Tag: john xun yang

BAMS Features John Xun Yang

April 12, 2024

The Bulletin of the American Meteorological Society (BAMS) featured the work of ESSIC/CISESS scientist John Xun Yang in their March 2024 issue.

SatERR is a bottom-up approach, where the four types of errors including measurement, preprocessing, observation operator, and representativeness errors are generated from sources and forward propagate through radiances, science products, and data assimilation systems. This approach can quantify and partition errors and uncertainties in science products, and capture leading features of the most important errors in a statistical sense for data assimilation.

Leveraging Satellite Observations with a Comprehensive Simulator

January 29, 2024

Satellite observations are vital for weather forecasts, climate monitoring, and environmental studies. In recent years, there has been a concerted effort to develop methods for quantifying and representing errors associated with satellite observations. ESSIC scientist John Xun Yang has led a team of scientists in the creation of an error inventory simulator, the Satellite Error Representation and Realization (SatERR).

Fig 1. The retrieved total precipitable water (TPW) and temperature (500 mb) from TROPICs are in good agreement with ECMWF analysis.

Atmospheric Sounding from the CubeSat TROPICS Mission

February 13, 2023

ESSIC/CISESS scientists John Xun Yang, Yong-Keun Lee, and Christopher Grassotti are co-authors on a new paper titled “Atmospheric humidity and temperature sounding from the CubeSat TROPICS mission: Early performance evaluation with MiRS” in Remote Sensing of Environment.

Time series of the noise of 183±1 and ±3 GHz with the fixed and adaptive window. There is a significant noise reduction with the adaptive window as much as over 50%

An Adaptive Calibration Window for Noise Reduction of Satellite Microwave Radiometers

June 28, 2022

ESSIC scientists John Xun Yang, Yalei You, and Ralph Ferraro are co-authors on a new paper in Institute of Electrical and Electronics Engineers (IEEE) that describes a newly developed adaptive window for calibration on microwave sounders at EUMETSAT and NOAA.

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

May 4, 2022

ESSIC/CISESS scientists John Xun Yang, Yalei You, and Rachael Kroodsma are co-authors on a new paper in IEEE Transactions on Geoscience and Remote Sensing alongside Sidharth Misra from NASA Jet Propulsion Laboratory and William Blackwell from MIT Lincoln Laboratory. Blackwell is also a two-time speaker for the ESSIC Seminar Series, the most recent of which can be viewed here.

Figure: This is a precipitation event on 29 August 2020 over the Pacific Ocean near the lower California peninsula. (a) the cross-track radiometer precipitation data, (b) the conical scanning radiometer precipitation data, (c) the reference data for the event, and (d) the “morphed” radiometer precipitation data. The box shows the area of improvement due to morphing.

Improving Satellite Precipitation Retrieval

August 24, 2021

ESSIC/CISESS Scientists Yalei You, John Xun Yang, and Jun Dong have a new article on using “morphing” to improve rain data from cross-track scanning radiometers. The paper, titled “Improving Cross-track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing”, is in press at the Journal of Hydrometeorology.

Figure. Comparing different algorithms for estimating NEDT against TVAC measurement. The new algorithm out-performs traditional algorithms of EUMETSAT, UK MetOffice and NOAA. While the 183 GHz is prone to 1/f noise, the new algorithm produces an accurate estimate of NEDT and is superior to traditional algorithms.

A New Algorithm to Estimate Noise Equivalent Delta Temperature

August 24, 2021

ESSIC/CISESS Scientists John Xun Yang and Hu Yang have a new article in press at IEEE Transactions on Geoscience and Remote Sensing titled “A New Algorithm for Determining the Noise Equivalent Delta Temperature of In-Orbit Microwave Radiometers”.