Learning spatial response functions from large multi-sensor AIRS and MODIS datasets

2021 ◽  
Author(s):  
Igor Yanovsky ◽  
Thomas S. Pagano ◽  
Evan M. Manning ◽  
Steven E. Broberg ◽  
Hartmut H. Aumann ◽  
...  
Keyword(s):  
Response Functions ◽  
Spatial Response

scholarly journals Radiance Comparisons of MODIS and AIRS Using Spatial Response Information

2010 ◽  
Vol 27 (8) ◽  
pp. 1331-1342 ◽  
Author(s):  
M. M. Schreier ◽  
B. H. Kahn ◽  
A. Eldering ◽  
D. A. Elliott ◽  
E. Fishbein ◽  
...  
Keyword(s):  
Spectral Response ◽  
Lapse Rate ◽  
Response Functions ◽  
Temperature Lapse Rate ◽  
Earth Observing System ◽  
Spatial Response ◽  
Response Information ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Water Vapor Mixing Ratio

Abstract The combination of multiple satellite instruments on a pixel-by-pixel basis is a difficult task, even for instruments collocated in space and time, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) on board the Earth Observing System (EOS) Aqua. Toward the goal of an improved collocation methodology, the channel- and scan angle–dependent spatial response functions of AIRS that were obtained from prelaunch measurements and calculated impacts from scan geometry are shown within the context of radiance comparisons. The AIRS spatial response functions are used to improve the averaging of MODIS radiances to the AIRS footprint, and the variability of brightness temperature differences (ΔTb) between MODIS and AIRS are quantified on a channel-by-channel basis. To test possible connections between ΔTb and the derived level 2 (L2) datasets, cloud characteristics derived from MODIS are used to highlight correlations between these quantities and ΔTb, especially for ice clouds in H2O and CO2 bands. Furthermore, correlations are quantified for temperature lapse rate (dT/dp) and the magnitude of water vapor mixing ratio (q) obtained from AIRS L2 retrievals. Larger values of dT/dp and q correlate well to larger values of ΔTb in the H2O and CO2 bands. These correlations were largely eliminated or reduced after the MODIS spectral response functions were shifted by recommended values. While this investigation shows that the AIRS spatial response functions are necessary to reduce the variability and skewness of ΔTb within heterogeneous scenes, improved knowledge about MODIS spectral response functions is necessary to reduce biases in ΔTb.

scholarly journals A Generalized Simulation Capability for Rotating Beam Scatterometers

2018 ◽  
Author(s):  
Zhen Li ◽  
Ad Stoffelen ◽  
Anton Verhoef
Keyword(s):  
Incidence Angle ◽  
Retrieval Algorithm ◽  
Pencil Beam ◽  
Wind Vector ◽  
Response Functions ◽  
Rotating Beam ◽  
Retrieval Performance ◽  
Wind Retrieval ◽  
Spatial Response

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT, WindRad on FY-3E and SeaWinds on QuikScat. Besides the comparison of the so-called 1st rank-solution retrieval skill of the input wind field, other Figure of Merits (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the Wind Vector Cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir, but also to investigate resolution and accuracy through incorporating and analysing the spatial response functions of the simulated Level-1B data for each WVC.

scholarly journals A physics-based approach to oversample multi-satellite, multispecies observations to a common grid

2018 ◽  
Vol 11 (12) ◽  
pp. 6679-6701 ◽  
Author(s):  
Kang Sun ◽  
Lei Zhu ◽  
Karen Cady-Pereira ◽  
Christopher Chan Miller ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Response Function ◽  
Gaussian Function ◽  
Computational Time ◽  
Response Functions ◽  
Fine Grid ◽  
Spatial Response ◽  
Sensitivity Distribution ◽  
Level 3 ◽  
Spatial Response Function ◽  
Level 2

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.

Measurement of spatial response functions of dosimetric detectors

2015 ◽  
Vol 60 (16) ◽  
pp. 6177-6194 ◽  
Author(s):  
Steffen Ketelhut ◽  
Ralf-Peter Kapsch
Keyword(s):  
Response Functions ◽  
Spatial Response

scholarly journals A physics-based approach to oversample multi-satellite, multi-species observations to a common grid

2018 ◽  
Author(s):  
Kang Sun ◽  
Lei Zhu ◽  
Karen Cady-Pereira ◽  
Christopher Chan Miller ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Response Function ◽  
Gaussian Function ◽  
Satellite Observation ◽  
Atmospheric Composition ◽  
Response Functions ◽  
Spatial Response ◽  
Sensitivity Distribution ◽  
Level 3 ◽  
Spatial Response Function ◽  
Level 2

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (Level 2) to a regular grid (Level 3). When averaging Level 2 data over a long time period to a target Level 3 grid significantly finer than Level 2 pixels, this process is referred to as "oversampling'". An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous approaches. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOV) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid resolution, the ground size of FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for high resolution oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grid resolutions finer than ~16 km. The generalized 2-D super Gaussian function also enables smoothing of the Level 3 results by decreasing the shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO2 products and IASI NH3 products, showing substantially improved visualization of trace gas distribution and local gradients.

SU-E-T-114: Characterization of the Spatial Response Functions of Ionization Chambers for Photon Beam Dosimetry

2012 ◽  
Vol 39 (6Part11) ◽  
pp. 3729-3729
Author(s):  
HK Looe ◽  
TS Stelljes ◽  
D Harder ◽  
B Poppe
Keyword(s):  
Photon Beam ◽  
Response Functions ◽  
Ionization Chambers ◽  
Spatial Response ◽  
Beam Dosimetry

scholarly journals A generalized simulation capability for rotating- beam scatterometers

2019 ◽  
Vol 12 (7) ◽  
pp. 3573-3594 ◽  
Author(s):  
Zhen Li ◽  
Ad Stoffelen ◽  
Anton Verhoef
Keyword(s):  
Incidence Angle ◽  
Retrieval Algorithm ◽  
Wind Vector ◽  
Response Functions ◽  
Rotating Beam ◽  
Retrieval Performance ◽  
Wind Retrieval ◽  
Figures Of Merit ◽  
Spatial Response

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.

A Modified Chair Geometry for Whole Body Counting

1976 ◽  
Vol 15 (05) ◽  
pp. 246-247
Author(s):  
S. C. Jain ◽  
G. C. Bhola ◽  
A. Nagaratnam ◽  
M. M. Gupta
Keyword(s):  
Marked Improvement ◽  
Point Of View ◽  
Whole Body ◽  
Spatial Response ◽  
Body Counting ◽  
The Subject ◽  
Whole Body Counting ◽  
The Buddha

SummaryIn the Marinelli chair, a geometry widely used in whole body counting, the lower part of the leg is seen quite inefficiently by the detector. The present paper describes an attempt to modify the standard chair geometry to minimise this limitation. The subject sits crossed-legged in the “Buddha Posture” in the standard chair. Studies with humanoid phantoms and a volunteer sitting in the Buddha posture show that this modification brings marked improvement over the Marinelli chair both from the point of view of sensitivity and uniformity of spatial response.

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