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Paolo Antonelli

Bio: Paolo Antonelli is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Hyperspectral imaging & Radiance. The author has an hindex of 9, co-authored 26 publications receiving 434 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra.
Abstract: A simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra. The study discusses the fundamental aspects of spectral correlation, distributions, and noise; the correlation between principal components (PCs) and atmospheric-level temperature and water vapor; and how an optimal subset of PCs is selected so a good compression ratio and high retrieval accuracy are obtained. Principal component analysis, principal component compression, and principal component regression under certain conditions are shown to provide 1) nearly full spectral information with little degradation, 2) noise reduction, 3) data compression with a compression ratio of approximately 15, and 4) tolerable loss of accuracy in temperature and water vapor retrieval. The techniques will therefore be valuable tools for data compression and the accurate retrieval of meteorological parameters from new-g...

137 citations

Journal ArticleDOI
TL;DR: In this paper, principal component analysis (PCA) was applied to reduce the random noise present in the hyperspectral infrared observations, and the results obtained depend on the variability of selected sets of observations and on specific instrument characteristics such as spectral resolution and noise statistics.
Abstract: [1] This paper describes the application of principal component analysis to reduce the random noise present in the hyperspectral infrared observations. Within a set of spectral observations the number of components needed to characterize the atmosphere is far less than the number of wavelengths observed, typically by a factor between 50 and 70. The higher-order components, which mainly serve to characterize noise, can be eliminated along with the noise that they characterize. The results obtained depend on the variability of the selected sets of observations and on specific instrument characteristics such as spectral resolution and noise statistics. For a set of 10,000 Fourier transform spectrometer (FTS) simulated spectra, whose standard deviation is about 10% of the mean, we were able to obtain noise reduction factors between 5 and 8. Results obtained from real FTS, with standard deviation of about 10% of the mean, indicated practical noise reduction between 5 and 6. To avoid loss of information in the presence of highly deviant observations, it is necessary to use a conservative number of principal components higher than the optimum to maximum noise reduction. However, even then, noise reduction factors of 4 are still achievable.

80 citations

Journal ArticleDOI
TL;DR: In this article, an improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements using CO2 sorting, which determines optimal channel pairs to which the CO2 slicing retrieval will be applied.
Abstract: An improvement to high-spectral-resolution infrared cloud-top altitude retrievals is compared to existing retrieval methods and cloud lidar measurements. The new method, CO2 sorting, determines optimal channel pairs to which the CO2 slicing retrieval will be applied. The new retrieval is applied to aircraft Scanning High-Resolution Interferometer Sounder (S-HIS) measurements. The results are compared to existing passive retrieval methods and coincident Cloud Physics Lidar (CPL) measurements. It is demonstrated that when CO2 sorting is used to select channel pairs for CO2 slicing there is an improvement in the retrieved cloud heights when compared to the CPL for the optically thin clouds (total optical depths less than 1.0). For geometrically thick but tenuous clouds, the infrared retrieved cloud tops underestimated the cloud height, when compared to those of the CPL, by greater than 2.5 km. For these cases the cloud heights retrieved by the S-HIS correlated closely with the level at which the CPL...

59 citations

Journal ArticleDOI
TL;DR: Retrieval exercises performed in simulation and with real observations lead us to conclude that the principal components space-based inverse approach is potentially superior over the current practice of using sparse channels.
Abstract: The problem of reducing the dimensionality of infrared atmospheric sounding interferometer (IASI) data space through a suitable transform and performing the retrieval process for thermodynamical parameters within the transformed data space is addressed in this paper. The reduction of dimensionality is performed with the principal components transform, which allows us to represent the full IASI spectrum with a few coefficients of the expansion. This truncated expansion could have a twofold beneficial effect: (i) it could improve the present exploitation and performance of IASI data for the retrieval of temperature and moisture; and (ii) it could save transmission bandwidth, data rate and costs for the dissemination to users of IASI data. A suitable form of the inverse/forward model completely embedded in the transformed space has been derived and applied to simulated and real IASI data. This methodology has allowed us to assess the IASI performance for temperature, water vapor and ozone based on the full IASI spectral coverage. The use of back-transformed spectral radiances (i.e. the filtered radiances obtained by the truncated expansion) instead of expansion coefficients has also been addressed and assessed. Retrieval exercises performed in simulation and with real observations lead us to conclude that the principal components space-based inverse approach is potentially superior over the current practice of using sparse channels. Copyright © 2011 Royal Meteorological Society

