Atmospheric & Oceanic Science Theses and Dissertations

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DEVELOPMENT AND APPLICATION OF PROPINQUITY MODELING FRAMEWORK FOR IDENTIFICATION AND ANALYSIS OF EXTREME EVENT PATTERNS
(2024) kholodovsky, vitaly; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damage. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: construction of essential field quantities, dimension reduction, spatial domain mapping, time series clustering, and threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale, well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heatwaves, especially during the summer months. Additionally, we compare empirical and statistical probabilities and intensities obtained through the Conventional Location Specific methods, which are deficient in geometric interconnectivity between individual spatial pixels and independent in time, with a new Propinquity modeling framework. We integrate the Spatio-Temporal Threshold Clustering algorithm and the conditional semi-parametric Heffernan and Tawn (2004) model into the Propinquity modeling framework to separate classes of models that can calculate process level dependence of large-scale extreme processes, primarily through the overall extreme spatial field. Our findings reveal significant differences between Propinquity and Conventional Location Specific methods, in both empirical and statistical approaches in shape and trend direction. We also find that the process of aggregating model results without considering interconnectivity between individual grid cells for trend construction can lead to significant variations in the overall trend pattern and direction compared with models that do account for interconnectivity. Based on these results, we recommend avoiding such practices and instead adopting the Propinquity modeling framework or other spatial EVA models that take into account the interconnectivity between individual grid cells. Our aim for the final application is to establish a connection between extreme essential field quantity intensity fields and large-scale circulation patterns. However, the Conventional Location Specific Threshold methods are not appropriate for this purpose as they are memoryless in time and not able to identify individual extreme episodes. To overcome this, we developed the Feature Finding Decomposition algorithm and used it in combination with the Propinquity modeling framework. The algorithm consists of the following three steps: feature finding, image decomposition, and large-scale circulation patterns connection. Our findings suggest that the Western Pacific Index, particularly its 5th percentile and 5th mode of decomposition, is the most significant teleconnection pattern that explains the variation in the trend pattern of the largest feature intensity.
IMPROVING SWAT MODELING AND INTEGRATING OBSERVATIONAL ANALYSIS TO ASSESS THE IMPACTS OF CLIMATE AND LAND USE CHANGE ON HYDROLOGICAL PROCESSES IN THE UNITED STATES
(2023) dangol, sijal; Liang, Xin-Zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Climate and land use have been changing significantly. Climate and land use changes are intricately linked to the hydrological processes that have a significant impact on water resources. The availability of water resources is crucial for food and energy production. In the U.S., about 42% of water is used for agricultural irrigation to improve the productivity of grain crops, including corn and soybeans. About 30% of the corn produced in the U.S. is used for ethanol production, which raises serious concerns regarding the food-fuel competition and detrimental environmental impacts. The expansion of corn production competes for water resources, as corn requires irrigation. To address these issues, the cultivation of bioenergy crops on marginal lands is proposed as a solution that also alleviates water demand, as they do not require irrigation. This cultivation could involve complex interactions among climate, crops, and hydrological processes, which may lead to significant environmental consequences. This necessitates an understanding of how land use and climate changes will affect hydrological processes and, subsequently, food production.To understand these processes, I use the hydrological model, Soil and Water Assessment Tool (SWAT). SWAT has a distinct advantage in its capability to simulate water and sediment transport, crop growth, agricultural management, and land management practices, and has specialized extensions for groundwater simulation (SWAT-MODFLOW). In this study, I focus on large agricultural watersheds in the U.S. Northern High Plains (NHP) Aquifer region. The NHP is a vast and critical agricultural area that relies heavily on groundwater irrigation, which has resulted in streamflow and groundwater level decline. Although the SWAT model can quantify the environmental impacts of climate and land use changes, its utility is limited by: (1) deficiencies in the auto-irrigation algorithms in SWAT that continue irrigation during the non-growing season, (2) optimization of the SWAT model with streamflow data only, without considering other hydrological components such as evapotranspiration and soil moisture, (3) failure to explicitly account for shoot and root biomass development when simulating bioenergy crops, and (4) standalone climate and land use change simulation that do not consider the influence of climate-crop feedback on hydrological processes. To provide an accurate assessment of the impact of climate and land use changes on hydrological processes in the NHP, my first effort involved improving the irrigation algorithms in both SWAT and its extension SWAT-MODFLOW to provide an improved representation of irrigational water use conditions. The use of SWAT-MODFLOW provides an improved representation of hydrological processes, particularly when considering the significant role of groundwater dynamics in the overall water budget of the NHP. The