measuring the short wavelength side of the water vapor band, this is not the case for geostationary imagers. Shorter wavelength (5–6 μm) spectral bands for imaging applications should be considered for observing moisture in the mid and lower troposphere because of several potential advantages offered by this spectral range. The short wavelength side of the water vapor band contains fewer additional absorbing gases that overlap the water vapor absorption lines. In addition, the shorter wavelengths would show less diffraction blurring which could enable finer spatial resolution for turbulence detection. This study considers some of the differences and potential advantages of the spectral information in the short wavelength side of the water vapor absorption band from the perspective of geostationary imaging. For dry conditions land heating could impact the qualitative use of observed brightness temperatures at 5.1 μm, while the solar reflection component over most clear‐sky scenes is small for a 5.1 μm band and essentially zero for a 5.6 μm band. Plain Language Summary: Long before images are sent back to Earth from satellites, the needed spectral bands have to be specified. The band specification includes attributes such as the wavelength centers, widths, and signal‐to‐noise ratio. This paper investigates the rationale for including data from a region of the electromagnetic spectrum that has not been used for imaging from geostationary orbit. If implemented, these short wavelength water vapor bands would provide unique information regarding low‐level moisture and mid‐level turbulence. This paper quantifies the expected reflected component from the sun and the influence of land heating on observations, both of which are relatively larger at shorter wavelengths. Key Points: Historically, the long wavelength side of the mid‐infrared water vapor absorption band has been used for imagingShort wavelength (near 5 μm) spectral bands should be considered, especially with finer spatial resolutionsOver most clear‐sky surfaces, the reflective component ranges from small to zero for 5.1 and 5.6 μm bands [ABSTRACT FROM AUTHOR]

Reconstructing the tectonic architecture of the southern Altaids is of great importance to piece together the continental growth history of the Central Asian continent. In this paper, we address the subduction‐accretion process of the Paleo‐Tianshan Ocean, one branch of the South Tianshan Ocean, in the southern Altaids, through a detailed geological and geochemical investigation of the Wulanmoren Accretionary Complex (WAC). The WAC is composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and turbiditic coherent units. In this study, we present key geological, petrological, and geochemical evidence indicating back‐arc and ocean island affinity of the igneous rocks of the WAC. Zircon U‐Pb dates of these rocks ages range between 380.2 ± 1.3 and 302.0 ± 5.3 Ma. The sedimentary matrix units of the mélanges were deposited in the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma), rather than Silurian strata as previously thought. However, the ages of some detrital zircons in the sedimentary matrix are Precambrian, pointing to a complex evolution involving multiple ocean island terrains and island arcs of different ages that accreted from the mid‐Paleozoic to the Early Mesozoic. Plain Language Summary: Well‐exposed rock outcrops of the Wulanmoren Accretionary Complex (WAC) offer an ideal opportunity to solve the enigma of the evolutionary history of the Paleo‐Tianshan Ocean (PTO) of Central Asia. We report field observations, geochemistry, and geochronology of the WAC in the southern Altaids, pointing to multiple tectonic settings of ages spanning from the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma). These reconstructions suggest closure time of the PTO occurred post‐Triassic. Based on detrital zircons provenance analysis, we propose a multi‐island ocean model to explain the tectonic evolution of the PTO in the time period studied. Key Points: The Wulanmoren Accretionary Complex was composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and coherent unitsThe Late Paleozoic MORB‐like and OIB‐type basalts, and ∼222 Ma coherent units indicate closure of the Paleo‐Tianshan Ocean occurred post‐TriassicThe southern Paleo‐Asian Ocean was an archipelago with several subduction systems which is similar to the modern southwest Pacific [ABSTRACT FROM AUTHOR]

A coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST-SM changes and the resulting ecological response across China. The results show that during 2000--2020, 24.5% of the land area in China experienced a warming-drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming-wetting trend leading to an 8.6% LAI increase. During 2015--2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long-term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short-term fluctuations in both typical regions and ecosystems despite consistent long-term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty-first century, but in terms of long-term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil-ecosystem anomalous events. [ABSTRACT FROM AUTHOR]

