Search results for "Infrared measurements"

showing 2 items of 2 documents

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
researchProduct

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
researchProduct