Theses and Dissertations from UMD
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Item NON-GAUSSIAN ENSEMBLE FILTERING AND ADAPTIVE INFLATION FOR SOIL MOISTURE DATA ASSIMILATION(2024) Dibia, Emmanuel; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The forecast error distribution in modern day land data assimilation systems is typically modeled as a Gaussian. The explicit tracking of only the first two moments can be problematic when trying to assimilate bounded quantities like soil moisture that are more accurately described using more general parameterizations. Given this issue, it is worthwhile to test how performance of land models is affected when the accompanying data assimilation system abides by a relatively more relaxed set of underlying assumptions. To study this problem, we perform experiments using the ensemble Kalman filter (EnKF) and rank histogram filter (RHF) to assimilate surface soil moisture content observations into the NASA Catchment land surface model. The EnKF acts as the traditional (Gaussian) standard of comparison whereas the RHF represents the novel and more general data assimilation method. An additional parameter of our tests is the usage of an adaptive inflation scheme that is only applied to the ensemble prior. This is done in an attempt to mitigate the negative effects of systematic deficiencies not accounted for by either filter. The examinations were carried out at a number of globally-distributed test locations, deliberately coinciding with sites used to validate NASA SMAP soil moisture retrieval products. Initial comparisons of the two filtering approaches in a perfect model context show both filters to provide significant benefits to the soil moisture modeling problem, with the RHF edging out the EnKF as the more performant filter. The relative performance gain of the RHF was most noticeable with respect to bias mitigation metrics and to the surface-level anomaly correlation scores, an interesting result given that neither filter is formulated to explicitly accommodate a systematic bias. When additionally applying adaptive inflation, both filters showed improvement in skill but such improvements were not significant. The use of synthetic observations and lack of a bias correction implementation may have led to exaggerated results. To address this concern, the experiments were performed again but using real observations from SMAP soil moisture retrievals, with in situ validation data proxying as truth. A robust bias correction scheme was used as well to more closely approximate practices used in operational settings. The RHF continues to show better metrics than the EnKF, but no longer in a statistically significant sense. A similar result was noted with respect to inflation usage. The most likely reason for this outcome is the low observation count. The findings obtained from the data assimilation experiments in this dissertation offer insight on how best to focus development efforts in soil moisture modeling and land data assimilation.Item Estimation and Spatiotemporal Analysis of All-sky Land Surface Temperature from Multiple Satellite Data(2023) Jia, Aolin; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The daily surface temperature variability, characterizing the dispersion of day-to-day temperature anomalies, is a fundamental aspect of the climate. It can be represented by the temperature standard deviation in a week. Studies reveal that daily temperature variability is a critical determinant of societal and natural outcomes, such as public health, crop yield, economic growth, etc. Although the overall warming trend is now well established in the scientific community, previous studies have shown little consensus about changes in daily temperature variability over the globe in recent decades; this is due to limited simulation accuracy and in-situ measurement distribution. Therefore, it is urgently needed to generate a reliable, global, long-term, observation-derived, daily temperature dataset in order to analyze variability changes and potential driving factors. The Advanced Very High-Resolution Radiometer (AVHRR) data provide an exceptional chance to record long-term land surface temperature (LST) over the entire globe. However, the AVHRR LST suffers from two restrictions: cloud contamination and orbital drift. Accordingly, we develop a surface energy balance (SEB)-based algorithm to recover the LST under clouds, and a two-step method to correct the artificial spurious temperature variation due to orbital drift. In the SEB method, 1) the hypothetical LST of missing pixels is first reconstructed by assimilating dispersed clear-sky retrievals into a continuous LST time-evolving model built by reanalysis data, and 2) the reconstructed LST is then corrected by superposing the cloud effect, estimated by satellite radiation products based on SEB theory. The two-step correction includes 1) calibrating the systematic bias of diurnal temperature cycles (DTCs) simulated from reanalysis data using