scholarly journals First application of the optimal estimation method to retrieve temperature from pure rotational raman scatter lidar measurements

2018 ◽  
Vol 176 ◽  
pp. 01011
Author(s):  
S. Mahagammulla Gamage ◽  
A. Haefele ◽  
R.J. Sica
Keyword(s):  
Estimation Method ◽  
Optimal Estimation ◽  
Forward Model ◽  
Temperature Calibration ◽  
Lidar Measurements ◽  
Raman Scatter ◽  
Calibration Coefficients

We present the application of the Optimal Estimation Method (OEM) to retrieve atmospheric temperatures from pure rotational Raman (PRR) backscatter lidar measurements. A forward model (FM) is developed to retrieve temperature and tested using synthetic measurements. The OEM offers many advantages for this analysis, including eliminating the need to determine temperature calibration coefficients.

scholarly journals Retrieval of temperature from a multiple channel pure rotational Raman backscatter lidar using an optimal estimation method

2019 ◽  
Vol 12 (11) ◽  
pp. 5801-5816 ◽  
Author(s):  
Shayamila Mahagammulla Gamage ◽  
Robert J. Sica ◽  
Giovanni Martucci ◽  
Alexander Haefele
Keyword(s):  
Estimation Method ◽  
Optimal Estimation ◽  
Uncertainty Budget ◽  
Temperature Measurements ◽  
New Method ◽  
Retrieval Algorithm ◽  
Coupling Constant ◽  
Raman Lidar ◽  
Multiple Channel ◽  
Lidar Measurements

Abstract. We present a new method for retrieving temperature from pure rotational Raman (PRR) lidar measurements. Our optimal estimation method (OEM) used in this study uses the full physics of PRR scattering and does not require any assumption of the form for a calibration function nor does it require fitting of calibration factors over a large range of temperatures. The only calibration required is the estimation of the ratio of the lidar constants of the two PRR channels (coupling constant) that can be evaluated at a single or multiple height bins using a simple analytic expression. The uncertainty budget of our OEM retrieval includes both statistical and systematic uncertainties, including the uncertainty in the determination of the coupling constant on the temperature. We show that the error due to calibration can be reduced significantly using our method, in particular in the upper troposphere when calibration is only possible over a limited temperature range. Some other advantages of our OEM over the traditional Raman lidar temperature retrieval algorithm include not requiring correction or gluing to the raw lidar measurements, providing a cutoff height for the temperature retrievals that specifies the height to which the retrieved profile is independent of the a priori temperature profile, and the retrieval's vertical resolution as a function of height. The new method is tested on PRR temperature measurements from the MeteoSwiss RAman Lidar for Meteorological Observations system in clear and cloudy sky conditions, compared to temperature calculated using the traditional PRR calibration formulas, and validated with coincident radiosonde temperature measurements in clear and cloudy conditions during both daytime and nighttime.

Studies for the Odin sub-millimetre radiometer. II. Retrieval methodology

2002 ◽  
Vol 80 (4) ◽  
pp. 341-356 ◽  
Author(s):  
Ph. Baron ◽  
Ph. Ricaud ◽  
J de la Noë ◽  
J EP Eriksson ◽  
F Merino ◽  
...  
Keyword(s):  
Frequency Band ◽  
Estimation Method ◽  
High Sensitivity ◽  
Optimal Estimation ◽  
Forward Model ◽  
Model Errors ◽  
Target Species ◽  
Spectral Parameters ◽  
Chlorine Monoxide ◽  
Standard Code

This paper presents the first algorithm developed to retrieve atmospheric vertical profiles of trace gases from calibrated spectra measured by the sub-millimetre radiometer (SMR) onboard the Odin satellite. An estimation of atmospheric profiles is obtained by means of an inversion of the spectra using the Optimal Estimation Method. Great attention is paid to the study of the simultaneous retrieval of several species and nonlinearity effects. The measurement response is defined to give the altitude domain of a good retrieval. Main sources of measurement and forward model errors are characterized and separated into two categories: the fixed errors and the variable errors. We define a standard retrieval strategy that can be applied to theoretically investigate any frequency band of any observing Odin mode. For each frequency band, two categories of species are defined: the target species, i.e., the main species to be retrieved, and the interfering species, i.e., molecules emitting an interfering radiance in the observed band. The standard code is based upon an inversion of spectra using a linearized forward model and simultaneously estimates target species and interfering species. As an example, inversions of synthetic noise-free spectra of ozone and chlorine monoxide within an autocorrelator band ranging from 501.18 to 501.58 GHz are shown to behave as expected in the middle stratosphere and in the lower mesosphere. The error analysis shows retrieval limitations in the lower stratosphere that are mainly induced by the high sensitivity of the retrieval to parameters such as tangent height, accuracy in the vertical profile of the interfering species, and spectral parameters of both target lines and interfering lines. PACS Nos.: 42.68Ay, 07.07Df, 07.57Kp

scholarly journals Validation of optimal estimation method retrievals of middle atmospheric temperature

2018 ◽  
Vol 176 ◽  
pp. 03001
Author(s):  
Ali Jalali ◽  
R. J. Sica ◽  
Alexander Haefele
Keyword(s):  
Quantitative Determination ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Atmospheric Temperature ◽  
Measurement Noise ◽  
Traditional Methods ◽  
Lidar Measurements ◽  
Random Uncertainties

