scholarly journals A principal component noise filter for high spectral resolution infrared measurements

2004 ◽  
Vol 109 (D23) ◽  
Author(s):  
P. Antonelli ◽  
H. E. Revercomb ◽  
L. A. Sromovsky ◽  
W. L. Smith ◽  
R. O. Knuteson ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
Principal Component ◽  
High Spectral Resolution ◽  
Noise Filter ◽  
Infrared Measurements

scholarly journals Deriving Atmospheric Temperature of the Tropopause Region–Upper Troposphere by Combining Information from GPS Radio Occultation Refractivity and High-Spectral-Resolution Infrared Radiance Measurements

2008 ◽  
Vol 47 (9) ◽  
pp. 2300-2310 ◽  
Author(s):  
Eva E. Borbas ◽  
W. Paul Menzel ◽  
Elisabeth Weisz ◽  
Dezso Devenyi
Keyword(s):  
Bayesian Estimation ◽  
Spectral Resolution ◽  
Radio Occultation ◽  
Estimation Method ◽  
Principal Component ◽  
Atmospheric Temperature ◽  
High Spectral Resolution ◽  
Statistical Regression ◽  
Infrared Radiance ◽  
Tropopause Region

Abstract Global positioning system radio occultation (GPS/RO) measurements from the Challenging Minisatellite Payload (CHAMP) and Satelite de Aplicaciones Cientificas-C (SAC-C) satellites are used to improve tropospheric profile retrievals derived from the Aqua platform high-spectral-resolution Atmospheric Infrared Sounder (AIRS) and broadband Advanced Microwave Sounding Unit (AMSU) measurements under clear-sky conditions. This paper compares temperature retrievals from combined AIRS, AMSU, and CHAMP/SAC-C measurements using different techniques: 1) a principal component statistical regression using coefficients established between real (and in a few cases calculated) measurements and radiosonde atmospheric profiles; and 2) a Bayesian estimation method applied to AIRS plus AMSU temperature retrievals and GPS/RO temperature profiles. The Bayesian estimation method was also applied to GPS/RO data and the AIRS Science Team operational level-2 (version 4.0) temperature products for comparison. In this study, including GPS/RO data in the tropopause region produces the largest improvement in AIRS–AMSU temperature retrievals—about 0.5 K between 100 and 300 hPa. GPS/RO data are found to provide valuable upper-tropospheric information that improves the profile retrievals from AIRS and AMSU.

scholarly journals Instrumental characteristics and Greenhouse gases measurement capabilities of the Compact High-spectral Resolution Infrared Spectrometer: CHRIS

2019 ◽  
Author(s):  
Marie-Thérèse El Kattar ◽  
Frédérique Auriol ◽  
Hervé Herbin
Keyword(s):  
Greenhouse Gases ◽  
Spectral Resolution ◽  
Complete Information ◽  
High Spectral Resolution ◽  
Gaseous Species ◽  
Future Space ◽  
Infrared Measurements ◽  
Thermal Region ◽  
Portable Spectrometers ◽  
Infrared Spectrometer

Abstract. Ground-based high spectral resolution infrared measurements are an efficient way to obtain accurate tropospheric abundances of different gaseous species and in particular GreenHouse Gases (GHG), such as CO2 and CH4. Many ground-based spectrometers are used in the NDACC and TCCON networks to validate the Level 2 satellite data, but their large dimensions and heavy mass makes them inadequate for field campaigns. To overcome these problems, the use of portable spectrometers was recently investigated. In this context, this paper deals with the CHRIS (Compact High-spectral Resolution Infrared Spectrometer) prototype with unique characteristics such as its high spectral resolution (0.135 cm-1 non-apodized) and its wide spectral range (680 to 5200 cm-1). Its main objective is the characterization of gases and aerosols in the infrared thermal region, that's why it requires high radiometric precision and accuracy, which is achieved by performing spectral and radiometric calibrations that will be presented in this paper. Also, CHRIS's capabilities to retrieve CO2 and CH4 vertical profiles are presented through a complete information content analysis, a channel selection and an error budget estimation in the attempt to join the ongoing campaigns, such as MAGIC, to monitor the GHG and validate the actual and future space missions.

scholarly journals Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis

2019 ◽  
Vol 11 (22) ◽  
pp. 2710 ◽  
Author(s):  
Sihui Fan ◽  
Wei Han ◽  
Zhiqiu Gao ◽  
Ruoying Yin ◽  
Yu Zheng
Keyword(s):  
Principal Component Analysis ◽  
Spectral Resolution ◽  
Principal Component ◽  
Component Analysis ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Data ◽  
Observation Error ◽  
Array Position

