Quantifying uncertainties in Atmospheric Infrared Sounder (AIRS) spatial response functions (SpatialRFs) is critical for enhancing the quality of climate data records. Previously, AIRS in-flight SpatialRF calibrations have utilized an incomplete set of pre-flight data obtained during instrument assembly. In our current work, we combined various pre-flight data sets to interpolate a complete set of pre-flight SpatialRFs. Concurrently, we employed two consecutive days of AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data to independently retrieve in-flight SpatialRFs for multiple channels and scan angles. Our methodology, based on our previous work, aligns AIRS and MODIS radiances to derive spatially corrected SpatialRFs. This paper compares in-flight SpatialRFs obtained from consecutive days and examines the discrepancies between pre-flight SpatialRFs from a completed set and in-flight SpatialRFs. Employing the total variation distance metric with two days of consecutive data revealed that the average uncertainties in in-flight SpatialRFs are approximately 5%, attributed mainly to noise, which establishes a baseline. In contrast, pre-flight SpatialRFs displayed an average uncertainty of about 16% when compared to the values derived in-flight. Our findings underscore the value of reconstruction techniques to derive in-flight SpatialRFs to validate pre-flight measurements, which is vital for ensuring the long-term reliability and precision of climate data records obtained from AIRS.
Accurate estimation of atmospheric wind velocity plays an important role in weather forecasting, flight safety assessment and cyclone tracking. Atmospheric data captured by infrared and microwave satellite instruments provide global coverage for weather analysis. Extracting wind velocity fields from such data has traditionally been done through feature tracking, correlation/matching or optical flow means from computer vision. However, these recover either sparse velocity estimates, oversmooth details or are designed for quasi-rigid body motions which over-penalize vorticity and divergence within the often turbulent weather systems. We propose a texture based optical flow procedure tailored for water vapor data. Our method implements an L1 data term and total variation regularizer and employs a structure-texture image decomposition to identify key features which improve recoveries and help preserve the salient vorticity and divergence structures. We extend this procedure to a multi-fidelity scheme and test both flow estimation methods on simulated over-ocean mesoscale convective systems and convective and extratropical cyclone datasets, each of which have accompanying ground truth wind velocities so we can qualitatively compare performances with existing optical flow methods.
We use large datasets from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive AIRS spatial response functions and study their potential variations over the mission. The new reconstructed spatial response functions can be used to reduce errors in the radiances in non-uniform scenes and improve products generated using both AIRS and MODIS data. AIRS spatial response functions are distinct for each of its 2378 channels and each of its 90 scan angles. We develop the mathematical model and the optimization framework for deriving spatial response functions for two AIRS channels with low water vapor absorption and various scan angles. We quantify uncertainties in the derived reconstructions and study how they differ from pre-flight spatial response functions. We show that our approach generates reconstructions that agree with the data more accurately compared to pre-flight spatial responses. We derive spatial response functions using data collected during successive dates in order to ascertain the repeatability of the reconstructed spatial response functions. We also compare the derived spatial response functions based on data collected in the beginning, the middle, and at the current state of the mission in order to study changes in reconstructions over time.
Microwave imaging has been widely used in the prediction and tracking of hurricanes, typhoons, and tropical storms. Due to the limitations of sensors, the acquired remote sensing data are usually blurry and have relatively low resolution, which calls for the development of fast algorithms for deblurring and enhancing the resolution. We propose an efficient algorithm for simultaneous image deconvolution and upsampling for low-resolution microwave hurricane data. Our model involves convolution, downsampling, and the total variation regularization. After reformulating the model, we are able to apply the alternating direction method of multipliers and obtain three subproblems, each of which has a closed-form solution. We also extend the framework to the multichannel case with the multichannel total variation regularization. A variety of numerical experiments on synthetic and real Advanced Microwave Sounding Unit and Microwave Humidity Sounder data were conducted. The results demonstrate the outstanding performance of the proposed method.
In the past decade, information theory has been studied extensively in computational imaging. In particular,
image matching by maximizing mutual information has been shown to yield good results in multimodal image
registration. However, there have been few rigorous studies to date that investigate the statistical aspect of
the resulting deformation fields. Different regularization techniques have been proposed, sometimes generating
deformations very different from one another. In this paper, we present a novel model for multimodal image
registration. The proposed method minimizes a purely information-theoretic functional consisting of mutual
information matching and unbiased regularization. The unbiased regularization term measures the magnitude of
deformations using either asymmetric Kullback-Leibler divergence or its symmetric version. The new multimodal
unbiased matching method, which allows for large topology preserving deformations, was tested using pairs of
two and three dimensional serial MRI images. We compared the results obtained using the proposed model to
those computed with a well-known mutual information based viscous fluid registration. A thorough statistical
analysis demonstrated the advantages of the proposed model over the multimodal fluid registration method when
recovering deformation fields and corresponding Jacobian maps.
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