On the determination of weighted mean temperature in Indonesia

2021 ◽  
Author(s):  
Nabila Putri ◽  
Johannes Boehm ◽  
Dudy D. Wijaya ◽  
Wedyanto Kuntjoro ◽  
Zamzam Tanuwijaya ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Water Vapor Pressure ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Mean Temperature ◽  
Weather Model ◽  
Weighted Mean Temperature ◽  
Global Navigation Satellite

<p>The mean temperature weighted with water vapor pressure (Tm) is an important parameter to obtain precipitable water vapor (PWV) from the Global Navigation Satellite Systems (GNSS) observations. This study investigates the possible impacts of equatorial troposphere on Tm estimates and its relation with surface temperature Ts. We calculated Tm in Indonesia from a Numerical Weather Model at nine InaCORS sites. We used 3-hourly ERA5 pressure, temperature, and humidity profiles for the year 2019. We found that Tm and surface temperature Ts in Indonesia have low correlation, less than 0.4. Seasonal and site-specific Tm-Ts relationships have slightly higher correlation, although the values can vary significantly. The highest correlation of around 0.7 is found at site CPUT in Kalimantan. We calculated Tm at nine additional stations in Kalimantan and found that stations located farther from the coast tend to have higher correlation, independent of the seasons. This suggests that Tm is also influenced by the vicinity to the coast. Based on our findings, the use of a general Tm-Ts relationship in Indonesia may not be appropriate. Further studies are necessary to develop an improved Tm over Indonesian region.</p>

scholarly journals Validating HY-2A CMR Precipitable Water Vapor Using Ground-based and Shipborne GNSS Observations

2020 ◽  
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
High Temporal Resolution ◽  
Satellite Systems ◽  
The Indian Ocean ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) onboard Haiyang-2A satellite provides wet tropospheric delays correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. Ground-based Global Navigation Satellite Systems (GNSS) provide precise PWV with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 IGS stations along the coastline and 56-day shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made to the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm in RMS within 100 km. Geographically, the RMS is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an RMS of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well with no obvious system error.

scholarly journals 50 anos de sinergia entre Geodésia Espacial e Meteorologia: do erro no posicionamento GNSS a aplicações de previsão de precipitação de curtíssimo prazo

2020 ◽  
Vol 72 ◽  
pp. 1509-1535
Author(s):  
Tayná Aparecida Ferreira Gouveia ◽  
João Francisco Galera Monico ◽  
Daniele Barroca Marra Alves ◽  
Luiz Fernando Sapucci ◽  
Felipe Geremia Nievinski
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Navigation Satellite ◽  
Satellite Systems ◽  
Global Navigation ◽  
Global Navigation Satellite

A atmosfera neutra (ou troposfera) causa refração nos sinais de radiofrequência, que resulta em erros nas medidas do Global Navigation Satellite Systems (GNSS) empregadas no posicionamento geodésico. Já para a Meteorologia esse efeito pode representar medidas importantes da concentração dos constituintes atmosféricos, principalmente em regiões onde não se pode realizar sondagem atmosférica convencional, por meio de radiossondas acopladas a balões. Duas técnicas GNSS podem ser empregadas para isso. A primeira utiliza receptores em estações terrestres que fornecem estimativas do conteúdo integrado verticalmente de umidade na atmosfera neutra (Precipitable Water Vapor - PWV). A segunda, com receptores localizados em plataformas espaciais, com os quais obtém perfis atmosféricos de pressão, temperatura e umidade, na técnica conhecida como Rádio-ocultação GNSS. Essas medidas têm um potencial significativo para aplicações em previsões de curtíssimo prazo (30 minutos) de eventos extremos de precipitação (>35 mm). O objetivo principal deste artigo é realizar uma revisão do estado da arte da sinergia entre a Geodésia e a Meteorologia na modelagem da atmosfera neutra (neutrosfera), seu efeito no posicionamento GNSS e na estimativa dos constituintes atmosféricos e suas aplicações. Além disso, apresenta os aprimoramentos e novos desafios desenvolvidos na modelagem do atraso para o posicionamento de alta acurácia.

scholarly journals Decadal variations in atmospheric water vapor time series estimated using ground-based GNSS

2016 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Galina Dick ◽  
Stefan Heise ◽  
Tzvetan Simeonov ◽  
Sibylle Vey ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Linear Trend ◽  
Precipitable Water ◽  
The Other ◽  
Global Navigation Satellite Systems ◽  
Positive Trend ◽  
Satellite Systems ◽  
Decadal Variations ◽  
Global Navigation Satellite

Abstract. Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we use time series from GNSS, European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data, and meteorological measurements to evaluate climate evolution in Central Europe. The assessment of climate change requires monitoring of different atmospheric variables such as temperature, PWV, precipitation, and snow cover. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim data, and 3) determined based on daily surface measurements of temperature and relative humidity. The other variables are available from surface meteorological stations or received from ERA-Interim. The PWV trend component estimated from GNSS data strongly correlates with that estimated from the other data sets. The linear trend is estimated by straight line fitting over 30 years of seasonally-adjusted PWV time series obtained using meteorological measurements. The results show a positive trend in the PWV time series at more than 60 GNSS sites with an increase of 0.3–0.6 mm/decade. In this paper, we compare the results of three stations. The temporal increment of the PWV correlates with the temporal increase in the temperature levels.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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 Estimating trends in atmospheric water vapor and temperature time series over Germany

2017 ◽  
Author(s):  
Fadwa Alshawaf ◽  
Kyriakos Balidakis ◽  
Galina Dick ◽  
Stefan Heise ◽  
Jens Wickert
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Temporal Trends ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Surface Measurements ◽  
Standard Deviations ◽  
Gnss Time Series

Abstract. Ground-based GNSS (Global Navigation Satellite Systems) have efficiently been used since the 1990s as a meteorological observing system. Recently scientists used GNSS time series of precipitable water vapor (PWV) for climate research. In this work, we compare the temporal trends estimated from GNSS time series with those estimated from European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data and meteorological measurements. We aim at evaluating climate evolution in Germany by monitoring different atmospheric variables such as temperature and PWV. PWV time series were obtained by three methods: 1) estimated from ground-based GNSS observations using the method of precise point positioning, 2) inferred from ERA-Interim reanalysis data, and 3) determined based on daily in situ measurements of temperature and relative humidity. The other relevant atmospheric parameters are available from surface measurements of meteorological stations or derived from ERA-Interim. The trends are estimated using two methods, the first applies least squares to seasonally-adjusted time series and the second using the Theil-Sen estimator. The trends estimated at 113 GNSS sites, with 10 and 19 year temporal coverage, varies between −1.5 and 2 mm/decade with standard deviations below 0.25 mm/decade. These values depend on the length and the variations of the time series. Therefore, we estimated the PWV trends using ERA-Interim and surface measurements spanning from 1991 to 2016 (26 years) at synoptic 227 stations over Germany. The former shows positive PWV trends below 0.5 mm/decade while the latter shows positive trends below 0.9 mm/decade with standard deviations below 0.03 mm/decade. The estimated PWV trends correlate with the temperature trends.

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