Physical‐statistical retrieval of water vapor profiles using SSM/T‐2 Sounder data

Rieder, Markus J.; Kirchengast, Gottfried

doi:10.1029/1999gl900244

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The choice of training dataset is thus essential since it holds all the physics of the problem. Iterative schemes adjust the estimated relative humidity profile using successive perturbations on an a priori state of the atmosphere (selected, for example, using a Bayesian approach; Rieder and Kirchengast, 1999; Rosenkranz, 2001) and the physical constraint comes from radiative transfer computations, the best profile minimizing a cost function (e.g. Wilheit and Al‐Khalaf, 1994).…”

Section: Information Content Of the 18331 Ghz Bandmentioning

confidence: 99%

Expected improvements in the atmospheric humidity profile retrieval using the Megha‐Tropiques microwave payload

Brogniez

Kirstetter

Eymard

2011

Quart J Royal Meteoro Soc

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The microwave payload of the Megha-Tropiques mission is explored to quantify the expected improvements in the retrieval of relative humidity profiles. Estimations of the profiles are performed using a generalized additive model that uses cubic smoothing splines to address the nonlinear dependencies between the brightness temperatures (T B ) in the 183.31 GHz band and the relative humidity of specified tropospheric layers. Under clear-sky and oceanic situations, the six-channel configuration of the SAPHIR radiometer clearly improves the retrieval and reduces by a factor of two the variance of the residuals with respect to the current spaceborne humidity sounders that have three channels in this band (AMSU-B, MHS). Additional information from the MADRAS radiometer (at 23.8 and 157 GHz) further improves the restitution with correlation coefficient higher than 0.89 throughout the troposphere.

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Section: Information Content Of the 18331 Ghz Bandmentioning

confidence: 99%

Expected improvements in the atmospheric humidity profile retrieval using the Megha‐Tropiques microwave payload

Brogniez

Kirstetter

Eymard

2011

Quart J Royal Meteoro Soc

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“…[22] Dependent on the quality of the a priori profile, the first or the first two iteration steps may need special aid with convergence due to linearization errors, which is often dealt with in extending the Gauss-Newton scheme to the Levenberg-Marquardt scheme [e.g., Rodgers, 2000;Rieder and Kirchengast, 1999]. We utilized the more simple but for the present purpose equivalently effective extension introduced by Liu et al [2000], termed ''D-rad'' method.…”

Section: Retrieval Algorithmmentioning

confidence: 99%

Temperature and humidity retrieval from simulated Infrared Atmospheric Sounding Interferometer (IASI) measurements

2002

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[1] An efficient temperature and humidity retrieval algorithm for radiometric measurements at high spectral resolution is introduced and applied to climatological profiles. The algorithm is developed for analyzing Infrared Atmospheric Sounding Interferometer (IASI) data of the European weather satellite METOP-1 (launch scheduled 2005) for climatological purposes but is also applicable for other purposes and to other similar data. The algorithm's core features are a channel selection methodology followed by a linearized optimal estimation. The key concept of the former is that a small subset (5-10%) of all available IASI channels ($8000) is selected based on maximizing a suitable information content measure at each retrieval level. This enables efficiency and robustness of the retrieval algorithm and curtails the high redundancy in the measurements. In addition to profile and error covariance estimates optimal estimation furnishes various sensitivity functions of which we used ''weighting functions'' for quantifying the utility of measurement channels and ''averaging kernel functions'' for assessing the resolution of retrieved profiles. Results based on simulated IASI spectra computed from a set of standard climatological profiles and a realistic radiometric noise model demonstrate, for clear air, the capabilities of high spectral resolution measurements for improving temperature and humidity soundings compared to current operational sensors. In the troposphere (below $200 hPa), retrieved profiles exhibit temperature errors of <1 K and specific humidity errors of <10% at most heights, associated with a vertical resolution of $1.5-2 km. Promising performance was found in the upper troposphere (500-200 hPa), where about five independent reliable values of temperature and humidity are available indicating the high potential of the IASI sensor for monitoring climatic changes in upper tropospheric moisture. Tests on the sensitivity of retrieved profiles to the quality of a priori profiles showed weak sensitivity of temperature but significant sensitivity of humidity. The results provide a solid basis and clear guidance for improvements of the presented algorithm for reliable large-scale application on cloud-free spectra.

