Comparison of total water vapor column from GOME-2 on MetOp-A against ground-based GPS measurements at the Iberian Peninsula

Román, Roberto; Antón, Manuel; Cachorro, Victoria E.; Loyola, Diego; Galisteo, José Pablo Ortiz de; Baraja, Angel Máximo de Frutos; Campos, Pedro Miguel Romero

doi:10.1016/j.scitotenv.2015.06.124

Cited by 25 publications

(35 citation statements)

References 37 publications

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“…Bins with less than 50 data points have been rejected. The dependence of distance satellite pixel -GPS ground-based station was not considered in this work, since Román et al (2015) did not show an important impact in the satellite IWV data. Fig.…”

Section: Discussionmentioning

confidence: 99%

“…IWV data at the nine GPS stations were available for this work from 2007 to 2012. These GPS data, which have a temporal resolution of one hour, have been used to perform other validation exercises on satellite IWV data (Román et al, 2015;Bennouna et al, 2013; Vaquero-Martínez et al, 2017a; Vaquero-Martínez et al, 2017b).…”

Section: = + Ztd Zhd Zwdmentioning

confidence: 99%

“…Among ground-based measurements there are microwave radiometers (Turner et al, 2007), sun-photometers (Ichoku et al, 2002), lunar-photometers (Barreto et al, 2013), star-photometers (Pérez-Ramírez et al, 2012), Lidar (Turner et al, 2002), GPS system (Ortiz de Galisteo et al, 2011), and radio-sounding (Torres et al, 2010). Space measurements are performed using satellites which collect information from different parts of the electromagnetic spectrum: microwave (Jones et al, 2009), visible (Román et al, 2015;Wang et al, 2014), near-infra-red (Grossi et al, 2015) and infra-red (Bennouna et al, 2013).…”

mentioning

confidence: 99%

“…The instruments are: Global Ozone Monitoring Instrument (GOME-2), Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, Ozone Monitoring Instrument (OMI), Spining Enhanced Visible and InfraRed Imager (SEVIRI), Atmospheric Infrared Sounder (AIRS), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). GOME-2 IWV data have been widely validated (Noël et al, 2008;Antón et al, 2015;Grossi et al, 2015;Román et al, 2015;Kalakoski et al, 2016), as well as MODIS water vapor products (Li et al, 2003;Gao and Li, 2008;Prasad and Singh, 2009;Bennouna et al, 2013;Chang et al, 2015;Ningombam et al, 2016;Vaquero-Martínez et al, 2017a). However, the validation of OMI IWV product has only been found in Wang et al (2016a) and Vaquero-Martínez et al (2017b), AIRS IWV products in Hagan et al (2004), Rama Varma Raja et al (2008, Milstein and Blackwell (2016), SCIAMACHY IWV products in Bovensmann et al (1999), Noël et al (2005), Schrijver et al (2009), du Piesanie et al 2013, and SEVIRI IWV products in (Hanssen et al, 2001;Schroedter-Homscheidt et al, 2008).…”

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confidence: 99%

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Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

Vaquero-Martínez

Antón

Galisteo

et al. 2018

Remote Sensing of Environment

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This paper focuses on the inter-comparison of integrated water vapor (IWV) products derived from the following satellite instruments: Global Ozone Monitoring Instrument (GOME-2), Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, Ozone Monitoring Instrument (OMI), Spining Enhanced Visible and InfraRed Imager (SEVIRI), Atmospheric Infrared Sounder (AIRS), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). IWV data from GPS in nine groundbased stations located in the Iberian Peninsula are used as reference. The study period extends from 2007 to 2012. The results show that, in general, OMI has good accuracy (pseudomedian of the relative differences between OMI and GPS IWV of (−0.7 ± 1.1)%). However, OMI, SCIAMACHY and AIRS show higher interquartile range (IQR) (which indicates lower precision) than the rest of satellite instruments. Both MODIS satellite instruments and SEVIRI products tend to slightly underestimate reference IWV data while GOME-2 exhibits a notable overestimation (16.7 ± 0.8%). All satellite instruments showed a tendency to reduce IWV extreme values: low IWV is overestimated while high IWV is underestimated. As for the influence of solar zenith angle (SZA), it can be observed that GOME-2 strongly overestimates the reference for high SZA values (by around 60% for SZA 60 − 80°). OMI shows, however, a high IQR for high SZA values. Both MODIS instruments show an increase in the pseudomedian of relative differences and IQR with SZA at daytime, with more stable values at night. Seasonal dependence is mainly due to the SZA and IWV typical values in each season. In general, in summer the tendency is to underestimate with low IQR (which happens when IWV is high and SZA is low), and in winter the trend is to overestimate with high IQR (which happens when IWV is low and SZA is high). SCIAMACHY shows a high pseudomedian in summer and autumn, and lower in winter and spring. It must be noted that GOME-2 shows a higher overestimation and OMI shows a higher IQR than other satellite instruments in winter and autumn. The influence of clouds was also studied, showing an increase of IQR as cloudiness increases in all satellites. Pseudomedian also worsens as cloudiness increases, generally. 1. Introduction Water vapor plays a crucial role in Earth's radiative balance, since it is the main absorber of the infrared radiation emitted from Earth's surface, and therefore responsible for air heating in the low layers. Regarding energy transport, water vapor's latent heat is a very effective mechanism. Water is evaporated at low latitudes, and water vapor is transported to higher latitudes where condensation releases high