modified SWAT and SWAT-MODFLOW were applied to the NHP which exhibited improved performance in simulating groundwater irrigation volume, groundwater level, and streamflow in the NHP. I also examined the effects of groundwater irrigation on the water cycle. Based on simulation results from SWAT-MODLFOW, historical irrigation has increased surface runoff, evapotranspiration, soil moisture, and groundwater recharge by 21.3%, 4.0%, 2.5%, and 1.5%, respectively. Irrigation also improved crop water productivity by nearly 27.2% for corn and 23.8% for soybean. To optimize the SWAT model for addressing climate and land use change issues in the NHP, I tried to identify the best approach to calibrate the SWAT. Traditionally, SWAT is calibrated using streamflow only. I hypothesize that calibrating SWAT with streamflow only, without considering broad hydrological processes like evapotranspiration and soil moisture causes model overfitting. I utilized the best available remotely sensed data, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE), to examine if multi-variate calibration could improve model performance. Contrary to my hypothesis, using remotely sensed data does not improve model performance because of two reasons: (a) remotely sensed data may lack physical constraints and has large uncertainty, and (b) SWAT may not provide an accurate representation of hydrological processes due to deficiencies in its model algorithms. Further investigation is required to address these issues. SWAT utilizes standalone climate change data without considering climate-crop feedback in environmental impact assessments of climate and land use change scenarios. To address this gap, I used another tool, the Climate-Weather and Research Forecasting (CWRF) model, both in standalone mode and coupled with the BioCro model. This approach incorporates the climate-crop feedback associated with the cultivation of perennial grasses on marginal lands. The climate data from these two scenarios: (i) Climate-only without climate-crop feedback, and (ii) Climate-crop feedback turned on, were used in SWAT to investigate hydrological responses to simulated climate-crop feedback in the NHP. The incorporation of climate-crop feedback produced cooler and wetter conditions in the NHP. These changes in temperature and precipitation have substantial environmental consequences. The cultivation of miscanthus on marginal lands increases evapotranspiration and decreases surface runoff, soil moisture, and percolation. At the watershed scale, surface runoff substantially increases during the growing season, both in the present (1985-2014) and the future (2031-2060). The differences in the extent of marginal land use for miscanthus cultivation between the Platte (4%) and Republican (20%) River basins result in different responses in streamflow and nitrogen loading. For example, in the future, annual streamflow and nitrogen loading increases by 5.5% and 2.3%, respectively, in the Platte River basin. In contrast, the Republican River basin exhibits negligible changes in annual streamflow and an 8.6% decrease in nitrogen loading during this period. These results highlight the importance of including climate-crop feedback in addition to the impact from land use change when assessing the impacts of climate and land use changes on hydrological processes. The SWAT model does not explicitly account for shoot and root biomass development. I integrated the grass growth sub-model from the DAYCENT into SWAT (SWAT–GRASSD) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASSM). Based on testing at eight sites in the U.S. Midwest, SWAT–GRASSM generally outperformed both SWAT and SWAT–GRASSD in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASSM can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. From these model experiments and analyses, I realized that there are still a lot of model and data gaps that need to be addressed. Consequently, the results of the impacts of the climate and land use changes on hydrological processes could be subjective due to inherent model deficiencies and calibration issues because of uncertainty in observed data. I, therefore, revisited the observed data screening approaches to identify near-natural streamflow data that are representative of actual watershed processes. Assuming that the hydrologic response of the watershed is primarily driven by precipitation and runoff processes, I applied lagged correlation analysis between precipitation and streamflow at various temporal scales, complemented by a systematic screening process using basin properties to select streamgages with near-natural streamflow. The screening method introduced here is objective and easy to implement in other data-sparse regions. The selected streamgages serve as valuable calibration and validation points for hydrological models, thereby it could help enhance the accuracy and confidence in model projections. In summary, my work improved the SWAT auto-irrigation algorithm for a better representation of irrigation water use and integrated the DAYCENT grass growth sub-model to enhance biomass yield predictions. The hydrological consequences of land use change for miscanthus cultivation highlighted the importance of incorporating climate-crop feedback into hydrological modeling. This integration had a substantial impact on the hydrological processes in the NHP. Despite the model improvements through changes in model algorithms and optimization, my work reveals that model and data gaps result in subjectivity in the study’s findings. To address this, I propose an objective screening process for streamgage selection that reflects natural watershed processes. The enhancements introduced to the modeling framework, coupled with the objective streamgage screening approach, will assist water resource managers and the regional hydrologic modeling community in the credible assessment of the hydrological impacts resulting from climate and land use changes.
