China Meteorological Administration Land Data Research Articles

Enhanced Wind Field Spatial Downscaling Method Using UNET Architecture and Dual Cross-Attention Mechanism

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data.

Deep learning‐based postprocessing for hourly temperature forecasting

AbstractIn this article, a prediction model based on spatiotemporal stacked ResNet (Res‐STS) for hourly temperature prediction is designed. On the timescale, the Res‐STS removes the gate structure of the long short‐term memory (LSTM) model, and the data of multiple consecutive time nodes are stacked together to preserve all temporal characteristics of the data. A point‐to‐point data mapping relationship is developed on the spatial scale to maximize the impact of large‐scale environmental background field characteristics on a single grid point. Based on the historical gridded data from the China Meteorological Administration land data assimilation system (CLDAS) and the optimal factor dataset of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF‐IFS) from 2017 to 2020, hourly temperature prediction models based on convolutional long short‐term memory (ConvLSTM) and Res‐STS model are developed, respectively. Furthermore, the prediction results of the two models in 2021 are compared with the ECMWF‐IFS. The results show that the root mean square error (RMSE) of the prediction results by ConvLSTM and Res‐STS models are both smaller than that of ECMWF‐IFS. Specially, the Res‐STS model performs best: it reduces the RMSE by 20.8% (24.5%) compared with the ConvLSTM (ECMWF‐IFS). Specifically, the RMSE peaks in the afternoon when the daily maximum temperature occurs, while it is relatively smaller at night. Res‐STS demonstrates a significant improvement in forecast performance compared with ECMWF‐IFS, while ConvLSTM's correction during the period of maximum temperature occurrence has been enhanced. Moreover, the forecast performance of the Res‐STS model is least affected by terrain compared with those of the ConvLSTM and ECMWF‐IFS. For the regions with terrain height greater than 1 km, the model Res‐STS evidently improves the RMSE.

The non-uniformity characteristics of the evaporation duct in the South China Sea based on CLDAS data

The non-uniformity of the evaporation duct has a significant impact on the propagation of electromagnetic waves. Based on the near real-time dataset from the China Meteorological Administration Land Data Assimilation System (CLDAS) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) datasets, the non-uniformity characteristics of evaporation duct height (EDH) in the South China Sea (SCS) region at different distances of electromagnetic propagation and the main influencing factors of EDH non-uniformity are analyzed. The findings are given as follows. First, the SCS monsoon has a significant impact on the spatial and temporal distribution of EDH, particularly in the western coastal areas of Luzon Island and the eastern coastal areas of the Indochina Peninsula. Secondly, the non-uniformity of EDH is more pronounced in the western part of Luzon Island, the eastern coast of the Indochina Peninsula, and the sea-land boundary area of the Beibu Gulf. Besides, the non-uniformity of EDH in the western part of Luzon Island and the eastern part of the Indochinese Peninsula is greatly influenced by air-sea temperature difference and relative humidity. Finally, it is advisable to consider the influence of the non-uniformity of EDH when the distance of electromagnetic propagation in coastal areas exceeds 50 km. However, it can be disregarded within a range of 100 km on the broad ocean.

Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model’s feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements.

Hybrid Assimilation of Snow Cover Improves Land Surface Simulations over Northern China

Abstract Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance.

Evaluation of ERA5 Reanalysis Precipitation Data in the Yarlung Zangbo River Basin of the Tibetan Plateau

Abstract Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial–temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB.

Assessing and Comparing Reference Evapotranspiration across Different Climatic Regions of China Using Reanalysis Products

This study aims to assess the accuracy of the reference evapotranspiration (ET0) estimated by CLDAS, ERA5 reanalysis products, and the quality of reanalysis weather variables required to calculate PM-ET0. For this purpose, the applicability of surface meteorological elements from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and observations, with a mean R2 of 0.96, 0.90 for the CLDAS maximum and minimum air temperatures, and 0.87, 0.84 for ERA5. For the reanalysis of solar radiation (Rs) and relative humidity (RH), an overestimation trend is shown for Rs and an underestimation trend is shown for RH. For reanalysis of wind speed, a relatively low accuracy is shown. The accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between the CLDAS estimates and site observations is higher, with a mean RMSE R2 of 0.91, 0.82 for CLDAS and 1.42, 0.70 for ERA5, respectively. Moreover, CLDAS reanalysis products are more effective in describing the boundary details of the study area.

