Atmospheric water vapor is one of the important parameters for weather and climate studies. Generally, atmospheric water vapor can be monitored by some techniques, such as the Global Navigation Satellite System (GNSS), radiosonde (RS), remote sensing and numerical weather forecast (NWF). However, the comprehensive retrieval and application of precipitable water vapor (PWV) using multi techniques has been hardly performed before, which becomes the focus of this study. A comprehensive PWV retrieval and application platform (CPRAP) is first established by combing the ground-based (GNSS), space-based (Fengyun-3A, Sentinel-3A) and reanalysis-based (the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasting, ERA5) techniques. Additionally, its applications are then extended to drought and rainfall monitoring using the CPRAP-derived PWV. The statistical result shows that PWV derived from ground-based GNSS has high accuracy in China, with the root mean square (RMS), Bias and mean absolute error (MAE) of 2.15, 0.05 and 1.65 mm, respectively, when the RS-derived PWV is regarded as the reference. In addition, the accuracy of PWV derived from the space-based (FY-3A and Sentinel-3A) techniques technique is also validated and the RMS, Bias and MAE of a Medium Resolution Spectral Imager (MERSI) onboard Fengyun-3A (FY-3A) and an Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A are 4.46/0.56/3.61 mm and 2.95/0.01/1.37 mm, respectively. Then, the performance of ERA5-derived PWV is evaluated based on GNSS-derived and RS-derived PWV. The result also shows good accuracy of ERA5-provided PWV with the averaged RMS, Bias and MAE of 1.86/0.11/1.48 mm and 0.90/−0.05/1.51 mm, respectively. Finally, the PWV data derived from the established CPRAP are further used for drought and rainfall monitoring. The applied results reveal that the calculated the standardized precipitation evapotranspiration index (SPEI) using the CPRAP-derived PWV can monitor the drought and the correlation coefficient ranges from 0.83 to 0.9 when compared with the SPEI. Furthermore, in this paper correlation analysis between PWV derived from the CPRAP and rainfall, and its potential for rainfall monitoring was also validated. Such results verify the significance of the established CPRAP for weather and climate studies.

Atmospheric weighted mean temperature (T_m) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical T_m models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal T_m model for PWV retrieval in China, this paper analyzes and evaluates some typical T_m models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the T_m model for China (CT_m), the Global Weighted Mean Temperature-H (GT_m-H) and the Global Tropospheric (GTrop) models. The T_m values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding T_m values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five T_m models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CT_m, GT_m-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CT_m, GT_m-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GT_m-H and CT_m models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain T_m for calculating PWV in China.

Noise-induced hidden hearing loss (HHL) is a type of hearing dysfunction caused by moderate noise exposure. It is clinically manifested as speech recognition impairment in noisy environments while the hearing threshold remains within the normal range. The mechanism by which noise exposure causes speech recognition impairment remains unclear. This study aimed to investigate the excitation-inhibition status in the auditory cortex of HHL mice and its roles in sound recognition disability in noisy environments. A model of HHL in mice was induced using 110 decibels (dB) of helicopter noise for 2h, the sound recognition-avoidance decision (SRAD) behavioral test was used to evaluate sound recognition ability. The activation level of excitatory neurons and the expression of vesicular glutamate transporter 1 (VGluT1) and glutamate decarboxylase 67 (GAD67) in the auditory cortex were observed. Mice were administered α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist NBQX. The results showed increased cFos expression in the auditory cortex of HHL mice and an upregulated ratio of activated CaMKⅡ⁺ neurons. Furthermore, the expression of VGluT1 and GAD67 increased in an imbalanced manner under an 80dB noise stimulus for 2h. The SRAD behavioral tests showed the HHL mice maintained normal sound recognition ability under quiet conditions but not in a noisy environment. The NBQX treatment improved sound recognition but did not restore normal status. This study suggested that the excitation-inhibition imbalance in the auditory cortex of mice with HHL might be the direct cause of sound recognition disorders.