OEM (Optimal Estimation Method) retrievals of temperature from lidar measurements are robust and practical (Sica and Haefele, 2015). They offer significant improvements over traditional methods. We will show a climatology of +360 nights of measurements from the Purple Crow Lidar and the improvements offered using an OEM, including the quantitative determination of the top altitude of the retrieval and the evaluation of the various systematic and random uncertainties due to measurement noise.

scholarly journals Stratospheric Ozone Density Retrieval Using the Optimal Estimation Method (OEM)

2018 ◽  
Vol 176 ◽  
pp. 03006
Author(s):  
Ghazal Farhani ◽  
R. J. Sica ◽  
Sophie Godin-Beekmann ◽  
Alexander Haefele
Keyword(s):  
Traditional Method ◽  
Inverse Method ◽  
Stratospheric Ozone ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Uncertainty Budget ◽  
Forward Model ◽  
Differential Absorption ◽  
Differential Absorption Lidar

We use an Optimal Estimation Method (OEM) to retrieve ozone profiles from the CANDAC Stratospheric Ozone Differential Absorption Lidar in Eureka, Canada. The OEM is a well known inverse method in which a forward model (FM) is used to describe the instrument and geophysical situation. We have developed a FM and are testing its validity using synthetic measurements. We will present the advantages of using OEM retrievals over the traditional method, including a full uncertainty budget.

scholarly journals Retrieval of Temperature From a Multiple Channel Pure Rotational Raman-Scatter Lidar Using an Optimal Estimation Method

2019 ◽  
Author(s):  
Shayamila Mahagammulla Gamage ◽  
Robert J. Sica ◽  
Giovanni Martucci ◽  
Alexander Haefele
Keyword(s):  
Estimation Method ◽  
Optimal Estimation ◽  
Temperature Measurements ◽  
New Method ◽  
Retrieval Algorithm ◽  
Coupling Constant ◽  
Raman Lidar ◽  
Night Time ◽  
Multiple Channel ◽  
Lidar Measurements

Abstract. We present a new method for retrieving temperature from Pure Rotational Raman (PRR) lidar measurements. Our Optimal Estimation Method (OEM) used in this study uses the full physics of PRR scattering and does not require any assumption of the form for a calibration function nor does it require fitting of calibration factors over a large range of temperatures. The only calibration required is the estimation of the ratio of the lidar constants of the two PRR channels (coupling constant) that can be evaluated at a single or multiple height bins using a simple analytic expression. The uncertainty budget of our OEM retrieval includes both statistical and systematic uncertainties, including the uncertainty in the determination of the coupling constant on the temperature. We show that the error due to calibration can be reduced significantly using our method, in particular in the upper troposphere when calibration is only possible over a limited temperature range. Some other advantages of our OEM over the traditional Raman lidar temperature retrieval algorithm include not requiring correction or gluing to the raw lidar measurements, providing a cutoff height for the temperature retrievals that specifies the height to which the retrieved profile is independent of the a priori temperature profile, and the retrieval's vertical resolution as a function of height. The new method is tested on PRR temperature measurements from the MeteoSwiss Raman Lidar for Meteorological Observations system in different sky conditions, compared to temperature calculated using the traditional PRR calibration formulas, and validated with coincident radiosonde temperature measurements in clear and cloudy conditions during both day and night time.

scholarly journals How to apply the optimal estimation method to your lidar measurements for improved retrievals of temperature and composition

2018 ◽  
Vol 176 ◽  
pp. 01025
Author(s):  
R. J. Sica ◽  
A. Haefele ◽  
A. Jalali ◽  
S. Gamage ◽  
G. Farhani
Keyword(s):  
Remote Sensing ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Uncertainty Budget ◽  
Ability To Work ◽  
Passive Remote Sensing ◽  
Random Uncertainty ◽  
Lidar Measurements ◽  
History Of ◽  
History Of Use

The optimal estimation method (OEM) has a long history of use in passive remote sensing, but has only recently been applied to active instruments like lidar. The OEM’s advantage over traditional techniques includes obtaining a full systematic and random uncertainty budget plus the ability to work with the raw measurements without first applying instrument corrections. In our meeting presentation we will show you how to use the OEM for temperature and composition retrievals for Rayleigh-scatter, Ramanscatter and DIAL lidars.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals On the limit to resolution and information on basal properties obtainable from surface data on ice streams

2008 ◽  
Vol 2 (2) ◽  
pp. 167-178 ◽  
Author(s):  
G. H. Gudmundsson ◽  
M. Raymond
Keyword(s):  
Flow Line ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Surface Flow ◽  
Ice Thickness ◽  
Ice Streams ◽  
Statistical Estimates ◽  
Surface Data ◽  
Highly Sensitive ◽  
Surface Measurements

Abstract. An optimal estimation method for simultaneously determining both basal slipperiness and basal topography from variations in surface flow velocity and topography along a flow line on ice streams and ice sheets is presented. We use Bayesian inference to update prior statistical estimates for basal topography and slipperiness using surface measurements along a flow line. Our main focus here is on how errors and spacing of surface data affect estimates of basal quantities and on possibly aliasing/mixing between basal slipperiness and basal topography. We find that the effects of spatial variations in basal topography and basal slipperiness on surface data can be accurately separated from each other, and mixing in retrieval does not pose a serious problem. For realistic surface data errors and density, small-amplitude perturbations in basal slipperiness can only be resolved for wavelengths larger than about 50 times the mean ice thickness. Bedrock topography is well resolved down to horizontal scale equal to about one ice thickness. Estimates of basal slipperiness are not significantly improved by accurate prior estimates of basal topography. However, retrieval of basal slipperiness is found to be highly sensitive to unmodelled errors in basal topography.

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