GSICS Microwave Sub-Group/ Lunar Calibration Focus Group meeting 2026-03-11

Agenda

1. Martin Burgdorf (University of Hamburg) – The Annual Variation in the Moon’s Brightness Temperature
2. Dezhang Li (Fudan University) – Initial analysis of Lunar Intrusion Events in AWS Deep-Space Views
3. Tim Hewison (EUMETSAT) – AWS Lunar Intrusion Analysis

Attendees

Tim Hewison (Chair)

TBD

Quick Recap

The meeting focused on analyzing lunar intrusion events in AWS Deep Space Views and discussing the calibration of microwave sounders. Martin presented his analysis of annual variations in the Moon’s brightness temperature, comparing observations with different models and highlighting discrepancies between them. Dezhang shared his initial analysis of lunar intrusion events in AWS data, which Tim provided context on and suggested improvements for. The group discussed the challenges of calibrating AWS due to inaccurate moon angle calculations and potential strategies to mitigate these issues. Tim shared his previous work on analyzing lunar intrusions in AWS data, including methods for deriving antenna temperature and beam width. The team also touched on the upcoming launch of the FY4 microwave satellite and Hao’s update on the development of a ground-based lunar observation system.

Summary

1. Moon Brightness Temperature Analysis

Martin presented a detailed analysis comparing different models and measurements of the Moon’s brightness temperature, focusing on annual variations due to the Moon’s distance from the Sun. He found that the model by Liu and Jin accurately predicted these variations, contradicting the earlier work by Bennett, which had suggested only a 1 Kelvin difference. Martin’s analysis, based on observations from multiple satellites including MHS, showed a peak-to-peak difference of about 4 Kelvin, which he attributed to the Sun-Moon distance. He concluded that the absolute uncertainty in brightness temperature measurements could be as low as 2 Kelvin, with the phase angle and frequency variations being well understood

Lunar Brightness Analysis

The team discussed Martin’s analysis of lunar brightness temperature variations, with Tim clarifying that the 95% confidence interval represents a range of 2.9 Kelvin, while Niutao explained his model treats the solar constant as a function of sun-moon distance, consistent with previous work. Tiger inquired about AWS data collection capabilities, leading Tim to clarify that while AWS has oversampled data with 25 space views, the additional data is not currently downlinked in Level 0 or Level 1 data products, though it could be considered for future end-of-life tests.

2. Lunar Intrusion Analysis Challenges

Dezhang presented initial analyses of lunar intrusion events in AWS Deep Space Views, highlighting abnormal enhancements in cold-view counts and discussing the challenges in accurately calculating moon angles. Tim explained that the current Level 1B moon angle data is approximately 90 degrees out and shared alternative calculations from an in-house prototype processor that accounts for each feed horn’s direction. Tiger suggested validating the moon angle calculations by plotting lunar angles against cold-space counts to identify lunar intrusion events, even with inaccurate angle data.

AWS Instrument Data Analysis

The team discussed the AWS instrument’s sampling and data collection, with Tim explaining that the instrument is in a sun-synchronous orbit and does not typically observe the sun. Tiger and Tim analyzed the impact of lunar and Earth contamination on the instrument’s measurements, with Tiger suggesting that different samples show varying magnitudes due to the antenna’s Gaussian response. Hao inquired about the color variation in the data, which Tim and Tiger explained was primarily due to instrument gain variations and thermal environment changes, with Earth contamination being a minor factor. The discussion concluded with Tiger offering to share simulation results and Tim mentioning he could share slides from a previous summer if time permitted.

3. Lunar Observations from Arctic Satellite

Tim presented an analysis of lunar observations from the Arctic Weather Satellite (AWS), which is operated by ESA and processed by OHB. He demonstrated how to calibrate and process the data to retrieve antenna temperature measurements, comparing his results with official values and finding reasonable agreement. Tim also showed animations of moon observations over multiple orbits and discussed the limitations of the current processing, noting that AWS was always intended to be a prototype mission.

AWS Moon Position and Calibration

Tim explained the differences in moon positions in the video due to different feed horns pointing at the reflector with varying offsets and angles. He clarified that the AWS and Sterner were made affordable by cutting out expensive parts of the optics. Tim also discussed the calibration strategy for AWS, explaining how lunar intrusions are identified and handled. Tiger inquired about the availability of AWS data and scan angle information, which Tim confirmed is available from the EUMETSAT data store. They also discussed the method for identifying and correcting lunar intrusions in the calibration process.

Lunar Intrusions in Antenna Measurements

The team discussed lunar intrusions and their impact on antenna measurements, with Tiger explaining that the smearing effect in the long scan direction is smaller than the ETMS, leading to differences in beamwidth measurements. Tim noted that antenna pattern measurements are not symmetric and can vary by 10-20% between different orientations. The group agreed to share data and results through a new platform that Niutao will set up, and Hao will provide an update on the ground-based lunar observation system for FY4 microwave metallic satellite, which is scheduled for launch before the end of the year.

AWS L1B data is available from EUMETSAT – Data Store

https://data.eumetsat.int/data/map/EO:EUM:DAT:0905

See Introduction to EUMETSAT Data Store: EUMETSAT – User Portal for details of getting an API

https://user.eumetsat.int/resources/user-guides/getting-started-using-data