GRACE-FO LRI1B HUST V01 Dataset

Update Description (20260210)

This update mainly focuses on optimizing and refining several engineering implementation details in the data processing workflow, while the core data processing methodology remains unchanged. The specific updates are summarized as follows:

1、CNR determination

Since the official LRI1A RL04 data released prior to July 1, 2019 do not contain the key parameters required for CNR calculation, all CNR values in the early version (20250328) were directly inherited from the JPL LRI1B RL04 product. In the updated version, the CNR handling strategy is as follows:

• For data prior to July 1, 2019, CNR values continue to be inherited from the JPL RL04 product;
• For data on and after July 1, 2019, CNR values are independently computed by our processing system.

2、Scale factor estimation

For LRI1A data segments with temporal discontinuities, the scale factor estimation method has been revised from a global estimation approach to a segmented estimation approach.

3、Improved handling of data gaps and missing days

To address the coarse treatment of LRI1A data discontinuities or missing dates in earlier versions, the following improvements have been implemented in this update:

• An additional low-CNR data screening step has been introduced, removing observations with CNR values lower than 80 dB;
• Data for certain dates with evident errors or anomalies (e.g., July 6, 2021) have been corrected.

4、Extension of phase anomaly quality flag association window

Considering that CRN filtering is applied during the generation of the LRI1B data, the temporal association window of the phase anomaly quality flag has been extended to 2 seconds before and after each detected anomaly.

Overall, this update concentrates on engineering-level optimizations and improvements in data quality, without modifying the core data processing methods. Therefore, the updated dataset continues to use the version number V01. Since its release in March 2025, the HUST V01 dataset has received valuable feedback from the research community, for which we express our sincere gratitude. We will continue to improve the data product and welcome further suggestions and comments (contact emails: lihongg@hust.edu.cn; zhouh@hust.edu.cn).