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

Baldysz, Zofia; Nykiel, Grzegorz

doi:10.3390/rs11171995

Cited by 7 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting ZWD is strictly related to the humidity in the atmosphere and can serve IWV estimation using the Bevis formula [26]. The water vapor weighted mean temperature, which is necessary for this procedure, can be estimated through, e.g., its linear relationship with the surface temperature [43,44].…”

Section: Methodsmentioning

confidence: 99%

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

et al. 2021

View full text Add to dashboard Cite

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

show abstract

Section: Methodsmentioning

confidence: 99%

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Böhm et al introduced global troposphere models with different resolutions, namely, 1 • × 1 • (GPT2w-1) and 5 • × 5 • (GPT2w-5) [8]. Zofia Baldysz and colleagues, using data from 109 radiosonde stations in Europe, derived a Tm model suitable for the European region through linear regression [9]. Many researchers, including Huang, Mo, and Yao, established Tm regression models involving single and multiple elements [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region

Tian,

Xiong,

Wang

et al. 2023

Atmosphere

View full text Add to dashboard Cite

The Qinghai–Tibet Plateau region has significant altitude fluctuations and complex climate changes. However, the current global weighted average temperature (Tm) model does not fully consider the impact of meteorological and elevation factors on it, resulting in existing models being unable to accurately predict the Tm in the region. Therefore, this study constructed a weighted average temperature refinement model (XTm) related to surface temperature, water vapor pressure, geopotential height, annual variation, and semi-annual variation based on measured data from 13 radiosonde stations in the Qinghai–Tibet Plateau region from 2008 to 2017. Using the Tm calculated via the numerical integration method of radiosonde observations in the Qinghai–Tibet Plateau region from 2018 to 2019 as a reference value, the quality of the XTm model was tested and compared with the Bevis model and GPT2w (global pressure and temperature 2 wet) model. The results show that for 13 modeling stations, the bias and root-mean-square (RMS) values of the XTm model were −0.02 K and 2.83 K, respectively; compared with the Bevis, GPT2-1, and GPT2w-5 models, the quality of XTm was increased by 47%, 38%, and 47%, respectively. For the four non-modeling stations, the average bias and RMS values of the XTm model were 0.58 K and 2.78 K, respectively; compared with the other three Tm models, the RMS values and the mean bias were both minimal. In addition, the XTm model was also used to calculate the global navigation satellite system (GNSS) precipitable water vapor (PWV), and its average values for the theoretical RMSPWV and RMSPWV/PWV generated by water vapor calculation were 0.11 mm and 1.03%, respectively. Therefore, in the Qinghai–Tibet Plateau region, the XTm model could predict more accurate Tm values, which, in turn, is important for water vapor monitoring.

show abstract

GNSS-retrieved precipitable water vapour in the Atlantic coast of France and Spain with GPT3 model

Perdiguer-Lopez,

Berne Valero,

Garrido-Villen

2023

Acta Geod Geophys

View full text Add to dashboard Cite

Water vapour is a critical atmospheric parameter to understand the Earth's climate system and it is characterized by a complex variability in time and space. GNSS observations have become an important source of information of the water vapour, thanks to its high temporal and spatial resolution. However, the lack of meteorological sites collocated with the GNSS site could hamper water vapour retrieval. The empirical blind models can fill this gap. This study analyses the temporal and spatial distribution of the water vapour using nine GNSS sites located on the Atlantic coast of Spain and France, with the empirical blind model GPT3 as the source of meteorological information. The observations were processed with Bernese 5.2 software on a double difference approach and validated with Zenith Total Delay EUREF REPRO2 values. Consequently, four-years series of water vapour was determined and validated using two matched radiosonde sites. The characterization of the water vapour on the area shows clear seasonal characteristics that the technique captures, using an empirical blind model for the whole process. Maximum values are observed in summer season and minimum in winter. The PWV tends to decrease with increasing latitude in the area of the study. The short-term variations can be reproduced by the high temporal resolution of the GNSS-retrieved water vapour and show a different behaviour over the area, but a similar pattern with a peak in the afternoon and minimum at night was found. Also, less variability is observed in winter season and higher in summertime.

show abstract

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

Cited by 7 publications

References 25 publications

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

A Refined Atmospheric Weighted Average Temperature Model Considering Multiple Factors in the Qinghai–Tibet Plateau Region

GNSS-retrieved precipitable water vapour in the Atlantic coast of France and Spain with GPT3 model

Contact Info

Product

Resources

About