53 citations

Journal ArticleDOI
TL;DR: The European AQUA Thermodynamic Experiment (EAQUATE) was held in Italy and the United Kingdom to demonstrate certain ground-based and airborne systems useful for validating hyperspectral satellite sounding observations as mentioned in this paper.
Abstract: The international experiment called EAQUATE (European AQUA Thermodynamic Experiment) was held in September 2004 in Italy and the United Kingdom to demonstrate certain ground-based and airborne systems useful for validating hyperspectral satellite sounding observations. A range of flights over land and marine surfaces were conducted to coincide with overpasses of the AIRS instrument on the EOS Aqua platform. Direct radiance evaluation of AIRS using NAST-I and SHIS has shown excellent agreement. Comparisons of level 2 retrievals of temperature and water vapor from AIRS and NAST-I validated against high quality lidar and drop sonde data show that the 1K/1km and 10%/1km requirements for temperature and water vapor (respectively) are generally being met. The EAQUATE campaign has proven the need for synergistic measurements from a range of observing systems for satellite cal/val and has paved the way for future cal/val activities in support of IASI on the European Metop platform and CrIS on the US NPP/NPOESS platform.

36 citations


Cited by
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TL;DR: The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment as discussed by the authors provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities).
Abstract: Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR soun...

463 citations

Journal ArticleDOI
TL;DR: In this article, the performance of the MODIS cloud mask algorithm is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar] and aircraft [Cloud Physics LIDar], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms.
Abstract: An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach. In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

371 citations

Journal ArticleDOI
TL;DR: The Infrared Atmospheric Sounding Interferometer (IASI) as discussed by the authors is the main sounding component of EUMETSAT's Metop-A satellite, which was launched in October 2006.
Abstract: Four years after launch, IASI has delivered significant advances in remote sensing capability for numerical weather prediction and atmospheric composition monitoring and promises an excellent dataset for climate studies. The Infrared Atmospheric Sounding Interferometer (IASI) forms the main infrared sounding component of EUMETSAT's Metop-A satellite (Klaes et al., 2007), which was launched in October 2006. This article presents the results of the first four years of the operational IASI mission. The performance of the instrument is shown to be exceptional in terms of calibration and stability the quality of the data has allowed the rapid use of the observations in operational numerical weather prediction (NWP) and the development of new products for atmospheric chemistry and climate studies, some of which were unexpected before launch. The assimilation of IASI observations in NWP models provides significant forecast impact; in most cases the impact has been shown to be at least as large as for any previous instrument. In atmospheric chemistry, global distributions of gases such as ozone and carbon monoxide can be produced in near-real time, and short-lived species such as ammonia or methanol can be mapped, allowing identification of new sources. The data have also shown the ability to track the location and chemistry of gaseous plumes and particles associated with volcanic eruptions and fires, providing valuable data for air quality monitoring and aircraft safety. IASI also contributes to the establishment of robust long term data records of several essential climate variables. The suite of products being developed from IASI continues to expand as the data are investigated, and further impacts are expected from increased use of the data in NWP and climate studies in the coming years. The instrument has set a high standard for future operational hyperspectral infrared sounders, and demonstrated that such instruments have a vital role in the global observing system.

361 citations

Journal ArticleDOI
01 Jun 2014-Talanta
TL;DR: The main objective of this article is to review the chemometric methods used in analytical chemistry to determine the elution sequence, classify various data sets, assess peak purity and estimate the number of chemical components.

276 citations

Journal ArticleDOI
TL;DR: The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations as mentioned in this paper, where the upper layer cloud is semitransparent.
Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System (EOS) Terra and Aqua platforms provides unique measurements for deriving global and regional cloud properties. MODIS has spectral coverage combined with spatial resolution in key atmospheric bands, which is not available on previous imagers and sounders. This increased spectral coverage/spatial resolution, along with improved onboard calibration, enhances the capability for global cloud property retrievals. MODIS operational cloud products are derived globally at spatial resolutions of 5 km (referred to as level-2 products) and are aggregated to a 1° equal-angle grid (referred to as level-3 product), available for daily, 8-day, and monthly time periods. The MODIS cloud algorithm produces cloud-top pressures that are found to be within 50 hPa of lidar determinations in single-layer cloud situations. In multilayer clouds, where the upper-layer cloud is semitransparent, the MODIS cloud pressure is representa...

259 citations