An Observation Study for Cold and Freezing Injury of Sugarcane Based on China Meteorological Administration Land Data Assimilation System, CLDAS Temperature Data

An Observation Study for Cold and Freezing Injury of Sugarcane Based on China Meteorological Administration Land Data Assimilation System, CLDAS Temperature Data

Multi-Model Ensemble Forecasts of Surface Air Temperatures in Henan Province Based on Machine Learning

Based on the China Meteorological Administration Land Data Assimilation System (CLDAS) reanalysis data and 12–72 h forecasts of the surface (2-m) air temperature (SAT) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three numerical weather prediction (NWP) models of the China Meteorological Administration (CMA-GFS, CMA-SH, and CMA-MESO), multi-model ensemble forecasts are conducted with a convolutional neural network (CNN) and a feed-forward neural network (FNN) to improve the SAT forecast in Henan Province, China. The results show that there are large errors in the 12–72 h forecasts of SAT from the CMA, while the ECMWF outperforms the other raw NWP models, especially in eastern and southern Henan. The CNN has the best short-term forecasting skills. The difference in the geographical distribution of the CNN forecast errors is small, without any apparent large-value areas. The CNN shows its advantages in its bias correction in the mountainous region (western Henan), indicating that the CNN can capture the spatial features of the atmospheric fields and is therefore more robust in regions with varied topography. In addition, the CNN can extract data features through the convolution kernel and focus on local features; it can assimilate the local features at a higher level and obtain global features. Therefore, the CNN takes advantage of the four models in the SAT forecast and further improves the forecast skill.

Spatio-temporal variations in dew and its relevance to drought in the Beijing-Tianjin-Hebei region of China

ABSTRACT The long-term trend and its spatio-temporal characteristics of dew formation over the Beijing-Tianjin-Hebei (BTH) region are still poorly understood. We examined dew and its climatic controls in BTH from 2008 to 2021 over China with the aid of model estimation based on the atmospheric data from the China Meteorological Administration Land Data Assimilation System (CLDAS). Besides, the relevance of dew to drought was also examined. Dew amount showed a decreasing tendency from northwest to southeast over BTH for the 14 years. The average monthly accumulative dew amount ranged from 0.56 mm to 15.87 mm. The annual dew amount was highest in Bashang and lowest in Plain area. Precipitation(P), air temperature (Ta) and relative humidity (RH) all showed positive correlation with dew over the three ecoregions, and among these, RH showed the most significant correlation with dew. The BTH showed a drier trend over the study period and dew exhibited higher over the dry area. Our study can not only provide a novel understanding of dew characteristics in the BTH, but also have an important potential effect on the water resources management in the context of climate change exacerbation.

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

Increased moist heat stress risk across China under warming climate

Heatwaves have afflicted human health, ecosystem, and socioeconomy and are expected to intensify under warming climate. However, few efforts have been directed to moist heat stress (MHS) considering relative humidity and wind speed, and moist heat stress risk (MHSR) considering exposure and vulnerability. Here we showed MHS and MHSR variations across China during 1998–2100 using China Meteorological Administration Land Data Assimilation System datasets, the 6th Coupled Model Intercomparison Project (CMIP6) merged datasets, Gross Domestic Product, population and leaf area index. We detected increased MHS across China under different Shared Socioeconomic Pathways (SSPs). Specifically, the historical MHS occurred mostly during mid-July to mid-August. We found increasing trends of 0.08%/year, 0.249%/year, and 0.669%/year in the MHS-affected areas under SSP126, SSP245, and SSP585, respectively. Furthermore, we observed the highest increasing rate of MHSR in Northwest and Southwest China, while the MHSR across Northeast and North China under SSP126 shifted from increasing to decreasing trends. Noteworthy is that the increasing trend of MHSR under SSP585 is 1.5–2.6 times larger than that under SSP245, especially in North and South China. This study highlights spatiotemporal evolutions of MHS and MHSR and mitigation to moisture heat stress in a warming climate.

Exploring the Spatio-Temporal Characteristics of Urban Thermal Environment during Hot Summer Days: A Case Study of Wuhan, China

An urban thermal environment is an area receiving special attention. In order to effectively explore its spatio-temporal characteristics during hot summer days, this study introduced the standard deviational ellipse (SDE) to construct an urban heat island index to describe the general spatial character of an urban thermal environment, and then used local Moran’s I to identify its local spatial cluster characteristics. Finally, the regressions of ordinary least squares (OLS) and spatial lag model (SLM) were adopted to explore the effect of woodland, water body and impervious surface on the thermal environment. Taking the city of Wuhan as a study area and using the air temperature on seven consecutive days, from 17 July to 23 July in 2018, from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), the results show that the urban heat island index can effectively represent the general characteristics of the thermal environment. The general trends of heat island intensity decrease first and then increase from 00:00 to 24:00. The heat island intensity is at its minimum from 10:00 to 16:00, and at its maximum from 22:00 to 4:00 the next day. Local Moran’s I values indicate that the clusters of high air temperature at 06:00 and at 22:00 are associated with the impervious surface and the water body. This is further illustrated by the regression analysis of OLS, which can explain 50–60% of the spatial variation of the air temperature. Then, the fitness of the SLM is greatly improved; the coefficients of determination at 06:00 and at 22:00 are all not less than 0.97. However, the explanation of the local land uses accounting for the spatial variation of the air temperature becomes lower. The regression analysis also shows that the woodland always has the effect of decreasing air temperature at 06:00, 14:00 and 22:00, implying that increasing the vegetation may be the most effective way to mitigate the adverse circumstance of the urban thermal environment.

