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The Three Gorges ground-based remote sensing vertical observation system, as the first pilot project of the "short board repair project" by the China Meteorological Administration, can monitor the fine structure and evolution of the atmospheric vertical profile in the Three Gorges Reservoir Region. The atmospheric temperature and humidity profiles are retrieved from the microwave radiometer in the Three Gorges ground-based remote sensing vertical observation system. Evaluating their retrieval bias and improving their accuracy is of great significance for precision monitoring of the vertical observation system in the Three Gorges Reservoir Region. By using microwave radiometer data, radiosonde data, automatic weather station data, and millimeter wave cloud gauge data of Three Gorges from November 2022 to June 2023, this study evaluates the bias of temperature and humidity at 83 height layers retrieved from microwave radiometer under sunny, cloudy, and rainy days. Then a linear regression correction method based on radiosonde data after the regrouping of the 83 height layers for temperature and humidity profile of the microwave radiometer was proposed. Finally, the applicability of the correction method by using the accuracy changes before and after the correction was evaluated using the observation from a rainy and snowy weather process. The results are as follows: (1) The temperature of the microwave radiometer has good consistency with radiosonde temperature under three weather conditions, and the bias of most height layers is less than 2 ℃. (2) On sunny days, the humidity of the microwave radiometer is higher than that of the radiosonde at all height layers. Above 6 km, the root mean square error (RMSE) of the humidity can reach 30% on cloudy and rainy days. (3) The correction method established after the regrouping of the 83 height layers can improve the temperature and humidity profile accuracy of the microwave radiometer. (4) The corrected temperature and humidity profiles of the microwave radiometer are closer to the reality in the case of a rain and snow event.

Real-time and effective detection of atmospheric profiles is of great significance in understanding the evolution of climate system. Ground-based microwave radiometers can provide atmospheric temperature and humidity profiles with extremely high temporal and spatial resolution. Domestic MWP967KV microwave radiometer has effectively made up for problems of imported microwave radiometers, but there are relatively few studies on the performance evaluation and application of this microwave radiometer. In order to better apply data and products of MWP967KV microwave radiometer, inversion data from June 2018 to July 2021 at Jinghe Station of Xi'an are compared with L-band radiosonde observation. The accuracy of atmospheric temperature, relative humidity and vapor density retrieved from microwave radiometer under clear skies and different cloudy skies (classified as low cloud, middle cloud and high cloud, respectively) are analyzed, and the applicability of the related products in precipitation is further explored. Results show that correlation coefficients of temperature between microwave radiometer and radiosonde are 0.99, correlation coefficients of vapor density are 0.97, and correlation coefficients of relative humidity are less than 0.50 under clear skies and cloudy skies, all passing 0.01 significant test. The difference of temperature between clear and cloudy skies is small, but root mean square error of relative humidity in cloudy skies is more than 25%, which is significantly larger than that in clear skies. It indicates that the presence of clouds reduces the accuracy of the humidity inversion, causing large errors, and the inversion accuracy is higher near the ground. Under different cloud types, the temperature difference is small, while root mean square error and bias of relative humidity in low cloud are the largest, which are 26.85% and 9.51%, respectively. In addition, a case analysis shows that relative humidity, liquid water content, atmospheric precipitable water vapor and liquid water path increase significantly before the occurrence of precipitation, which can be used as indicators of the possible occurrence of precipitation. Statistic results show that the atmospheric precipitable water vapor reaches 4 cm and liquid water path reaches 0.2 mm during several precipitation cases, and these indexes can be used as the reference threshold for judging the precipitation of Xi'an.

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To take full advantage of FY-4A temperature profile data to understand the evolutions of weather processes and nowcasting, based on the atmospheric temperature profile of radiosonde, microwave radiometer and FY-4A satellite from 1 January 2021 to 31 March 2022, 897 samples are matched and their deviation characteristics are evaluated. The results show that the correlation coefficient between FY-4A satellite temperature and that of microwave radiometer is 0.9891, and the correlation coefficient between FY-4A satellite temperature and that of radiosonde is 0.9820. The mean temperature of FY-4A satellite is 0.51℃ smaller than that of the radiosonde below 10 km height, and the standard deviation is 0.50℃. The mean temperature of FY-4A satellite is 0.53℃ larger than that of the microwave radiometer below 10 km, and the standard deviation is 0.75℃. FY-4A temperature is consistent with the mean deviation trend of the radiosonde at 0000 UTC and 1200 UTC. Compared with 0000 UTC, the deviation sample of FY-4A at 1200 UTC is less discrete. When there is precipitation, the temperature deviation of microwave radiometer and FY-4A gradually increases above 600 m height, and the deviation reaches the maximum (about 9.35℃) near 1500 m height. In the range of 3000-8500 m height, the deviation ranges from 1.35℃ to 5.10℃, and the standard deviation ranges from 1.41℃ to 4.99℃. In the case of precipitation, the deviation values and standard deviations of FY-4A temperature and radiosonde are small. Although the deviation values and standard deviations of FY-4A temperature and radiosonde are different at different heights in the whole layer, the deviation is between -0.31 and 3.60℃. When there are clouds, the mean deviation between FY-4A temperature and that of microwave radiometer is -0.40℃, and the mean standard deviation is 3.79℃. The overall mean deviation between FY-4A temperature and radiosonde is 0.31℃, and the mean standard deviation is 2.66℃. Both the deviation and standard deviation between FY-4A temperature and that of microwave radiometer are larger than those between FY-4A and radiosonde when there are clouds. The deviation and standard deviation of microwave radiometer temperature and that of radiosonde with FY-4A are small in clear sky. The above conclusions can provide reference for the further use of FY-4A satellite data, as well as for the quality control of FY-4A satellite data and its application in weather analysis and forecast.

