Atmospheric & Oceanic Science Theses and Dissertations

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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.
ADVANCES IN SEQUENTIAL DATA ASSIMILATION AND NUMERICAL WEATHER FORECASTING: AN ENSEMBLE TRANSFORM KALMAN-BUCY FILTER, A STUDY ON CLUSTERING IN DETERMINISTIC ENSEMBLE SQUARE ROOT FILTERS, AND A TEST OF A NEW TIME STEPPING SCHEME IN AN ATMOSPHERIC MODEL
(2012) Amezcua, Javier; Kalnay, Eugenia; Ide, Kayo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.
Air Pollutant Concentrations and Trends over the Eastern U.S. and China: Aircraft Measurements and Numerical Simulations
(2012) He, Hao; Dickerson, Russell R.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In the last several decades, efforts have been made to mitigate air pollution all around the world. With surface observations showing substantial decrease of criteria pollutants, including O3, NOx, CO and SO2, the long-term aircraft measurements over the eastern U.S. provide a unique opportunity to study the trend of the air pollutant column contents and the regional transport in the free troposphere. Analyses of the historical data indicated ~2.0 Dobson Unit/decade decrease in tropospheric O3 columns over the eastern U.S. with a similar decreasing trend of CO. The statistical analysis also showed a significant decreasing trend for tropospheric SO2. Analyses of the EPA CEMS emission data showed parallel reductions. A case study of tropospheric O3 and SO2 over downwind area of Baltimore showed that the regional transport by westerly wind from Ohio and Pennsylvania play an important role in the local air quality issues. As the second largest economy in the world, China's rapid economic growth in the last decade lead to a dramatic increase in energy demand, which relied heavily on coal burning. The enormous amount of SO2 emissions caused severe environmental issues including acid deposition and particulate matter pollution. To mitigate these air quality problems, strict control measures and regulations were applied to abate sulfur emissions, especially before and during the 2008 Beijing Olympics. Aircraft measurements of tropospheric SO2 were conducted over central China in spring 2008, where intense measurements are lacking. A substantial amount of SO2 was observed in the free troposphere, which is important to regional transport and remote sensing. I successfully validated the SO2 columns with satellite retrievals, and proved that the new OMI SO2 algorithm performs better than the conventional algorithm. An emission inventory was evaluated through a combination of model simulations and satellite products. Between 2006 and 2008, the SO2 emissions had been reduced substantially over middle and eastern China. I also analyzed the model simulations, and find the SO2 lifetime is ~ 38 h during spring in China and that ~50% of Chinese emissions are exported to the western Pacific.
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.
Air Pollution Response to Changing Weather and Power Plant Emissions in the Eastern United States
(2008-11-20) Bloomer, Bryan Jaye; Dickerson, Russell R; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Air pollution in the eastern United States causes human sickness and death as well as damage to crops and materials. NOX emission reduction is observed to improve air quality. Effectively reducing pollution in the future requires understanding the connections between smog, precursor emissions, weather, and climate change. Numerical models predict global warming will exacerbate smog over the next 50 years. My analysis of 21 years of CASTNET observations quantifies a climate change penalty. I calculate, for data collected prior to 2002, a climate penalty factor of ~3.3 ppb O3/°C across the power plant dominated receptor regions in the rural, eastern U.S. Recent reductions in NOX emissions decreased the climate penalty factor to ~2.2 ppb O3/°C. Prior to 1995, power plant emissions of CO2, SO2, and NOX were estimated with fuel sampling and analysis methods. Currently, emissions are measured with continuous monitoring equipment (CEMS) installed directly in stacks. My comparison of the two methods show CO2 and SO2 emissions are ~5% lower when inferred from fuel sampling; greater differences are found for NOX emissions. CEMS are the method of choice for emission inventories and commodity trading and should be the standard against which other methods are evaluated for global greenhouse gas trading policies. I used CEMS data and applied chemistry transport modeling to evaluate improvements in air quality observed by aircraft during the North American electrical blackout of 2003. An air quality model produced substantial reductions in O3, but not as much as observed. The study highlights weaknesses in the model as commonly used for evaluating a single day event and suggests areas for further investigation. A new analysis and visualization method quantifies local-daily to hemispheric-seasonal scale relationships between weather and air pollution, confirming improved air quality despite increasing temperatures across the eastern U.S. Climate penalty factors indicate amplified smog formation in areas of the world with rising temperatures and increasing emissions. Tools developed in this dissertation provide data for model evaluation and methods for establishing air quality standards with an adequate margin of safety for cleaning the air and protecting the public's health in a world with changing climate.