The China‐France ocean satellite (CFOSAT) has obtained synchronized observations of sea winds and sea waves for the first time in the world, providing accurate data support for the study of swells. In this paper, the accuracy of key parameters and the wave spectrum of CFOSAT surface waves investigation and monitoring (SWIM) L2 data are analyzed and evaluated using a spatial‐temporal matching criterion. The accuracies of the significant wave height (SWH), spectrum peak period (Tp) and dominant azimuth (Azi) are 0.76 m, 2.89 s and −24.02°, respectively, in the coastal area of the South China Sea (SCS) and 0.30 m, 0.97 s and 13.02° in the offshore area of the SCS. Calculated based on CFOSAT SWIM data, the annual average SWH of mixed waves in the SCS is 1.47 m, and 80% of them are between 0.5 and 3 m. There are high value areas in the central, northeastern and southwestern parts of the SCS. The average annual SWH of swells is 0.93 m, and 99% of swells are less than 2 m. A high swell SWH also exists in the central part of the SCS. Plain Language Summary: Swells are an important marine resource. Due to their long propagation distance and fast propagation speed, they can effectively forecast storm surge. They also have large energy and good stability, which are conducive to acquisition and conversion, and they have become a new method for offshore power generation. China‐France ocean satellite provides a convenient means to observe waves and to verify the accuracy of wave parameters, which is an important step for wave detection. The South China Sea, with its vast sea area and abundant swell energy reserves, is an ideal area for offshore power generation. Understanding the distribution of swell energy in the South China Sea (SCS) provides an opportunity to explore a sustainable and reliable renewable energy source. The SCS is also important and significant to the development and utilization of swell energy and for the site selection of offshore power stations. Key Points: Precision valuation of surface waves investigation and monitoring L2 data such as significant wave height (SWH), spectrum peak period (Tp), dominant azimuth (Azi) were carried out and it was determined that the data were stableThe annual average SWH in the South China Sea is 1.47 m and 80% of them are between 0.5 and 3 mWind‐swell separation was carried out, and the distribution of swell SWH shows a high value in the central part of the South China Sea [ABSTRACT FROM AUTHOR]

Ice clouds are mostly composed of different ice crystal habits. It is of great importance to classify ice crystal habits seeing as they could greatly impact single‐scattering properties of ice crystal particles. The single‐scattering properties play an important role in the study of cloud remote sensing and the Earth's atmospheric radiation budget. However, there are countless ice crystals with different shapes in ice clouds, and the task of empirical classification based on naked‐eye observations is unreliable, time consuming and subjective, which leads to classification results having obvious uncertainties and biases. In this paper, the images of ice crystals observed from airborne Cloud Particle Imager in China are used to establish an ice crystal data set called Ice Crystals Database in China, which consists of 10 habit categories containing over 7,000 images. We propose an automatic classification model of ice crystal habits, called TL‐ResNet152, which is a deep convolutional neural network based on the newly developed method of transfer learning. The results show that the TL‐ResNet152 model could achieve reliable performance in ice crystal habits classification with the accuracy of 96%, which is far more accurate than traditional classification methods. Achieving high‐precision automatic classification of ice crystal habits will help us better understand the radiation characteristics of ice clouds. Plain Language Summary: In recent years, Convolutional Neural Networks (CNNs), as one of the representative algorithms used in deep learning, have been widely applied in the field of image classification and have achieved remarkable results. However, to the best of our knowledge, there are few studies regarding the application of deep CNNs to ice crystal image classification. In this paper, we propose an automatic classification model of ice crystal habits called TL‐ResNet152, which is a deep CNN based on a newly developed method of transfer learning. Using the TL‐ResNet152 model, high precision and automatic classification of ice crystal habits could be achieved, furthering our understanding of radiation characteristics of ice clouds. We have set up an ice crystal data set, called Ice Crystals Database in China, which consists of 10 habit categories with 7,282 images. As far as we know, it is the first publicly available ice crystals data set in China, pertaining to ice crystals observed in natural ice clouds. The publication of this database will promote more and more researches into understanding the physical process of ice clouds based on ice crystal habits classification. Key Points: An ice crystal data set called Ice Crystals Database in China (ICDC) containing over 7,000 images has been first establishedWe propose an automatic classification model of ice crystal habits, TL‐ResNet152, which is a convolutional neural network created using transfer learningThe high‐precision automatic classification of ice crystal habits is achieved by using this classification model [ABSTRACT FROM AUTHOR]

The reduction of the runoff to lake has become a common phenomenon of the inland lakes in northwest China, and the quantitative calculation of the driving factors affecting the runoff change has always been a challenge in lake research. In this pursuit, the present study was performed on Lake Daihai as an example, based on the relevant data from 1960 to 2018. The quantitative calculations of the runoff driving factors of Lake Daihai from 1982 to 2004 and from 2004 to 2017 were based on the slope change ratio of cumulative quantity (SCRCQ). It was concluded that human activities were the main factors affecting runoff change after 1982. The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basis. The results show that the dominant factor of runoff changes in Lake Daihai was found to be the climate change before 1975, and it changed into human activities after 1975. Finally, based on the comparative analysis of the results of SCRCQ and improved SCRCQ, the advantages of improved SCRCQ were discussed. In addition, the reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed. Key Points: This paper analyzes the influence of climate change and human activities on the change in runoff volume of Lake Daihai according to the slope change ratio of cumulative quantity (SCRCQ)The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basisThe reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed [ABSTRACT FROM AUTHOR]