Bridging Gaussian and non-Gaussian Data Assimilation for High-Dimensional Geophysical Models
(2023) Kurosawa, Kenta; Poterjoy, Jonathan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Strongly nonlinear model dynamics and observation operators can induce bias in Gaussian-based data assimilation methods commonly used for numerical weather prediction, such as ensemble Kalman filters (EnKFs) and the 4D variational method (4DVar). This limitation is apparent for multiscale weather prediction systems that exhibit large uncertainty in smaller scales, or when observations are sensitive to cloud processes. Several methods have been proposed for improving data assimilation performance in nonlinear regimes. Examples include the adoption of an "outer loop" in variational methods, which helps reduce bias caused by linear assumptions. Likewise, numerous "iterative ensemble methods" exist, which periodically re-linearize model and measurement operators in the same manner. While the convergence properties of the latter methods are not completely known, numerical experiments performed by several previous studies suggest they can provide accurate solutions for mildly nonlinear problems. Another strategy that has gained momentum in recent years is to apply dimension-reduction procedures (namely, localization) to particle filters (PFs). PFs avoid the parametric estimation of Bayesian posterior densities, thus providing great flexibility for solving non-Gaussian data assimilation problems. However, these methods are more easily affected by sampling error than Gaussian-based methods—even when using localization. My research introduces new approaches that bridge Gaussian and non-Gaussian data assimilation for geophysical models. To begin, the first part of this study investigates intrinsic limitations in data assimilation methods that are currently used for nonlinear applications in geoscience. We then propose novel data assimilation strategies for combining PFs with Gaussian-based methods that are more robust to sampling error. We demonstrate that the approaches have significant value within modern high-resolution regional atmospheric modeling systems, which are designed specifically for predicting tropical cyclones and severe convective storms. We further emphasize that this research has general implications for data assimilation within Earth-system models.
Exploring the Observation Impacts and Enhancing the Predictability for Ensemble-Based Coupled Data Assimilation
(2023) Chang, Chu-Chun; Kalnay, Eugenia EK; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This research aims to explore the observation impacts in coupled data assimilation (CDA) and improve the predictability of coupled systems by advanced DA approaches. Three topics are discussed in this dissertation: (1) An enhanced application of the correlation cutoff method (Yoshida and Kalnay, 2018) as a spatial localization is introduced. We investigated the feasibility and characteristics of the traditional distance-dependent (Gaspari and Cohn, 1999) and the correlation-dependent localizations preliminary on the Lorenz (1996) model with the local ensemble transform Kalman filter (LETKF). We further discussed the potential of integrative localization strategies and the application of the correlation cutoff method on Mars DA. (2) We found that the surface sea temperature (SST) relaxation operationally used in the Climate Forecast System version 2 (CFSv2) is not effective in reducing existing SST biases. To address this issue, we replaced the SST relaxation with the weakly coupled data assimilation (WCDA) of satellite-retrieved SST products. A series of experiments with real observations were conducted on the CFSv2-LETKF (Sluka et al., 2018) to investigate the impacts of SST WCDA on the CFSv2 analysis and the forecasts. (3) The Ensemble Forecast Sensitivity to Observations (EFSO, Kalnay et al., 2012) is a powerful tool to identify the beneficial or detrimental impact of every observation and has been widely used in atmospheric ensemble-based DA. However, EFSO has not yet been applied to any ocean or coupled DA due to the lack of a proper error norm for oceanic variables. This study first introduces a novel density-based error norm that simultaneously includes sea temperature and salinity forecast errors, by which EFSO becomes available to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF for quantifying the individual impact of ocean observations and explored the great potential of EFSO to be extended as a data selection criterion to improve the CFSv2 forecasts.