Analyses of multiple global and regional aerosol products: investigation of aerosol effects and artifacts
(2005-12-02) Jeong, Myeong Jae; Li, Zhanqing; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Multiple aerosol products derived from satellite, ground-based, and air-borne instruments were analyzed with a focus on satellite-based aerosol products. Aerosol measurements based on different techniques were utilized to investigate the effects and the artifacts of aerosols and clouds by taking advantages of respective techniques. The global aerosol products derived from Advanced Very High Resolution Radiometer (AVHRR) and Total Ozone Mapping Spectrometer (TOMS), were analyzed for extracting synergic information. Global distributions of dominant aerosol type(s) were derived and the two products were combined to acquire an extended spatial coverage of aerosol optical thickness (AOT) at a common wavelength (0.55um). It was shown that the derived AOT agreed reasonably with AOT from the state-of-the-art Moderate Resolution Imaging Spectroradiometer (MODIS). In-depth comparison of aerosol products derived from the MODIS and the AVHRR was performed. New insights and understanding were gained for the discrepancies between the two prominent aerosol products, allowing for bridging the current and past products. Several factors causing the discrepancies were investigated. Cloud-screening techniques and aerosol models employed by the retrieval algorithms were found to be the most important factors explaining the observed discrepancies. The column aerosol humidification effect (AHE) was investigated. The column AHE was shown to be sensitive to changes in relative humidity (RH). Six methods to infer the column AHE were introduced. The knowledge of the AHE helps investigate aerosol properties and retrievals near clouds, enabling separation of aerosol real effects from artifacts associated with clouds. Finally, apparent correlations between AOT and cloud amount from ground- and satellite-based measurements were investigated. Several factors including air convergence, cloud contamination and uncertainty in cloud cover estimation, the AHE, cloud-processed/new particle genesis were studied to explain the correlations. We showed that the correlation found in ground-based measurements is mostly due to real effects while satellite-based measurements are significantly influenced by artifacts caused by clouds.
An analysis of convective transport, Lightning NO.sub.x production, and chemistry in midlatitude and subtropical thunderstorms
(2006-10-18) Ott, Lesley Elaine; Dickerson, Russell R.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The impact of lightning NO.sub.x production and convective transport on tropospheric chemistry was studied in four thunderstorms observed during field projects using a 3-dimensional (3-D) cloud-scale chemical transport model (CSCTM). The dynamical evolution of each storm was simulated using a cloud-resolving model, and the output used to drive the off-line CSCTM which includes a parameterized source of lightning NO.sub.x based on observed cloud-to-ground (CG) and intracloud (IC) flash rates. Simulated mixing ratios of tracer species were compared to anvil aircraft observations to evaluate convective transport in the model. The production of NO per CG flash (P.sub.CG) was estimated based on mean observed peak current, and production per IC flash (P.sub.IC) was scaled to P.sub.CG. Different values of P.sub.IC/P.sub.CG were assumed and the results compared with in-cloud aircraft measurements to estimate the ratio most appropriate for each storm. The impact of lightning NO.sub.x on ozone and other species was examined during the storm in the CSCTM and following each storm in the convective plume using a chemistry-only version of the model which includes diffusion but without advection, and assumes clear-sky photolysis rates. New lightning parameterizations were implemented in the CSCTM. One parameterization uses flash length data, rather than flash rates, as input, and production per meter of flash channel length is estimated. A second parameterization simulates indivdual lightning flashes rather than distributing lightning NO_x uniformly among a large number of gridcells to better reproduce the variability of observations. The results suggest that P_IC is likely on the order of P_CG and not significantly less as has been assumed in many global modeling studies. Mean values of P_CG=500 moles NO and P_IC=425 moles NO have been estimated from these simulations of midlatitude and subtropical continental thunderstorms. Based on the estimates of production per flash, and an assumed ratio of the number of IC to CG flashes and global flash rate, a global annual lightning NO source of 8.6 Tg N yr^-1 is estimated. Based on these simulations, vertical profiles of lightning NO_x mass for subtropical and midlatitude continental regimes have been computed for use in global and regional chemical transport models.
