Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements

Rosenkranz, P. W.

doi:10.1109/36.964979

Cited by 123 publications

(77 citation statements)

References 24 publications

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“…In the vertical, the AIRS level 2 processing provides measurements within 1 km layers in the troposphere and 3-5 km in the stratosphere. AMSU-A (Advanced Microwave Sounding Unit A), also aboard the AQUA spacecraft, is a microwave temperature/humidity radiometer formed with two independent modules: AMSU-A1 (12 channels between 50-58 GHz and 1 channel at 89 GHz) and AMSU-A2 (2 channels at 23.8 GHz and 31.4 GHz) (Rosenkranz et al, 2001). Because microwave radiation, unlike infrared radiation, is not sensitive to clouds, nine AIRS 13.5 km footprint infrared data are combined with one 40 km AMSU footprint in the microwave, to provide a single "cloud-clear" infrared spectrum (Aumann et al, 2003 andSusskind et al, 2003 for more details).…”

Section: Airs/amsumentioning

confidence: 99%

Evaluation of balloon and satellite water vapour measurements in the Southern tropical and subtropical UTLS during the HIBISCUS campaign

et al. 2009

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Abstract. Balloon water vapour in situ and remote measurements in the tropical upper troposphere and lower stratosphere (UTLS) obtained during the HIBISCUS campaign around 20 • S in Brazil in February-March 2004 using a tunable diode laser (µSDLA), a surface acoustic wave (SAW) and a Vis-NIR solar occultation spectrometer (SAOZ) on a long duration balloon, have been used for evaluating the performances of satellite borne remote water vapour instruments available at the same latitude and measurement period. In the stratosphere, HALOE displays the best precision (2.5%), followed by SAGE II (7%), MIPAS (10%), and SCIAMACHY (35%), all of which show approximately constant H 2 O mixing ratios between 20-25 km. Compared to HALOE of ±10% accuracy between 0.1-100 hPa, SAGE II and SAOZ show insignificant biases, MIPAS is wetter by 10% and SCIAMACHY dryer by 20%. The currently available GOMOS profiles of 25% precision show a positive vertical gradient in error for identified reasons. Compared to these, the water vapour of the Reprobus Chemistry Transport Model, forced at pressures higher than 95 hPa by the ECMWF analyses, is dryer by about 1 ppmv (20%).In the lower stratosphere between 16-20 km, most notable features are the steep degradation of MIPAS precision below 18 km, and the appearance of biases between instruments far larger than their quoted total uncertainty. HALOE and Correspondence to: N. Montoux (nadege.montoux@latmos.ipsl.fr) SAGE II (after spectral adjustment for reducing the bias with HALOE at northern mid-latitudes) both show decreases of water vapour with a minimum at the tropopause not seen by other instruments or the model, possibly attributable to an increasing error in the HALOE altitude registration. Between 16-18 km where the water vapour concentration shows little horizontal variability, and where the µSDLA balloon measurements are not perturbed by outgassing, the average mixing ratios reported by the remote sensing instruments are substantially lower than the 4-5 ppmv observed by the µSDLA. Differences between µSDLA and HALOE and SAGE II (of the order of −2 ppmv), SCIAMACHY, MIPAS and GOMOS (−1 ppmv) and SAOZ (−0.5 ppmv), exceed the 10% uncertainty of µSDLA, implying larger systematic errors than estimated for the various instruments.In the upper troposphere, where the water vapour concentration is highly variable, AIRS v5 appears to be the most consistent within its 25% uncertainty with balloon in-situ measurements as well as ECMWF. Most of the remote measurements show less reliability in the upper troposphere, losing sensitivity possibly because of absorption line saturation in their spectral ranges (HALOE, SAGE II and SCIA-MACHY), instrument noise exceeding 100% (MIPAS) or imperfect refraction correction (GOMOS). An exception is the SAOZ-balloon, employing smaller H 2 O absorption bands in the troposphere.

