Abstract
One of the limitations in using spaceborne, microwave radiometer data for atmospheric remote sensing is the nonuniform spatial resolution. Remapping algorithms can be applied to the data to ameliorate this limitation. In this paper, two remapping algorithms, the Backus–Gilbert inversion (BGI) technique and the filter algorithm (AFA), widely used in the operational data preprocessing of the Advanced Technology Microwave Sounder (ATMS), are investigated. The algorithms are compared using simulations and actual ATMS data. Results show that both algorithms can effectively enhance or degrade the resolution of the data. The BGI has a higher remapping accuracy than the AFA. It outperforms the AFA by producing less bias around coastlines and hurricane centers where the signal changes sharply. It shows no obvious bias around the scan ends where the AFA has a noticeable positive bias in the resolution-enhanced image. However, the BGI achieves the resolution enhancement at the expense of increasing the noise by 0.5 K. The use of the antenna pattern instead of the point spread function in the algorithm causes the persistent bias found in the AFA-remapped image, leading not only to an inaccurate antenna temperature expression but also to the neglect of the geometric deformation of the along-scan field-of-views.
Highlights
Microwave radiometers have wide applications in atmospheric remote sensing and provide essential inputs to numerical weather prediction models [1,2]
The applications of spaceborne, microwave radiometer data in atmospheric remote sensing are often hampered by the nonuniform spatial resolutions available from various sensors and at different frequencies
In this paper, presented was a comparison of two typical methods, the Backus–Gilbert inversion (BGI) and a filter algorithm (AFA), remapping in spatial and frequency domains, respectively. They both have long been applied in Advanced Technology Microwave Sounder (ATMS) operational data preprocessing
Summary
Microwave radiometers have wide applications in atmospheric remote sensing and provide essential inputs to numerical weather prediction models [1,2]. Since the development by Hu et al [30] of a deconvolution algorithm based on a convolutional neural network, the deep learning technique has been applied to microwave radiometer data to enhance their resolution [31,32,33]. Among these algorithms, only a few manage to match the intrinsic antenna pattern of the observations. The result is an estimate of the antenna temperature (Ta) distribution the sensor would have measured, given a prescribed antenna pattern that is different from the actual one
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