satellite product climatology, 2) correcting the calibrated DTCs in detail by historical AVHRR LSTs during the years 1981-2021, and averaging the corrected DTCs to get daily mean LSTs. Global, 5-km, all-sky, daily mean LSTs from 1982 to 2021 are produced for the daily variability analysis. In order to mitigate the impact of orbital drift, the SEB method is examined by MODIS and VIIRS LST products. Ground validation suggests that the cloudy-sky VIIRS LST exhibits a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, and R2 of 0.94, comparable to the accuracy of clear-sky LST and the MODIS results. Thus, the algorithm is sensor independent and also works for AVHRR data. To obtain satellite-derived DTC climatology for calibrating simulated DTCs, an optimization module is created to extend the feasibility of the SEB method at night. By collecting clear-sky LSTs from geostationary satellite sensors and two MODIS sensors, global, hourly, 5 km, all-sky LSTs from 2011 to 2021 are produced. The overall RMSE of the hourly LSTs is 3.38 K, with a bias of −0.53 K based on 197 global sites. Finally, after integrating the SEB method and two-step correction method, the target AVHRR LST is recovered with an RMSE of 1.97 K over the globe and few biases. Spatiotemporal analysis of the AVHRR LST suggests that the globally averaged daily LST variability does not have a significant trend from 1982 to 2021 under the global warming background, whereas it showed diverse variation both regionally and seasonally. A significant decrease/increase is detected at high/low latitudes, which matches previous simulation conclusions. However, contrary to the simulation, it reveals significant variability increases in the mid-latitudes, such as the western US, the Mediterranean Basin, and northern China. Historical auxiliary observations indicate that the variability decrease at high-latitudes is driven by downward longwave (DLW) radiation. Arctic amplification mitigates cold temperature anomalies at high latitudes in winter. The enhanced atmospheric convection in the tropics causes the increasing variability of cloud cover and downward shortwave radiation (DSR), and the LST variability has also increased. Climate internal variability, DLW, and DSR all show considerable impact at mid-latitudes. This study proposed innovative cloud-sky LST estimation and orbital drift correction methods. The first global, all-sky, 5-km, daily mean LST product (1982 - 2021) was generated, which shows great potential for long-term energy budget and hydrological cycling analysis. Furthermore, the study fills the knowledge gap about the unknown daily temperature variability trend over the globe and provides an attribution based on historical observations, which will assist the community in understanding the mechanism of high-frequency temperature change, improving model prediction, and coordinating resources for extreme weather adaptation.Item Estimating terrestrial water budget components across high mountain Asia using remote sensing, data assimilation, and machine learning(2021) Ahmad, Jawairia; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Contemporary studies have predicted a vulnerable future for key water budget components across high mountain Asia (HMA) and the adjoining areas. Considering the regional population and its dependence on agrarian economies, it is imperative that efforts be channelized towards improving the estimation of the hydrologic cycle across HMA. In this study, data assimilation methods were employed to assimilate remotely-sensed observations into land surface models to improve snow mass, soil moisture, and runoff estimates. The NASA Land Information System was used to simulate the hydrologic cycle across HMA and the adjoining areas using the Noah-MP land surface model. In an effort to improve snow mass estimation, passive microwave brightness temperature spectral differences (∆Tb) from the Advanced Microwave Scanning Radiometer-2 (AMSR2) were assimilated into Noah-MP snow mass estimates. Support vector machine regression, a supervised machine learning technique, was used as the observation operator to map the geophysical states into the observed ∆Tb space. Evaluation of the assimilation routine highlighted the decrease in domain-wide snow mass bias. The assimilation framework proved to be more effective during the (dry) snow accumulation season resulting in decreased snow mass bias and RMSE at 76% and 58% of the comparative locations, respectively. Diagnostic metrics such as the innovation sequence were studied to assess the snow-related observation error characteristics of AMSR2 ∆Tb. To improve the spatiotemporal variability of modeled soil moisture estimates, Soil Moisture Active Passive (SMAP) soil moisture retrievals were assimilated into Noah-MP. Assimilation was carried out using bias corrected (via CDF-matching) and raw (without CDF-matching) SMAP retrievals. Comparison against in-situ soil moisture measurements across the Tibetan Plateau highlighted the improvement