Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

Vey, Sibylle; Dietrich, Reinhard; Rülke, Axel; Fritsche, Mathias; Steigenberger, Peter; Rothacher, M.

doi:10.1175/2009jcli2787.1

Cited by 96 publications

(75 citation statements)

References 85 publications

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“…This variation of the bias as a function of the IWV value has also already been described in the literature (e.g. Ohtani and Naito, 2000;Deblonde et al, 2005;Prasad and Singh, 2009;Vey et al, 2010), but alternative explanations were formulated. For instance, Ohtani and Naito (2000) considered the effects of the seasonal variation of the GPS mapping function as one of the causes for their observed annual variation of the GPS-RS biases.…”

Section: Summary For the Nh Intercomparisonsupporting

confidence: 72%

“…2). Its accuracy depends thus mainly on errors in the pressure measurements, causing offsets in the IWV estimates of at most 0.8 mm when considering an error in the pressure of 2 hPa (Vey et al, 2010). Other surface pressure uncertainties reported in the literature are 1 hPa (Deblonde et al, 2005) and 1.65 hPa (Wang et al, 2007), corresponding respectively to 0.4 and 0.6 mm uncertainty in IWV.…”

Section: Uncertainty Of the Gps-based Iwvmentioning

confidence: 98%

“…for GOME-2 there can be two overpasses per day with a time difference of about 1.5 h. a Deblonde et al (2005); Wang et al (2007); Vey et al (2010). b Miloshevich et al (2009);Smit et al (2013); Wang et al (2013).…”

Section: Ztd To Iwv Conversion Schemementioning

confidence: 99%

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A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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Abstract. Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of groundbased instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2).This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.

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Section: Summary For the Nh Intercomparisonsupporting

confidence: 72%

Section: Uncertainty Of the Gps-based Iwvmentioning

confidence: 98%

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A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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“…The IGSSuomiNet data include 1160 stations worldwide and are available from 1995 to 2012. The ground-based GPS data have been extensively used to validate other TCWV measurements and data assimilation products (Wang and Zhang, 2008;Sibylle et al, 2010;Mears et al, 2015). The GPS TCWV retrieval error is estimated to be 1.5 mm (Wang et al, 2007).…”

Section: Ncar's Ground-based Gps Datamentioning

confidence: 99%

Validation and update of OMI Total Column Water Vapor product

Wang¹,

Abad²,

Liu³

et al. 2016

Atmos. Chem. Phys.

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Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7-465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's "clear" sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV < 15 mm and about 1 mm lower when TCWV > 15 mm.

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“…Vários estudos buscam avaliar a qualidade das informações de água precipitável (AP) fornecidas pelas reanálises do National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) com informações obtidas de dados observados e/ou gerados por outros modelos numéricos da atmosfera e normalmente ressaltam a boa qualidade destas Mo e Higgins, 1996;Sudradjat et al 2005;Vey et al 2010;Koulali et al 2012). Vey et al (2010) observaram que sobre a Europa e grande parte da América do Norte, o ciclo sazonal e as variações interanuais na AP de global positioning system (GPS) e NCEP concordam muito bem.…”

Section: Introductionunclassified

Tendência do Vapor D'Água na Atmosfera Mediante Dados do NCEP/NCAR

Cavalcanti

Mariano

2016

Rev. bras. meteorol.

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ResumoNeste estudo foi analisando a tendência anual da Água Precipitável (AP) média global no período de 1984 a 2014. Foram utilizados dados extraídos das reanálises do National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) e apresentados na forma de tabela. A distribuição espacial da tendência de Água Precipitável também foi avaliada. Os testes de significância estatísticas de Mann-Kendall e t de Student foram aplicados para as tendências das séries globais. O valor médio de AP para todo o período é de 24 mm correspondendo a um volume estimado para toda a atmosfera do globo de cerca de 12 trilhões de metros cúbicos. Os resultados apontam tendências significativas de 0,026; 0,013 e 0,019 mm/ano para o H.N.; H.S. e todo o Globo, respectivamente. Áreas específicas do globo apontam tendências positivas de 0,09 mm/ano e outras negativas de até -0,09 mm/ano demonstrando assim uma distribuição não homogênea dessa tendência no globo. Palavras-chave: água precipitável, reanálises do NCEP/NCAR, tendência climática. Water Vapor Trend in the Atmosphere by NCEP/NCAR DATA AbstractIn this study the mean global annual precipitable water is analyzed for the period of 1984 to 2014. For this purpose the data extracted from the reanalyses of NCEP/NCAR was used and presented in the form of a table. The spacial distribution of the tendency of precipitable water also was presented. Mann-Kendall and student t significant tests were applied for the tendency of global series. The mean value of precipitable water for the period is of 24 mm corresponding to the volume estimated for the entire global atmosphere of about 12 x 10 12 m 3 . The results indicate significant tendencies of 0.026, 0.013 and 0.019 mm/year for the northern hemisphere, southern hemisphere and the entire globe, respectively. Specific areas of the globe show positive tendencies of 0.09 mm/year and the others show negative tendencies of the order of -0.09 mm/year demonstrating a non-homogeneous global tendency.

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Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

Cited by 96 publications

References 85 publications

A multi-site intercomparison of integrated water vapour observations for climate change analysis

A multi-site intercomparison of integrated water vapour observations for climate change analysis

Validation and update of OMI Total Column Water Vapor product

Tendência do Vapor D'Água na Atmosfera Mediante Dados do NCEP/NCAR

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