scholarly journals Evaluation of ERA-5 Precipitable Water Vapor Data in Plateau Areas: A Case Study of the Northern Qinghai-Tibet Plateau

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1367
Author(s):  
Jie Zhao ◽  
Tiejian Li ◽  
Kaifang Shi ◽  
Zhen Qiao ◽  
Zhongye Xia
Keyword(s):  
Water Vapor ◽  
Cloud Cover ◽  
Coincidence Degree ◽  
Precipitable Water ◽  
Climatic Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tibet Plateau ◽  
Total Cloud Cover ◽  
Qinghai Tibet Plateau

In order to verify the accuracy of precipitable water vapor (PWV) in remote sensing and reanalysis datasets under different climatic conditions and ensure the reliability of analysis results, the performances of ERA-5 reanalysis PWV data and the Atmospheric Infrared Sounder (AIRS) remotely-sensed PWV data were tested in the northern Qinghai-Tibet Plateau by using weather balloon radiosonde data from meteorological stations from 2002 to 2016. The coincidence degree of total cloud cover was also verified, and then the PWV data precision with different levels of cloud cover was analyzed. The results show that: (1) Both ERA-5 and AIRS data underestimate PWV in the studied high plateau region, and higher altitude leads to greater deviation. (2) Compared with AIRS data, ERA-5 data have better consistency with radiosonde data in PWV and total cloud cover. (3) For the long-term trend of PWV, the ERA-5 data are the opposite to the radiosonde data with a clear sky, but both datasets showed a significant increasing trend in cloudy skies. It can be concluded that in high altitude areas, the ERA-5 data can be used for general analysis, but are not well qualified to reflect the changing trend of PWV under climate change.

scholarly journals Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Raquel Perdiguer-López ◽  
José Luis Berné-Valero ◽  
Natalia Garrido-Villén
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
The North ◽  
Radiosonde Station ◽  
Observation Method ◽  
North Of Spain

A processing methodology with GNSS observations to obtain Zenith Tropospheric Delay using Bernese GNSS Software version 5.2 is revised in order to obtain Precipitable Water Vapor (PWV). The most traditional PWV observation method is the radiosonde and it is often used as a standard to validate those derived from GNSS. For this reason, a location in the north of Spain, in A Coruña, which has a GNSS station with available data and also a radiosonde station, was chosen. Two GPS weeks, in different weather conditions were calculated. The result of the comparison between the GNSS- retrieved PWV and Radiosonde-PWV is explained in the last section of this paper.

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 Precipitable water vapor content from ESR/SKYNET Sun-sky radiometers: validation against GNSS/GPS and AERONET over three different sites in Europe

2017 ◽  
Author(s):  
Monica Campanelli ◽  
Alessandra Mascitelli ◽  
Paolo Sanò ◽  
Henri Diémoz ◽  
Victor Estellés ◽  
...  
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Climatic Conditions ◽  
Precipitable Water Vapor ◽  
Water Vapor Content ◽  
Atmospheric Parameter ◽  
High Temporal Resolution ◽  
Photometric Calibration ◽  
Wide Range ◽  
Calibration Parameters

Abstract. The estimation of the precipitable water vapor content (W) with high temporal and spatial resolution is of great interest in both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed, in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun-sky radiometers, or sun-photometers, operating in several international networks, allowed the development of automatic estimations of W from these instruments with high temporal resolution. However the great problem of this methodology is the estimation of the sun-photometric calibration parameters. The objective of this paper is to validate a new methodology based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance at 940 nm are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site. To obtain the calibration parameters some simultaneously seasonal independent measurements of W taken over a large range of solar zenith angle and covering a wide range of W, are needed. In this work yearly GNSS/GPS dataset were used for obtaining a table of photometric calibration constants and the methodology was applied and validated in three European ESR-SKYNET network sites, characterized by different atmospheric and climatic conditions: Rome, Valencia and Aosta. Results were validated against the GNSS/GPS and AErosol Robotic NETwork (AERONET) W estimations. In both the validations the agreement was very high with a percentage RMSD of about 6 %, 13 % and 8 % in the case of GPS intercomparison at Rome, Aosta and Valencia, respectively, and of 8 % in the case of AERONET comparison in Valencia. Analysing the results by W classes, the present methodology was found to clearly improve W estimation at low W content when compared against AERONET in term of %Bias, bringing the agreement with the GPS (considered the reference one), from a %Bias of 5.76 to 0.52.

scholarly journals WATER VAPOR CONVERSION FACTOR OVER QINGHAI-TIBET PLATEAU REGION CONSIDERING ELEVATION, LATITUDE AND FINE SEASONAL VARIATION IS CONSTRUCTED