The Geostationary Interferometric Infrared Sounder (GIIRS) is the first high-spectral resolution advanced infrared (IR) sounder onboard the new-generation Chinese geostationary meteorological satellite FengYun-4A (FY-4A). The GIIRS has 1650 channels, and its spectrum ranges from 700 to 2250 cm−1 with an unapodized spectral resolution of 0.625 cm−1. It represents a significant breakthrough for measurements with high temporal, spatial and spectral resolutions worldwide. Many GIIRS channels have quite similar spectral signal characteristics that are highly correlated with each other in content and have a high degree of information redundancy. Therefore, this paper applies a principal component analysis (PCA)-based denoising algorithm (PDA) to study simulation data with different noise levels and observation data to reduce noise. The results show that the channel reconstruction using inter-channel spatial dependency and spectral similarity can reduce the noise in the observation brightness temperature (BT). A comparison of the BT observed by the GIIRS (O) with the BT simulated by the radiative transfer model (B) shows that a deviation occurs in the observation channel depending on the observation array. The results show that the array features of the reconstructed observation BT (rrO) depending on the observation array are weakened and the effect of the array position on the observations in the sub-center of the field of regard (FOR) are partially eliminated after the PDA procedure is applied. The high observation and simulation differences (O-B) in the sub-center of the FOR array notably reduced after the PDA procedure is implemented. The improvement of the high O-B is more distinct, and the low O-B becomes smoother. In each scan line, the standard deviation of the reconstructed background departures (rrO-B) is lower than that of the background departures (O-B). The observation error calculated by posterior estimation based on variational assimilation also verifies the efficiency of the PDA. The typhoon experiment also shows that among the 29 selected assimilation channels, the observation error of 65% of the channels was reduced as calculated by the triangle method.

scholarly journals A High-Spectral-Resolution Radiative Transfer Model for Simulating Multilayered Clouds and Aerosols in the Infrared Spectral Region

2015 ◽  
Vol 72 (2) ◽  
pp. 926-942 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Xu Liu
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Present Model ◽  
Reference Model ◽  
Mineral Dust ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Multiple User ◽  
Infrared Measurements

Abstract A fast and flexible model is developed to simulate the transfer of thermal infrared radiation at wavenumbers from 700 to 1300 cm−1 with a spectral resolution of 0.1 cm−1 for scattering–absorbing atmospheres. In a single run and at multiple user-defined levels, the present model simulates radiances at different viewing angles and fluxes. Furthermore, the model takes into account complicated and realistic scenes in which ice cloud, water cloud, and mineral dust layers may coexist within an atmospheric column. The present model is compared to a rigorous reference model, the 32-stream Discrete Ordinate Radiative Transfer model (DISORT) code. For an atmosphere with three scattering layers (water, ice, and mineral dust), the root-mean-square error of the simulated brightness temperatures at the top of the atmosphere is approximately 0.05 K, and the relative flux errors at the boundary and internal levels are much smaller than 1%. Within the same computing environment, the fast model runs more than 10 000, 6000, and 4000 times faster than DISORT under single-layer, two-layer, and three-layer cloud–aerosol conditions, respectively. With its computational efficiency and accuracy, the present model may optimally facilitate the forward radiative transfer simulations involved in remote sensing implementations based on high-spectral-resolution and narrowband infrared measurements and in the data assimilation applications of the weather forecasting system. The selected 0.1-cm−1 spectral resolution is an obstacle to extending the present model to strongly absorptive bands (e.g., 600–700 cm−1). However, the present clear-sky module can be substituted by a more accurate model for specific applications involving spectral bands with strong absorption.

scholarly journals Noise Reduction of Atmospheric Emitted Radiance Interferometer (AERI) Observations Using Principal Component Analysis

2006 ◽  
Vol 23 (9) ◽  
pp. 1223-1238 ◽  
Author(s):  
D. D. Turner ◽  
R. O. Knuteson ◽  
H. E. Revercomb ◽  
C. Lo ◽  
R. G. Dedecker
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Spectral Element ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Atmospheric Variability ◽  
Temporal Sampling ◽  
Noise Filter ◽  
Empirical Function ◽  
Number Of Principal Components

Abstract A principal component noise filter has been applied to ground-based high-spectral-resolution infrared radiance observations collected by the Atmospheric Emitted Radiance Interferometers (AERIs) deployed by the Atmospheric Radiation Measurement (ARM) program. The technique decomposes the radiance observations into their principal components, selects the ones that describe the most variance in the data, and reconstructs the data from these components. An empirical function developed for chemical analysis is utilized to determine the number of principal components to be used in the reconstruction of the data. Statistical analysis of the noise-filtered minus original radiance data, as well as side-by-side analysis of data from two AERI systems utilizing different temporal sampling, demonstrates the ability of the noise filter using this empirical function to retain most of the atmospheric signal above the AERI noise level in the filtered data. The noise filter is applied to data collected at ARM’s tropical, midlatitude, and Arctic sites, demonstrating that the random variability in the data is reduced by 5% to over 450%, depending on the spectral element and location of the instrument. A seasonal analysis of the number of principal components required by the noise filter for each site shows a strong seasonal dependence in the atmospheric variability at the Arctic and midlatitude sites but not at the tropical site.

