Accurate global observations from space are critical for global climate change study. However, atmospheric temperature trend derived from spaceborne microwave instruments remains a subject of debate, due mainly to the uncertainty in characterizing the long-term drift of instrument calibration. Thus, a highly stable target with a well-known microwave radiation is required to evaluate the long-term calibration stability. This paper develops a new model to simulate the lunar emission at microwave frequencies, and the model is then used for monitoring the stability of the Advanced Technology Microwave Sounder (ATMS) onboard Suomi NPP satellite. It is shown that the ATMS cold space view of lunar radiation agrees well with the model simulation during the past five years and this instrument is capable of serving the reference instrument for atmospheric temperature trending studies, and connecting the previous generation of microwave sounders from NOAA-15 to the future Joint Polar Satellite System Microwave Sounder onboard NOAA-20 satellite.

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.

Reported here are disk-integrated Moon surface microwave brightness temperature ( $Tb$ ) retrievals covering the frequency range of 23–183 GHz. Full Moon observations obtained from the advanced technology microwave sounder (ATMS) onboard the NOAA-20 satellite during a special spacecraft pitch–maneuver operation forms the basis of the retrievals. Instrument nonlinearity, Earth sidelobe contamination, cosmic background radiation, and reflector thermal emission corrections are applied to the observations to obtain accurate values of the Moon’s $Tb$ at all frequencies. The measured full Moon $Tb$ ranges from ~240 to 293 K with frequency increases from 23 to 183 GHz. A clear frequency trend is detected when the brightness temperature increases.

One of the data fusion issues for observations from multiple space-borne microwave sensors is the non-uniform spatial resolution. Although the Backus-Gilbert Inversion (BGI) algorithm has long been used for the Advanced Technology Microwave Sounder (ATMS) antenna pattern matching, previous studies showed that it has difficulty in accurate remapping from the coarser to the finer observations. Since BGI tends to enhance the data’s high spatial frequency components including both information and noise, it is a challenge to increase the spatial resolution while maintaining an acceptable noise level. This study unveils that the main cause of this issue is the insufficiency of the information provided by the conventional fixed reconstruction window. An adaptive window method is applied to provide sufficient information for the reconstruction at each scan position. In addition, a new noise tuning method is proposed to eliminate the scan-angle-dependent features in the noise caused by the sensor’s cross-track scanning manner. Results from simulations and NOAA ATMS data show that compared to the fixed window, the new method can significantly reduce the bias stemming from the resolution difference. The issue of the deterioration of the resolution enhancement capability near the scan edge in the fixed window method has been largely ameliorated. The overall Root-Mean-Square error is declined by 30%. The new noise tuning method is capable of suppressing the noise level at around 0.6 K over scan.

We develop a new monthly zonal mean climatology (MMC) in the upper troposphere and lower stratosphere from September 2006 to July 2023 using the dry temperature profiles from multiple global navigation satellite system (GNSS) radio occultation (RO) missions processed by the GNSS RO Science and Data Center (SDC) at the NOAA Center for Satellite Applications and Research (STAR). The multiple RO missions include Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC‐1), SPIRE, and Meteorological Operational satellite (MetOp)‐A, ‐B, ‐C. We compare collocated profiles from multiple STAR‐processed RO missions to ensure the consistency of the data used in the construction of STAR MMC. Because the Constellation Observing System for Meteorology, Ionosphere, and Climate‐2 (COSMIC‐2) contains a noticeable temperature difference compared with other RO missions, we decided not to include it. We validate the robustness of the sampling error correction method through three reanalyses: ERA‐5, MERRA‐2, and JRA‐55. The result shows the uncertainty caused by using different reanalyses is negligible. This STAR MMC is then compared with the ROM SAF MMC and the MMC derived from these three reanalyses, exhibiting general consistency. Various climate signals, including quasi‐biennial oscillation (QBO) and El Niño–Southern Oscillation (ENSO), can be identified from STAR MMC. The global temperature trends from STAR MMC show a prominent warming of 0.310 ± 0.128 K/Decade in the upper troposphere and a robust cooling of −0.295 ± 0.145 K/Decade in the mid‐stratosphere. These results demonstrate that STAR MMC can capture climate signals and monitor long‐term climate change.