Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze

Vogelmann, Hannes; Sussmann, Ralf; Trickl, Thomas; Reichert, Andreas

doi:10.5194/acp-15-3135-2015

Cited by 58 publications

(70 citation statements)

References 47 publications

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“…The atmospheric distribution of water vapor is highly variable at spatial and temporal scales relevant to weather and climate (e.g., [8]). This imposes strong observational constraints on water vapor measurements; sensor intercomparisons must take the spatio-temporal mismatch into account [9,10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

et al. 2016

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Understanding the atmospheric distribution of water (H 2 O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of −3.1% ± 17.7%, reasonable consistency over different locations (station bias of −3.1% ± 9.5%) and very good correlation with TCCON (R = 0.95). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH 2 O amounts can reach 6000-6500 ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH 2 O product, after public release, can already be useful for water cycle studies.

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Section: Introductionmentioning

confidence: 99%

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

et al. 2016

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“…Meanwhile, Vogelmann et al [10] evaluated spatial variability of IWV using a ground-based FTS and a differential absorption lidar (DIAL) located on the summit of the Zugspitze (2962 m a.s.l., Germany) and on the steep slope of the Zugspitze (2675 m a.s.l. ), respectively.…”

Section: Spatial Variability Of Xh 2 Omentioning

confidence: 99%

“…Since the range of SZA for the intercomparison between the three XH 2 O products was~20 • -60 • , the standard deviations of the difference between the TANSO-FTS TIR and SWIR XH 2 O for 20 • -30 • bin and 50 • -60 • bin were applied to the σ TIR−SWIR term in Equation (10). For the spatial coincidence criterion of 200 km, when the standard deviation for 20 • -30 • bin (i.e., 7.6%) was used as σ TIR−SWIR , the σ TIR , σ SWIR , and σ S values were 6.5%, 3.1% and 18.8%, respectively.…”

Section: Precision Of the Tanso-fts Xh 2 O Datamentioning

confidence: 99%

“…Because of high spatial and temporal variability of atmospheric H 2 O, it is difficult to rigorously estimate the measurement precision by the intercomparison of IWV, even if side-by-side observations are performed (e.g., [3][4][5][6][7][8]). The spatial and temporal variability can be estimated by selecting subsets of data from both instruments with optimal volume matching and acceptable temporal matching [9,10]. These strict coincidence criteria, however, result in a statistically insufficient number of pairs.…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

et al. 2017

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Spatial and temporal variability of atmospheric water vapor (H 2 O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H 2 O data by a simple comparison between the data derived from two different instruments. We determined the measurement precisions of column-averaged dry-air mole fractions of H 2 O (XH 2 O) retrieved independently from spectral radiances in the thermal infrared (TIR) and the short-wavelength infrared (SWIR) regions measured using a Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT), by an intercomparison between the two TANSO-FTS XH 2 O data products and the ground-based FTS XH 2 O data. Furthermore, the spatial variability of XH 2 O was also estimated in the intercomparison process. Mutually coincident XH 2 O data above land for the period ranging from April 2009 to May 2014 were intercompared with different spatial coincidence criteria. We found that the precisions of the TANSO-FTS TIR and TANSO-FTS SWIR XH 2 O were 7.3%-7.7% and 3.5%-4.5%, respectively, and that the spatial variability of XH 2 O was 6.7% within a radius of 50 km and 18.5% within a radius of 200 km. These results demonstrate that, in order to accurately evaluate the measurement precision of XH 2 O, it is necessary to set more rigorous spatial coincidence criteria or to take into account the spatial variability of XH 2 O as derived in the present study.

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“…7). Vogelmann et al (2015) use DIAL (differential absorption lidar) for estimating temporal mismatches in vertical profiles. Their Fig.…”

Section: Data Availabilitymentioning

confidence: 99%

Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review

et al. 2016

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Abstract. In the lower/middle troposphere, {H 2 O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUltiplatform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H 2 O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H 2 O,δD} data pairs.First, we briefly resume the particularities of an {H 2 O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H 2 O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H 2 O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H 2 O,δD} pair distributions due to incomplete processing of the remote sensing products.

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Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze

Cited by 58 publications

References 47 publications

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review

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Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze

Cited by 58 publications

References 47 publications

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

Comparison of XH2O Retrieved from GOSAT Short-Wavelength Infrared Spectra with Observations from the TCCON Network

Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison

Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H&lt;sub&gt;2&lt;/sub&gt;O,&lt;i&gt;δ&lt;/i&gt;D} pairs – a review

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Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H<sub>2</sub>O,<i>δ</i>D} pairs – a review