Dr. Tim H. Hultberg Profile

Dr. Tim H. Hultberg

at EUMETSAT

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Author

Publications (5)

Proceedings Article | 12 September 2021 Presentation + Paper

Cloud liquid and ice water content estimation from satellite: a regression approach based on neural networks

Pietro Mastro, Guido Masiello, Domenico Cimini, Filomena Romano, Elisabetta Ricciardelli, Francesco Di Paola, Tim Hultberg, Thomas August

Proceedings Volume 11859, 118590H (2021) https://doi.org/10.1117/12.2600124

KEYWORDS: Medium wave, Clouds, Data modeling, Neural networks, Principal component analysis, Liquids, Satellites, Microwave radiation, Infrared radiation, Earth's atmosphere

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Proceedings Article | 12 September 2019 Presentation

First results from Metop-C of the EUMETSAT Polar System (EPS) (Conference Presentation)

K. Dieter Klaes, Manfred Lugert, Bojan Bojkov, Jörg Ackermann, Régis Borde, Dorothée Coppens, Stephanie Guedj, Thomas August, Christian Marquardt, Craig Anderson, Tim Hultberg, Marc Crapeau, Ruediger Lang, Rosemary Munro, Alessandra Cacciari

Proceedings Volume 11127, 111270V (2019) https://doi.org/10.1117/12.2527395

KEYWORDS: Satellites, Meteorological satellites, Environmental sensing, Microwave radiation, Satellite navigation systems, Atmospheric monitoring, Satellite communications, Infrared radiation, Interferometers, Radiometry

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Proceedings Article | 29 September 2017 Paper

Removal of instrument artefacts by harmonisation of hyperspectral sensor data from multiple detectors

Tim Hultberg, Thomas August

Proceedings Volume 10423, 1042324 (2017) https://doi.org/10.1117/12.2278587

KEYWORDS: Sensors, Signal detection, Data compression, Satellites, Atmospheric sensing, Data processing, Remote sensing

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Proceedings Article | 29 September 2017 Presentation + Paper

Local or global? How to choose the training set for principal component compression of hyperspectral satellite measurements: a hybrid approach

Tim Hultberg, Thomas August, Flavia Lenti

Proceedings Volume 10423, 104231G (2017) https://doi.org/10.1117/12.2278349

KEYWORDS: Satellites, Sensors, Data compression, Interference (communication), Signal attenuation, Data processing, Remote sensing

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Principal Component (PC) compression is the method of choice to achieve band-width reduction for dissemination of hyper spectral (HS) satellite measurements and will become increasingly important with the advent of future HS missions (such as IASI-NG and MTG-IRS) with ever higher data-rates. It is a linear transformation defined by a truncated set of the leading eigenvectors of the covariance of the measurements as well as the mean of the measurements. We discuss the strategy for generation of the eigenvectors, based on the operational experience made with IASI. To compute the covariance and mean, a so-called training set of measurements is needed, which ideally should include all relevant spectral features. For the dissemination of IASI PC scores a global static training set consisting of a large sample of measured spectra covering all seasons and all regions is used. This training set was updated once after the start of the dissemination of IASI PC scores in April 2010 by adding spectra from the 2010 Russian wildfires, in which spectral features not captured by the previous training set were identified. An alternative approach, which has sometimes been proposed, is to compute the eigenvectors on the fly from a local training set, for example consisting of all measurements in the current processing granule. It might naively be thought that this local approach would improve the compression rate by reducing the number of PC scores needed to represent the measurements within each granule. This false belief is apparently confirmed, if the reconstruction scores (root mean square of the reconstruction residuals) is used as the sole criteria for choosing the number of PC scores to retain, which would overlook the fact that the decrease in reconstruction score (for the same number of PCs) is achieved only by the retention of an increased amount of random noise. We demonstrate that the local eigenvectors retain a higher amount of noise and a lower amount of atmospheric signal than global eigenvectors. Local eigenvectors do not increase the compression rate, but increase the amount of atmospheric loss and should be avoided. Only extremely rare situations, resulting in spectra with features which have not been observed previously, can lead to problems for the global approach. To cope with such situations we investigate a hybrid approach, which first apply the global eigenvectors and then apply local compression to the residuals in order to identify and disseminate in addition any directions in the local signal, which are orthogonal to the subspace spanned by the global eigenvectors.

Proceedings Article | 27 August 2008 Paper

Validation of the IASI temperature and water vapor profile retrievals by correlative radiosondes

Nikita Pougatchev, Thomas August, Xavier Calbet, Tim Hultberg, Osoji Oduleye, Peter Schlüssel, Bernd Stiller, Karen St. Germain, Gail Bingham

Proceedings Volume 7081, 70810J (2008) https://doi.org/10.1117/12.795382

KEYWORDS: Error analysis, Satellites, Humidity, Statistical analysis, Systems modeling, Atmospheric sensing, Temperature metrology, Data modeling, Mathematical modeling, Statistical modeling

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