The Experts below are selected from a list of 2535 Experts worldwide ranked by ideXlab platform
Sarah L Dance - One of the best experts on this subject based on the ideXlab platform.
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
Liang Feng - One of the best experts on this subject based on the ideXlab platform.
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
Paul I Palmer - One of the best experts on this subject based on the ideXlab platform.
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
H Bosch - One of the best experts on this subject based on the ideXlab platform.
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
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estimating surface co 2 fluxes from space borne co 2 dry air mole fraction observations using an ensemble kalman filter
Atmospheric Chemistry and Physics, 2008Co-Authors: Liang Feng, Paul I Palmer, H Bosch, Sarah L DanceAbstract:Abstract. We have developed an ensemble Kalman Filter (EnKF) to estimate 8-day regional surface fluxes of CO2 from space-borne CO2 dry-air mole fraction observations (XCO2) and evaluate the approach using a series of synthetic experiments, in preparation for data from the NASA Orbiting Carbon Observatory (OCO). The 32-day duty cycle of OCO alternates every 16 days between nadir and glint measurements of backscattered solar radiation at short-wave infrared wavelengths. The EnKF uses an ensemble of states to represent the error covariances to estimate 8-day CO2 surface fluxes over 144 geographical regions. We use a 12×8-day Lag Window, recognising that XCO2 measurements include surface flux information from prior time Windows. The observation operator that relates surface CO2 fluxes to atmospheric distributions of XCO2 includes: a) the GEOS-Chem transport model that relates surface fluxes to global 3-D distributions of CO2 concentrations, which are sampled at the time and location of OCO measurements that are cloud-free and have aerosol optical depths
Zahir M. Hussain - One of the best experts on this subject based on the ideXlab platform.
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Adaptive instantaneous frequency estimation of multicomponent FM signals using quadratic time-frequency distributions
2014Co-Authors: Zahir M. Hussain, Boualem BoashashAbstract:Abstract—An adaptive approach to the estimation of the instan-taneous frequency (IF) of nonstationary mono- and multicompo-nent FM signals with additive Gaussian noise is presented. The IF estimation is based on the fact that quadratic time–frequency dis-tributions (TFDs) have maxima around the IF law of the signal. It is shown that the bias and variance of the IF estimate are func-tions of the Lag Window length. If there is a bias-variance tradeoff, then the optimal Window length for this tradeoff depends on the un-known IF law. Hence, an adaptive algorithm with a time-varying and data-driven Window length is needed. The adaptive algorithm can utilize any quadratic TFD that satisfies the following three conditions: First, the IF estimation variance given by the chosen distribution should be a continuously decreasing function of the Window length, whereas the bias should be continuously increasing so that the algorithm will converge at the optimal Window length for the bias-variance tradeoff; second, the time-Lag kernel filter of the chosen distribution should not perform narrowband filtering in the Lag direction in order to not interfere with the adaptive Window in that direction; third, the distribution should perform effective cross-terms reduction while keeping high resolution in order to be efficient for multicomponent signals. A quadratic distribution with high resolution, effective cross-terms reduction and no Lag fil-tering is proposed. The algorithm estimates multiple IF laws by using a tracking algorithm for the signal components and utilizing the property that the proposed distribution enables nonparametric component amplitude estimation. An extension of the proposed TFD consisting of the use of time-only kernels for adaptive IF esti-mation is also proposed. Index Terms—Amplitude estimation, instantaneous frequency estimation, multicomponent signals, reduced interference distri-butions, time–frequency analysis. I
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Adaptive instantaneous frequency estimation of multicomponent FM signals using quadratic time-frequency distributions
IEEE Transactions on Signal Processing, 2002Co-Authors: Zahir M. HussainAbstract:An adaptive approach to the estimation of the instantaneous frequency (IF) of nonstationary mono- and multicomponent FM signals with additive Gaussian noise is presented. The IF estimation is based on the fact that quadratic time-frequency distributions (TFDs) have maxima around the IF law of the signal. It is shown that the bias and variance of the IF estimate are functions of the Lag Window length. If there is a bias-variance tradeoff, then the optimal Window length for this tradeoff depends on the unknown IF law. Hence, an adaptive algorithm with a time-varying and data-driven Window length is needed. The adaptive algorithm can utilize any quadratic TFD that satisfies the following three conditions: First, the IF estimation variance given by the chosen distribution should be a continuously decreasing function of the Window length, whereas the bias should be continuously increasing so that the algorithm will converge at the optimal Window length for the bias-variance tradeoff, second, the time-Lag kernel filter of the chosen distribution should not perform narrowband filtering in the Lag direction in order to not interfere with the adaptive Window in that direction; third, the distribution should perform effective cross-terms reduction while keeping high resolution in order to be efficient for multicomponent signals. A quadratic distribution with high resolution, effective cross-terms reduction and no Lag filtering is proposed. The algorithm estimates multiple IF laws by using a tracking algorithm for the signal components and utilizing the property that the proposed distribution enables nonparametric component amplitude estimation. An extension of the proposed TFD consisting of the use of time-only kernels for adaptive IF estimation is also proposed.