When many cities need quantitative forecasts of air quality to adjust industrial production plans and urbanization development, how to build an efficient forecast model remain a challenge. Methodology for quantitative prediction of air quality can no longer rely on single‐site observations, and thus approaches that require fewer input data and are more efficient and more reliable need to be explored. This paper proposes the principles and steps of a new model using the empirical orthogonal function (EOF) and principal component regression (PCR), which is a hybrid EOF‐PCR approach that decomposes the panel data of PM2.5 and predictors into spatial structures (EOFs) and time expansion coefficients (ECs) by EOF analysis, establishes the PCR of the ECs of PM2.5, and simulates the PM2.5 concentrations in each city by projecting the fitted ECs through EOFs. The very heart of the new model is the PCR modeling and projection. The results are presented for PM2.5 concentrations over Jiangsu Province in eastern China. The results show that this EOF‐PCR model, which is based on EC1s with a cumulative variance contribution rate above 90%, has an average prediction accuracy of 65%. The model performs best in spring and autumn, better in summer and worst in winter. Most predictors have a maximum lag of a week, and they are quite different among seasons. Considering the influence of the spatial distributions of predictors, rather than covariates at a single site, this model can reflect regional influences and effectively improve the simulation effect. Plain Language Summary: This study aims to develop a hybrid statistical model for regional forecasting of PM2.5 concentrations using EOF analysis and principal component regression, based on daily observations of PM2.5 concentrations and meteorological predictors. In the study area, the emission sources are relatively fixed, and meteorological conditions represent the main factor affecting short‐term air quality. The panel data of PM2.5 concentration and predictors are decomposed into spatial structures (EOFs) and temporal expansion coefficients (ECs) by EOF analysis. A principal component regression model was built to simulate the ECs of the PM2.5 concentrations, with the ECs of the predictors. Finally, through the inverse operation of the EOF analysis, the fitting values of the PM2.5 ECs are projected as PM2.5 concentrations at each site. The proposed model can reduce input requirements, improve computational efficiency, and emphasize the synergistic effect of meteorological factor distributional modes, rather than a point influence. Key Points: Proposed new regional forecast approach has less demand for input data, especially emissions dataForecasting accuracy was improved and reached nearly 70%, and computational efficiency and speed also increasedPM2.5 concentrations are sensitive to regional distributions of different meteorological predictors, that is, EOFs [ABSTRACT FROM AUTHOR]

This paper presents our efforts to characterize the candidate landing region (109°–133°E, 23°–30°N) for Tianwen‐1, China's first mission to Mars, in terms of engineering safety and scientific significance. Topographic analysis reveals that the region has a low elevation around −4,230 m, and 98% of the region have slopes smaller than 8°. The geomorphological mapping and analysis show that the region has an average crater density of about 28 craters (≥200 m in diameter) per 100 square kilometers, with several clusters of high crater densities distributed around the center of the region. There are also pitted cones distributed mainly in the southern part of the region, with a density of approximately 6.6 cones per 100 square kilometers in specific local areas. The region has rock abundances ranging from 1% to 23%, with local clusters of low and high rock abundances. The region comprises four main geological units, including a lowland unit formed in the Late Hesperian and a volcanic unit formed in the Amazonian and Hesperian period. Their specific surface ages are estimated through the analysis of crater size‐frequency distribution. Combining the engineering constraints on surface slopes, crater density, cone density, and rock abundance, a hazard map of the candidate landing region is generated for landing site evaluation and safety assessment. Based on the results, we further discuss the potential scientific outcomes from the exploration in this region. The findings will be helpful for the mission planning and maximization of the scientific return from Tianwen‐1, and complement existing Martian scientific research. Key Points: Topographic, geomorphological, and geologic characterization of the candidate landing region of Tianwen‐1A hazard map of the region combining engineering constraints on slopes, crater density, cone density, and rock abundanceTwo local areas in the region are highlighted as potential landing sites for their low hazard and scientific significance [ABSTRACT FROM AUTHOR]

