Near real‐time GPS sensing of atmospheric water vapor

Rocken, Christian; Hove, Teresa Van; Ware, Randolph

doi:10.1029/97gl03312

Cited by 204 publications

(126 citation statements)

References 17 publications

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“…Issoé obtido ao aplicar uma estratégia adequada que minimize os demais erros presentes nas determinações GPS (Sapucci, 2001), além de funções de mapeamento que relacionam o atraso na direção satélite-receptor com o mesmo na direção zenital (Spilker, 1996). Na estimativa do ZTD para obtenção dos valores do IWV-GPS em tempo real o maior problema está relacionado com os erros nas efemérides (posição dos satélites) e nos relógios dos satélites GPS utilizados no processamento (Rocken et al, 1997a). As opções para solucionar esse problema são basicamente duas.…”

Section: Determinação Das Estimativas Do Atraso Zenital Troposférico unclassified

“…Na primeira opção, as posições dos satélites são preditas a partir do processamento dos dados GPS coletados por receptores pertencentes a redes de monitoramento contínuo. Essas efemérides são utilizadas no processamento em tempo real visando obter as estimativas do ZTD, levando em consideração aépoca em que foram geradas, pois a qualidade de sua prediçãoé degradada no tempo (Rocken et al, 1997a). A segunda opçãoé através da utilização das efemérides ultra-rápidas disponibilizadas pelos centros de análise do IGS (International GNSS Service ), as quais foram criadas especialmente para esse fim (NASA, 2005).…”

Section: Determinação Das Estimativas Do Atraso Zenital Troposférico unclassified

“…Esse intervalo de tempo está associado com o tamanho da janela de dados utilizado no processamento. Assim, os valores do atraso podem ser estimados com uma latência de 1,5 h (Marel, 2000), 1 h (Reigber et al, 2001) e até 30 min ou menos (Rocken et al, 1997a), dependendo do tamanho da janela de dados utilizada e da precisão requerida. A determinação do tamanho da janela idealé algo que merece atenção (Foster et al, 2005), pois ela está associada com a eficiência do processo e a qualidade final das estimativas do IWV-GPS.…”

Section: Determinação Das Estimativas Do Atraso Zenital Troposférico unclassified

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Assimilação do IWV-GPS no Brasil: otimização das estimativas do atraso zenital troposférico em tempo real

Sapucci¹,

Machado²,

Herdies³

et al. 2007

Rev. Bras. Geof.

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ABSTRACT. IWV (Integrated Water Vapor) values from the Brazilian Network for Continuous Monitoring (RBMC) of GPS (Global Positioning System) signals are additional sources of humidity information available for being used in Numerical Weather Prediction (NWP) models operating in the Brazilian meteorological centers.In order to obtain IWV-GPS values in an efficient approach, an optimization of the GPS data processing for estimating the ZTD values (Zenithal Tropospheric Delay) is involved, which should search for the best result with lowest computational cost. In this optimization process it is necessary to determine the ideal size of the so called sliding-window data involved in the real time data processing, which must be the as small as possible but providing results with the required quality. In this paper is investigated the ideal size of such sliding-window. An experiment was carried out in which GPS sliding-windows of different sizes were evaluated using simulations of a real time data processing. Comparing the obtained results with those from post-processed GPS data, it is observed that a sliding-window containing 7 hours of GPS data generated the best results from the operational point of view (rms of 1.61 and 1.82 kg m −2 for latency of 60 and 120 minutes, respectively).Keywords: IWV-GPS, ZTD, NWP, data assimilation. RESUMO. Valores do IWV (Integrated Water Vapor -Vapor D'água Integrado na coluna atmosférica) provenientes da Rede Brasileira de Monitoramento Contínuo(RBMC) dos Satélites GPS (Global Positioning System -Sistema de Posicionamento Global) compõem uma fonte adicional de informações da umidade para a assimilação em modelos operacionais de Previsão Numérica de Tempo (PNT) existentes no Brasil. Para que os valores do IWV-GPS sejam obtidos com maior eficiênciá e necessário uma otimização do processamento de dados GPS para a estimativa do ZTD (Zenithal Tropospheric Delay -Atraso Zenital Troposférico), de forma que resultados com qualidade adequada possam ser obtidos com custo computacional baixo. Nessa otimização deve-se definir o tamanho ideal da chamada janela deslizante (sliding window ) de dados GPS envolvidos no processamento em tempo real, a qual deve ser a menor possível, mas fornecendo resultados satisfatórios. O objetivo desse trabalhoé investigar sobre o tamanho ideal dessa janela de dados. Um experimento foi realizado envolvendo janelas de dados GPS de diferentes tamanhos, as quais foram testadas através de simulações de processamento em tempo real. Comparando os resultados simulados com aqueles obtidos no modo pós-processado, observou-se que do ponto de vista operacional o emprego de uma janela com 7 horas de dados proporciona os melhores resultados (Erro médio Quadrático de 1,61 e 1,82 kg m −2 para 60 e 120 minutos de latência, respectivamente).

