Comparison of the Remapping Algorithms for the Advanced Technology Microwave Sounder (ATMS)

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.

Spatial Resolution Matching of Microwave Radiometer Measurements Using Iterative Deconvolution With Close Loop Priors (ICLP)

“…The spatial resolution can be obtained by calculating the footprint size at the nadir [10], as shown in Fig. 6.…”

“…Furthermore, the local data in the land-sea transition zone are severely contaminated by the relatively sizeable measured footprint covering both terrestrial and ocean [6], [7], [8], leading to poor local data integrity. To match to lower-frequency channels with a low spatial resolution for multichannel joint retrieval, one common strategy is to average high-frequency channel data with a high spatial resolution [9], [10]. However, the multifrequency channel data with the finer spatial resolution are desperate to be obtained when collecting the small regional-scale distribution parameters with highprecision requirements [11].…”

Diurnal Variation in Cloud Liquid Water Path Derived from Five Cross-Track Microwave Radiometers Onboard Polar-Orbiting Satellites

“…The ATOVS and AVHRR Pre-processing Package (AAPP) algorithm employed in this study has been widely used in the operational processing of ATMS internationally [15][16][17]. Recently, Zhou and Yang [18] compared the AAPP algorithm with the Backus-Gilbert BG method for ATMS remapping. They showed that both algorithms have comparable noise reduction properties and can effectively increase or decrease the resolution of the ATMS for the window and sounding channels, respectively.…”

Improving ATMS Remapping Accuracy Using Adaptive Window and Noise-Tuning Method in Backus–Gilbert Inversion

“…One is the conversion of the swath-based measurements to a regular Earth-centered grid, equivalent to antenna pattern deconvolution [2]- [5]. The other is the matching of antenna pattern, which makes estimation of the antenna temperature (Ta) the sensors would have measured with the prescribed target antenna pattern [1][6] [7]. In both methods, if the generated measurements have finer (coarser) spatial resolution than the raw data, this process is also known as spatial resolution enhancement (degradation).…”

“…The Fourier filtering algorithm, manipulating the antenna pattern in frequency domain, also has applications in ATMS and MWRI data remapping [17]- [18]. The comparison of the BGI and the Fourier filtering algorithms [7] showed that the former has higher remapping accuracy than the latter one in both resolution enhancement and degradation. In recent years, various Convolutional Neural Networks have been designed for spatial resolution enhancement for microwave sensors, such as the MWRI onboard Fengyun-3 series of satellites [19] - [23].…”