A Cautionary Note on the Use of Gaussian Statistics in Satellite-Based UTH Climatologies

John, Viju O.; Buehler, Stefan; Courcoux, N.

doi:10.1109/lgrs.2005.859350

Cited by 13 publications

(14 citation statements)

References 26 publications

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“…More details on the retrieval methodology are provided by Buehler and John [2005]. UTH data are not affected by the limb effect because we use appropriate regression coefficients for each viewing angle [ John et al , 2006]. The data set has been validated using high‐quality radiosonde and satellite measurements [ Buehler et al , 2004; John and Buehler , 2005; Buehler et al , 2008; Milz et al , 2009; Moradi et al , 2010].…”

Section: Methodsmentioning

confidence: 99%

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

et al. 2011

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[1] We use microwave retrievals of upper tropospheric humidity (UTH) to estimate the impact of clear-sky-only sampling by infrared instruments on the distribution, variability, and trends in UTH. Our method isolates the impact of the clear-sky-only sampling, without convolving errors from other sources. On daily time scales, IR-sampled UTH contains large data gaps in convectively active areas, with only about 20-30 % of the tropics (30°S-30°N) being sampled. This results in a dry bias of about −9 %RH in the area-weighted tropical daily UTH time series. On monthly scales, maximum clear-sky bias (CSB) is up to −30 %RH over convectively active areas. The magnitude of CSB shows significant correlations with UTH itself (−0.5) and also with the variability in UTH (−0.6). We also show that IR-sampled UTH time series have higher interannual variability and smaller trends compared to microwave sampling. We argue that a significant part of the smaller trend results from the contrasting influence of diurnal drift in the satellite measurements on the wet and dry regions of the tropics.

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Section: Methodsmentioning

confidence: 99%

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

et al. 2011

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“…Since the Jacobians for the different angles are not identical, the altitude weighting for the UTH from the different viewing angles is slightly different. However, the resulting differences in the UTH climatology are very small if the correct coefficients are used, as demonstrated by John et al [2006, Figure 6].…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1 shows maps of the median of the monthly medians of retrieved UTH for each of the three satellites. We show the median instead of the mean here, as this was found by John et al [2006] to be more appropriate for UTH, due to the non‐Gaussian nature of the UTH distribution. The maps for all three instruments are very similar.…”

Section: Data Set Propertiesmentioning

confidence: 99%

An upper tropospheric humidity data set from operational satellite microwave data

et al. 2008

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[1] 183.31 GHz observations from the Advanced Microwave Sounding Unit B (AMSU-B) instruments onboard the NOAA 15, 16, and 17 satellites were used to derive a new data set of Upper Tropospheric Humidity (UTH). The data set consist of monthly median and mean data on a 1.5°latitude-longitude grid between 60°S and 60°N, and covers the time period of January 2000 to February 2007. The data from all three instruments are very consistent, with relative difference biases of less than 4% and relative difference standard deviations of 7%. Radiometric contributions by high ice clouds and by the Earth's surface affect the measurements in certain areas. The uncertainty due to clouds is estimated to be up to approximately 10%RH in areas with deep convection. The uncertainty associated with contamination from surface emission can exceed 10%RH in midlatitude winter, where the data therefore should be regarded with caution. Otherwise the surface influence appears negligible. The paper also discusses the UTH median climatology and seasonal cycle, which are found to be broadly consistent with UTH climatologies from other sensors. Finally, the paper presents an initial validation of the new data set against IR satellite data and radiosonde data. The observed biases of up to 9%RH (wet bias relative to HIRS) were found to be broadly consistent with expectations based on earlier studies. The observed standard deviations against all other data sets were below 6%RH. The UTH data are available to the scientific community on http://www.sat.ltu.se.

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“…where T b is the radiance expressed in brightness temperature in kelvins and a and b are linear fit coefficients which are available separately for different viewing angles, so that radiances do not need to be limb corrected [32]. Microwave radiances are less sensitive to clouds than infrared radiances [33], but sufficiently optically thick ice clouds can affect microwave data [34].…”

Section: Methodsmentioning

confidence: 99%

Evaluating Instrumental Inhomogeneities in Global Radiosonde Upper Tropospheric Humidity Data Using Microwave Satellite Data

Moradi

Buehler

John

et al. 2013

IEEE Trans. Geosci. Remote Sensing

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In this paper, the overall quality of the water vapor profiles of global operational radiosonde data for the period 2000-2009 is investigated using upper tropospheric humidity (UTH) retrieved from microwave satellite data. Overall, the nighttime radiosonde data showed a dry bias (−5% to −15%) over Europe, Australia, and New Zealand and systematically moist bias (greater than 30%) over China and the former Soviet Union. The nighttime sonde data from the U.S. and Canada showed a bias between −10% and 20%. Most stations indicated a daytime radiation dry bias, except for a few stations from the U.S. and the former Soviet Union. A sensorwise comparison showed a large nighttime wet bias for the Russian (MRZ-3A and MARS) and Chinese GZZ-2 sensors, a relatively small nighttime wet bias for the U.S. Sippican and VIZ-B2 sensors, and a nighttime dry bias for the Chinese GTS1, Vaisala (RS80-A, RS80-H, RS90, RS92K, and RS92-SGP), and the U.S. VIZ-MKII sensors. All sensors had a daytime radiation dry bias, except for the Russian MRZ-3A sensor that had a daytime radiation wet bias that could be because of the daytime radiation bias correction. Because of the large differences between different radiosonde sensors, it is essential for UTH studies to only use the data measured using a single type of sensor at any given station.Index Terms-Microwave remote sensing (RS), radiosonde data, satellite data, tropospheric humidity, water vapor.

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A Cautionary Note on the Use of Gaussian Statistics in Satellite-Based UTH Climatologies

Cited by 13 publications

References 26 publications

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

Clear-sky biases in satellite infrared estimates of upper tropospheric humidity and its trends

An upper tropospheric humidity data set from operational satellite microwave data

Evaluating Instrumental Inhomogeneities in Global Radiosonde Upper Tropospheric Humidity Data Using Microwave Satellite Data

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