Aiming at the problem that the flight altitude of traditional radiosonde is difficult to break through to 30～80 km, a rocket drop radiosonde for detecting the atmospheric environment was designed at an altitude of 30～80 km, including a radiosonde temperature sensor composed of thermocouple array, which can reduce the temperature measurement error caused by high-altitude solar radiation. Firstly, the numerical solution of radiation error of thermocouple temperature probes with different sizes was studied by computational fluid dynamics (CFD). Secondly, the radiation error of thermocouple probes coated with different solar radiation absorptivity materials was calculated by CFD method, and the ambient temperature was calculated by the ratio of radiation error between each probe. Thirdly, the solar radiation error was solved and processed by support vector machine (SVM) algorithm. Finally, the low pressure wind tunnel and solar simulator experimental platform were built to carry out experimental verification research. The experimental results show that the average measurement error of the temperature sensor on the rocket drop radiosonde is 0.107 ℃, and the root mean square error is 0.22 ℃. [ABSTRACT FROM AUTHOR]

Objectives: Atmospheric weighted average temperature Tm is essential for calculating water vapour conversion factor and atmospheric precipitation. Methods: Based on the global geodetic observing system (GGOS) Atmosphere Tm grid data and European centre for medium‑range weather forecasts (ECMWF) 2 m temperature data from 2007 to 2017, a Tm model (qTm) suitable for Australia was established and the seasonal and daily variation of Tm residuals was taken into account. In addition, GGOS Atmosphere Tm grid data and radiosonde data in 2018 were selected to evaluate the model. Results: The results reveal that the qTm model is more accurate and applicable in the Australian region. Compared with GGOS Atmosphere Tm, the annual average Bias and RMSE(root mean square error) of the qTm model are －0.31 K and 1.97 K, respectively. While compared to the GPT2w(global pressure and temperature 2 wet) ‑ 1 and GPT2w ‑ 5, RMSE of qTm model increased by 21.8% and 25.9%, respectively. qTm model values are more consis‑ tent with sounding integral values, and the model's annual average Bias and RMSE are －0.44 K and 2.45 K, respectively. Compared with GPT2w ‑1 and GPT2w‑5, they increased by 10.2% and 11.8%, respectively. Conclusions: The qTm model can provide accurate Tm values for the Australian region and is a basis for the region's atmospheric moisture analysis and El Niño studies. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) tomography technique has become one of the most potential techniques for retrieving the three ‐dimensional (3D) distribution of water vapor with the advantages of high precision observations, low cost and all ‐weather monitoring. Methods: High horizontal resolution water vapor information provided by remote sensing satellites is introduced. We propose a GNSS water vapor tomography algorithm constrained with high horizontal resolution precipitable water vapor (PWV) data for the first time, which supplements and improves the constraints of existing water vapor tomography algorithms. In the proposed algorithm, firstly, the high horizontal resolution PWV observations are calibrated, and then the PWV constraint equations are constructed based on the densified tomographic voxels. Finally, the PWV constraint equations are included into the GNSS tomography model, which optimizes the constraint conditions and improves the tomographic results. Results: GNSS data and remote sensing water vapor data from FY‐3A satellite over Xuzhou area, China in August 2017 are used to assess the feasibility and accuracy of the proposed algorithm. Taking the high precision radiosonde water vapor profile and 3D water vapor density field from ERA5 as reference values, it can be observed that the proposed algorithm is superior to traditional method in retrieving water vapor profile and 3D water vapor distribution. Three kinds of accuracy indexes of the tomographic results have been significantly improved using the proposed method, with the mean root mean square error (RMSE) decreasing from 2.73 g/m3 to 1.78 g/m³, showing an improvement of 34.80%.Conclusions: This highlights that the improved tomography algorithm has significant potential to reconstruct the accurate and reliable 3D atmospheric water vapor distribution. [ABSTRACT FROM AUTHOR]

