The evaluation of the integrated water vapour annual cycle over the Iberian Peninsula from EOS‐MODIS against different ground‐based techniques

Bennouna, Yasmine; Torres, Benjamín; Cachorro, Victoria E.; Galisteo, José Pablo Ortiz de; Toledano, Carlos

doi:10.1002/qj.2080

Cited by 36 publications

(34 citation statements)

References 53 publications

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“…The AERONET version 3.0 is distributed with three quality levels (Giles et al, 2019): Level 1.0 (raw data), Level 1.5 (cloud-screened and quality controlled), and Level 2.0 (cloud-screened and quality-assured). Because the long-term dataset (> 25 years) and standard quality protocols, numerous studies have applied this dataset as ground-truth reference for validating MODIS water vapor products (Bennouna et al, 2013;Diedrich et al, 2015;Shi et al, 2018;Bright et al, 2018).…”

Section: Aeronet Datamentioning

confidence: 99%

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

Martins

Lyapustin

Wang

et al. 2019

Atmospheric Research

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The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS error lower than 0.250 cm. The binned error analysis revealed an underestimation (~10%) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 cm. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 cm. While the accuracy is relatively similar between 1.0 and 5.0 cm for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (>5.0 cm). The expected error was defined as ±15%, with >68% of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (~0.2 cm/decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000)(2001)(2002)(2003)(...

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Section: Aeronet Datamentioning

confidence: 99%

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

Martins

Lyapustin

Wang

et al. 2019

Atmospheric Research

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“…Many studies compare various IWV measurements in different geographical regions and for different time periods using different criteria for temporal and spatial matching and elevation corrections (cf. Bennouna et al, 2013;Martin et al, 2006;Morland et al, 2009;Palm et al, 2010;Schneider et al, 2010;Torres et al, 2010). Frequently these comparisons involve data sets with more than 1 h temporal and more than 20 km spatial differences as well as with different horizontal resolutions.…”

Section: Rsmentioning

confidence: 99%

Assessment of small-scale integrated water vapour variability during HOPE

et al. 2015

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Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP) 2 ) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤ 1 kg m −2 ) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering.For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3-4 km or time of 10-15 min induce IWV variabilities on the order of 0.4 kg m −2 . This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds.Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m −2 ) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

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“…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).…”

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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).…”

mentioning

confidence: 99%

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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The evaluation of the integrated water vapour annual cycle over the Iberian Peninsula from EOS‐MODIS against different ground‐based techniques

Abstract: The annual cycle of precipitable water vapour is inferred from the MODIS thermal infrared (IR) and near-infrared (NIR

Cited by 36 publications

References 53 publications

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

Assessment of small-scale integrated water vapour variability during HOPE

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

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