Comparison of atmospheric parameters derived from GPS, VLBI and a ground-based microwave radiometer in Italy

Pacione, R.; Fionda, E.; Ferrara, R.; Lanotte, R.; Sciarretta, C.; Vespe, F.

doi:10.1016/s1474-7065(02)00005-0

Cited by 31 publications

(19 citation statements)

References 25 publications

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“…3 represents the differences in PWV measurement between the two equipments in time series, the average is 1.1 mm and the standard deviation is represented as 1.2 mm. This is a result similar to average value of 0.5 mm and standard deviation of 1.4 mm for differences between two equipments measured by Pacione et al (2002), respectively. In summer when there is a large amount of water vapor in the atmosphere and the range of temporal variation of PWV is large, the average value of differences between two equipments is represented as 1.9 mm, and the standard deviation is represented as 1.4 mm while the temperature (Rose & Czekala 2009).…”

Section: Comparative Analysis Of Pwvsupporting

confidence: 88%

“…The observation values of radiosonde which has been used for the longest period of time are used often for verification, calibration and analysis of characteristics for values measured by other systems. For example, in a relative comparison with values measured by MWR, average difference from radiosonde results was represented as 1-5 mm, and standard deviation was represented as 1-2 mm (Westwater et al 1989, Liou et al 2001, Basili et al 2002, Pacione et al 2002, and in a relative comparison with values measured by GPS, average difference from radiosonde results was represented as 1-4 mm, and standard deviation was represented as 1-5 mm (Moon et al 1999, Kwon et al 2007.…”

Section: Introductionmentioning

confidence: 99%

“…The GPS equipment consists of Trimble's NetRS receiver, Choko Ring antenna and Paroscientific's MET3A meteorological sensor and collects the observational data automatically at every 10 minutes. MWR is RPG-HATPRO model manufactured by Radiometer Physics Gmbh, Germany and consists of 7 channels (22.2-31.4 GHz) to measure the humidity in the atmosphere and 7 channels (51.2-58.5 GHz) to measure searches of cross-correlated comparison on observation values and data assimilation cases in order to use them as basic data for model to predict the rapidly changing weather (Pacione et al 2002, Ha et al 2007, Jeon et al 2008. In recent years, the researches using observational data by satellites are actively under way (Birkenheuer & Gutman 2006, Prasad & Singh 2009.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Characteristics of Precipitable Water Vapor Measured by Global Positioning System and Microwave Radiometer

Sohn¹,

Park²,

Won³

et al. 2012

Journal of Astronomy and Space Sciences

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In this study, global positioning system (GPS)-derived precipitable water vapor (PWV) and microwave radiometer (MWR)-measured integrated water vapor (IWV) were compared and their characteristics were analyzed. Comparing those two quantities for two years from August 2009, we found that GPS PWV estimates were larger than MWR IWV. The average differ ence over the entire test period was 1.1 mm and the standard deviation was 1.2 mm. When the discrepancies between GPS PWV and MWR IWV were analyzed depending on season, the average difference was 0.7 mm and 1.9 mm in the winter and summer months, respectively. Thus, the average difference was about 2.5 times larger in summer than that in winter. However, MWR IWV measurements in the winter months were over-estimated than those in the summer months as the water vapor content got larger. The results of the diurnal analysis showed that MWR IWV was underestimated in the daytime, showing a difference of 0.8 mm. In the early morning hours, MWR IWV has a tendency to be over-estimated, with a difference of 1.3 mm with respect to GPS PWV.

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Section: Comparative Analysis Of Pwvsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of the Characteristics of Precipitable Water Vapor Measured by Global Positioning System and Microwave Radiometer

Sohn¹,

Park²,

Won³

et al. 2012

Journal of Astronomy and Space Sciences

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“…Pacione et al, 2002;Rózsa, 2014) or by combining the uncertainties of the input variables used in order to obtain the uncertainty of the GNSS IWV (e.g. Wang et al, 2007;Ning et al (2013);and Van Malderen et al, 2014).…”

Section: T Ning Et Al: the Uncertainty Of The Gnss-derived Iwvmentioning

confidence: 99%

The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations

et al. 2016

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Abstract. Within the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) there is a need for an assessment of the uncertainty in the integrated water vapour (IWV) in the atmosphere estimated from ground-based global navigation satellite system (GNSS) observations. All relevant error sources in GNSS-derived IWV are therefore essential to be investigated. We present two approaches, a statistical and a theoretical analysis, for the assessment of the uncertainty of the IWV. The method is valuable for all applications of GNSS IWV data in atmospheric research and weather forecast. It will be implemented to the GNSS IWV data stream for GRUAN in order to assign a specific uncertainty to each data point. In addition, specific recommendations are made to GRUAN on hardware, software, and data processing practices to minimise the IWV uncertainty. By combining the uncertainties associated with the input variables in the estimations of the IWV, we calculated the IWV uncertainties for several GRUAN sites with different weather conditions. The results show a similar relative importance of all uncertainty contributions where the uncertainties in the zenith total delay (ZTD) dominate the error budget of the IWV, contributing over 75 % of the total IWV uncertainty. The impact of the uncertainty associated with the conversion factor between the IWV and the zenith wet delay (ZWD) is proportional to the amount of water vapour and increases slightly for moist weather conditions. The GRUAN GNSS IWV uncertainty data will provide a quantified confidence to be used for the validation of other measurement techniques.

