The paper by Swenson and Wahr addresses the issue of correlated errors in GRACE (Gravity Recovery and Climate Experiment) data, which manifest as long, linear features (stripes) in maps of surface mass variability. These stripes indicate correlations in the gravity field coefficients. The authors examine the spectral signature of these errors and propose a method to remove them. They derive a filter that smooths the Stokes coefficients for a particular order using a quadratic polynomial in a moving window, effectively isolating and removing coefficients of like parity. The filter is applied to GRACE data, showing significant reduction in striping while preserving geophysical signals. The effectiveness of the filter is further demonstrated by applying it to a model of surface mass variability, which shows minimal degradation of the underlying signals. The study concludes that the filter can significantly reduce the impact of correlated errors, enhancing the accuracy of GRACE data.The paper by Swenson and Wahr addresses the issue of correlated errors in GRACE (Gravity Recovery and Climate Experiment) data, which manifest as long, linear features (stripes) in maps of surface mass variability. These stripes indicate correlations in the gravity field coefficients. The authors examine the spectral signature of these errors and propose a method to remove them. They derive a filter that smooths the Stokes coefficients for a particular order using a quadratic polynomial in a moving window, effectively isolating and removing coefficients of like parity. The filter is applied to GRACE data, showing significant reduction in striping while preserving geophysical signals. The effectiveness of the filter is further demonstrated by applying it to a model of surface mass variability, which shows minimal degradation of the underlying signals. The study concludes that the filter can significantly reduce the impact of correlated errors, enhancing the accuracy of GRACE data.