Advances in ATMS Sensor Data Record (SDR) Algorithm Sciences
From ATMS TDR data, the differences between brightness temperature observations and simulated observations are calculated based on numerical weather predictions (aka O-B). For the upper ATMS temperature sounding channels, O-B exhibits a clear striping pattern (Bormann et al., 2013) in along-track direction. We propose to firstly use the principal component analysis (PCA) to isolate scan-dependent features such as the cross-track striping from the atmospheric signal, and then to use an Ensemble Empirical Mode Decomposition (EEMD) to extract the striping noise in ATMS Earth scene brightness temperature observations for both temperature and water vapor sounding channels. It is shown that the PC coefficient of the first PC mode, which mainly describes a scan-dependent feature of cross-track radiometer measurements, captures the striping noise. The EEMD is then applied to the PC coefficient to extract the first three high-frequency intrinsic mode functions (IMFs), which are denoted as the PC1/IMF3 noise. When the PC1/IMF3 noise is removed from the data, the striping noise is imperceptible in the global distribution of O-B for ATMS temperature sounding channels 1-16. Using the same method, it is demonstrated that the striping noise is also present in ATMS water vapor sounding channels 17-22. The magnitude of the ATMS striping noise is about 0.3 K for the temperature sounding channels and 1.0 K for the moisture sounding channels. The same technique is also applied to AMSU-A, AMSU-B and MHS. The striping noise is undetectable for AMSU-A but is present in both AMSU-B and MHS data.
The cross-calibrated measurements from Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit-A (AMSU-A) on board different NOAA polar-orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. Since ATMS inherited most of the sounding channels from its predecessor of AMSU, is important to extend AMSU data records with ATMS observations. However, the ATMS field of view is different from that of AMSU. In this study, the Backus-Gilbert method is used for optimally remapping the ATMS FOVs to AMSU-A like FOVs. Differences in ATMS brightness temperatures introduced by remapping are firstly illustrated over the region of Hurricane Sandy which occurred in October 2012. Using the simultaneous nadir overpass (SNO) method, AMSU and ATMS remap observations are then collocated in space and time and the inter-sensor biases are derived for each pair of channels. It is shown that the brightness temperatures from SNPP ATMS are now well merged into the AMSU data family after remap and cross-calibration.