R. Klees

View Pure Profile

Master thesis (1)

Student report (1)

2 records found

Regularizing GRACE mascon solutions using river run-off data

Master thesis (2021) - O. Lucassen (author), P.G. Ditmar (mentor), R. Klees (graduation committee member), S.C. Steele-Dunne (graduation committee member), M. Hrachowitz (graduation committee member)

Accurate estimates of terrestrial water storage variations (TWSV) are critical for a variety of applications, e.g., model calibration and climate studies. This study aims to find the added value of river run-off data for regularizing GRACE mascon solution, from which TWSV can be estimated. Most subbasins of the Mississippi Basin show an exponential relationship between run-off and TWSV with moderate to good predictive value. The mean value of the explained variance (R^2) for the models is 0.4, when excluding the Lower Mississippi and Middle Missouri subbasins the mean value increases to 0.5. The Lower Mississippi and Middle Missouri subbasins are the only subbasins with no clear correlation between run-off and storage, most likely due to aquifer depletion.

Since ready-to-used GRACE mascon solutions cannot be modified, the mascon method developed at the Geoscience and Remote Sensing Department of the Delft University of Technology is used in this study. This study proposes modifications to tailor this method to the Mississippi Basin and mass anomalies of hydrological origin. After applying standard Tikhonov regularization, the exponential relationship is used to determine the most probable model outcome for each subbasin, adding a new term to the estimator. The resulting GRACE mascon solutions (Tikhonov and run-off regularized solutions) have a root-mean-square deviation (RMSD) of around 3.5 cm when compared to ready-to-use mascon solutions from the Jet Propulsion Laboratory and Goddard Space Flight Center. The run-off regularized solutions show smaller deviation (on average 7 %) than the Tikhonov regularized solutions with respect to an independent validation data set. The largest difference between the Tikhonov and run-off regularized solutions occur in the dryer subbasins (Platte, Middle, and Upper Missouri, and Upper Mississippi subbasin). These areas show a stronger inter-annual trend in TWSV, these trends are captured better by the run-off regularized solutions. Additional research, co-estimating the regularization bias, shows that the run-off regularized solutions induce less bias into the solutions. This study shows that using run-off data when processing GRACE data could be of added value, especially in semi-humid to arid areas, since the TWSV in these areas is more susceptible to inter-annual variations.

Analysis of noise in K-Band Ranging data

An investigation of noise in KBR ranging data of NASA’s and GFZ’s GRACE Follow-On mission. Detection of outliers and estimation on the noise power spectral density.

Student report (2020) - A.C. Eijgenraam (author), P.G. Ditmar (mentor), R. Klees (graduation committee member)

The GRACE Follow-On mission is using classical K-Bandranging (KBR) and a new laser-ranging interferometry (LRI) method. The lattergives ranging data two orders of magnitude more accurate compared to theclassical K-Band ranging data (Dahl C et al. 2016). This gives a newopportunity for analyzing KBR noise by defining KBR noise as the differencebetween the KBR and LRI ranging data. In order to get a realization of KBR noise, the KBR andLRI epochs should be aligned and outliers have to be removed. An interpolation on the LRI data to make the LRI epochsaligned with the KBR epochs did not give sufficient results. Therefore only theoverlapping epochs were used as input for the next step of outlier detection, atotal of 2,250,000 epochs. The outlier detection method starts by taking theabsolute difference between the KBR and LRI range-rates. The largestdifferences were investigated where the original range-rate was compared to anestimation of the true value. This ‘true’ value is found by the use of a thirddegree polynomial function through the six range-rates that lie next to thesuspected outlier. The outlier detection method removes LRI range-rates thatdiffer more than 2.0*10^-8 m/s from the estimated true value and KBRrange-rates that differ more than 7.7*10^-7 m/s. 3764 LRI epochs and362 KBR epochs were labelled as outliers. After the outlier detection a histogram was made for theKBR range-rate noise. It was found that in order to get a normal distributionof KBR range-rate noise, the interval had to be in the range of <-3*10^-7 m/s, 3*10^-7m/s>. 59,000 still fell out of this noise range. 54,000 of these pointsformed 10 major clusters together where a subtraction of the KBR and LRIrange-rates did not give a realization of noise, but a trend similar to theoriginal signal and are therefore likely related to a clocking error. These54,000 points were therefore also removed from the dataset. To the remaining KBR range-rate noise values a thresholdof 3 was applied, removing an additional 4972 noisevalues that were larger than 4.03*10^-7 m/s. Finally , an estimation of the PSD of the KBR range-ratenoise was made and compared to an oldPSD image of December 2008 of the GRACEmission. The PSD plot of this project shows three peaks between 10^-4Hz and 10^-3 Hz. These peaks are not present in the PSD plot found inliterature of the GRACE mission. Except from the second and the third peak, thePSD values of the KBR range-rate noise of this project were lower, up to twoorders of magnitude in the lower frequency range (around 10^-4 Hz)and one order of magnitude in the higher frequency range (around 10^-1Hz).