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“…Aunque la componente hidrostática tiene un mayor efecto en el retraso troposférico, su valor es muy estable, pudiendo ser estimado a partir de medidas de presión atmosférica en superficie, lo que facilita su cálculo mediante modelos, como por ejemplo el modelo de Saastamoinen, con una precisión mejor de 1% (Roberts y Rizos, 2001; Pacione et al, 2002). Las pequeñas variaciones que se producen en los valores del ZHD son proporcionales a los cambios de la presión en superficie debidos a su vez a las variaciones locales atmosféricas.…”

Section: Estimación Del Contenido De Vapor De Agua Atmosférico Con Téunclassified

Análisis y predicción de lluvias intensas en la Comunidad Valenciana basados en la estimación del contenido de vapor de agua atmosférico obtenido con técnicas GNSS

Santos¹,

Enríque²

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AgradecimientosA Maru, de la que he aprendido que cada día es el mejor del año, a mis hijos, mi mejor fortuna, y a mis padres, mis primeros maestros.A mis tutores, José Luis Berné y Andrés Seco, por su amistad, sus consejos, su confianza y trasmitirme la perseverancia necesaria para realizar esta tesis.A los compañeros del grupo de investigación de la Universidad Pública de Navarra, especialmente a Eduardo Serna, por su paciencia y ayuda con los procesados, sin olvidarme de Xavier Archanco y Eduardo Prieto, a todos ellos por su atención en mis visitas a Pamplona.A Pascual Garrigues, Emilio Benítez y Fernando Cruz, que me han facilitado la colocación de los equipos GNSS. A Pascual agradecerle también todo el apoyo logístico, y a Emilio la ayuda en la descarga de datos y cálculos.A Rafael Doménech, por facilitarme el acceso a los datos de las estaciones GNSS de la Autoridad Portuaria de Valencia.A José Ángel Núñez (AEMET) y Álvaro Muñoz (UPV), por gestionarme de forma eficiente el suministro de los muchos datos meteorológicos que les he solicitado.A Mónica López y Albert Barniols (TVE) por su asesoramiento meteorológico.A Raquel Capilla y Mar Rubio por su apoyo material y humano desde el ICV.A Txomin Hermosilla, por atenderme y facilitarme todo aquello que le he pedido para materializar esta tesis.A Alfonso Fernández por estar siempre predispuesto a cualquier tipo de ayuda.A Josep Pardo y Luis Ángel Ruiz, por sus consejos y traducciones. ResumenLos Sistemas Globales de Navegación por Satélite (GNSS) han transformado las técnicas de posicionamiento, la navegación y el tiempo, llegando a ser indispensables en muchas de las actividades de la vida diaria. Aparte de las aplicaciones civiles y comerciales ya conocidas, la tecnología GNSS puede utilizarse como sistema de observación atmosférica, puesto que se ha demostrado su capacidad para estimar el contenido de vapor de agua en la atmosfera a partir del retraso troposférico que sufre la propagación de la señal en la dirección satélite-receptor.La distribución y evolución del vapor de agua es determinante en el funcionamiento climático y meteorológico de la atmosfera terrestre, siendo una de las variables climáticas esenciales de observación sistemática que actualmente ha definido el Sistema de Observación del Clima Mundial (GCOS, Global Climate Observing System). El comportamiento del vapor de agua atmosférico es esencial para estudios de cambio climático, puesto que es el principal gas de efecto invernadero que influye en el calentamiento global. Además, es un componente primordial en la estabilidad vertical de la atmósfera, interviniendo activamente en la evolución de los sistemas tormentosos.Hasta hace poco tiempo, esta variable atmosférica no estaba bien definida al no disponerse de medidas con la suficiente resolución para representar su alta inestabilidad espacial y temporal. El incremento en los últimos años de estaciones de referencia permanentes GNSS a escala mundial, supone un importante avance para la monitorización del vapor de agua o cantidad de agua prec...

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Comparison of atmospheric parameters derived from GPS, VLBI and a ground-based microwave radiometer in Italy

Cited by 31 publications

References 25 publications

Comparison of the Characteristics of Precipitable Water Vapor Measured by Global Positioning System and Microwave Radiometer

Comparison of the Characteristics of Precipitable Water Vapor Measured by Global Positioning System and Microwave Radiometer

The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations

Análisis y predicción de lluvias intensas en la Comunidad Valenciana basados en la estimación del contenido de vapor de agua atmosférico obtenido con técnicas GNSS

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