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Evaluation of Three Air Temperature Reanalysis Datasets in the Alpine Region of the Qinghai–Tibet Plateau

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP.

Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China

Reference Crop evapotranspiration (ET0) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET0 (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET0 is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET0, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

To Accurately and Lightly Downscale the Temperature Field by Deep Learning

Abstract Downscaling is essential in atmospheric science, aiming to infer the fine-scale field from the coarse-scale field. To obtain the high-resolution temperature field, our team proposed a deep learning–based model, the China Meteorological Administration land data assimilation system statistical downscaling model (CLDASSD). Inspired by some works in computer vision, we proposed the improved version, Light-CLDASSD, which is a lightweight model with fewer parameters. The modified model has the characteristics of light training and fewer parameters. What is more, we introduced station observation data in the model to make the downscaling results more accurate. Taking temperature as the research object, we performed experiments in the Beijing–Tianjin–Hebei region and downscaled the temperature field from 1/16° (0.0625°) to 0.01°. Experiments show that Light-CLDASSD can get robust results. As for spatial distribution, Light-CLDASSD can reconstruct fine and accurate spatial distribution on complex mountains and reconstruct small-scale characteristics in plain areas that other models cannot achieve. As for temporal change, Light-CLDASSD performs better at local noon and warm seasons. Furthermore, Light-CLDASSD achieves better performance than other models and is comparable with High-Resolution China Meteorological Administration’s Land Assimilation System (HRCLDAS). The root-mean-square error (RMSE) of Light-CLDASSD is 0.08°C lower than HRCLDAS, and the bias distribution is more concentrated at 0°C. This article is an upgrade of the CLDASSD model and preliminary exploration of the back-calculation for high-resolution historical data. Significance Statement This work proposes a deep learning–based statistical downscaling model named Light China Meteorological Administration land data assimilation system statistical downscaling model (Light-CLDASSD), which can downscale the temperature field generated by CLDAS from 1/16° (0.0625°) to 0.01°. Introducing observation data improves the performance, and the model results are comparable to HRCLDAS products. Our research is of great significance to developing high-resolution data and the back-calculation of historical assimilation data.

A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data.

Daily spatial complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the Global Navigation Satellite System (GNSS) satellites to measure SM. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The Moderate-Resolution Imaging Spectroradiometer (MODIS) product (i.e., vegetation index [VI] and land surface temperature [LST]) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to fuse the CYGNSS and MODIS data for daily spatial complete SM retrieval. First, for CYGNSS data, the surface reflectivity (SR) is proposed as a proxy to evaluate its ability to estimate daily SM. Second, the LST output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) and MODIS LST (1 × 1 km) are fused to generate spatial complete and temporally continuous LST maps. An Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model is proposed to estimate SM derived from MODIS data at high spatial resolution. Finally, the final SM estimation model is constructed from the back-propagation artificial neural network (BP-ANN) fusing the CYGNSS point, E-NWSVI data, and ancillary data, and applied to get the daily continuous SM result over southeast China. The results show that the estimation SM are comparable and promising (R = 0.723, root mean squared error [RMSE] = 0.062 m3 m−3, and MAE = 0.040 m3 m−3 vs. in situ, R = 0.714, RMSE = 0.057 m3 m−3, and MAE = 0.039 m3 m−3 vs. CLDAS). The proposed algorithm contributes from two aspects: (1) validates the CYGNSS derived SM by taking advantage of the dense in situ networks over Southeast China; (2) provides a point-surface fusion model to combine the usage of CYGNSS and MODIS to generate the temporal and spatial complete SM. The proposed approach reveals significant potential to map daily spatial complete SM using CYGNSS and MODIS data at a regional scale.

A method for estimating spatially continuous soil moisture from the synergistic use of geostationary and polar-orbit satellite data

Soil moisture (SM) is a key variable in the surface energy balance and water cycle, and its spatiotemporal dynamics are of great significance to climate, agriculture and other fields. Optical remote sensing has been widely used to estimate SM with relatively fine spatial resolution. However, optical observations are easily contaminated by clouds, making it difficult to obtain spatially continuous SM over large regions. In the present study, a semimonthly SM dataset over the study area of the entire Inner Mongolia region with nearly full spatial coverage was derived from the synergistic use of China’s Feng-Yun (FY) geostationary (FY-4A) and polar-orbit (FY-3D) observations, following a previously developed trapezoid feature space in a pixel-to-pixel manner. A preliminary assessment was conducted to evaluate the performances of the proposed method over two main dominant land cover types (grassland and cropland) in the study region, where the China Meteorological Administration Land Data Assimilation System (CLDAS) and the Soil Moisture Active Passive (SMAP) SM products were provided as references. The results indicated that the estimated SM was well correlated to the referenced SM datasets, with a significant correlation coefficient varying from 0.5 to 0.8. Furthermore, for the grassland (cropland), unbiased root mean square errors of approximately 0.062 (0.097) m3/m3 and 0.055 (0.069) m3/m3 can be found when comparing the estimated SM with the CLDAS and the SMAP product, respectively.