scholarly journals Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

2014 ◽  
Vol 7 (6) ◽  
pp. 5601-5650
Author(s):  
U. Amato ◽  
L. Lavanant ◽  
G. Liuzzi ◽  
G. Masiello ◽  
C. Serio ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Spectral Resolution ◽  
Principal Component ◽  
Real Data ◽  
Scientific Basis ◽  
High Spectral Resolution ◽  
Initial Assessment ◽  
Data Sets ◽  
Infrared Imager ◽  
Cloud Mask

Abstract. We introduce a classification method (Cumulative Discriminant Analysis) of the Discriminant Analysis type to discriminate between cloudy and clear sky satellite observations in the thermal infrared. The tool is intended for the high spectral resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The Cumulative Discriminant Analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A Principal Component Analysis prior step is also introduced which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80%, except at high latitudes in their winter seasons.

scholarly journals Instrumental characteristics and potential greenhouse gas measurement capabilities of the Compact High-Spectral-Resolution Infrared Spectrometer: CHRIS

2020 ◽  
Vol 13 (7) ◽  
pp. 3769-3786
Author(s):  
Marie-Thérèse El Kattar ◽  
Frédérique Auriol ◽  
Hervé Herbin
Keyword(s):  
Greenhouse Gases ◽  
Spectral Resolution ◽  
Complete Information ◽  
Atmospheric Composition ◽  
High Spectral Resolution ◽  
Gaseous Species ◽  
Infrared Measurements ◽  
Gas Measurement ◽  
Portable Spectrometers ◽  
Infrared Spectrometer

Abstract. Ground-based high-spectral-resolution infrared measurements are an efficient way to obtain accurate tropospheric abundances of different gaseous species, in particular greenhouse gases (GHGs) such as CO2 and CH4. Many ground-based spectrometers are used in the NDACC and TCCON networks to validate the Level 2 satellite data, but their large dimensions and heavy mass make them inadequate for field campaigns. To overcome these problems, the use of portable spectrometers was recently investigated. In this context, this paper deals with the CHRIS (Compact High-Spectral-Resolution Infrared Spectrometer) prototype with unique characteristics such as its high spectral resolution (0.135 cm−1 nonapodized) and its wide spectral range (680 to 5200 cm−1). Its main objective is the characterization of gases and aerosols in the thermal and shortwave infrared regions. That is why it requires high radiometric precision and accuracy, which are achieved by performing spectral and radiometric calibrations that are described in this paper. Furthermore, CHRIS's capabilities to retrieve vertical CO2 and CH4 profiles are presented through a complete information content analysis, a channel selection and an error budget estimation in the attempt to join ongoing campaigns such as MAGIC (Monitoring of Atmospheric composition and Greenhouse gases through multi-Instruments Campaigns) to monitor GHGs and validate the actual and future space missions such as IASI-NG and Microcarb.

scholarly journals Cloud mask via cumulative discriminant analysis applied to satellite infrared observations: scientific basis and initial evaluation

2014 ◽  
Vol 7 (10) ◽  
pp. 3355-3372 ◽  
Author(s):  
U. Amato ◽  
L. Lavanant ◽  
G. Liuzzi ◽  
G. Masiello ◽  
C. Serio ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Spectral Resolution ◽  
Principal Component ◽  
Real Data ◽  
Scientific Basis ◽  
High Spectral Resolution ◽  
Initial Assessment ◽  
Data Sets ◽  
Infrared Imager ◽  
Cloud Mask

Abstract. We introduce a classification method (cumulative discriminant analysis) of the discriminant analysis type to discriminate between cloudy and clear-sky satellite observations in the thermal infrared. The tool is intended for the high-spectral-resolution infrared sounder (IRS) planned for the geostationary METEOSAT (Meteorological Satellite) Third Generation platform and uses IASI (Infrared Atmospheric Sounding Interferometer) data as a proxy. The cumulative discriminant analysis does not introduce biases intrinsic with the approximation of the probability density functions and is flexible enough to adapt to different strategies to optimize the cloud mask. The methodology is based on nine statistics computed from IASI spectral radiances, which exploit the high spectral resolution of the instrument and which effectively summarize information contained within the IASI spectrum. A principal component analysis prior step is also introduced, which makes the problem more consistent with the statistical assumptions of the methodology. An initial assessment of the scheme is performed based on global and regional IASI real data sets and cloud masks obtained from AVHRR (Advanced Very High Resolution Radiometer) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) imagers. The agreement with these independent cloud masks is generally well above 80 %, except at high latitudes in the winter seasons.

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