Atmospheric & Oceanic Science
Permanent URI for this communityhttp://hdl.handle.net/1903/2264
Formerly known as the Department of Meteorology.
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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 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.Item Exploring Regional Emissions and Tropospheric Ozone in the Eastern United States Using Air Quality Models and Data Products(2019) Ring, Allison Marie; Canty, Timothy P; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tropospheric ozone (O3) is a harmful pollutant regulated by the US Environmental Protection Agency (EPA) through the use of designated air quality standards. Within the United States, approximately 110 million people live within counties designated as in non-attainment of the O3 standard. In this work, analysis is performed to examine the influence of anthropogenic emissions on tropospheric O3 production within the framework of the CMAQ regulatory air quality model. Adjustments are recommended to improve emission representation from the largest (class 3) commercial marine vessels (c3 Marine). Model results with the implemented corrections show improved comparison to surface O3 observations from AQS sites. Characterization of the photochemical O3 production regime (VOC or NOx sensitive) is performed using the ratio of formaldehyde (HCHO) and nitrogen dioxide (NO2) tropospheric column observations from the satellite borne Ozone Monitoring Instrument (OMI), and whole air sampling canisters in the Long Island Sound (LIS) collected on May 17th and 18th, 2017. Evidence for the importance of anthropogenic VOCs in the New York City pollution plume and their role in tropospheric O3 production is presented. Aircraft O3 observations are used to evaluate model performance of the National Oceanic Atmospheric Administration (NOAA) National Air Quality Forecast Capability system CMAQ model for the O3 event in the LIS. Finally, a series of CMAQ simulations are performed to suggest the likely inventory sector (non-road mobile) most responsible for the significant O3 production downwind of coastal urban centers like New York and Chicago. Important air quality policy implications of these findings are discussed.Item Multi-instrument approach for measuring spectral aerosol absorption properties in UV and VIS wavelengths(2017) Mok, Jungbin; Li, Zhanqing; Krotkov, Nickolay A.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The spectral dependence of light absorption by atmospheric particulate matter (PM) has major implications for air quality, surface ultraviolet (UV) radiation, and tropospheric oxidation capacity, but remains highly uncertain. Quantifying the spectral dependence of aerosol absorption at UV and visible wavelengths is important for the accurate air pollution characterization using current (e.g., Aura/OMI) and future (e.g., TROPOMI, TEMPO, GEMS) satellite measurements, photolysis rates calculations in chemical and aerosol transport models and surface radiation modeling. Measurements of column atmospheric absorption and its spectral dependence remain the most difficult part of atmospheric radiation measurements. Currently available ground measurements of spectral aerosol absorption properties (e.g., column effective imaginary refractive index (k), single scattering albedo, (SSA), and aerosol absorption optical depth (AAOD)) are limited to the cloud free conditions and few discrete wavelength bands in the visible spectral region by AERONET almucantar inversions. To address the lack of spectral aerosol and gaseous absorption measurements in the UV, a suite of complementary ground-based instruments, modified UV Multifilter Rotating Shadowband Radiometer (UV-MFRSR) was established in 2002 and is currently in use at NASA Goddard Space Flight Center (NASA/GSFC) in Greenbelt, Maryland. In addition, several field campaigns have been carried out to measure aerosol absorption properties in UV and VIS from different sources in different locations. In September-October 2007 biomass-burning season in the Amazon basin (Santa Cruz, Bolivia), light absorbing (chromophoric) organic or “brown” carbon (BrC) is studied with surface and space-based remote sensing. It is found that BrC has negligible absorption at visible wavelengths, but significant absorption and strong spectral dependence at UV wavelengths. Using the ground-based inversion of column effective imaginary refractive index (k) at UV wavelengths down to 305 nm, a strong spectral dependence of specific BrC absorption is quantified in the UV implying more strongly reduced ultraviolet B (UV-B) radiation reaching the surface. Reduced UV-B means less erythema, plant damage, but also a slower ozone photolysis rate. A photochemical box model is used to show that relative to black carbon (BC) alone, the combined optical properties of BrC and BC slow the net rate of production of ozone by up to 18% and lead to reduced concentrations of radicals OH, HO2, and RO2 by up to 17%, 15%, and 14%, respectively. The optical properties of BrC aerosol change in subtle ways the generally adverse effects of smoke from biomass burning. The objective of this thesis is to develop a new method to infer column effective spectral absorption properties (k, SSA, and AAOD) of PM using the ground-based measurements from AERONET in the visible wavelengths and UV-MFRSR in the UV and ozone and NO2 from ground-based (Pandora and Brewer) or satellite spectrometers, such as Ozone Monitoring Instrument (OMI) on NASA EOS Aura satellite. This represents the first effort to separate effects of gaseous (ozone and NO2) and aerosol absorption and partition black and brown (light-absorbing organic) carbon absorption in the short UV-B wavelengths. These measurements are essential to answer key science questions of the atmospheric composition and improve data products from the current and future satellite atmospheric composition missions.