in modeled soil moisture with reductions in mean bias and RMSE by 8.4% and 9.4%, respectively, even though assimilation occurred during <10% of the total study period across the Tibetan Plateau. More importantly, SMAP retrieval assimilation corrected biases that were generated due to unmodeled hydrologic phenomenon (i.e., surface irrigation associated with agricultural production). Improvements in soil moisture translated into changes in the modeled evapotranspiration. Further, the improvement in fine-scale (0.05 degree) modeled soil moisture estimates by assimilating coarse-scale soil moisture retrievals (36 km) indicated the potential of the described methodology for soil moisture estimation over data scarce regions. Soil moisture assimilation also increased the gridded total runoff (particularly baseflow) and volumetric streamflow across irrigated areas; however, limited impact was noted in terms of volumetric streamflow along high-flow river tributaries. In this study, data assimilation was leveraged to advance contemporary land surface modeling of the terrestrial water budget components across HMA. The study objectives explored how assimilation systems could be used to improve critical geophysical state estimation for a better informed future of regional water resources.Item Strongly Coupled Ocean-Atmosphere Data Assimilation with the Local Ensemble Transform Kalman Filter(2018) Sluka, Travis Cole; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current state-of-the-art coupled data assimilation systems handle the ocean and atmosphere separately when generating an analysis, even though ocean atmosphere models are subsequently run as a coupled system for forecasting. Previous research using simple 1-dimensional coupled models has shown that strongly coupled data assimilation (SCDA), whereby a coupled system is treated as a single entity when creating the analysis, reduces errors for both domains when using an ensemble Kalman filter. A prototype method for SCDA is developed with the local ensemble transform Kalman filter (LETKF). This system is able to use the cross-domain background error covariance from the coupled model ensemble to enable assimilation of atmospheric observations directly into the ocean. This system is tested first with the intermediate complexity SPEEDYNEMO model in an observing system simulation experiment (OSSE), and then with real observations and an operational coupled model, the Climate Forecasting System v2 (CFSv2). Finally, the development of a major upgrade to ocean data assimilation used at NCEP (the Hybrid-GODAS) is presented, and shown how this new system could help present a path forward to operational strongly coupled DA.Item DATA-DRIVEN SIMULATIONS OF WILDFIRE SPREAD AT REGIONAL SCALES(2018) Zhang, Cong; Trouve, Arnaud; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current wildfire spread simulators lack the ability to provide accurate prediction of the active flame burning areas at regional scales due to two main challenges: a modeling challenge associated with providing accurate mathematical representations of the multi-physics multi-scale processes that induce the fire dynamics, and a data challenge associated with providing accurate estimates of the initial fire position and the physical parameters that are required by the fire spread models. A promising approach to overcome these limitations is data assimilation: data assimilation aims at integrating available observations into the fire spread simulator, while accounting for their respective uncertainties, in order to infer a more accurate estimate of the fire front position and to produce a more reliable forecast of the wildfire behavior. The main objective of the present study is to design and evaluate suitable algorithms for regional-scale wildfire spread simulations, which are able to properly handle the variations in wildfire spread due to the significant spatial heterogeneity in the model inputs and to the temporal changes in the wildfire behavior. First we developed a grid-based spatialized parameter estimation approach where the estimation targets are the spatially-varying input model parameters. Then we proposed an efficient and robust method to compute the discrepancy between the observed and simulated fire fronts, which is based on a front shape similarity measure inspired from image processing theory. The new method is demonstrated in the context of Luenberger observer-based state estimation strategy. Finally we developed a dual state-parameter estimation method where we estimate both model state and model parameters simultaneously in order to retrieve more accurate physical values of model parameters and achieve a better forecast performance in terms of fire front positions. All these efforts aim at designing algorithmic solutions to overcome the difficulties associated with spatially-varying environmental conditions and potentially complex fireline shapes and topologies. It paves the way towards real-time monitoring and forecasting