Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.
Soil moisture (SM) is an essential variable in the hydrological cycle. Quantifying the magnitude of SM is crucial for the climate system. In this letter, we present a new model with multi-view multi-task learning (MVMTL) to estimate SM over continental U.S. Specifically, the multi-view component is used to make full use of spatial and temporal features of each grid cell (0.25° × 0.25°). Meanwhile, the multi-task component aims to capture the spatial correlations and to perform coestimations between different grid cells in the study area. To evaluate the effectiveness of MVMTL, we compare it with several retrieval methods in terms of the SM product from the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim) and in situ SM measurements. The experimental results show that the MVMTL model can achieve higher performance than the other methods.
Long-term analysis of climate trends and patterns relies on continuous and high-frequency observation data sets. Still, due to limitations in historical meteorological observation techniques and national policies, most weather stations worldwide can only provide three, four, or eight observations per day, hindering climate change research progress. To solve the problem of low-frequency daily observation in part of global meteorological stations, we propose a time-downscaling model of observation series based on deep learning, Land Surface Observation Simulator-Time Series Version (LOS-T), taking 2m air temperature as an example. LOS-T, combined with multimodal technology and Transformer architecture, effectively merges multiple types of data, including low-frequency observations, ERA5-land, and geographic information, to convert low-frequency observations into hourly high-frequency observations. The model showed significant accuracy improvements by training on millions of meteorological observations worldwide, especially on downscaling the data, which only has three observations per day. The results showed that LOS-T substantially improved over baseline models such as Bilinear and vanilla Transformer on several metrics such as MAE, RMSE, COR, and R2. In addition, case studies have confirmed that LOS-T can effectively utilize ERA5-land's high-frequency temperature change information to improve the accuracy and robustness of predictions, even when there is a significant deviation between ERA5-land data and Ground Truth. In short, LOS-T provides new ways to refine global meteorological observation data and helps advance climate science.
Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
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 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.
Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for IoU_s, F1_s, and MPA, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics.
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.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)