scholarly journals Characterisation of GOME-2 formaldehyde retrieval sensitivity

2013 ◽  
Vol 6 (2) ◽  
pp. 371-386 ◽  
Author(s):  
W. Hewson ◽  
H. Bösch ◽  
M. P. Barkley ◽  
I. De Smedt
Keyword(s):  
Sensitivity Analysis ◽  
Pacific Ocean ◽  
Systematic Error ◽  
Differential Absorption ◽  
Satellite Measurements ◽  
Error Sources ◽  
Spectral Fitting ◽  
Tropospheric Photochemistry ◽  
Ozone Monitoring ◽  
Set Up

Abstract. Formaldehyde (CH2O) is an important tracer of tropospheric photochemistry, whose slant column abundance can be retrieved from satellite measurements of solar backscattered UV radiation, using differential absorption retrieval techniques. In this work a spectral fitting sensitivity analysis is conducted on CH2O slant columns retrieved from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument. Despite quite different spectral fitting approaches, the retrieved CH2O slant columns have geographic distributions that generally match expected CH2O sources, though the slant column magnitudes and corresponding uncertainties are particularly sensitive to the retrieval set-up. The choice of spectral fitting window, polynomial order, I0 correction, and inclusion of minor absorbers tend to result in the largest modulations of retrieved slant column magnitude and fit quality. However, application of a reference sector correction using observations over the remote Pacific Ocean is shown to largely homogenise the resulting CH2O vertical columns obtained with different retrieval settings, thereby largely reducing any systematic error sources from spectral fitting.

scholarly journals Characterisation of GOME-2 formaldehyde retrieval sensitivity

2012 ◽  
Vol 5 (5) ◽  
pp. 7095-7139
Author(s):  
W. Hewson ◽  
H. Bösch ◽  
M. P. Barkley ◽  
I. De Smedt
Keyword(s):  
Sensitivity Analysis ◽  
Pacific Ocean ◽  
Uv Radiation ◽  
Differential Absorption ◽  
Satellite Measurements ◽  
Spectral Fitting ◽  
Tropospheric Photochemistry ◽  
Ozone Monitoring ◽  
Geographic Distributions ◽  
Set Up

Abstract. Formaldehyde (HCHO) is an important tracer of tropospheric photochemistry, whose slant column abundance can be retrieved from satellite measurements of solar backscattered UV radiation, using differential absorption retrieval techniques. In this work a spectral fitting sensitivity analysis is conducted on HCHO slant columns retrieved from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument. Despite quite different spectral fitting approaches, the retrieved HCHO slant columns have geographic distributions that generally match expected HCHO sources, though the slant column magnitudes and corresponding uncertainties are particularly sensitive to the retrieval set-up. The choice of spectral fitting window, polynomial order, I0 correction, and inclusion of minor absorbers tend to have the largest impact on the fit residuals. However, application of a reference sector correction using observations over the remote Pacific Ocean, is shown to largely homogenise the resulting HCHO vertical columns, thereby largely reducing any systematic erroneous spectral fitting.

scholarly journals DOAS measurements of NO<sub>2</sub> from an ultralight aircraft during the Earth Challenge expedition

2012 ◽  
Vol 5 (8) ◽  
pp. 2057-2068 ◽  
Author(s):  
A. Merlaud ◽  
M. Van Roozendael ◽  
J. van Gent ◽  
C. Fayt ◽  
J. Maes ◽  
...  
Keyword(s):  
High Sensitivity ◽  
Large Field ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Satellite Measurements ◽  
Po Valley ◽  
The Earth ◽  
Ozone Monitoring ◽  
Set Up

Abstract. We report on airborne Differential Optical Absorption Spectroscopy (DOAS) measurements of NO2 tropospheric columns above South Asia, the Arabic peninsula, North Africa, and Italy in November and December 2009. The DOAS instrument was installed on an ultralight aircraft involved in the Earth Challenge project, an expedition of seven pilots flying on four ultralight aircraft between Australia and Belgium. The instrument recorded spectra in limb geometry with a large field of view, a set-up which provides a high sensitivity to the boundary layer NO2 while minimizing the uncertainties related to the attitude variations. We compare our measurements with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment 2) tropospheric NO2 products when the latter are available. Above Rajasthan and the Po Valley, two areas where the NO2 field is homogeneous, data sets agree very well. Our measurements in these areas are 0.1 ± 0.1 to 3 ± 1 × 1015 molec cm−2 and 2.6 ± 0.8 × 1016 molec cm−2, respectively. Flying downwind of Riyadh, our NO2 measurements show the structure of the megacity's exhaust plume with a higher spatial resolution than OMI. Moreover, our measurements are larger (up to 40%) than those seen by satellites. We also derived tropospheric columns when no satellite data were available if it was possible to get information on the visibility from satellite measurements of aerosol optical thickness. This experiment also provides a confirmation for the recent finding of a soil signature above desert.

