Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall.

Due to the limitations of hyperspectral optical imaging, hyperspectral images have a dilemma between spectral and spatial resolutions. Hyperspectral and multispectral image (HSI-MSI) fusion, which combines a low-spatial-resolution hyperspectral image (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI), can generate a high-spatial-resolution hyperspectral image (HR-HSI). In existing methods for hyperspectral and multispectral fusion, correlation between spectral and spatial domains in HSIs is mostly neglected. To address this issue, an unsupervised encoder-decoder network under spatial and spectral guidance for hyperspectral and multispectral image fusion (uEDSSG) was proposed in this study. To learn more accurate abundances of a LR-HSI and a HR-MSI, multi-hierarchical encoders under spatial and spectral guidance were designed to extract multi-hierarchical fused features from the LR-HSI and HR-MSI with the guidance of the HR-MSI and LR-HSI, respectively. In the new method, deep coupling of the point spread function (PSF) or spectral response function (SRF) and edge of the HSIs was designed to maintain the spatial and spectral details of the HR-HSI; a spatial-spectral constraint was constructed to establish the relationship of the HSIs. Both visual and quantitative evaluation results of experiments based on both synthetic and real datasets showed that the proposed method outperformed seven common methods. The results suggest that the new method by maintaining the correlation between spectral and spatial domains can improve the result of HSI-MSI fusion.

As one of the most important parameters for the atmospheric water vapor contents, precipitable water vapor (PWV) derived from Global Navigation Satellite Systems (GNSS) has been a valuable source of information for forecasting rainfall events in recent years due to the distinctive advantages of high accuracy, high temporal-spatial resolution, wide coverage and low cost of GNSS data. In this study, a new gated recurrent unit (GRU) based model for forecasting rainfall events was developed using training dataset in the 9-year period of 2010−2018 at 54 GNSS stations located in the US. Moreover, the length of input windows and the effectiveness of various combinations of seven meteorological parameters were also investigated for the determination of the optimal ones to be used in the new model. The performance of the new model was evaluated using the test dataset in the 2-year period of 2019−2020, and its results were also compared with that of the threshold-based model developed in our previous study. It is showed that: 1) the optimal lengths of input windows at different stations were different, thus they need to be specifically determined for each station; 2) the model was improved by incorporating four meteorological parameters into the input data; 3) the mean values of probability of detection (POD) and false alarm rate (FAR) resulting from the new model at all the above 54 stations were 93% and 45%, respectively, which were significantly improved over a threshold-based model. These results suggest that the GRU-based model can effectively forecast most rainfall events due to its utilization of more meteorological data in input data.

Hyperspectral superresolution reconstruction technique obtains a high-resolution hyperspectral image (HR-HSI) by fusing both a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image. Existing methods of hyperspectral superresolution reconstruction are mostly concentrated on the global low-rank property while spatial and spectral information in individual regions is not considered. To address this issue, the decomposition of low-rank and sparse tensor is proposed in this study. First, HR-HSI was decomposed into low-rank and sparse components. The former was further separated into spatial and spectral domains according to the spectral low-rank property, and the latter was used to compensate for information loss caused by the low-rank property. Then, a nonlocal constraint of adaptive manifold extracting structural details by the manifold structure was designed to enforce nonlocal self-similarity of the spatial domain. In order to ensure the same spatial structure of different bands and reduce the false individual regions in the sparse component, a surface-aware regularization combined with group sparsity was utilized. Finally, HR-HSI was constructed by the alternating direction method of multipliers. Experiment results on three datasets show that the proposed method outperforms five common existing methods by means of both visual and quantitative evaluations. It is concluded that the new method by taking into account the low-rank and sparse properties can improve the result of the reconstruction.

The tropospheric tomography is an ill-posed inversion problem due to the sparsity of global navigation satellite systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, and then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2-D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use prefixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10% and 15% when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2-D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this article can improve the performance of the iterative methods for GNSS tropospheric tomography.

Global Navigation Satellite Systems (GNSS) tomography is a modern technique that leverages GNSS satellites and ground-based network stations for meteorological applications, with a particular focus on studying the dynamics of water vapor. Over the past two decades, various approaches have been proposed, but assessing the accuracy of these methods remains challenging. In this study, we introduce an empirical stochastic model for GNSS tomography. This model is formulated as a fully populated covariance-variance matrix that incorporates the variance of the GNSS-Integrated Water Vapor (IWV) solution, the spherical distance between station-satellite signal paths, and an empirical covariance-variance function. Our findings indicate that combining the functional model with the stochastic model significantly improves the estimation of unknown parameters, suggesting a method for enhancing the accuracy and reliability of GNSS tomographic solutions. We compared the standard tomography solution, both with and without stochastic information, with data from radiosondes, ERA5 reanalysis, and the WRF model. The results demonstrate that incorporating the empirical stochastic model can improve the solution accuracy by 10%. More importantly, it provides a valuable tool for evaluating the trustworthiness of tomographic solutions.