REMOTE SENSING OF ATMOSPHERIC TRACE GASES FROM SPACEBORNE UV MEASUREMENTS
(2022) Huang, Xinzhou; Yang, Kai; Dickerson, Russell R.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Satellite measurements of atmospheric trace gases provide continuous long-term information for monitoring the atmospheric chemical environment and air quality at local, regional, and global scales. Trace gas retrievals play a critical role in chemical data assimilation, air quality modeling and forecast, and regulatory decision-making. In this dissertation, I present retrievals of three trace gases species (O3, SO2, and NO2) from measurements of Ultraviolet (UV) radiation made from the imaging spectrometers onboard operational satellites, including the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR), the Ozone Mapping and Profiler Suite - Nadir Mapper (OMPS-NM) onboard Suomi-NPP (SNPP), and the OMPS-NM onboard NOAA-20 satellite. The retrievals of the trace gas vertical columns are achieved through the Direct Vertical Column Fitting (DVCF) algorithm, which is designed to maximize the absorption signature from the Earth’s atmosphere in the UV spectral range. This dissertation first demonstrates the theoretical basis and mathematical procedures of the DVCF algorithm used for retrieving total vertical columns of ozone (O3) and sulfur dioxide (SO2) from DSCOVR EPIC. We describe algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulted from EPIC’s band-to-band misregistrations. Furthermore, we present detailed error analyses to quantify retrieval uncertainties from various sources, assess EPIC-observed volcanic plumes, and validate O3 and SO2 retrievals with correlative data. The second part of this dissertation presents a suite of efforts to retrieve the tropospheric and stratospheric NO2 vertical columns from the new NOAA-20 OMPS hyperspectral Ultraviolet-Visible (UV-Vis) instrument, covering retrieval algorithm, Stratosphere-Troposphere Separation (STS) scheme, measurement sensitivity assessment, inter-comparison with the Ozone Monitoring Instrument (OMI), evaluation with ground-based Pandora spectrometers, as well as a case study of drastic NO2 changes during COVID-19 pandemic. The third part of my dissertation focuses on validation and algorithm improvements for the tropospheric NO2 retrievals from SNPP OMPS UV measurements. OMPS column NO2 was validated against coincidence measurements from two ground-based MAX-DOAS spectrometers deployed in eastern China. To achieve higher retrieval accuracy, we developed and implemented a series of algorithm improvements, including an explicit aerosol correction scheme to account for changes in measurement sensitivity caused by aerosol scattering and absorption, the replacement of climatological a priori NO2 profile with more accurate NO2 vertical distribution from high-resolution CMAQ model simulations, and the application of model-derived spatial weighting kernel to account for the effect of heterogeneous subpixel distribution. These improvements yield more accurate OMPS NO2 retrievals in better agreement with MAX-DOAS NO2 measurements. The analysis concluded that explicit aerosol correction and a priori profile adjustment are critical for improving satellite NO2 observations in highly polluted regions and spatial downscaling is helpful in resolving NO2 subpixel variations.
IMPROVEMENTS IN THE ASSIMILATION OF DOPPLER RADIAL WINDS AT NCEP IN REGIONAL AND GLOBAL FORECAST SYSTEMS
(2022) Lippi, Donald; Kleist, Daryl T; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Doppler radial winds have been an underutilized observation in U.S. operational forecast systems. This has typically been owing to limitations in formulation of the observation operator, the amount of data thinning via super-obbing, or simple exclusion from assimilation in global modeling systems. In this work we explore some of the more feasible aspects of radial wind assimilation that could more readily be applied to the operational systems with the main goal to improve the use of radial winds in the operational forecast systems used by NOAA. It will be shown that improvements could be made to systems not only operating at the convective scale, but also that global systems could benefit from the assimilation of radial winds. Experiments featuring results from the regional NAM version 4 forecast system along with the GFS version 15 and 16 will be shown. The GFS experiments explore the potential radial wind impact via hypothetical observing networks tested with observing system simulation experiments. We further extend this study to a real-data case with a land falling tropical cyclone event and novel, rapidly-updated version of the GFS.This work is a first step toward improving the use of radial wind observations and tests their use for the very first time in the GFS. This effort demonstrates potential for radial wind assimilation in the GFS, a potentially important observation type as we increase update cadence and spatial resolution.
