Engineering topics

John Xun Yang

Publications and source records attributed to John Xun Yang.

3 records

NASA NTRS2022

Precipitation Phase Determination by Brightness Temperatures From ATMS

Previous studies used the temperature-related variables from model outputs (e.g., 2-m temperature) for precipitation phase determination (i.e., rain–snow separation). This study presents a new idea for precipitation phase determination using brightness temperatures (TBs) from Advanced Technology Microwave Sounder (ATMS). It is found that TB-based phase discrimination shows comparable determination performance to that from model outputs over land. In contrast, TB-based phase discrimination over the ocean performs noticeably worse than that from model outputs. Further analyses reveal that the phase determination performance over land from TBs slightly depends on the satellite’s local zenith angle. However, the determination performance over the ocean strongly depends on the satellite’s local zenith angle, with the skill score decreasing sharply from 0.74 near the nadir to 0.55 near the edge. These results imply that over land, TBs may be used directly for phase determination, which can be extended to other operational and future microwave sounders with similar channels available.

Yalei You↗

NASA NTRS2021

Quantifying and Characterizing Striping of Microwave Humidity Sounder with Observation and Simulation

Striping has been observed in the MetOp-A Microwave Humidity Sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W/G bands of MHS. Here we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and 1/f noise and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz that has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and 1/f noise remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that 1/f noise generates sharp, non-periodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of 1/f to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and190 GHz with the striping percentage over 50%, while lower in183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact in data assimilation and science products.

John Xun Yang↗

NASA NTRS2021

Improving Cross-track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing

Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.

Yalei You↗