of wildfire dynamics at regional scales.Item ASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICA(2017) Xue, Yuan; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Snow is a critical component in the global energy and hydrologic cycle. It is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. To accurately estimate the depth of snow and mass of water within a snow pack across regional or continental scales is a challenge, especially in the presence of dense vegetations since direct quantification of SWE is complicated by spatial and temporal variability. To overcome some of the limitations encountered by traditional SWE retrieval algorithms or radiative transfer-based snow emission models, this study explores the use of a well-trained support vector machine to merge an advanced land surface model within a variant of radiance emission (i.e., brightness temperature) assimilation experiments. In general, modest improvements in snow depth, and SWE predictability were witnessed as a result of the assimilation procedure over snow-covered terrain in North America when compared against available snow products as well as ground-based observations. These preliminary findings are encouraging and suggest the potential for global-scale snow estimation via the proposed assimilation procedure.Item Data-Driven Wildfire Propagation Modeling with FARSITE-EnKF(2016) Theodori, Maria Faye; Trouve, Arnaud; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The goal of this study is to provide a framework for future researchers to understand and use the FARSITE wildfire-forecasting model with data assimilation. Current wildfire models lack the ability to provide accurate prediction of fire front position faster than real-time. When FARSITE is coupled with a recursive ensemble filter, the data assimilation forecast method improves. The scope includes an explanation of the standalone FARSITE application, technical details on FARSITE integration with a parallel program coupler called OpenPALM, and a model demonstration of the FARSITE-Ensemble Kalman Filter software using the FireFlux I experiment by Craig Clements. The results show that the fire front forecast is improved with the proposed data-driven methodology than with the standalone FARSITE model.Item Breeding Analysis of Growth and Decay in Nonlinear Waves and Data Assimilation and Predictability in the Martian Atmosphere(2014) Zhao, Yongjing; Kalnay, Eugenia; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The effectiveness of the breeding method in determining growth and decay characteristics of certain solutions to the Kortweg-de Vries (KdV) equation and the Nonlinear Schrödinger equation is investigated. Bred vectors are a finite amplitude, finite time generalization of Leading Lyapunov Vectors (LLV), and breeding has been used to predict large impending fluctuations in many systems, including chaotic systems. Here, the focus is on predicting fluctuations associated with extreme waves. The bred vector analysis is applied to the KdV equation with two types of initial conditions: soliton collisions, and a Gaussian distribution which decays into a group of solitons. The soliton solutions are stable, and the breeding analysis enables tracking of the growth and decay during the interactions. Furthermore, this study with a known stable system helps validate the use of breeding method for waves. This analysis is also applied to characterize rogue wave type solutions of the NLSE, which have been used to describe extreme ocean waves. In the results obtained, the growth rate maxima and the peaks of the bred vector always precede the rogue wave peaks. This suggests that the growth rate and bred vectors may serve as precursors for predicting energy localization due to rogue waves. Finally, the results reveal that the breeding method can be used to identify numerical instabilities. Effective simulation of diurnal variability is an important aspect of many geophysical data assimilation systems. For the Martian atmosphere, thermal tides are particularly prominent and contribute much to the Martian atmospheric circulation, dynamics and dust transport. To study the Mars diurnal variability (or thermal tides), the GFDL Mars Global Climate Model (MGCM) with the 4D-Local Ensemble Transform Kalman Filter (4D-LETKF) is used to perform a reanalysis of spacecraft temperature retrievals. We find that the use of a "traditional" 6-hr assimilation cycle induces spurious forcing of a resonantly-enhanced semi-diurnal Kelvin waves represented in both surface pressure and mid-level temperature by forming a wave 4 pattern in the diurnal averaged analysis increment that acts as a "topographic" stationary forcing. Different assimilation window lengths in the 4D-LETKF are introduced to remove the artificially induced resonance. It is found that short assimilation window lengths not only remove the spurious resonance, but also push the migrating semi-diurnal temperature variation at 50 Pa closer to the estimated "true" tides even