A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Aires, Filipe; Marquisseau, Francis; Prigent, Catherine; Sèze, Geneviève

doi:10.1175/mwr-d-10-05012.1

Cited by 24 publications

(20 citation statements)

References 37 publications

(30 reference statements)

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“…It is not unexpected that the HIRS sensor outperforms the counterpart MW sensors in detecting ice clouds. The measurements at IR channels are known to be strongly sensitive to thin ice clouds, as reported by many authors (Aires et al 2011;Stein, Delanoë, and Hogan 2011;Kahn et al 2014). Therefore, the information content obtained from IR-based measurements can add valuable skills towards ice cloud detection.…”

Section: Resultsmentioning

confidence: 95%

Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Islam

Srivastava

Dai

et al. 2014

Remote Sensing Letters

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An algorithm for ice cloud detection aided by support vector machine (AID-SVM) is presented. The AID-SVM algorithm is applied and tested for the Advanced Microwave Sounding Unit-A, microwave humidity sounder (MHS), and high resolution infrared radiation sounder (HIRS) instruments onboard NOAA-19 satellite. The algorithm is based on satellite brightness temperature measurements and developed as well as validated by using collocated ice/no-ice cloud information acquired from the CloudSat cloud-profiling radar. The algorithm is tested over both ocean and land surfaces. Overall, the results exhibit very promising potential to acquire ice/no-ice cloud information using the passive satellite sensors. It is found that infrared satellite sensor such as HIRS is more efficient in detecting ice clouds than the counterpart microwave satellite sensors. Furthermore, the combined measurements using microwave/infrared synergy perform no better than the infrared-only measurements.

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Section: Resultsmentioning

confidence: 95%

Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Islam

Srivastava

Dai

et al. 2014

Remote Sensing Letters

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“…Remote sensing under cloudy conditions would also certainly benefit from the synergy between the VIS, IR, and MW domains. First, multi‐wavelength observations will benefit the retrieval of cloud characteristics [ Aires et al , 2011a]. Second, with clouds better constrained, the atmospheric retrieval will be facilitated.…”

Section: Discussionmentioning

confidence: 99%

Synergistic multi‐wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp‐A

et al. 2012

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.[1] In this paper, synergy refers to a process where the use of multiple satellite observations makes the retrieval more precise than the best individual retrieval. Two general strategies can be used in order to use multi-wavelength observations in an inversion scheme. First, the multi-wavelength observations are merged in the input of the retrieval scheme. This means that the various satellite observations are used simultaneously and that their possible interactions can be exploited by the retrieval scheme. Second, each multi-wavelength observations are used independently to retrieve a same geophysical variable and then, these independent retrievals are combined a posteriori using for example a simple weighted averaging. In this paper, it is shown that the first approach provides better synergy results: The retrieval is better suited to optimize the use of all the information available because they are provided to the algorithm simultaneously. In particular, the retrieval process is able, in this case, to exploit the possible interactions between the various input information. The two retrieval approaches are tested and compared using an application for the retrieval of atmospheric profiles and integrated column quantities (temperature, water vapor, and ozone) using MetOp-A observations from IASI, AMSU-A and MHS instruments. Although real satellite observations are considered in this analysis, the results are dependent on the correlation structure in the training data set (i.e. ECMWF analysis) used to calibrate the retrieval algorithm. However, it can be seen that the infrared and microwave observations have a good synergy for the retrieval of atmospheric temperature, water vapor, and for ozone thanks to an indirect synergy.Citation: Aires, F., O. Aznay, C. Prigent, M. Paul, and F. Bernardo (2012), Synergistic multi-wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp-A, J. Geophys.

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“…In this paper, it is not possible to list all the ways to define a rigorous distance on each one of the relationship diagnostics that have been presented: Euclidean distance can be used on the regression parameters or the sensitivity coefficients, or two weather regime frequencies can be measured using confusion matrices (e.g. Aires et al, 2011). The distance needs to be adapted to the relationship diagnostic.…”

Section: Pattern-oriented Approachesmentioning

confidence: 99%

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

et al. 2013

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Abstract. Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climatecarbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable Published by Copernicus Publications on behalf of the European Geosciences Union. A. M. Foley et al.: Evaluation of biospheric components in Earth system modelsand no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process-and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

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A Land and Ocean Microwave Cloud Classification Algorithm Derived from AMSU-A and -B, Trained Using MSG-SEVIRI Infrared and Visible Observations

Cited by 24 publications

References 37 publications

Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

Synergistic multi‐wavelength remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp‐A

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

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