Monitoring and predicting ionospheric space weather is important for global navigation satellite system (GNSS) navigation, positioning, and communication. Ionospheric total electron content (TEC) is a vital indicator to measure ionospheric space weather. This study utilizes a multichannel convolutional long short-term memory (ConvLSTM) with attention mechanism to predict ionospheric TEC maps considering the relevance of physical observations for TEC variations. The MConvLSTM-Attention method is trained and tested on regional ionospheric maps (RIMs) for three years (2015 to 2017) by using GNSS observations from the Crustal Movement Observation Network of China (CMONOC). The experimental results show that the root mean square error (RMSE) values of MConvLSTM-Attention model during quiet, moderate, and geomagnetic storm periods are 2.58, 2.60, and 3.21 TECU, respectively. Also, the MConvLSTM-Attention model performs better than MConvLSTM, ConvLSTM, and international reference ionosphere (IRI) 2016 models in predicting regional ionospheric maps (RIMs). In addition, the MConvLSTM-Attention prediction model shows good generalization performance and relatively good stability and high precision during both geomagnetic quiet and storm time.

Atmospheric weighted mean temperature (Tm) is a crucial parameter that links precipitable water vapor (PWV) and zenith wet delay (ZWD). To address the challenge of balancing the quality and timeliness of Tm data, this study introduced infrared remote-sensing technology based on meteorological satellites for the first time to retrieve Tm. We developed separate Tm estimation models for FengYun-4A (FY4A) observations under both clear and cloudy conditions and combined them to enable real-time retrieval of all-weather Tm. This combined model, called the all-weather Tm estimation model, is based on the linear relationship between Tm and surface temperature as well as remote sensing retrieval theories related to surface temperature and cloud-top properties. This grouping modeling approach allows continuous spatiotemporal Tm data to be estimated at minute intervals, even under cloudy conditions. Radiosonde-derived and ERA5-derived Tm data from 2022 were used to assess the accuracy of FY4A-derived Tm for Australia. Compared to radiosonde-derived Tm, the root mean square error (RMSE)/bias values for FY4A-derived Tm were 1.37/0.05, 1.45/0.06, and 1.38/0.06 K for all-time, daytime, and nighttime, respectively. Compared to the ERA5-derived Tm, the RMSE/bias values for FY4A-derived Tm were 1.26/0.01, 1.33/0.01, and 1.37/0.03 K under all-weather, clear, and cloudy conditions, respectively. The validation results indicated that the satellite-based Tm retrieval model possesses the advantages of real-time monitoring, all-weather capability, high accuracy, and high spatiotemporal resolution. Thus, it has tremendous potential for deepening interdisciplinary collaboration between the meteorology and navigation fields.

In recent years, several models have been extensively investigated for short-term rainfall forecasts based on global navigation satellite system (GNSS)-derived precipitable water vapor (PWV). However, the predictors of these models are mainly developed from the PWV, zenith total delay (ZTD), and their combinations, and new and independent predictors have not been introduced for improving the rainfall forecast accuracy. In addition to PWV, convective available potential energy (CAPE) is an effective index for reflecting the atmospheric stability. We proposed a novel linear rainfall forecast (NLRF) model by combining PWV and CAPE, which includes six independent predictors (PWV value, PWV variation and its derivation, CAPE value, and CAPE variation and its derivation). We selected hourly PWV, CAPE, and rainfall over a period of one year at six GNSS stations in Taiwan to develop the NLRF model. The optimal thresholds for PWV and CAPE predictors were determined based on the percentile theory, which followed the principle of highest true detected rate (TDR) and lowest false forecast rate (FFR), and their optimal percentile thresholds are 5 percentile in most years and seasons at the six stations. The optimal performances of these predictors and their combinations were evaluated using the seven designed schemes. The statistical results indicate that the NLRF model with six predictors can achieve the highest accuracy for rainfall forecasts, with TDR and FFR values of 94.94%–24.09%, respectively. Finally, compared to that of latest studies, the forecast accuracy of the NLRF model used in this study reached the highest level and had more application potential. However, the generalization ability of the NLRF model requires further validation across various research regions (such as extratropical and polar regions), different rainfall types (like non-convective rainfall), and different weather phenomena (like convective gales).

Nowadays, a huge amount of GNSS continuously operating reference stations (CORS) have been established around the world, which have already been and will continue providing massive troposphere zenith total delay (ZTD) data. This paper proposes an efficient deep learning-based troposphere ZTD dataset generation method including ZTD series segmentation, addictive and innovational outlier detection, and missing data imputation. The overall standard deviation of first-order ZTD difference series between adjacent epochs of the selected CORS station in January 2018 is reduced from 0.84 to 0.26 mm with the 3-sigma rule and the wavelet decomposition strategy for outlier elimination. A dense neural network (DNN) is subsequently designed to impute missing ZTD data. Only 3.4 minutes are required for the DNN training using a NVIDIA GeForce RTX 3090Ti GPU. The complete ZTD series with missing ZTD data imputed by the well-trained DNN model has a good agreement with the ZTD series before the data imputation in terms of mean value (-0.0184 vs -0.0187 mm) and standard deviation (5.43 vs 5.26 mm). Complete ZTD series of 120 CORS stations are generated to further evaluate the computational efficiency of the proposed method. An average of 2.56/5.38/5.77/4.33 hours are necessary to generate the ZTD dataset for January/April/July/October in 2018, respectively. Our study confirms that the proposed method can efficiently generate CORS-based ZTD dataset, which could be extended to applications including troposphere temporal-spatial pattern exploration and ZTD augmentation on high-precision GNSS positioning.

GPS-based precipitable water vapor (PWV) estimation has been proven as a cost-effective approach for numerical weather prediction. Most previous efforts focus on the performance evaluation of post-processed GPS-derived PWV estimates using International GNSS Service (IGS) satellite products with at least 3-9-h latency. However, the suggested timeliness for meteorological nowcasting is 5-30 min. Therefore, the latency has limited the GPS-based PWV estimation in real-time meteorological nowcasting. The limitation has been overcome since April 2013 when IGS released real-time GPS orbit and clock products. This becomes the focus of this paper, which investigates real-time GPS precise point positioning (PPP)-based PWV estimation and its potential for rainfall monitoring and forecasting. This paper first evaluates the accuracy of IGS CLK90 real-time orbit and clock products. Root-mean-square (RMS) errors of <; 5 cm and ~0.6 ns are revealed for real-time orbit and clock products, respectively, during July 4-10, 2013. Second, the real-time GPS PPP-derived PWV values obtained at IGS station WUHN are compared with the post-processed counterparts. The RMS difference of 2.4 mm has been identified with a correlation coefficient of 0.99. Third, two case studies, including a severe rainfall event and a series of moderate rainfall events, have been presented. The agreement between the real-time GPS PPP-derived PWV and ground rainfall records indicates the feasibility of real-time GPS PPP-derived PWV for rainfall monitoring. Moreover, the significantly reduced latency demonstrates a promising perspective of real-time GPS PPP-based PWV estimation as an enhancement to existing forecasting systems for rainfall forecasting.