scholarly journals Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li
Keyword(s):  
Neural Network ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Surface ◽  
Satellite Measurements ◽  
Surface Parameters ◽  
The North ◽  
The North Pacific ◽  
Bpnn Model

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

scholarly journals JWST Noise Floor. II. Systematic Error Sources in JWST NIRCam Time Series

2021 ◽  
Vol 161 (3) ◽  
pp. 115
Author(s):  
Everett Schlawin ◽  
Jarron Leisenring ◽  
Michael W. McElwain ◽  
Karl Misselt ◽  
Kenneth Don ◽  
...  
Keyword(s):  
Time Series ◽  
Systematic Error ◽  
Error Sources ◽  
Noise Floor

scholarly journals New-generation NASA Aura Ozone Monitoring Instrument (OMI) volcanic SO<sub>2</sub> dataset: Algorithm description, initial results, and continuation with the Suomi-NPP Ozone Mapping and Profiler Suite (OMPS)

2016 ◽  
Author(s):  
Can Li ◽  
Nickolay A. Krotkov ◽  
Simon Carn ◽  
Yan Zhang ◽  
Robert J. D. Spurr ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
Climate Impacts ◽  
Wavelength Shift ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Monitoring Instrument ◽  
Spectral Fitting ◽  
Ozone Monitoring ◽  
Pca Algorithm ◽  
New Generation

Abstract. Since the fall of 2004, the Ozone Monitoring Instrument (OMI) has been providing global monitoring of volcanic SO2 emissions, helping to understand their climate impacts and to mitigate aviation hazards. Here we introduce a new generation OMI volcanic SO2 dataset based on a principal component analysis (PCA) retrieval technique. To reduce retrieval noise and artifacts as seen in the current operational linear fit (LF) algorithm, the new algorithm, OMSO2VOLCANO, uses characteristic features extracted directly from OMI radiances in the spectral fitting, thereby helping to minimize interferences from various geophysical processes (e.g., O3 absorption) and measurement details (e.g., wavelength shift). To solve the problem of low bias for large SO2 total columns in the LF product, the OMSO2VOLCANO algorithm employs a table lookup approach to estimate SO2 Jacobians (i.e., the instrument sensitivity to a perturbation in the SO2 column amount) and iteratively adjusts the spectral fitting window to exclude shorter wavelengths where the SO2 absorption signals are saturated. To first order, the effects of clouds and aerosols are accounted for using a simple Lambertian equivalent reflectivity approach. As with the LF algorithm, OMSO2VOLCANO provides total column retrievals based on a set of pre-defined SO2 profiles from the lower troposphere to the lower stratosphere, including a new profile peaked at 13 km for plumes in the upper troposphere. Examples given in this study indicate that the new dataset shows significant improvement over the LF product, with at least 50 % reduction in retrieval noise over the remote Pacific. For large eruptions such as Kasatochi in 2008 (~ 1700 kt total SO2) and Sierra Negra in 2005 (> 1100 DU maximal SO2), OMSO2VOLCANO generally agrees well with other algorithms that also utilize the full spectral content of satellite measurements, while the LF algorithm tends to underestimate SO2. We also demonstrate that, despite the coarser spatial and spectral resolution of the Suomi National Polar-orbiting Partnership (Suomi-NPP) Ozone Mapping and Profiler Suite (OMPS) instrument, application of the new PCA algorithm to OMPS data produces highly consistent retrievals between OMI and OMPS. The new PCA algorithm is therefore capable of continuing the volcanic SO2 data record well into the future using current and future hyperspectral UV satellite instruments.

scholarly journals Averaging bias correction for the future space-borne methane IPDA lidar mission MERLIN

