Comparison of columnar water-vapor measurements from solar transmittance methods

Schmid, Beat; Michalsky, Joseph; Slater, D. W.; Barnard, James C.; Halthore, R. N.; Liljegren, J. C.; Holben, B. N.; Eck, T. F.; Livingston, J. M.; Russell, Philip B.; Ingold, Thomas; Slutsker, I.

doi:10.1364/ao.40.001886

Cited by 137 publications

(156 citation statements)

References 31 publications

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“…where a and b are constants and m is the relative optical air mass (Schmid et al, 2001). From this relationship, IWV can be derived with an accuracy of 10 % (Alexandrov et al, 2009).…”

Section: Sun Photometermentioning

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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“…where a and b are constants and m is the relative optical air mass (Schmid et al, 2001). From this relationship, IWV can be derived with an accuracy of 10 % (Alexandrov et al, 2009).…”

Section: Sun Photometermentioning

confidence: 99%

Assessment of small-scale integrated water vapour variability during HOPE

et al. 2015

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“…Using a power law parameterisation, a conversion of the slant optical depth to the slant IWV can be obtained (e.g. Bruegge et al, 1992;Schmid et al, 2001). Uncertainties in this parameterisation and the Langley plot regression (due to variable atmospheric water vapour amounts) as well as deficits in the filter characterisation are the leading error sources.…”

Section: Cimel Sun Photometermentioning

confidence: 99%

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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“…At Izaña, we determine the water vapour columns by the modified Langley plot method. Therefore, the relation between the slant optical depth and the water vapour slant column amounts is approximated by a power law parameterisation (e.g., Bruegge et al, 1992;Schmid et al, 2001). Uncertainties in this parameterisation and the Langley regression (due to variable atmospheric water vapour amounts) as well as deficits in the filter characterisation are the leading error sources.…”

Section: Cimel Sunphotometermentioning

confidence: 99%

Continuous quality assessment of atmospheric water vapour measurement techniques: FTIR, Cimel, MFRSR, GPS, and Vaisala RS92

et al. 2010

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Abstract. At the Izaña Observatory, water vapour amounts have been measured routinely by different techniques for many years. We intercompare the total precipitable water vapour (PWV) amounts measured between 2005 and 2009 by a Fourier Transform Infrared (FTIR) spectrometer, a Multifilter Rotating Shadow-band Radiometer (MFRSR), a Cimel sunphotometer, a Global Positioning System (GPS) receiver, and daily radiosondes (Vaisala RS92). The long-term characteristics of our study allows a reliable and extensive empirical quality assessment of long-term validity, which is an important prerequisite when applying the data to climate research. We estimate a PWV precision of 1% for the FTIR, about 10% for the MFRSR, Cimel, and GPS (when excluding rather dry conditions), and significantly better than 15% for the RS92 (the detection of different airmasses avoids a better constrained estimation). We show that the MFRSR, Cimel and GPS data quality depends on the atmospheric conditions (humid or dry) and that the restriction to clear-sky observations introduces a significant dry bias in the FTIR and Cimel data. In addition, we intercompare the water vapour profiles measured by the FTIR and the Vaisala RS92, which allows the conclusion that both experiments are able to detect lower to upper tropospheric water vapour mixing ratios with a precision of better than 15%.

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Comparison of columnar water-vapor measurements from solar transmittance methods

Cited by 137 publications

References 31 publications

Assessment of small-scale integrated water vapour variability during HOPE

Assessment of small-scale integrated water vapour variability during HOPE

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

Continuous quality assessment of atmospheric water vapour measurement techniques: FTIR, Cimel, MFRSR, GPS, and Vaisala RS92

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