APPLICATION OF NEURAL NETWORKS TO EMULATION OF RADIATION PARAMETERIZATIONS IN GENERAL CIRCULATION MODELS
(2012) Belochitski, Alexei; Baer, Ferdinand; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
A novel approach based on using neural network (NN) techniques for approximation of physical components of complex environmental systems has been applied and further developed in this dissertation. A new type of a numerical model, a complex hybrid environmental model, based on a combination of deterministic and statistical learning model components, has been explored. Conceptual and practical aspects of developing hybrid models have been formalized as a methodology for applications to climate modeling and numerical weather prediction. The approach uses NN as a machine or statistical learning technique to develop highly accurate and fast emulations for model physics components/parameterizations. The NN emulations of the most time consuming model physics components, short and long wave radiation (LWR and SWR) parameterizations have been combined with the remaining deterministic components of a general circulation model (GCM) to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The high accuracy, which is of a paramount importance for the approach, and a speed-up of model calculations when using NN emulations, open the opportunity for model improvement. It includes using extended NN ensembles and/or more frequent calculations of full model radiation resulting in an improvement of radiation-cloud interaction, a better consistency with model dynamics and other model physics components. First, the approach was successfully applied to a moderate resolution (T42L26) uncoupled NCAR Community Atmospheric Model driven by climatological SST for a decadal climate simulation mode. Then it has been further developed and subsequently implemented into a coupled GCM, the NCEP Climate Forecast System with significantly higher resolution (T126L64) and time dependent CO2 and tested for decadal climate simulations, seasonal prediction, and short- to medium term forecasts. The developed highly accurate NN emulations of radiation parameterizations are on average one to two orders of magnitude faster than the original radiation parameterizations. The NN approach was extended by introduction of NN ensembles and a compound parameterization with quality control of larger errors. Applicability of other statistical learning techniques, such as approximate nearest neighbor approximation and random trees, to emulation of model physics has also been explored
APPLICATIONS OF ENSEMBLE FORECAST SENSITIVITY TO OBSERVATIONS FOR IMPROVING NUMERICAL WEATHER PREDICTION
(2018) Chen, Tse-Chun; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Massive amounts of observations are assimilated every day into modern Numerical Weather Prediction (NWP) systems, and more are being deployed. The large volume of data prevents thorough monitoring and screening (QC) the impact of each assimilated observation using standard observing system experiments (OSEs). The presence of so many observations also makes very difficult to estimate the impact of a new observing system using OSEs. Forecast Sensitivity to Observation using adjoint formulation (AFSO, Langland and Baker, 2004) provides an efficient impact evaluation of each observation on forecasts. We propose 3 applications using the simpler ensemble formulation of FSO (EFSO, Kalnay et al., 2012) to improve NWP, namely (1) online monitoring tool, (2) data selection, and (3) proactive quality control (PQC). We first demonstrate PQC on a simple Lorenz (1996) model, showing that EFSO is able to identify artificially '`flawed" observations. We then show that PQC improves the quality of analysis and forecast of the system, even if the observations are flawless, and the improvement is robust against common sub-optimal of DA configurations in operation. A PQC update method reusing the original Kalman gain is found to be both accurate and computationally efficient. EFSO and PQC are then explored with realistic GFS systems. A close-to-operation GFS-GSI Hybrid En-Var system is used to examine the data monitoring and selection applications. The benefit of the online observation monitoring and data rejection based on EFSO is very apparent. Identifying and deleting detrimental radiance channels results in a forecast improvement. Results obtained on a lower resolution GFS system show that PQC significantly improves the quality of analysis and 5-day forecasts for all variables over the globe. Most of the improvement comes from "cycling" PQC, which accumulates improvements brought by deleting detrimental observations over many cycles, rather than from deleting detrimental observations in the current cycle. Thus we avoid using "future data" in PQC and its implementation is shown to be computationally feasible in NCEP operations.
