Refining the empirical global pressure and temperature model with the ERA5 reanalysis and radiosonde data

2021 ◽  
Vol 95 (3) ◽  
Author(s):  
Tao Li ◽  
Lei Wang ◽  
Ruizhi Chen ◽  
Wenju Fu ◽  
Beizhen Xu ◽  
...  
Keyword(s):  
Radiosonde Data ◽  
Temperature Model ◽  
Global Pressure

scholarly journals A Refined Regional Model for Estimating Pressure, Temperature, and Water Vapor Pressure for Geodetic Applications in China

2020 ◽  
Vol 12 (11) ◽  
pp. 1713
Author(s):  
Junyu Li ◽  
Bao Zhang ◽  
Yibin Yao ◽  
Lilong Liu ◽  
Zhangyu Sun ◽  
...  
Keyword(s):  
Water Vapor ◽  
Vapor Pressure ◽  
Meteorological Parameters ◽  
Water Vapor Pressure ◽  
Regional Model ◽  
Radiosonde Data ◽  
New Model ◽  
Gnss Positioning ◽  
Global Pressure ◽  
Gpt2w Model

Pressure, temperature, and water vapor pressure are basic meteorological parameters that are frequently required in Global Navigation Satellite System (GNSS) positioning/navigation and GNSS meteorology. Although models like Global Pressure and Temperature (GPT) and Global Pressure and Temperature 2 wet (GPT2w) were developed for these demands, their spatial resolutions are lower than 0.75° and temporal resolutions are below 6 h, which limits their achievement. The publication of European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 hourly 0.25° × 0.25° data offers the opportunity to lift this limitation. In this work, the ERA5 surface data are used to evaluate the temporal variabilities of pressure, temperature, and water vapor pressure in the area of China. We characterize their diurnal variations using hourly data and take into account their geographical variations by 0.25° × 0.25° grids. In addition, we improve the height corrections for the three parameters employing the ERA5 pressure level data. Through these efforts, we build a new regional model named Chinese pressure, temperature, and water vapor pressure (CPTw), which has the advanced resolution of 0.25° × 0.25° and temporal resolution of 1 h. We evaluate the performance using ERA5 data and radiosonde data compared with the approved GPT2w model. Results demonstrate that the accuracies of the new model are superior to the GPT2w model in all meteorological parameters. The validation with the radiosonde data shows RMS for pressure, temperature, and water vapor pressure of the CPTw model is reduced by 14.1%, 25.8%, and 4.8%, compared with that of the GPT2w model. The new model catches especially well the diurnal changes in pressure, temperature, and water vapor pressure, which have never been realized before. Since the CPTw model can provide accurate empirical pressure, temperature, and water vapor pressure for any time and location in China and its surrounding areas, it can not only meet the need of empirical meteorological parameters in real-time geodetic applications like GNSS positioning and navigation, but it is also useful for GNSS meteorology.

GENERAILIZED TWO-TEMPERATURE MODEL FOR COUPLED PHONONS IN NANOSIZED GRAPHENE

2018 ◽  
Author(s):  
Meng An ◽  
Qichen Song ◽  
Xiaoxiang Yu ◽  
Han Meng ◽  
Dengke Ma ◽  
...  
Keyword(s):  
Temperature Model ◽  
Two Temperature

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals Modeling of Thermal Effects in Semiconductor Structures

VLSI Design ◽  
1998 ◽  
Vol 8 (1-4) ◽  
pp. 53-58
Author(s):  
Christopher M. Snowden
Keyword(s):  
Thermal Effects ◽  
Large Scale ◽  
High Electron Mobility Transistors ◽  
Thermal Modelling ◽  
High Electron ◽  
Temperature Model ◽  
Active Devices ◽  
Electron Mobility Transistors ◽  
Fully Coupled ◽  
Scale Surface

A fully coupled electro-thermal hydrodynamic model is described which is suitable for modelling active devices. The model is applied to the non-isothermal simulation of pseudomorphic high electron mobility transistors (pHEMTs). A large-scale surface temperature model is described which allows thermal modelling of semiconductor devices and monolithic circuits. An example of the application of thermal modelling to monolithic circuit characterization is given.

Evaluation of daily and diurnal signals of total precipitable water (TPW) over the Indian Ocean based on TMI retrieved 3-day composite estimates and radiosonde data

2007 ◽  
Vol 27 (6) ◽  
pp. 761-770 ◽  
Author(s):  
V. Sajith ◽  
Jimmy O. Adegoke ◽  
Santosh K. Raghavan ◽  
H. S. Ram Mohan ◽  
Vinod Kumar ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Precipitable Water ◽  
Radiosonde Data ◽  
Total Precipitable Water ◽  
The Indian Ocean
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