in the absence of a radiatively active water ice cloud parameterization. In order to compare the performance of different assimilation window lengths, short-term to long-term forecasts based on the hour 00 and 12 assimilation are evaluated and compared. Results show that during NH summer, it is not the assimilation window length, but the radiatively active water ice cloud that influences the model prediction. A "diurnal bias correction" that includes bias correction fields dependent on the local time is shown to effectively reduce the forecast root mean square differences (RMSD) between forecasts and observations, compensate for the absence of water ice cloud parameterization, and enhance Martian atmosphere prediction. The implications of these results for data assimilation in the Earth's atmosphere are also discussed.Item Ensemble Assimilation of Global Large-scale Precipitation(2014) Lien, Guo-Yuan; Kalnay, Eugenia; Miyoshi, Takemasa; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many attempts to assimilate precipitation observations in numerical models have been made, but they have resulted in little or no forecast improvement at the end of the precipitation assimilation. This is due to the nonlinearity of the model precipitation parameterization, the non-Gaussianity of precipitation variables, and the large and unknown model and observation errors. In this study, we investigate the assimilation of global large-scale satellite precipitation using the local ensemble transform Kalman filter (LETKF). The LETKF does not require linearization of the model, and it can improve all model variables by giving higher weights in the analysis to ensemble members with better precipitation, so that the model will "remember" the assimilation changes during the forecasts. Gaussian transformations of precipitation are applied to both model background precipitation and observed precipitation, which not only makes the error distributions more Gaussian, but also removes the amplitude-dependent biases between the model and the observations. In addition, several quality control criteria are designed to reject precipitation observations that are not useful for the assimilation. Our ideas are tested in both an idealized system and a realistic system. In the former, observing system simulation experiments (OSSEs) are conducted with a simplified general circulation model; in the latter, the TRMM Multisatellite Precipitation Analysis (TMPA) data are assimilated into a low-resolution version of the NCEP Global Forecasting System (GFS). Positive results are obtained in both systems, showing that both the analyses and the 5-day forecasts are improved by the effective assimilation of precipitation. We also demonstrate how to use the ensemble forecast sensitivity to observations (EFSO) to analyze the effectiveness of precipitation assimilation and provide guidance for determining appropriate quality control. These results are very promising for the direct assimilation of satellite precipitation data in numerical weather prediction models, especially with the forthcoming Global Precipitation Measurement (GPM) sensors.Item Nonlinear and Multiresolution Error Covariance Estimation in Ensemble Data Assimilation(2012) Rainwater, Sabrina; Hunt, Brian R; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Ensemble Kalman Filters perform data assimilation by forming a background covariance matrix from an ensemble forecast. The spread of the ensemble is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. Usually the ensemble members are evolved with the same model. The first part of my dissertation presents and tests a modified Local Ensemble Transform Kalman Filter (LETKF) that takes its background covariance from a combination of a high resolution ensemble and a low resolution ensemble. The computational time and the accuracy of this mixed-resolution LETKF are explored and compared to the standard LETKF on a high resolution ensemble, using simulated observation experiments with the Lorenz Models II and III (more complex versions of the Lorenz 96 model). The results show that, for the same computation time, mixed resolution ensemble analysis achieves higher accuracy than standard ensemble analysis. The second part of my dissertation demonstrates that it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. This technique, denoted ``forecast spread adjustment'' provides a tunable parameter that is complementary to covariance inflation, which cumulatively increases the ensemble spread to compensate for underestimation of uncertainty. As the adjustable parameter approaches zero, the filter approaches the Extended Kalman Filter when the ensemble size is sufficiently large. The improvement provided by forecast spread adjustment depends on ensemble size, observation error, and model error. The results indicate that it is most effective for smaller ensembles, smaller observation errors, and larger model error, though the effectiveness depends significantly on the type of model error.