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Section: Discussionmentioning

confidence: 99%

Section: = + Ztd Zhd Zwdmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

Vaquero-Martínez

Antón

Galisteo

et al. 2018

Remote Sensing of Environment

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“…This effect on the MODIS retrieval algorithm was particularly evident in the MODIS Aqua PWV estimation which was somewhat high during both the dry and wet season. MODIS Terra also showed deviations but were related to an underestimation during the wet season which can be related to the so-called shielding effect (i.e., clouds are probably occulting water vapor underneath them) [13,44]. The differences between the MODIS Aqua and MODIS Terra estimations could be attributed to their different passing times over the Central American Isthmus (MODIS Aqua crosses the equator in the afternoon, whereas MODIS Terra does it in the morning) and to the use of different radiations to estimate the water vapor during the day and night.…”

Section: Discussionmentioning

confidence: 99%

GPS Precipitable Water Vapor Estimations over Costa Rica: A Comparison against Atmospheric Sounding and Moderate Resolution Imaging Spectrometer (MODIS)

Campos-Arias

Esquivel‐Hernández

Valverde-Calderón

et al. 2019

Climate

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The quantification of water vapor in tropical regions like Central America is necessary to estimate the influence of climate change on its distribution and the formation of precipitation. This work reports daily estimations of precipitable water vapor (PWV) using Global Positioning System (GPS) delay data over the Pacific region of Costa Rica during 2017. The GPS PWV measurements were compared against atmospheric sounding and Moderate Resolution Imaging Spectrometer (MODIS) data. When GPS PWV was calculated, relatively small biases between the mean atmospheric temperatures (T m ) from atmospheric sounding and the Bevis equation were found. The seasonal PWV fluctuations were controlled by two of the main circulation processes in Central America: the northeast trade winds and the latitudinal migration of the Intertropical Convergence Zone (ITCZ). No significant statistical differences were found for MODIS Terra during the dry season with respect GPS-based calculations (p > 0.05). A multiple linear regression model constructed based on surface meteorological variables can predict the GPS-based measurements with an average relative bias of −0.02 ± 0.19 mm/day (R 2 = 0.597). These first results are promising for incorporating GPS-based meteorological applications in Central America where the prevailing climatic conditions offer a unique scenario to study the influence of maritime moisture inputs on the seasonal water vapor distribution.Climate 2019, 7, 63 2 of 18 of atmospheric water vapor content are needed to improve the predictability of rainfall and the understanding of and feedback in climate related processes [5,6].A quantifiable parameter useful for studying water vapor is the precipitable (or integrated) water vapor (PWV). Precipitable water vapor mainly comprises tropospheric water vapor and the less abundant stratospheric water and can be used to analyze water vapor variability and its contributions to climate change [6]. The classical approach to gather information about PWV is using atmospheric sounding based on radiosonde profiles [7]. However, due to high costs, radiosonde networks lack spatial and temporal resolutions and, thus, provide limited information to carry out detailed studies of weather and climate. For example, radiosondes are usually launched 1-2 times per day in monitoring stations spaced several hundred kilometers from each other. In recent years, the fast development of ground-based GPS networks allows a new source of water vapor information. As atmospheric water changes the atmospheric refractivity, satellite-receiver path delays provide a unique information on the total water vapor within the troposphere and stratosphere. Therefore, GPS has become a standard technique for measuring PWV with some noticeable advantages over radiosondes. For instance, GPS can be used in all weather conditions and has low operation costs, allowing for a high temporal resolution with numerous records throughout the daytime and nighttime [8][9][10]. In Costa Rica, there are 14 Global Navigation Satellite Syst...

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Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3)

Bock

Pacione

Ahmed

et al. 2019

Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate

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Comparison of total water vapor column from GOME-2 on MetOp-A against ground-based GPS measurements at the Iberian Peninsula

Cited by 25 publications

References 37 publications

Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula

GPS Precipitable Water Vapor Estimations over Costa Rica: A Comparison against Atmospheric Sounding and Moderate Resolution Imaging Spectrometer (MODIS)

Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3)

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