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Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research. [ABSTRACT FROM AUTHOR]

In this paper, we present a method for retrieving sea surface wind speed (SSWS) from Fengyun-3D (FY-3D) microwave radiation imager (MWRI) data. In contrast to the conventional point-to-point (P2P) retrieval methods, we propose a field-to-field (F2F) SSWS retrieval method based on the basic framework of a Convolutional Neural Network (CNN). Considering the spatial continuity and consistency characteristics of wind fields within a certain range, we construct the model based on the basic framework of CNN, which is suitable for retrieving various wind speed intervals, and then synchronously obtaining the smooth and continuous wind field. The retrieval results show that: (1) Comparing the retrieval results with the label data, the root-mean-square error ( R M S E ) of wind speed is about 0.26 m/s, the F2F-NN model is highly efficient in training and has a strong fitting ability to label data. Comparing the retrieval results with the buoys (NDBC and TAO) data, the R M S E of F2F-NN wind speed is less than 0.91 m/s, the retrieval accuracy is better than the wind field products involved in the comparison. (2) In the hurricane ( S a m ) area, the F2F-NN model greatly improves the accuracy of wind speed in the FY-3D wind field. Comparing five wind field products with the Stepped-Frequency Microwave Radiometer (SFMR) data, the overall accuracy of the F2F-NN wind data is the highest. Comparing the five wind field products with the International Best Track Archive for Climate Stewardship (IBTrACS) data, the F2F-NN wind field is superior to the other products in terms of maximum wind speed and maximum wind speed radius. The structure of the wind field retrieved by F2F-NN is complete and accurate, and the wind speed changes smoothly and continuously. [ABSTRACT FROM AUTHOR]

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing. [ABSTRACT FROM AUTHOR]

Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data, Earth scientists must further improve DA methods. Recent studies have introduced machine learning (ML) methods to assist the DA process. In this paper, we explore the potential of a four‐dimensional variational (4DVar) constrained neural network (NN) method for accurate DA. Our NN is trained to approximate the solution of the variational problem, thereby avoiding the need for expensive online optimization when generating the initial fields. In the context that the full‐field system truths are unavailable, our approach embeds the system's kinetic features described by a series of analysis fields into the NN through a 4DVar‐form loss function. Numerical experiments on the Lorenz96 physical model demonstrate that our method can generate better initial fields than most traditional DA methods at a low computational cost, and is robust when assimilating observations with higher error outside of the distributions where it is trained. Furthermore, our NN‐based DA model is effective against Lorenz96 physical models with larger variable numbers. Our approach exemplifies how ML methods can be leveraged to improve both the efficiency and accuracy of DA techniques. Plain Language Summary: The use of machine learning (ML) to approximate mappings from data has made a significant impact on numerical weather prediction. In the data assimilation (DA) process, several recent studies have applied ML to accelerate or improve the accuracy of DA output. In this paper, we investigate the potential of employing physical constraints based on four‐dimensional variational (4DVar) DA to further enhance the accuracy of an end‐to‐end ML‐based DA model. Our objective is to determine whether the 4DVar‐constrained ML model can perform the DA task more efficiently and produce comparable accuracy to the traditional DA methods. We trained our NN‐based model without true values as labels and tested it on the Lorenz96 physical model. Several experiments have been applied to verify that the 4DVar‐constrained ML model can be used as a potential substitute for the DA process. Key Points: A physics‐informed neural network trained without ground truths can provide accurate initial fields for numerical predictionsThe system's kinetic features are embedded into the model through our four‐dimensional variational form loss functionWe show on Lorenz96 that the proposed method can be used directly for accurate data assimilation as a low computational cost [ABSTRACT FROM AUTHOR]

In this paper, we present a method for retrieving sea surface wind field (SSWF) from HaiYang-2B (HY-2B) scatterometer data. In contrast to the conventional algorithm, i.e., using a point-to-point (P2P) method based on geophysical model functions (GMF) to retrieve SSWF by spaceborne scatterometer, we introduce a more accurate field-to-field (F2F) retrieval method based on convolutional neural network (CNN). We fully consider the spatial correlation and continuity between adjacent observation points, and input the observation data of continuous wind field within a certain range into the neural network to construct the neural network model, and then synchronously obtain the wind field within the range. The wind field obtained by our retrieval method maintains its continuity and solves the problem of ambiguity removal in traditional wind direction retrieval methods. Comparing the retrieval results with the buoy data, the results show that the root mean square errors (RMSE) of wind direction and wind speed are less than 0.18 rad (10.31°) and 0.75 m/s, respectively. The retrieval accuracy is better than the L2B product of HY-2B. [ABSTRACT FROM AUTHOR]

Hyper-spectral infrared radiance data play an important role in cloud detection. To improve the cloud detection accuracy, this study proposes a novel cloud detection method based on the logistic regression model that uses the Infrared Atmospheric Sounding Interferometer (IASI) radiance data of four characteristic channels as the training features. Due to significant differences in the terrain between the land and the sea, the data from the oceans and continents are trained separately. Thereafter, the proposed scheme is verified and compared with existing methods. The results show that the accuracy of the proposed method (97% at sea and 88% on land) outperforms that of the existing Advanced Very High Resolution Radiometer (AVHRR)/IASI scheme (75% at sea and 55% on land). In addition, the proposed method uses only IASI observations as input and thus does not require the use of other auxiliary data. [ABSTRACT FROM AUTHOR]

The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm. This paper is intended to improve the accuracy of spatial interpolation towards wind fields. A new composited multi-scale anisotropic kernel function for weather processes using two-dimensional space information is proposed. To fix the underfitting in this kernel caused by unilateral space information, multiple variables (wind direction, air temperature, and atmospheric pressure) are introduced, which generates a multivariate correction model based on the novel kernel function and Gaussian process regression. Focusing on different weather processes, two multivariate correction models are designed. The new models pave a new way to employ multi-scale local information, and extract the anisotropy and structure information. The experiments on 10 m wind fields for the weather processes without cyclones and for the weather processes with cyclones validate the efficiency. The mean RMSE of the multivariate correction model for the weather processes without cyclones is reduced by around 15% for the u wind component compared with that of a simple composited kernel. For the weather processes with cyclones, the mean RMSE of the novel model declines by around 55% compared to that of spline, and by about 95% compared to that of back propagation neural networks. [ABSTRACT FROM AUTHOR]