Jingshu Liang

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoid clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation of 93%, 6.4% higher than the RF model without data expansion, 3–6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution.

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoids clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation to 93%, 6.4% higher than the RF model without data expansion, 3%-6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution.

The accurate measurement of water vapor is essential for research about and the applications of meteorology, climatology, and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) can be retrieved with the precise point positioning (PPP) software (XTW-PPP version 0.0). The experiment was conducted in Beijing in January 2023. Three solutions were designed with PPP using the BeiDou system only, the GPS system only, and the BeiDou-GPS combined solution. Real-time PWVs for the three solutions were validated with the ERA5 reanalysis data. Between the PWV values from the single BeiDou and ERA5, there was a bias of 0.7 mm and an RMSE of 1.8 mm. For the GPS case, the bias was 0.73 mm and the RMSE was 1.97 mm. The biases were less than 1 mm and RMSEs were less than 2 mm. Both the BeiDou and the GPS processing performed very well. But little improvement was found for the BeiDou-GPS combined solution, compared with the BeiDou system-only and the GPS system-only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with a single system at the first step and then obtain the optimization by Bayesian model averaging.

Accurate measurement of water vapor is essential for researches and applications of meteorology, climatology and hydrology. Based on the BeiDou PPP-B2b service, real-time precipitable water vapor (PWV) is retrieved with the precise point positioning (PPP) software. The experiment was conducted in Beijing in January, 2023. Three solutions are designed with PPP using the BeiDou system only, the GPS system only and the BeiDou-GPS combined solution. Real-time PWVs for the 3 solutions are validated with the ERA5 reanalysis data. Between PWVs from single BeiDou and ERA, there are a bias of 0.7 mm and a RMSE of 1.8 mm. While for the GPS case, the bias is 0.73 mm and RMSE is 1.97 mm. The biases are less than 1 mm and RMSEs are less than 2mm. Both the BeiDou and the GPS processing performs very well. But little improvement is found for the BeiDou-GPS combined solution, comparing with the BeiDou system only and the GPS system only solution. This may be due to the poor handling of two different kinds of errors for the GPS and the BeiDou systems in our PPP software. A better PWV estimation with the two systems is to estimate PWV with single system at first step, and then to obtain the optimization by Bayesian model averaging.

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.

The ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts (ECMWF) in the summers from 2015 to 2020 was used to compare and analyze the features of the precipitable water vapor (PWV) observed by six ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in the Yunnan–Guizhou Plateau. The correlation coefficients of the two datasets ranged between 0.804 and 0.878, the standard deviations ranged between 4.686 and 7.338 mm, and the monthly average deviations ranged between −4.153 and 9.459 mm, which increased with the altitude of the station. Matching the quality-controlled ground precipitation data with the PWV in time and space revealed that most precipitation occurred when the PWV was between 30 and 65 mm and roughly met the normal distribution. We used the vertical integral of divergence of moisture flux (∇_p) and S-band Doppler radar networking products combined with the PWV to study the convergence and divergence process and the water vapor delivery conditions during the deep convective weather process from August 24 to 26, 2020, which can be used to analyze the real-time observation capability and continuity of PWV in small-scale and mesoscale weather processes. Furthermore, the 1 h precipitation and the cloud top temperature (ctt) data at the same site were used to demonstrate the effect of PWV on the transit of convective weather systems from different time–space scales.