China has lots of funds, materials, and human resources in cloud seeding operations to help with water shortage problems across the country. But there is little guide about when and where the operations would obtain better benefits. To fill this gap, the modified Water Utilization Level (WUL) map of China mainland was plotted with the acquirable water utilization data on sub‐watershed scales in this study. Then the agricultural drought levels map of different counties were integrated with the modified WUL Map under preset rules to generate an integrated water scarcity map. Lastly, the cloud seeding demand maps of different seasons were drawn by integrating the agricultural drought seasons map of different counties with the integrated water scarcity map and the modified WUL map. Results showed that the middle north of China should have the highest priorities for cloud seeding operations during spring and summer, and some regions at the south during winter and spring, a few scattered regions at the northwest during spring and summer and a few regions at the middle of southeast China during summer and autumn should also have high priorities of cloud seeding operations. This research can help rain enhancement operations to obtain better effects on easing water scarcity problems and deepen the cooperation of water resources department and weather modification department in China. The designed intersecting rules of different maps in this paper also might inspire other relevant researches. Key Points: The results gave a clear demand from water resources field to weather modification department in China mainlandThe middle north areas of China have the severest water scarcity problems, especially during spring and summerSome less developed regions at the south, the northwest and the southeast have high water scarcity levels in different seasons [ABSTRACT FROM AUTHOR]

China's first Mars exploration mission (Tianwen‐1), launched on July 23, 2020 with the goal of "orbiting, landing, and roving," and its tentative landing areas (TLAs) are Chryse Planitia (TLA 1) and Isidis‐Elysium Planitia (TLA 2). Dust storm is a typical activity on Mars; it has strong interaction with atmospheric circulation and will influence the visibility of the Martian surface. To ensure the safety and accuracy for landing, it is meaningful to analyze the probability of dust storm activity during Entry‐Descent‐Landing (EDL) season in TLAs. First, based on Mars Orbiter Camera (MOC) and Mars Color Imager (MARCI) Daily Global Maps (MDGMs) from Mars Years 24–31, we identified 882 dust storms within the 2,000 km radius monitor circle of TLA 2 (short for TLA 2‐C). Second, the average daily probability of dust storm activity (Adp_ds) in TLA 2‐C was calculated, with a range of 0% to 14.13% showing obvious seasonality and discontinuity. Third, we divided TLA 2‐C into 0.5° longitude × 0.5° latitude square grids and calculated their spatial probability of dust storm activity. The spatial probability of dust storm in TLA 2‐C ranged from 0% to 11.87% and generally reduced from north to south. Finally, according to the temporal and spatial dust storm probability in TLA 2 during EDL season, we suggested the optimal landing time of China's Tianwen‐1 mission was in Ls = 20–41° and selected five preferred landing areas with the spatial probability of dust storm <3% for this mission. Plain Language Summary: The first China's Mars exploration mission (Tianwen‐1), will achieve the targets of "orbiting, landing, and roving." There are two tentative landing areas (TLAs) in the latitude range of 5–30°, Chryse Planitia (TLA 1) and Isidis‐Elysium Planitia (TLA 2). TLA 2 is on the south of Utopia which is an important origination area of dust storm sequences in the northern hemisphere. The probability of dust storm activity is an important criterion of atmospheric risk assessment for a Martian surface mission. Thus, it is crucial to evaluate the probabilities and characteristics of dust storm activity in and around TLA 2. In this paper, we used Mars Daily Global Maps (MDGMs) of Mars Orbiter Camera (MOC) and Mars Color Imager (MARCI) to (1) identify and catalog the dust storms in and around TLA 2; (2) calculate and analyze the temporal and spatial probability of dust storms in and around TLA 2; and (3) select the preferred landing time and areas for Tianwen‐1 mission based on the temporal and spatial probability of dust storm activity in TLA 2. Key Points: The temporal probability of dust storm activity in Isidis‐Elysium Planitia shows obvious seasonal and inconsecutive activityThe spatial probability of dust storm activity in Isidis‐Elysium Planitia is generally reduced from north to southThe optimal landing time was in Ls = 20–41°, and five preferred landing areas were selected for Tianwen‐1 [ABSTRACT FROM AUTHOR]

The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection. Plain Language Summary: Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance. Key Points: A new laser altimeter system design consisting of a laser altimeter, two laser footprint cameras, and a laser optical axis surveillance camera equipped with the Gao Fen 7 satellite is introducedThe application of the Gao Fen 7 laser altimeter system including ground control point measurement, polar ice sheet monitoring, and forest biomass estimation is presentedScientific experiments on the Gao Fen 7 laser altimeter system to be performed in November 2019 are given. Six levels of data products are listed and will be provided to users worldwide [ABSTRACT FROM AUTHOR]