A 20-YEAR CLIMATOLOGY OF GLOBAL ATMOSPHERIC METHANE FROM HYPERSPECTRAL THERMAL INFRARED SOUNDERS WITH SOME APPLICATIONS
(2022) Zhou, Lihang; Warner, Juying; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Atmospheric Methane (CH4) is the second most important greenhouse gas after carbon dioxide (CO2), and accounts for approximately 20% of the global warming produced by all well-mixed greenhouse gases. Thus, its spatiotemporal distributions and relevant long-term trends are critical to understanding the sources, sinks, and global budget of atmospheric composition, as well as the associated climate impacts. The current suite of hyperspectral thermal infrared sounders has provided continuous global methane data records since 2002, starting with the Atmospheric Infrared Sounder (AIRS) onboard the NASA EOS/Aqua satellite launched on 2 May 2002. The Cross-track Infrared Sounder (CrIS) was launched onboard the Suomi National Polar Orbiting Partnership (SNPP) on 28 October 2011 and then on NOAA-20 on 18 November 2017. The Infrared Atmospheric Sounding Interferometer (IASI) was launched onboard the EUMETSAT MetOp-A on 19 October 2006, followed by MetOp-B on 17 September 2012, then Metop-C on 7 November 2018. In this study, nearly two decades of global CH4 concentrations retrieved from the AIRS and CrIS sensors were analyzed. Results indicate that the global mid-upper tropospheric CH4 concentrations (centered around 400 hPa) increased significantly from 2003 to 2020, i.e., with an annual average of ~1754 ppbv in 2003 and ~1839 ppbv in 2020. The total increase is approximately 85 ppbv representing a +4.8% change in 18 years. More importantly, the rate of increase was derived using satellite measurements and shown to be consistent with the rate of increase previously reported only from in-situ observational measurements. It further confirmed that there was a steady increase starting in 2007 that became stronger since 2014, as also reported from the in-situ observations. In addition, comparisons of the methane retrieved from the AIRS and CrIS against in situ measurements from NOAA Global Monitoring Laboratory (GML) were conducted. One of the key findings of this comparative study is that there are phase shifts in the seasonal cycles between satellite thermal infrared measurements and ground measurements, especially in the middle to high latitudes in the northern hemisphere. Through this, an issue common in the hyperspectral thermal sensor retrievals were discovered that was unknown previously and offered potential solutions. We also conducted research on some applications of the retrieval products in monitoring the changes of CH4 over the selected regions (the Arctic and South America). Detailed analyses based on local geographic changes related to CH4 concentration increases were discussed. The results of this study concluded that while the atmospheric CH4 concentration over the Arctic region has been increasing since the early 2000s, there were no catastrophic sudden jumps during the period of 2008-2012, as indicated by the earlier studies using pre-validated retrieval products. From our study of CH4 climatology using hyperspectral infrared sounders, it has been proved that the CH4 from hyperspectral sounders provide valuable information on CH4 for the mid-upper troposphere and lower stratosphere. Future approaches are suggested that include: 1) Utilizing extended data records for CH4 monitoring using AIRS, CrIS, and other potential new generation hyperspectral infrared sensors; 2). Improving the algorithms for trace gas retrievals; and 3). Enhancing the capacity to detect CH4 changes and anomalies with radiance signals from hyperspectral infrared sounders.
