China Meteorological Administration Land Data Assimilation System 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.

Investigating transformer‐based models for spatial downscaling and correcting biases of near‐surface temperature and wind‐speed forecasts

AbstractHigh‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m () and wind speed at 10 m () over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance.

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.

Forecast and Analysis of a Rainstorm Case in East China Based on the Blown-Up Theory

Practical application has shown that the blown-up theory has great predictive ability for predicting transitional weather systems, especially catastrophic weather systems. This study applies the blown-up theory to analyze and predict a rainstorm case in Jiangsu Province of East China to explore the applicability of the blown-up theory. At the same time, a numerical simulation experiment is conducted using the Weather Research and Forecasting Model (WRF) v4.2. The numerical results are compared with the European Center for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) data and the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) products. It is found that there is a deviation in the simulation for the precipitation center, and further analysis indicates that it is likely related to the position of the simulated low-level shear line. On the other hand, the blown-up analyses are consistent with the actual situation and provide additional information besides the numerical simulation results. These results indicate that the blown-up charts and V-3θ diagrams are able to predict the weather system transformation, the rainfall area, and the evolution of the rainstorm, which confirms the applicability of the blown-up theory to rainstorm forecasts. This provides an auxiliary analysis method in addition to numerical simulations for rainstorm forecasts.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

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.

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.

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.

Multiscale Assessments of Three Reanalysis Temperature Data Systems over China

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as “true values” in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models.

Multi-Scale and Multi-Depth Validation of Soil Moisture From the China Land Data Assimilation System

Soil moisture (SM) plays a critical role in vegetation growth, terrestrial hydrological cycle, and regional climate change. In this article, the China Meteorological Administration Land Data Assimilation System (CLDAS) SM was validated at multiscale and multidepth against in situ measurements over the Tibetan plateau, and the Global Land Data Assimilation System (GLDAS) SM was also used as a reference for the validation process. The results revealed the following: First, in validations using the large, medium, and small scale networks, the small network achieved comparatively superior accuracy with a higher fitting degree and smaller statistical error, proving the reliability and robustness of densely clustered in situ stations. Second, the CLDAS SM can correctly present the promotion effect from precipitation. The most significant response of the CLDAS SM to precipitation lagged from 1 to 3 days in terms of growing depth, whereas promotion to SM tended to decrease as the soil depth increased. Third, CLDAS and GLDAS reasonably fitted in situ measurements in the frozen and unfrozen seasons, while their bias gradually increased with increasing depth. The SM errors in the snow-covered region were relatively stable. Diurnal land surface temperature exhibited a positive correlation with the bias of top-layer SM. In summary, this article shows that CLDAS SM retrievals favorably reproduced values and dynamics of in situ measurements and can be expected to act as a valuable reference for water cycle studies and climate evolution analysis.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS), we blended the Climate Prediction Center (CPC) morphing technique (CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China (CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2 (GLDAS-V2.1) precipitation, and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement (GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Evaluation of CLDAS and GLDAS Datasets for Near-Surface Air Temperature over Major Land Areas of China

As one of the most principal meteorological factors to affect global climate change and human sustainable development, temperature plays an important role in biogeochemical and hydrosphere cycle. To date, there are a wide range of temperature data sources and only a detailed understanding of the reliability of these datasets can help us carry out related research. In this study, the hourly and daily near-surface air temperature observations collected at national automatic weather stations (NAWS) in China were used to compare with the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the Global Land Data Assimilation System (GLDAS), both of which were developed by using the advanced multi-source data fusion technology. Results are as follows. (1) The spatial and temporal variations of the near-surface air temperature agree well between CLDAS and GLDAS over major land of China, except that spatial details in high mountainous areas were not sufficiently displayed in GLDAS; (2) The near-surface air temperature of CLDAS were more significantly correlated with observations than that of GLDAS, but more caution is necessary when using the data in mountain areas as the accuracy of the datasets gradually decreases with increasing altitude; (3) CLDAS can better illustrate the distribution of areas of daily maximum above 35 °C and help to monitor high temperature weather. The main conclusion of this study is that CLDAS near-surface air temperature has a higher reliability in China, which is very important for the study of climate change and sustainable development in East Asia.

Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) forcing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against observations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah-MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–ss10 cm in mainland China, especially in the eastern monsoon area such as the Huang-Huai-Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m3 m−3 for the SM simulations, and from 12.9 to 11.4 mm month−1 for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes.

Combined Dynamical and Statistical Downscaling for High-Resolution Projections of Multiple Climate Variables in the Beijing–Tianjin–Hebei Region of China

AbstractHigh-resolution combined dynamical and statistical downscaling for multivariables (HDM) was performed in the Beijing–Tianjin–Hebei (BTH) region by using observations from China Meteorological Administration Land Data Assimilation System (CLDAS), a regional climate model (RCM), and quantile mapping. This resulted in the production of a daily product with six variables (daily mean, maximum, and minimum temperature; precipitation; relative humidity; and wind speed), five ensemble members, a multidecadal time span (1980–2099), and a high resolution (6.25 km) for climate change projections under the RCP4.5 scenario. The evaluation showed that the HDM output could reproduce well the mean states of all variables and most extreme indices except the consecutive dry and wet days. The biases in the magnitude of interannual variability in HDM were mostly inherited from the RCM. By using the HDM, future projection over BTH was conducted. The results indicated that the annual mean temperature and precipitation as well as extreme heat and heavy precipitation events will increase over most regions. The warming magnitudes over the mountainous and coastal area at the northern BTH and the wetting magnitudes over the Daqinghe River basin (DRB) within BTH will be relatively stronger. The increases in extreme heat events will be much larger in the plain area. More than one-half of regions with the large extreme precipitation increase will be located within DRB. Both the number of models with the same sign of change and the ensemble standard deviation were used to estimate the projection uncertainty. The projected changes and uncertainties over DRB and subregions and Xiong’an city within the basin for each season are also discussed.

Determination of all-sky surface soil moisture at fine spatial resolution synergistically using optical/thermal infrared and microwave measurements

Soil moisture (SM) is highly involved in a number of fundamental agricultural activities and hydrological processes. Currently, most of the available microwave SM products are too coarse to be used in these relevant applications at regional or watershed scales, whereas optical/thermal infrared measurements are valid over clear-sky conditions only. The present study aims to provide a novel framework for the retrieval of all-sky surface SM at fine spatial resolution synergistically using optical/thermal infrared and microwave measurements. In this framework, SM for each clear-sky pixel is determined from a novel pixel-to-pixel scheme of land surface temperature-vegetation index (Ts-VI) trapezoid, whereas cloudy pixel SM is derived from coarse microwave-based product disaggregation where gridded meteorological measurements are provided as inputs. Mainland China is selected as the study area to obtain all-sky SM at the spatial resolution of 0.05° over the period from August to October in 2017. A preliminary comparison of the estimated all-sky SM with the China Meteorological Administration Land Data Assimilation System (CLDAS)-derived SM product at the same spatial resolution is conducted to assess the proposed approach. Results indicate that the two datasets show similar spatial patterns throughout mainland China. Moreover, the estimated SM over 300 random pixels correlates well with the CLDAS product, with RMSE ranging from 0.070 to 0.083 m3/m3 and a slight bias varying from −0.001 to 0.011 m3/m3 on six selected days. The present study therefore successfully provides a framework for the retrieval of all-sky SM content from currently available satellite products.