2018 ◽  
Vol 11 (10) ◽  
pp. 5865-5884 ◽  
Author(s):  
Yoann Tellier ◽  
Clémence Pierangelo ◽  
Martin Wirth ◽  
Fabien Gibert ◽  
Fabien Marnas
Keyword(s):  
Systematic Error ◽  
Optical Depth ◽  
Random Noise ◽  
Reference Value ◽  
Surface Flux ◽  
Differential Absorption ◽  
Satellite Mission ◽  
Space Agency ◽  
Future Space ◽  
Air Mixing

Abstract. The CNES (French Space Agency) and DLR (German Space Agency) project MERLIN is a future integrated path differential absorption (IPDA) lidar satellite mission that aims at measuring methane dry-air mixing ratio columns (XCH4) in order to improve surface flux estimates of this key greenhouse gas. To reach a 1 % relative random error on XCH4 measurements, MERLIN signal processing performs an averaging of data over 50 km along the satellite trajectory. This article discusses how to process this horizontal averaging in order to avoid the bias caused by the non-linearity of the measurement equation and measurements affected by random noise and horizontal geophysical variability. Three averaging schemes are presented: averaging of columns of XCH4, averaging of columns of differential absorption optical depth (DAOD) and averaging of signals. The three schemes are affected both by statistical and geophysical biases that are discussed and compared, and correction algorithms are developed for the three schemes. These algorithms are tested and their biases are compared on modelled scenes from real satellite data. To achieve the accuracy requirements that are limited to 0.2 % relative systematic error (for a reference value of 1780 ppb), we recommend performing the averaging of signals corrected from the statistical bias due to the measurement noise and from the geophysical bias mainly due to variations of methane optical depth and surface reflectivity along the averaging track. The proposed method is compliant with the mission relative systematic error requirements dedicated to averaging algorithms of 0.06 % (±1 ppb for XCH4=1780ppb) for all tested scenes and all tested ground reflectivity values.

Sensitivity Analysis of Error Motions and Geometric Errors in the Case of Sphere-Shaped Workpiece

2020 ◽  
Author(s):  
Zongze Li ◽  
Ryuta Sato ◽  
Keiichi Shirase
Keyword(s):  
Sensitivity Analysis ◽  
Machine Tools ◽  
End Milling ◽  
Geometric Errors ◽  
Machined Surface ◽  
Error Sources ◽  
Motion Error ◽  
Machining Center ◽  
Machining Errors ◽  
Five Axis

Abstract Motion error of machine tool feed axes influences the machined workpiece accuracy. However, the influences of each error sources are not identical; some errors do not influence the machined surface although some error have significant influences. In addition, five-axis machine tools have more error source than conventional three-axis machine tools, and it is very tough to predict the geometric errors of the machined surface. This study proposes a method to analyze the relationships between the each error sources and the error of the machined surface. In this study, a kind of sphere-shaped workpiece is taken as a sample to explain how the sensitivity analysis makes sense in ball-end milling. The results show that the method can be applied for the axial errors, such as motion reversal errors, to make it clearer to obverse the extent of each errors. In addition, the results also show that the presented sensitivity analysis is useful to investigate that how the geometric errors influence the sphere surface accuracy. It can be proved that the presented method can help the five-axis machining center users to predict the machining errors on the designed surface of each axes error motions.

Multiparameter Sensitivity Analysis of Operational Energy Efficiency for Inland River Ships Based on Backpropagation Neural Network Method

2015 ◽  
Vol 49 (1) ◽  
pp. 148-153 ◽  
Author(s):  
Xinping Yan ◽  
Xing Sun ◽  
Qizhi Yin
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Energy Efficiency ◽  
Variable Parameter ◽  
Energy Efficiency Improvement ◽  
Inland River ◽  
Network Method ◽  
Sensitivity Analysis Method ◽  
Operational Energy ◽  
Set Up

AbstractWith the introduction of energy efficiency operational indicator (EEOI) to inland river ships, a multiparameter sensitivity analysis method was proposed to analyze the parameters affecting the operational energy efficiency of inland river ships. On the basis of experimental data, a model based on a backpropagation artificial neural network (BP-ANN) for predicting the EEOI was set up. The accuracy of this predictive model was verified. On the basis of weights and threshold values of each variable parameter gained in the trained BP-ANN, a Garson algorithm was used for calculating the parameter sensitivity factors. Results showed that, besides the engine speed, the environment conditions would also play a big part in the operational energy efficiency of inland river ships. The conclusion provides a foundation for engaging the energy efficiency improvement strategies for inland river ships.

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