Applications of the LETKF to adaptive observations, analysis sensitivity, observation impact and the assimilation of moisture
(2007-11-26) Liu, Junjie; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In this thesis we explore four new applications of the Local Ensemble Transform Kalman Filter (LETKF), namely adaptive observations, analysis sensitivity, observation impact, and multivariate humidity assimilation. In each of these applications we have obtained promising results. In the adaptive observation studies, we found that ensemble spread strategy, where adaptive observations are selected among the points with largest ensemble spread (with the constraint that observations cannot be contiguous in order to avoid clusters of adaptive observations) is very effective and close to optimal sampling. The application on simulated Doppler Wind Lidar (DWL) adaptive observation studies shows that 3D-Var is as effective as LETKF with 10% adaptive observations sampled with the ensemble spread strategy. With 2% adaptive observations, 3D-Var is not as effective as the LETKF. In the analysis sensitivity study, we proposed to calculate this quantity within the LETKF with low additional computational time. Unlike in 4D-Var (Cardinali et al., 2004), in the LETKF, the computation is exact and satisfies the theoretical value limits (between 0 and 1). The results from simulated experiments show that the trace of analysis sensitivity qualitatively reflects the observation impact obtained from independently computed data addition or data denial OSSE experiments. In the observation impact study, we derived a formula to estimate the impact of observations on short-range forecasts as in Langland and Baker (2004), but without using an adjoint model. Both methods estimate more than 90% accuracy the actual observation impact on the short-range forecast error improvement. Like the adjoint method, the method we proposed detects observations that have either large random error or unaccounted bias. This method can be easily calculated within the LETKF, and provides a powerful tool to estimate the quality of observations. Finally, for the first time, we assimilate humidity observations multivariately in both perfect model experiments and real data assimilation. We found that multivariate assimilation is better than univariate assimilation. The assimilation of pseudo-RH (Dee and da Silva, 2003) is better than the choice of specific humidity and relative humidity. The multivariate assimilation of AIRS specific humidity retrievals on NCEP GFS system shows positive impact on the winds analysis.
APPLICATIONS OF THE OZONE MONITORING INSTRUMENT IN OBSERVING VOLCANIC SULFUR DIOXIDE PLUMES AND SULFATE DEPOSITION
(2021) Fedkin, Niko Markovich; Dickerson, Russell R; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Sulfur dioxide (SO2), a gas emitted by both volcanoes and anthropogenic activity, is a major pollutant and a precursor to sulfate aerosols. Sulfates can be deposited back to the ground where they have adverse impact on the environment or reside in the stratosphere as aerosols and affect radiative forcing. I investigated two components that stem from SO2: the deposition of sulfate, and the remote sensing of the SO2 layer height, important for aviation safety and chemical modeling. In the first study, I used column SO2 data from the Ozone Monitoring Instrument (OMI), and sulfate wet deposition data from the National Atmospheric Deposition Program to investigate the temporal and spatial relationship between trends in SO2 emissions and the downward sulfate wet deposition over the northeastern U.S. from 2005 to 2015. The results showed that emission reductions are reflected in deposition reductions within this same region. Emission reductions along the Ohio River Valley led to decreases in sulfate deposition not only in eastern OH and western PA, but also further downwind at sites in Delaware and Maryland. The findings suggested that emissions and wet deposition are linked through not only the location of sources relative to the observing sites, but also photochemistry and weather patterns characteristic to the region in winter and summer. The second part of this dissertation focuses on SO2 layer height retrievals and their applications. To this end I applied the Full Physics Inverse Learning Machine (FP-ILM) algorithm to OMI radiances in the spectral range of 310-330 nm. This approach utilized radiative transfer calculations to generate a large dataset of synthetic radiance spectra for a wide range of geophysical parameters. The spectral information was then used to train a neural network to predict the SO2 height. The main advantage of the algorithm is its speed, retrieving plume height in less than 10 min for an entire OMI orbit. I also compared the SO2 height retrievals to other data sources and explored some potential applications, in particular their use in volcanic SO2 plume forecasts and estimating the total mass emitted from volcanic eruptions.