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 271-277
Author(s):  
H. Peng ◽  
L. K. Huang ◽  
C. Li ◽  
L. L. Liu ◽  
S. Wang ◽  
...  
Keyword(s):  
Water Vapor ◽  
High Precision ◽  
Conversion Factor ◽  
Conversion Coefficient ◽  
Reference Value ◽  
Radiosonde Data ◽  
Tibet Plateau ◽  
Grid Data ◽  
Plateau Region ◽  
Qinghai Tibet Plateau

Abstract. In this paper, the conversion factor K model of Qinghai-Tibet plateau region was established based on the QTm model which is established using high-precision the Global Geodetic Observing System (GGOS) Atmosphere grid data from 2007 to 2014. The model took into account the influence of elevation fluctuation and latitude change on the model, and analyzed the relevant characteristics with seasonal changes. The 2015 GGOS grid data and radiosonde data were used as the reference value for accuracy assess. The established QTm model was compared with GPT2w model in bias and RMS. Compared with GGOS grid data, the average annual bias and RMS of QTm model were -0.28K and 2.70k respectively. The RMS of GPT2w-5 and GPT2w-1 were 58.16% and 28.84% higher, respectively. Compared with radiosonde data, QTm model has 1.13k average annual bias and the RMS error of 2.92k. Compared with GPT2w-5 and GPT2w-1, the RMS value of QTm model was improved by 25.08% and 29.43%, respectively. The value of atmospheric water vapor conversion coefficient was calculated by the integral method calculated by radio sounding data in the Qinghai-Tibet region in 2015 was used as the reference value for assess the performance of conversion factor K, and compared and analyzed the conversion coefficient K which provided by QTm and GPT2w. The results show that the value of Tm provided by QTm model has the highest accuracy, which is 25.07% higher than that of GPT2w-5 and 29.42% higher than that of GPT2w-1. QTm models can achieve GPS-PWV retrieval precision of better than 2 mm. Which has potential application for high-precision real-time GNSS-PWV retrieving in Qinghai-Tibet region.

Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Soundings

2012 ◽  
Vol 500 ◽  
pp. 390-396 ◽  
Author(s):  
Sheng Lan Zhang ◽  
Li Sheng Xu ◽  
Ji Lie Ding ◽  
Hai Lei Liu ◽  
Xiao Bo Deng
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Radiative Transfer ◽  
Rbf Neural Network ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Rbf Network

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) observations is proposed. An exact radial basis function (RBF) network is selected, in which the at-sensor brightness temperatures are the input variables, and PWV is the output variable. The training data sets for the RBF network are mainly simulated from the fast radiative transfer model (Community Radiative Transfer Model, CRTM) and the latest global assimilation data. The algorithm is validated by retrieving the PWV over west area in China using AIRS data. Compared with the AIRS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.67 g/cm2, and a comparison between the retrieved PWV and radiosonde data is carried out. The result suggests that the RBF neural network based algorithm is applicable and feasible in actual conditions. Furthermore, spatial resolution of water vapor derived by RBF neural network is superior as compared to that of AIRS-L 2 standard product. Finally a PCA scheme is used for the preliminary investigation of the compression of AIRS high dimension observations.

scholarly journals Variations of precipitable water vapor from 2012 to 2019 over the Philippines using radiosondes

2021 ◽  
Vol 880 (1) ◽  
pp. 012001
Author(s):  
Y Rivera ◽  
K C Capacete ◽  
S K Rodriguez ◽  
A R David ◽  
E Macalalad
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Kendall Test ◽  
The Philippines ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Upward Trend ◽  
Annual Periodicity ◽  
Increasing Trend ◽  
Moderate Positive Correlation

Abstract Precipitable Water Vapor (PWV) refers to the content of water vapor in the atmosphere which is significant in observing climate changes. The trends and variations of precipitable water vapor in Laoag, Legazpi, Mactan, and Puerto Princesa from 2012-2019, are presented through the use of radiosonde data derived from the database of the Integrated Global Radiosonde Archives (IGRA). These data were analyzed for possible patterns through a time series of its daily, monthly, and annual mean, together with a Lomb-Scargle periodogram, and Mann-Kendall test. The results observed varying trends and variability. Legazpi and Puerto Princesa with a minimum value of 20 mm, observed a gradual downward trend of PWV. Laoag and Mactan detected an upward trend of PWV with a minimum of 10 mm and 20 mm, respectively. It also showed an annual and bi-annual periodicity of PWV. Furthermore, all cities detected an increase of PWV during the wet months of May to September, while the dry months of October to April with slight variations over 8 years. In terms of seasonality, only Laoag observed a slightly different dry season, with January, February, and March experiencing around 5 mm less in monthly PWV variation compared to the other cities. The correlation of surface temperature and relative humidity of PWV observed an overall increasing trend while showing a general moderate positive correlation. This study can be used for future references for meteorologists for upcoming forecasting on the likelihood of different weather phenomena in the Philippines.

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