Assimilation of Precipitation and Nonlocal Observations in the LETKF, and Comparison of Coupled Data Assimilation Strategies with a Coupled Quasi-geostrophic Atmosphere-Ocean Model
(2022) Da, Cheng; Eugenia, Kalnay; Tse-chun, Chen; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Among the data assimilation methods, the Ensemble Kalman Filter (EnKF) has gained popularity due to its ease of implementation and incorporation of the “errors of the day” [Kalnay, 2003]. While the EnKF can successfully assimilate a wide range of observations, it encounters difficulty handling two types of observations: a) observations with non-Gaussian errors such as hydrometeors and precipitation, and b) nonlocal (i.e., path-integrated) observations such as radiance, both of which are vital for weather monitoring and forecasting, since non-Gaussian observations are often associated with severe weather, and nonlocal observations contribute the most to the improved weather forecast skill in the modern assimilation systems. The satellite mission, the Global Precipitation Measurement (GPM), provides several products belonging to these two types of observations since its launch in 2014. Different strategies are developed in this dissertation to assimilate these two types of observations in the EnKF system. To assimilate GPM surface precipitation with non-Gaussian errors, we extended the Gaussian transformation approach developed by Lien et al. [2013, 2016a, b] to a regional model. We transformed the observed and modeled precipitation into Gaussian variables, whose errors also become more Gaussian. We then allowed the transformed precipitation to adjust the dynamic variables and hydrometeors directly through the ensemble error covariance in the EnKF so that the model could “remember” the correct dynamics. Four typhoon cases in 2015 were studied to investigate the impact of GPM precipitation assimilation on typhoon forecast. Results show that model analysis by additional precipitation assimilation agrees more favorably with various independent observations, which leads to an improved typhoon forecast up to 72 hours. Localizing nonlocal observations in the EnKF is another challenging problem. Observation localization is needed in the EnKF to reduce sampling errors caused by the small ensemble size. Unlike conventional observations with single observed locations, those nonlocal observations such as radiance are path-integrated measurements and do not have single observed locations. One common empirical single-layer vertical localization (SLVL) approach localizes nonlocal observations at their weighting function (WF) peaks with symmetric Gaussian-shape localization functions. While the SLVL approach is appropriate for observations with symmetric Gaussian-shaped WFs, it might have difficulty handling observations properly with broad asymmetric WFs or multiple WF peaks, which are typical for clear-sky radiance from sounding or trace-gas sensitive channels of hyperspectral infrared sensors. A multi-layer vertical localization (MLVL) method is developed as an extension of the SLVL, which explicitly considers the WF shape in the formulation and generates the localization value based on the cumulative influences from all components that constitute the nonlocal observations. Observing system simulation experiments assimilating 1-D and 3-D nonlocal observations show that the MLVL has comparable or better performance than the SLVL when assimilating narrow-WF observations, and superior performance than the SLVL when assimilating observations with broad WFs or multiple WF peaks. In the last part, we switch our focus to coupled data assimilation in preparation for assimilating GPM precipitation into different earth components through strongly-coupled data assimilation. Few studies have systematically compared ensemble and variational methods with different coupled data assimilation (CDA) strategies (i.e., uncoupled DA (UCDA), weakly-coupled DA (WCDA), and strongly-coupled DA (SCDA)) for coupled models, though such comparison are essential to understand different methods and have been extensively conducted for uncoupled models. We developed a coupled data assimilation testbed for a coupled quasi-geostrophic atmosphere-ocean model that allows systematic comparison between ensemble and variational methods under different CDA strategies. Results show that WCDA and SCDA improve the coupled analysis compared with UCDA for both 3D-Var and ETKF. It is found that the ocean analysis by SC ETKF is consistently better than the one by WC ETKF, a phenomenon not observed for the 3D-Var method. Different SCDA methods are then compared together under different observation networks. When both atmosphere and ocean observations are assimilated, the SC incremental 4D-Var and ETKF share a similar analysis RMSE smaller than SC 3D-Var, for both atmosphere and ocean. An ECMWF CERA-like assimilation system, which adopts the outer-loop-coupling approach instead of utilizing the coupled-state background error covariance, achieves a similar RMSE as the SC 4D-Var and ETKF. When only atmospheric observations are assimilated, all variational-based DA methods using static background error covariance fail to stabilize the RMSE for the ocean within the experiment periods (about 27.4 years), while the flow-dependent ETKF does stabilize the analysis after about 10 years. Among all the variational systems, CERA shows larger ocean analysis RMSE than SC 3D-Var and 4D-Var, which indicates the outer-loop-coupling alone is not enough to replace the role of a coupled-state background error covariance.
