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GFZ daily GRACE/GRACE-FO Kalman filter solutions
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The satellite missions GRACE (Gravity Recovery And Climate Experiment) and GRACE-Follow-on (GRACE-FO) measure gravity field variations with homogeneous global coverage. Standard GRACE/GRACE-FO solutions are usually provided by different analysis centers as monthly mean gravity fields. An increase in the temporal resolution reduces the accuracy of the solutions so that it is not possible to compute daily solutions without taking into account additional information about the temporal correlations between the subsequent solutions. The assessment of this correlation is based on the knowledge of the stochastic properties of the underlying geophysical processes governing the variations of the gravity field on sub-monthly time scales. This method is known as Kalman filter approach.In the frame of the Research Unit (RU) New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), we investigate the possibility of obtaining Kalman daily gravity field solutions using refined stochastic information about two main geophysical processes causing high-frequency variations in the gravity field: non-tidal atmospheric and oceanic variations and hydrology. We present the first results for two test years, 2007 and 2020. To validate the obtained daily solutions, comparisons between our daily gravity fields and different flood events which took place during these test years were performed.
Title: GFZ daily GRACE/GRACE-FO Kalman filter solutions
Description:
The satellite missions GRACE (Gravity Recovery And Climate Experiment) and GRACE-Follow-on (GRACE-FO) measure gravity field variations with homogeneous global coverage.
Standard GRACE/GRACE-FO solutions are usually provided by different analysis centers as monthly mean gravity fields.
An increase in the temporal resolution reduces the accuracy of the solutions so that it is not possible to compute daily solutions without taking into account additional information about the temporal correlations between the subsequent solutions.
The assessment of this correlation is based on the knowledge of the stochastic properties of the underlying geophysical processes governing the variations of the gravity field on sub-monthly time scales.
This method is known as Kalman filter approach.
In the frame of the Research Unit (RU) New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV), we investigate the possibility of obtaining Kalman daily gravity field solutions using refined stochastic information about two main geophysical processes causing high-frequency variations in the gravity field: non-tidal atmospheric and oceanic variations and hydrology.
We present the first results for two test years, 2007 and 2020.
To validate the obtained daily solutions, comparisons between our daily gravity fields and different flood events which took place during these test years were performed.
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