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Section: Determinação Das Estimativas Do Atraso Zenital Troposférico unclassified

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Assimilação do IWV-GPS no Brasil: otimização das estimativas do atraso zenital troposférico em tempo real

Sapucci¹,

Machado²,

Herdies³

et al. 2007

Rev. Bras. Geof.

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“…Existing networks (not specifically designed for meteorological purposes) and specifically designed dense GNSS networks have been used for monitoring the distribution of water vapor in the atmosphere, with particular reference to its lower layer, that is, the troposphere. At least three different approaches have been applied: (1) investigation of the vertical column over a single station (Rocken et al 1997), (2) exploitation of existing national GNSS networks (Seko et al 2007;Inoue and Inoue 2007), and (3) implementation of specifically designed dense and hyperdense GNSS networks (Zhang et al 2008;Realini et al 2012;Sato et al 2013;Tsuda et al 2013;Oigawa et al 2015). In all aforementioned studies, which are surely not exhaustive, the water vapor content was monitored to support weather forecasting, which is useful for interpreting severe meteorological events.…”

Section: Gnss Meteorology: State Of the Artmentioning

confidence: 99%

2D PWV monitoring of a wide and orographically complex area with a low dense GNSS network

Ferrando

Federici

Sguerso

2018

Earth Planets Space

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This study presents an innovative procedure to monitor the precipitable water vapor (PWV) content of a wide and orographically complex area with low-density networks. The procedure, termed G4M (global navigation satellite system, GNSS, for Meteorology), has been developed in a geographic information system (GIS) environment using the free and open source GRASS GIS software (https://grass.osgeo.org). The G4M input data are zenith total delay estimates obtained from GNSS permanent stations network adjustment and pressure (P) and temperature (T) observations using existing infrastructure networks with different geographic distributions in the study area. In spite of the wide sensor distribution, the procedure produces 2D maps with high spatiotemporal resolution (up to 250 m and 6 min) based on a simplified mathematical model including data interpolation, which was conceived by the authors to describe the atmosphere's physics. In addition to PWV maps, the procedure provides ∆PWV and heterogeneity index maps: the former represents PWV variations with respect to a "calm" moment, which are useful for monitoring the PWV evolution; and the latter are promising indicators to localize severe meteorological events in time and space. This innovative procedure is compared with meteorological simulations in this paper; in addition, an application to a severe event that occurred in Genoa (Italy) is presented.

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“…The distribution of water vapor and the development of precipitation systems mutually affect each other [Shoji, 2013]. Remarkable progress in using ground-based GPS receivers for remote sensing of the atmospheric water vapor has been achieved during the last decades [Bevis et al, 1992;Rocken et al, 1997;Fang et al, 1998;Gendt et al, 2004;Li et al, 2014] as GPS has several significant advantages of low-operating expense, all-weather capability, and high spatiotemporal resolution. The zenith total delay (ZTD), which is closely related to the integrated water vapor above the station, is the basic observable in GPS meteorology [Bevis et al, 1992].…”

Section: Introductionmentioning

confidence: 99%

Retrieving high‐resolution tropospheric gradients from multiconstellation GNSS observations

Zus

et al. 2015

Geophysical Research Letters

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The developing multi‐Global Navigation Satellite Systems (GNSS) constellations have the potential to provide accurate high‐resolution tropospheric gradients. Such data, closely linked to strong humidity gradients accompanying severe weather phenomena, are considered a new important data source for meteorological studies, e.g., nowcasting of severe rainfall events. Here we describe the development of a multi‐GNSS processing system for the precise retrieval of high‐resolution tropospheric gradients. The retrieved products were validated by using independent water vapor radiometer (WVR) observations and numerical weather model (NWM) data. The multi‐GNSS high‐resolution gradients agree well with those, derived from NWM and WVR, especially for the fast‐changing peaks which were mostly associated with synoptic fronts. Compared to GPS‐only gradients, the correlations with the validation data are significantly improved up to 20–35%. The new data product has significant potential to improve numerical weather prediction and to advance meteorological studies.

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Near real‐time GPS sensing of atmospheric water vapor

Cited by 204 publications

References 17 publications

Assimilação do IWV-GPS no Brasil: otimização das estimativas do atraso zenital troposférico em tempo real

Assimilação do IWV-GPS no Brasil: otimização das estimativas do atraso zenital troposférico em tempo real

2D PWV monitoring of a wide and orographically complex area with a low dense GNSS network

Retrieving high‐resolution tropospheric gradients from multiconstellation GNSS observations

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