LEE- Geomatik Mühendisliği Lisansüstü Programı

Bu topluluk için Kalıcı Uri

http://hdl.handle.net/11527/19289

Gözat

Yazar "Akyılmaz, Orhan" ile LEE- Geomatik Mühendisliği Lisansüstü Programı'a göz atma
  • Öge
    Filling the data gap between grace and grace follow-on missions using deep learning algorithms
    (Graduate School, 2022) Keleş, Merve ; Akyılmaz, Orhan ; 709316 ; Geomatics Engineering Programme
    The main purpose of GRACE (Gravity Recovery and Climate Experiment) satellites, which was launched on 17 March, 2002, is to monitor the changing gravity field of the Earth due to mass changes in the hydrosphere, cryosphere and ocean then record all the data on a monthly basis. GRACE satellites provide the dynamics of terestrial water storage anomalies (TWSA) on a global scale with very high accuracy, both spatially and temporally. These data are used in sustainable water resources management, hydrologic and climatic studies. The mission of the GRACE satellites ended on 17 October, 2017 due to battery problems. Thereupon, the GRACE-Follow on (GRACE-FO) was launched with the same mission. To that end, there was an 11-months data gap between GRACE and GRACE-FO. It is predicted that such a data gap in the TWSA time series will lead to significant bias and uncertainties in the model predictions. This study covers the period between December 2013 and December 2020. In the study process, besides the data gap between two missions, there is also 12-months data gap due to technical reasons. As a result of this study, a total of 23 months of data was filled by using deep learning (DL) algorithms. As input data, ERA5-Land driver data which include monthly temperature, precipitation, cumulative water storage changes and ERA5L-derived TWSA were used. As an additional input, long wavelet of the gravitational signal derived from SWARM TWSA were used. All data used in the study were temporally rescaled monthly and spatially at 1°x1° resolution before deep learning process. The L2 data obtained from the SWARM satellite was not used directly, but was first subjected to corrections applied as in CSR mascon solutions (degree 1 correction, C20/C30 and GIA corrections). SWARM Sh (Spherical Harmonic) models truncated at degree and order 12. Then, Gaussian smoothing filter with a radius of 1000 km was applied to reduce the noise in the recovered SWARM-derived TWSA. In this study, it is aimed to fill the data gap by using three different Deep Learning (DL) algorithms, namely, Convolutional Nural Network (CNN), Deep Convolutional Autoencoders (DCAE) and Bayesian Convolutional Neural Network (BCNN). The time period covering the study is 84 months, including gaps (December, 2013 - December, 2020). The mascon solutions are available for 61 months within the study period. 13 months randomly selected from the available mascon solutions were used for the test and the remaining 48 months were used for training the DL models. Finally, totally 23 months of TWSA data gap was successfully filled using DL algorithms.
  • Öge
    Global gravity field recovery from low-low satellite-to-satellite tracking with enhanced spatiotemporal resolution using deep learning paradigm
    (Graduate School, 2023-05-24) Uz, Metehan ; Akyılmaz, Orhan ; 501162610 ; Geomatic Engineering
    Understanding climate system and ensuring survival of the planet require more attention to monitoring water resources and water-related natural disasters. Therefore, monitoring water storage is crucial for the global climate and natural ecosystems. Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions have revealed new insights into mass transport within the Earth system. For the first 15 years, beginning in 2002, time series of terrestrial water storage (TWS) variations on the Earth were recovered from the measurements of the GRACE mission. After a gap of 11 successive months, the GFO mission has been performing this task since May 2018. Hence, over the last 20 years, TWS variations from GRACE/GFO measurements have provided an unique information on the Earth's water cycle to a wide range of hydrology, glaciology, and solid earth activities. Numerous scientific investigations have been conducted in the light of this data. Some of these efforts include estimating time-variable gravity field models with high accuracy from GRACE/GFO measurements using satellite gravimetry techniques and/or enhancing the temporal and spatial resolutions of TWS anomalies (TWSA). In this thesis, two major efforts have been investigated by applying the energy balance approach (EBA), which is a kind of satellite gravimetry technique based on the principles of energy conservation. The preliminary aim is to estimate spherical harmonic coefficients (SHC) of time-variable gravity field models of the Earth and new hybrid deep learning (DL) algorithms, namely residual deep convolutional autoencoders (ResDCAE) and super-resolution residual deep convolutional autoencoders (SR-ResDCAE). The next objective is to enhance temporal and spatial resolutions of TWSA maps that are derived from the SHCs. The SHCs are highly sensitive to the systematic errors and high-frequency noise sources in range-rate observations of GRACE/GFO K/Ka Band Ranging (KBR) as well as the orbit configurations. This is why the estimated geopotential differences (GPD) from EBA have direct relations to range rate dataset due to applied KBR alignment approach. Under these circumstances, the temporal models are estimated to have comparable accuracy with other institution models for up to degree/order (d/o) 20, but are less accurate for higher degrees of SHCs. In order to mitigate these error and noise sources, KBR empirical parameter estimation or the Bayesian filter (BF) is applied to estimated GPDs. When the number of empirical parameters are increased (from one to three cycle-per-revolution (CPR)), the heavier effect of North-South (N-S) stripes is drastically reduced, particularly in months with poor orbit configuration. However, this results in a loss of strength in the long-wavelength component of the gravitational signal. On the other hand applying both the forward filtering (FF) and backward smoothing (BS) steps of BF to the GPD residuals, high-frequency noises caused by the satellite's temperature changes are reduced and there is no signal loss in SHCs estimated by these filtered and smoothed GPDs. However, this result did not lead to any improvement in the mitigation of high-degree SHC correlations. Since the estimated GPDs are also highly sensitive to orbital configurations to represent mass variations, it is concluded that a regularization process is required in the gravity inversion step to eliminate correlations in higher-order SHCs and reduce N-S stripes in an unconstrained solution without signal loss. In the second step, the TWSA that are calculated from estimated SHCs are downscaled from monthly and 100 km resolutions to daily and 25 km resolutions using in-house developed DL, i.e., ResDCAE and SR-ResDCAE, applying step-by-step simulations from lower to higher resolutions. Internally, the performance of each GRACE-like TWSA simulation is validated using mathematical metrics such as root mean squared error (RMSE) and Nash-Sutcliffe efficiency (NSE), as well as comparisons to previous studies. Contrary to internal validation, the simulated TWSAs are also externally validated by comparison to the performance of filling the gap between GRACE and GFO missions and to non-GRACE datasets, such as the El Nino/La Nina sea surface temperature index and global mean sea level (GMSL) changes. In addition, the capability of the daily simulations to detect long- and short-term variations in the TWSA signal caused by natural disasters such as the 2011 and 2019 Missouri River Floods, Hurricane Harvey, and the 2012–2017 drought in California for Contiguous United States (CONUS) region is investigated. The droughts experienced in Türkiye during the GRACE/GFO time period, which occurred in 2007–2008 and 2013–2014, are also evaluated using daily simulations considering Fırat Dicle Basin (FDB) and Konya Close Basin (KCB), separately. Both the filling of TWSA data gaps and the simulation of daily time series using the ResDCAE algorithm have been successfully simulated. Nevertheless, the spatial downscaling step of the SR-ResDCAE algorithm requires additional physical investigation regarding the establishment of spatio-temporal correlations during training. In addition, leakage bias effects have emerged as a result of the post-processing filters used to eliminate errors in time-varying gravity field models obtained unconstrained by the EBA method. Due to post-processing filters, the true signal magnitude of TWSA is diminished. Therefore, the temporal and spatial pattern of the simulated TWSA time series is comparable to that of the other compared simulations and models, but signal power loss is readily apparent.