Objectives：GNSS tomography technique is one of the most important methods to obtain the three‐dimensional water vapor information. However, due to lack of enough initial prior information in the process of building the tomography model, the design matrix of tomography model is unstable and the tomographic result is poor, which has become an urgent problem to be resolved.Methods: First of all, the grid data derived from European Center for Medium‐Range Weather Forecasting (ECMWF) is used to calculate the initial value of water vapor density in every voxel of interest area. And then, sophisticating the traditional tomography modeling using the calculated initial water vapor values. Finally, the influence of weightings of different equations in tomography model on tomographic result is also considered.Results: Comparing to the traditional methods, the proposed approach can enhance the accuracy of tomographic result by 41.2%, and its root mean squared error (RMSE) decreases from 1.82 g/m³ to 1.07 g/m³ . Additionally, the mean absolute error (MAE), Bias and standard deviation (STD) also show a better performance than those of the traditional methods.Conclusions: The purpose is to improve the accuracy of tomographic water vapor profiles and used to sophisticate the established tomography model. [ABSTRACT FROM AUTHOR]

Microwave radiometer (MWR) can retrieve atmospheric profiles with a temporal resolution of several minutes, which compensates for the low temporal resolution of the meteorological radiosonde data. In this study, we use the data of MWR and radiosonde observed from August to October in 2017 at Ganzi, a station on the eastern side of the Qinghai-Tibet Plateau (QTP), to analyze the accuracy of atmospheric profiles retrieved from the MWR observation and investigate the properties of atmospheric thermal profiles and cloud liquid water content profiles over Ganzi. The results show that the correlation coefficient between the MWR and radiosonde observations is high. The general deviations of MWR-retrieved temperature, relative humidity and vapour density against the data of radiosonde were 1.3℃, -2%, and 0.71 g/m3, with the corresponding root mean square errors of 2.9℃, 20%, and 1.08 g·m-3, respectively. The impact of precipitation weather on the accuracy of observed data by the MWR was weak except for heavy rainfall weather conditions. The deviation of temperature profile between the MWR and radiosonde was less than 2℃ in most height layers, which can meet the deviation requirement of meteorological operations. The relative humidity deviation between the MWR and radiosonde under non-precipitation condition was about 10% in most height layers, while it was less than 5% below 3.5 km under precipitation conditions. Based on the statistical analysis of the MWR data in Ganzi, it was found that the atmosphere was dry and hot in daytime while wet and cold at nighttime during the observation period. At the same time, the cloud liquid water content was lower in daytime and larger at nighttime, and the development of low-level clouds was weaker during the daytime with higher cloud base heights and stronger during the nighttime with lower cloud base heights. Moreover, the cloud liquid water content showed a similar vertical structure under cloudy and rainy conditions, in which the cloud liquid water content rapidly increased with height and significantly decreased after a fluctuation within a certain height. This variation well indicated the characteristics of entering and leaving clouds. In addition, the cloud body of low-level clouds under cloudy conditions mainly located at the height of 0.1-2.5 km, while the cloud body of low-level clouds under rainy condition widely spread from the surface to 3.5 km. These analysis results showed that the atmospheric profiles retrieved from the MWR observation were credible at the Ganzi station, and the MWR can provide actual data for quantitative study of cloud characteristics.

The number of signal rays passing through a research area are often limited due to the geometric distribution of satellite constellations and receivers. To solve this issue, an approach is proposed that imposes virtual slant signals on the tomographic model. This approach increases the number of signal rays passing through a research area and. improves accuracy. An experiment tested the feasibility and accuracy of proposed approach by comparing with the results using Global Positioning System and meteorological data for 31 days in May, 2013, provided by Hong Kong Satellite Positioning Reference Station Network and those derived from radiosonde data. The Root Mean Square water vapor profile and error statistics for proposed method are all superior to that of traditional method. The experiment shows that the proposed approach can improve the accuracy of tomographic results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In existing Global Navigation Satellite System (GNSS) water vapor tomographic techniques, only signals which penetrate the entire research area are used. Due to the specific geometric distribution of satellite constellations and receivers as well as the specificity of tomography area, many signals penetrating from the side of a research area are excluded as ineffective information, which undermines the tomographic result. To solve this issue, a method is proposed for improving the utilization of signal rays by adding an assisted tomographic area, which allows the signal information crossed from the side of a research area to be utilized as well. An experiment using data from the Continuously Operation Reference Stations (CORS) network of Texas in UAS, is compared with radiosonde data of 72 249. The results show that the proposed method can enhance the accuracy of tomographic results, the improvement rate of Root Mean Square Error (RMS) was 14.6%. [ABSTRACT FROM AUTHOR]

Based on the observation data of ground PM2.5/PM10 , GPS Precipitable Water Vapor (PWV) and Radiosonde PWV in Beijing in 2013, the change among PM2.5/PM10, GPS PWV and Radiosonde PWV per layer of PWV was compared. We found that there is significantly positive correlation between ground PM2.5/PM10 variation and ground GPS PWV change during autumn, winter, and spring. Such a correlation does not exist in the rainy summer months. According to the comparisons between ground PM2.5/PM10 observations and aerial PWV Radiosonde observations made in autumn, winter, and spring, the correlation between ground PM2.5/PM10 variation and total aerial PWV change is also significantly positively related. The correlation between PM2.5/PM10 variation and the 3th/4th water vapor layer (850-3 000 m) PWV change is the most significantly positively related value. [ABSTRACT FROM AUTHOR]