0000000001317129

AUTHOR

David Malmgren-hansen

showing 4 related works from this author

Spatial noise-aware temperature retrieval from infrared sounder data

2020

In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine Learningbusiness.industryComputer scienceDimensionality reductionFeature extraction0211 other engineering and technologiesWord error ratePattern recognitionRegression analysis02 engineering and technologyMachine Learning (cs.LG)Principal component analysisLinear regression0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceElectrical Engineering and Systems Science - Signal ProcessingbusinessSpatial analysis021101 geological & geomatics engineering
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Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval

2018

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceFeature extraction0211 other engineering and technologiesTranfer learningFOS: Physical sciences02 engineering and technologyAtmospheric modelInfrared atmospheric sounding interferometercomputer.software_genreConvolutional neural networkMachine Learning (cs.LG)0202 electrical engineering electronic engineering information engineeringInfrared measurements021101 geological & geomatics engineeringArtificial neural networkStatistical modelNumerical weather predictionParameter retrievalPhysics - Atmospheric and Oceanic PhysicsKernel method13. Climate actionAtmospheric and Oceanic Physics (physics.ao-ph)Convolutional neural networks020201 artificial intelligence & image processingData miningcomputerCurse of dimensionalityIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Statistical retrieval of atmospheric profiles with deep convolutional neural networks

2019

Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…

010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesWeather forecasting02 engineering and technologycomputer.software_genreAtmospheric measurements01 natural sciencesConvolutional neural networkLinear regressionRedundancy (engineering)Information retrievalInfrared measurementsComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesArtificial neural networkbusiness.industryDeep learningDimensionality reductionPattern recognitionAtomic and Molecular Physics and OpticsComputer Science Applications13. Climate actionNoise (video)Artificial intelligencebusinesscomputerNeural networksISPRS Journal of Photogrammetry and Remote Sensing
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IASI dataset v1

2020

The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series measures the infrared spectrum with high resolution. The ground footprint resolution of the instruments is 12 km at nadir, and a spectral resolution of 0.25cm −1 in the spectrum between 645 cm −1 and 2760 cm −1 . This results in 8461 spectral samples covering 2200km scan-swath with 60 points per line. IASI is an ideal instrument for monitoring different physical/chemical parameters in the atmosphere e.g. temperature, humidity and trace gases such as ozone. Energy from different altitudes returns a different spectral shift. In this way atmospheric profiles can be obtained and these provides important …

Climatology13. Climate actionAtmospheric sciences not elsewhere classifiedAtmospheric aerosols
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