scholarly journals ATMOSPHERIC WEIGHTED MEAN TEMPERATURE MODEL IN GUILIN

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1155-1160
Author(s):  
Z. X. Mo ◽  
L. K. Huang ◽  
H. Peng ◽  
L. L. Liu ◽  
C. L. Kang
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weighted Mean ◽  
New Model ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Radiosonde Station ◽  
Variation Characteristics ◽  
Tm Model ◽  
The Impact

Abstract. Atmospheric water vapor is an important part of the earth's atmosphere, and it has a great relationship with the formation of precipitation and climate change. In CNSS-derived precipitable water vapor (PWV), atmospheric weighted mean temperature, Tm, is the key factor in the progress of retrieving PWV. In this study, using the profiles of Guilin radiosonde station in 2017, the spatiotemporal variation characteristics and relationships between Tm and surface temperature (Ts) are analyzed in Guilin, an empirical Tm model suitable for Guilin is constructed by regression analysis. Comparing the Tm values calculated from Bevis model, Li Jianguo model and new model, it is found that the root mean square error (RMSE) of new model is 2.349 K, which are decreased by 14% and 19%, respectively. Investigating the impact of different Tm models on GNSS-PWV, the Tm-induced error from new model has a smaller impact on PWV than other two models. The results show that the new Tm model in Guilin has a relatively good performance and it can improve the reliability of the regional GNSS water vapor retrieval to some extent.

scholarly journals Improved Empirical Coefficients for Estimating Water Vapor Weighted Mean Temperature over Europe for GNSS Applications

2019 ◽  
Vol 11 (17) ◽  
pp. 1995
Author(s):  
Baldysz ◽  
Nykiel
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Linear Interpolation ◽  
Satellite System ◽  
Time Of Day ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Empirical Coefficients ◽  
The Impact

Development of the so-called global navigation satellite system (GNSS) meteorology is based on the possibility of determining a precipitable water vapor (PWV) from a GNSS zenith wet delay (ZWD). Conversion of ZWD to the PWV requires application of water vapor weighted mean temperature (Tm) measurements, which can be done using a surface temperature (Ts) and its linear dependency to the Tm. In this study we analyzed up to 24 years (1994–2018) of data from 49 radio-sounding (RS) stations over Europe to determine reliable coefficients of the Tm-Ts relationship. Their accuracy was verified using 109 RS stations. The analysis showed that for most of the stations, there are visible differences between coefficients estimated for the time of day and night. Consequently, the ETm4 model containing coefficients determined four times a day is presented. For hours other than the primary synoptic hours, linear interpolation was used. However, since this approach was not enough in some cases, we applied the dependence of Tm-Ts coefficients on the time of day using a polynomial (ETmPoly model). This resulted in accuracy at the level of 2.8 ± 0.3 K. We also conducted an analysis of the impact of this model on the PWV GNSS. Analysis showed that differences in PWV reached 0.8 mm compared to other commonly used models.

scholarly journals An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

2020 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Pedro Mateus ◽  
João Catalão ◽  
Virgílio B. Mendes ◽  
Giovanni Nico
Keyword(s):  
Water Vapor ◽  
Air Temperature ◽  
Surface Pressure ◽  
Surface Air Temperature ◽  
Precipitable Water ◽  
Weighted Mean ◽  
Zenith Wet Delay ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Zenith Hydrostatic Delay

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

scholarly journals CONSTRUCTION OF REGIONAL WEIGHTED MEAN TEMPERATURE MODEL BASED ON OPTIMIZATION BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1099-1105
Author(s):  
Z. X. Chen ◽  
L. L. Liu ◽  
L. K. Huang ◽  
Q. T. Wan ◽  
X. Q. Mo
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Bp Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Single Station

Abstract. The tropospheric weighted mean temperature (Tm) is one of the key characteristic parameters in the troposphere, which plays an important role in the conversion of Zenith Wet Delay (ZWD) to atmospheric Precipitable Water Vapor (PWV). The precision of Global Navigation Satellite System (GNSS) inversion of PWV can be significantly improved with the accurate calculation of Tm. Due to the strong nonlinear mapping ability of Back Propagation (BP) neural network, the algorithm can be used to excavate the law with massive data. In term of the nonlinear and non-stationary characteristics of GNSS precipitable water vapor, in this paper, we proposes a forecast method of GNSS precipitable water vapor based on BP neural network, which can modelling the weighted mean temperature of troposphere. The traditional BP neural network has some shortcomings, such as large amount of calculation, long training time and easy to appear “over-fitting” phenomenon and so on. In order to optimize the deficiency and numerical simulation, the three characteristic values include water vapor pressure, surface pressure and surface temperature provided are selected as input parameters, named as BP_Tm. The optimal initialization parameters of the model were obtained from the 2016 radiosonde data of 89 radiosonde stations in China, and the modeling and accuracy verification were conducted with the 2017 radiosonde data，and the accuracy of the new model was compared with the common regional Tm model. The results show the BP_Tm model has good simulation accuracy, the average deviation is −0.186K, and the root mean square error is 3.144K. When simulating the weighted mean temperature of a single station, the accuracy of the four models to simulate Tm is compared and analyzed, which the BP_Tm model can obtain good accuracy and reflect better stability and reliability.

scholarly journals A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM

2021 ◽  
Vol 13 (15) ◽  
pp. 3004
Author(s):  
Wenliang Gao ◽  
Jingxiang Gao ◽  
Liu Yang ◽  
Mingjun Wang ◽  
Wenhao Yao
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Short Term Memory ◽  
Precipitable Water ◽  
Satellite System ◽  
Site Location ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
Modeling Strategy

In the meteorology of Global Navigation Satellite System, the weighted mean temperature (Tm) is a key parameter in the process of converting the zenith wetness delay into precipitable water vapor, and it plays an important role in water vapor monitoring. In this research, two deep learning algorithms, namely, recurrent neural network (RNN) and long short-term memory neural network (LSTM), were used to build a high-precision weighted mean temperature model for China using their excellent time series memory capability. The model needs site location information and measured surface temperature to predict the weighted mean temperature. We used data from 118 stations in and around China provided by the Integrated Global Radiosonde Archive from 2010 to 2015 to train the model and data from 2016 for model testing. The root mean square error (RMSE) of the RNN_Tm and LSTM_Tm models were 3.01K and 2.89K, respectively. Compared with the values calculated by the empirical GPT3 model, the accuracy was improved by 31.1% (RNN_Tm) and 33.9% (LSTM_Tm). In addition, we selected another 10 evenly distributed stations in China and used the constructed model to test the prediction capability of the weighted mean temperature from 2010 to 2016. The RMSE values were 2.95K and 2.86K, which proved that the model also exhibits high generalization in non-modeling sites in China. In general, the RNN_Tm and LSTM_Tm models have a good performance in weighted mean temperature prediction.

scholarly journals Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 169
Author(s):  
Fengyang Long ◽  
Wusheng Hu ◽  
Yanfeng Dong ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Meteorological Factor ◽  
Profile Data ◽  
Weighted Mean ◽  
Mean Temperature ◽  
Weighted Mean Temperature ◽  
New Models ◽  
Atmospheric Profile ◽  
Tm Model

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

Sign in / Sign up

Export Citation Format

Share Document