Radiance Comparisons of MODIS and AIRS Using Spatial Response Information

Schreier, M. M.; Kahn, Brian H.; Eldering, A.; Elliott, Denis A.; Fishbein, Evan; Irion, F. W.; Pagano, Thomas S.

doi:10.1175/2010jtecha1424.1

Cited by 34 publications

(37 citation statements)

References 25 publications

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“…The matching approach uses a nearest-neighbor technique weighted by the sensor spatial response function (Schreier et al, 2010). The mean, variance, and skewness of MODIS cloud properties at 1 or 5 km resolution is retained within a larger 45 km resolution AIRS/AMSU field of regard (FOR), while MERRA's 1/2 • × 2/3 • resolution thermodynamic and dynamic variables are matched to the nearest AIRS/AMSU FOR.…”

Section: Datamentioning

confidence: 99%

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

et al. 2017

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Abstract. The global-scale patterns and covariances of subtropical marine boundary layer (MBL) cloud fraction and spatial variability with atmospheric thermodynamic and dynamic fields remain poorly understood. We describe an approach that leverages coincident NASA A-train and the Modern Era Retrospective-Analysis for Research and Applications (MERRA) data to quantify the relationships in the subtropical MBL derived at the native pixel and grid resolution. A new method for observing four subtropical oceanic regions that capture transitions from stratocumulus to trade cumulus is demonstrated, where stratocumulus and cumulus regimes are determined from infrared-based thermodynamic phase. Visible radiances are normally distributed within stratocumulus and are increasingly skewed away from the coast, where trade cumulus dominates. Increases in MBL depth, wind speed, and effective radius (r e ), and reductions in 700-1000 hPa moist static energy differences and 700 and 850 hPa vertical velocity correspond with increases in visible radiance skewness. We posit that a more robust representation of the cloudy MBL is obtained using visible radiance rather than retrievals of optical thickness that are limited to a smaller subset of cumulus. The method using the combined A-train and MERRA data set has demonstrated that an increase in r e within shallow cumulus is strongly related to higher MBL wind speeds that further correspond to increased precipitation occurrence according to CloudSat, previously demonstrated with surface observations. Hence, the combined data sets have the potential of adding global context to process-level understanding of the MBL.

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Section: Datamentioning

confidence: 99%

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

et al. 2017

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“…7, the balance between tessellation and discretization errors depends on both the target grid resolution and the deviation of satellite spatial response function from an ideal 2-D boxcar shape. The uncertainty in the knowledge of 5 the spatial response functions is not considered here, but the spatial response function can be characterized prelaunch and validated on-orbit (Schreier et al, 2010;de Graaf et al, 2016;Sihler et al, 2017). For all three cases, the tessellation error significantly outweighs the discretization error at 1 km oversampling resolution, by a factor of 4 for IASI and over 200 for OMI.…”

Section: Balancing the Errors From Tessellation And Discretization Ofmentioning

confidence: 99%

“…However, depending on target grid resolution and the spatial response function of specific satellite observations, this may be too strong of an assumption. For example, Schreier et al (2010) characterized the complex spatial response function of the AIRS instrument and used it to improve the comparison of radiances measured by AIRS and MODIS. de Graaf et al (2016) and Sihler et al (2017) derived in-flight spatial response function of 10 OMI using collocated MODIS radiance.…”

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confidence: 99%

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

Sun¹,

Zhu²,

Cady‐Pereira³

et al. 2018

Preprint

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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 obser-5 vation 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 10 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 15 shape-determining exponents, useful for high noise level or sparse satellite datasets. This physical oversampling is applied to OMI NO 2 products and IASI NH 3 products, showing substantially improved visualization of trace gas distribution and local gradients.1 Atmos. Meas. Tech. Discuss., https://doi

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“…An average AIRS SpatialRF is first calculated for the observation (see Schreier et al, 2010). The SpatialRF varies by scan angle and spectral channel, and we make a simple average of the SpatialRF using only the channels used in the retrieval.…”

Section: Modis Cloud a Priori Information And Mapping To Airs Footprintmentioning

confidence: 99%

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

Irion¹,

Kahn²,

Schreier³

et al. 2017

Preprint

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Abstract. Single-footprint Atmospheric Infrared Sounder spectra are used in an optimal estimation-based algorithm (AIRS-OE) for simultaneous retrieval of atmospheric temperature, water vapor, surface temperature, cloud-top temperature, effective cloud optical depth and effective cloud particle radius. In a departure from currently operational AIRS retrievals (AIRS-V6), cloud 10 scattering and absorption are in the radiative transfer forward model, and Level 1b AIRS thermal infrared data are used directly rather than Level 2 cloud-cleared spectra. Coincident MODIS Level 2 cloud data are used for cloud a priori. Using Level 1b spectra improves the horizontal resolution of the AIRS retrieval from ~45 km to ~13.5 km at nadir, but as microwave data are not used, retrieval is not made at altitudes below thick clouds. An outline of the AIRS-OE retrieval procedure and information content analysis is presented. Initial comparison of AIRS-OE to AIRS-V6 results show increased horizontal detail in the water 15 vapor and relative humidity fields in the free troposphere above clouds. Comparisions of temperature, water vapor and relative humidity profiles against coincident radiosondes show good agreement. Future improvements to the retrieval algorithm, and to the forward model in particular, are discussed.

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Radiance Comparisons of MODIS and AIRS Using Spatial Response Information

Cited by 34 publications

References 25 publications

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer

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

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

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