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“…Of course, the use of a retrieval technique (e.g., neural network or 1-D-variational) using prior physical information should further improve the estimation: for instance, the surface layer should clearly benefit from prior knowledge of the surface temperature and total water vapor content. In fact, a comparison with existing works based on methods combining physical constraints with statistical tools (Kuo et al, 1994;Cabrera-Mercadier and Staelin, 1995;Rieder and Kirchengast, 1999;Liu and Weng, 2005;Aires et al, 2013) applied to on similar radiometers with less channels in the 183.31 GHz line, such as AMSU-B or MHS, shows that the current approach gives similar performance (root mean square errors of about 10 %RH in the mid-troposphere). It is also consistent with the layer-averaged RH profiles estimated by the Indian team involved in the Megha-Tropiques mission, although further constraining the retrieval by NCEP/NCAR outputs (Venkat Ratnam et al, 2013;Gohil et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, most of the approaches are physically based iterative techniques such as a n-dimensional variational algorithm that converges to the least biased profile using other inputs as prior knowledge of the system under study (such as surface emissivity, temperature profile and sometimes a prior water vapor profile for BT simulations). These variational techniques are well established (Kuo et al, 1994;Cabrera-Mercadier and Staelin, 1995;Rieder and Kirchengast, 1999;Blankenship et al, 2000;Liu and Weng, 2005) and it would be unnecessary to reinvent a similar algorithm. Here, the selected approach is to learn the relationship between the inputs (i.e., the BTs) and the output (i.e., the averaged RH in a specific atmospheric layer) directly from a training set that implicitly contains all the relevant information such as the statistical distribution of the atmospheric RH or the radiative transfer equation from the set of BTs.…”

Section: R G Sivira Et Al: Relative Humidity Profiling With Megha-mentioning

confidence: 99%

A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

et al. 2015

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Abstract.A statistical method trained and optimized to retrieve seven-layer relative humidity (RH) profiles is presented and evaluated with measurements from radiosondes. The method makes use of the microwave payload of the Megha-Tropiques platform, namely the SAPHIR sounder and the MADRAS imager. The approach, based on a generalized additive model (GAM), embeds both the physical and statistical characteristics of the inverse problem in the training phase, and no explicit thermodynamical constraint -such as a temperature profile or an integrated water vapor content -is provided to the model at the stage of retrieval. The model is built for cloud-free conditions in order to avoid the cases of scattering of the microwave radiation in the 18.7-183.31 GHz range covered by the payload. Two instrumental configurations are tested: a SAPHIR-MADRAS scheme and a SAPHIR-only scheme to deal with the stop of data acquisition of MADRAS in January 2013 for technical reasons. A comparison to learning machine algorithms (artificial neural network and support-vector machine) shows equivalent performance over a large realistic set, promising low errors (biases < 2.2 %RH) and scatters (correlations > 0.8) throughout the troposphere (150-900 hPa). A comparison to radiosonde measurements performed during the international field experiment CINDY/DYNAMO/AMIE (winter 2011-2012) confirms these results for the mid-tropospheric layers (correlations between 0.6 and 0.92), with an expected degradation of the quality of the estimates at the surface and top layers. Finally a rapid insight of the estimated large-scale RH field from Megha-Tropiques is presented and compared to ERA-Interim.

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Physical‐statistical retrieval of water vapor profiles using SSM/T‐2 Sounder data

Cited by 8 publications

References 8 publications

Expected improvements in the atmospheric humidity profile retrieval using the Megha‐Tropiques microwave payload

Expected improvements in the atmospheric humidity profile retrieval using the Megha‐Tropiques microwave payload

Temperature and humidity retrieval from simulated Infrared Atmospheric Sounding Interferometer (IASI) measurements

A layer-averaged relative humidity profile retrieval for microwave observations: design and results for the Megha-Tropiques payload

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