AIR POLLUTION EMISSIONS FROM HIGHWAY VEHICLES: QUANTIFYING IMPACTS OF HUMIDITY, AMBIENT TEMPERATURE, AND COVID-19–RELATED TRAVEL RESTRICTIONS
(2022) Hall-Quinlan, Dolly; Dickerson, Russell R; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Air pollution adversely affects human health and climate at both local and regional scales. With vehicles representing the dominant source of several important air pollutants, more work is needed to improve our understanding of the factors impacting vehicular emissions to further reduce pollution levels. In this dissertation, I use ambient, near-road (NR) observations of nitrogen oxides (NOx), carbon monoxide (CO), black carbon (BC), carbon dioxide (CO2), and traffic to characterize vehicular emissions and the influence of weather and traffic patterns. The first part focuses on how vehicular emissions respond to ambient temperature. The second part investigates traffic pattern changes resulting from COVID-19 travel restrictions and the effects on mobile emissions.Chapter 2 discusses the temperature and specific humidity sensitivity of vehicular NOx, CO, and CO2 emissions. Using NR (along Interstate 95) observations during the cold season, I calculated hourly ΔCO/ΔNOx, ΔCO2/ΔNOx, and ΔCO2/ΔCO ratios to infer emissions ratios from vehicular exhaust. Chapter 3 builds on this work by extending the temperature analysis to BC emissions using ∆BC/∆CO and ∆BC/∆CO2. Results show a factor of two decrease in NOx (−5°C to 25°C) and a ~50% increase in BC emissions (−5°C to 20°C). Combined with traffic observations, we trace this effect to diesel-powered trucks. The observed trends are then used to evaluate the temperature sensitivity in modeled mobile emissions. Important public policy decisions regarding air quality often depend on models that generate accurate emissions estimates from various sectors, including mobile sources. The US EPA estimates vehicular emissions for air quality models using the MOtor Vehicle Emissions Simulator (MOVES). Our analysis shows that MOVES underestimates the temperature effect in NOx emissions and does not adjust BC emissions, indicating that more work is needed to improve the temperature sensitivity in the model. Chapter 4 examines the impact of changing traffic patterns on I-95 in April 2020 on mobile emissions revealing ~60% fewer on-road cars and ~10% fewer trucks, resulting in faster highway speeds and less stop-and-go traffic. Coupled with an analysis of emission ratios, the results of this study suggest a significant decrease in BC emissions from diesel-powered trucks attributable to improved traffic flow.
REGIONAL PREFERENCES IN THE SEASONAL AND MULTIDECADAL LOSS OF ARCTIC SEA ICE: THE ROLE OF CONTINENTAL RUNOFF
(2022) Eager, Rebecca; Nigam, Sumant; Ruiz-Barradas, Alfredo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Arctic sea ice is of great importance as both a key indicator and a driver of climate change. Sea ice is highly sensitive to temperature changes of the overlying atmosphere and the underlying ocean. The declining trend of Arctic sea ice, especially in late summer when seasonal ice extent is also a minimum, is widely considered a key indicator of the global warming of the planet. This dissertation finds that the observed trends in late summer Arctic sea ice are greatly impacted by natural decadal-to-multidecadal climate variability, mainly by sea surface temperature variability in the Atlantic and Pacific Oceans. The Atlantic Multidecadal Oscillation and Pacific Decadal Variability – via a Pan Pacific mode – each contribute a loss of 3-4% of sea ice concentration (SIC) per decade to the overall loss of 24% per decade since 1979.To better understand the mechanisms driving these trends, the impact of decadal and multidecadal climate variability on the Arctic atmosphere, ocean, and continental hydrology is investigated. Multidecadal climate variability leads to regional and seasonal impacts on atmospheric circulation, ocean heat content, and ocean salinity that vary across the Arctic. Modification of the atmospheric circulation on decadal and multidecadal time scales impacts warm inflow into the Arctic from the North Atlantic and North Pacific, and also leads to redistribution of sea ice in the Arctic. Vertical profiles of ocean temperature and salinity near the mouth of the Arctic rivers provides a means to investigate the impact of variability in continental hydrology on the Arctic marginal seas through the input of freshwater and heat. In the Beaufort Sea, the Atlantic Multidecadal Oscillation leads to increased ocean temperatures and decreased ocean salinity at the mouth of the Mackenzie River, corresponding in time to the annual June peak in river discharge. Finally, the impact of Mackenzie River discharge variability on the freshwater content, temperatures, and SIC in the Beaufort Sea is assessed.

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