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Section: Airs/amsumentioning

confidence: 99%

Evaluation of balloon and satellite water vapour measurements in the Southern tropical and subtropical UTLS during the HIBISCUS campaign

et al. 2009

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“…Neural networks were already used in Cabrera-Mercader and Staelin (1995) for retrievals of moisture profiles from synthetic radiances at channels resembling the AMSU channels, with performance comparable or superior to a complex physical and iterative retrieval algorithm. Physical iterative inversions, such as Rosenkranz (2001), are also valid inversion tools, but the non-linearity of the mapping between radiances and UTH makes them computationally expensive. The neural network approach is very efficient as a whole set of measurements can be retrieved at once.…”

Section: Introductionmentioning

confidence: 99%

“…The data seems less exploited for a direct estimation of UTH. Temperature and moisture profiles were retrieved by an iterative minimum variance algorithm from measured AMSU-A and AMSU-B radiances in Rosenkranz (2001), but no derivation of UTH values is reported. Green-wald and Christopher (2002) studied the effect of clouds in UTH derived from AMSU-B radiances, with the UTH derived from a simplified relationship between UTH and brightness temperature originally developed for infrared data (Soden and Bretherton, 1996), but no precision estimates of the retrieval were given.…”

Section: Introductionmentioning

confidence: 99%

A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

et al. 2005

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Abstract.A neural network algorithm inverting selected channels from the Advance Microwave Sounding Unit instruments AMSU-A and AMSU-B was applied to retrieve layer averaged relative humidity. The neural network was trained with a global synthetic dataset representing clear-sky conditions. A precision of around 6% was obtained when retrieving global simulated radiances, the precision deteriorated less than 1% when real mid-latitude AMSU radiances were inverted and compared with co-located data from a radiosonde station. The 6% precision outperforms by 1% the reported precision estimate from a linear single-channel regression between radiance and weighting function averaged relative humidity, the more traditional approach to exploit AMSU data. Added advantages are not only a better precision; the AMSU-B humidity information is more optimally exploited by including temperature information from AMSU-A channels; and the layer averaged humidity is a more physical quantity than the weighted humidity, for comparison with other datasets. The training dataset proved adequate for inverting real radiances from a mid-latitude site, but it is limited by not considering the impact of clouds or surface emissivity changes, and further work is needed in this direction for further validation of the precision estimates.

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“…Ground-based microwave radiometers have been designed, fabricated and used to sense water vapor and temperature profiles from ground to 10 km altitude (Iturbide-Sanchez et al, 2007;Solheim, et al, 1998). Satellite-based instruments like Advanced Microwave Sounding Units (AMSU-A and B) on board NOAA-15 (Susskind et al, 2011;Rosenkranz, 2001) (Rao, et al, 2013) have been used to retrieve humidity and temperature profiles in addition to a range of other parameters. The AMSU-A and B channels operate close to the 22.235, 60 and 183 GHz absorption lines as well as at the 89 GHz window frequency.…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

Panda

Sahoo

Pandithurai

2017

Geosci. Instrum. Method. Data Syst.

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Abstract. Ground-based microwave measurements performed at water vapor and oxygen absorption line frequencies are widely used for remote sensing of tropospheric water vapor density and temperature profiles, respectively. Recent work has shown that Bayesian optimal estimation can be used for improving accuracy of radiometer retrieved water vapor and temperature profiles. This paper focuses on using Bayesian optimal estimation along with time series of independent frequency measurements at K-and V-bands. The measurements are used along with statistically significant but short background data sets to retrieve and sense temporal variations and gradients in water vapor and temperature profiles.

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Retrieval of temperature and moisture profiles from AMSU-A and AMSU-B measurements

Cited by 123 publications

References 24 publications

Evaluation of balloon and satellite water vapour measurements in the Southern tropical and subtropical UTLS during the HIBISCUS campaign

Evaluation of balloon and satellite water vapour measurements in the Southern tropical and subtropical UTLS during the HIBISCUS campaign

A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

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