The Assimilation of Hyperspectral Satellite Radiances in Global Numerical Weather Prediction
(2008-04-29) Jung, James Alan; Kalnay, Eugenia; Le Marshall, John F.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Hyperspectral infrared radiance data present opportunities for significant improvements in data assimilation and Numerical Weather Prediction (NWP). The increase in spectral resolution available from the Atmospheric Infrared Sounder (AIRS) sensor, for example, will make it possible to improve the accuracy of temperature and moisture fields. Improved accuracy of the NWP analyses and forecasts should result. In this thesis we incorporate these hyperspectral data, using new assimilation methods, into the National Centers for Environmental Prediction's (NCEP) operational Global Data Assimilation System/Global Forecast System (GDAS/GFS) and investigate their impact on the weather analysis and forecasts. The spatial and spectral resolution of AIRS data used by NWP centers was initially based on theoretical calculations. Synthetic data were used to determine channel selection and spatial density for real time data assimilation. Several problems were previously not fully addressed. These areas include: cloud contamination, surface related issues, dust, and temperature inversions. In this study, several improvements were made to the methods used for assimilation. Spatial resolution was increased to examine every field of view, instead of one in nine or eighteen fields of view. Improved selection criteria were developed to find the best profile for assimilation from a larger sample. New cloud and inversion tests were used to help identify the best profiles to be assimilated in the analysis. The spectral resolution was also increased from 152 to 251 channels. The channels added were mainly near the surface, in the water vapor absorption band, and in the shortwave region. The GFS was run at or near operational resolution and contained all observations available to the operational system. For each experiment the operational version of the GFS was used during that time. The use of full spatial and enhanced spectral resolution data resulted in the first demonstration of significant impact of the AIRS data in both the Northern and Southern Hemisphere. Experiments were performed to show the contribution to the improvements in global weather forecasts from the increase in spatial and spectral resolution. Both spatial and spectral resolution increases were shown to make significant contributions to forecast skill. New methods were also developed to check for clouds, inversions and for estimating surface emissivity. Overall, an improved methodology for assimilating hyperspectral AIRS data was achieved.
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.
Assimilation of trace gas retrievals with the Local Ensemble Transform Kalman Filter
(2009) Kuhl, David Derieg; Kalnay, Eugenia; Szunyogh, Istvan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Over the 50 year history of Numerical Weather Prediction (NWP), the focus has been on the modeling and prediction of meteorological parameters such as surface pressure, temperature, wind, and precipitation. However, due to concerns over pollution and to recent advancements in satellite technologies, an increasing number of NWP systems have been upgraded to include capabilities to analyze and predict the concentration of trace gases. This dissertation explores some of the specific issues that have to be addressed for an efficient modeling of the concentration of the trace gases. These issues include modeling the effects of convective mixing on the concentration of the trace gases and the multivariate assimilation of space-based observations of the concentration of the trace gases. In this dissertation, we assimilate observations of the concentration of trace gases with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) NWP model. We use a modified version of the NCEP GFS model that was operational in 2004 at resolution T62/L28. We modify the model by adding parameterization for the process of convective mixing of the trace gases. We consider two specific trace gases: ozone (O₃) and carbon monoxide (CO). We incorporate these gases into the model by using 3-dimensional time-dependent O₃ and CO production-loss values from the Real-time Air Quality Modeling System (RAQMS) global chemical model. The O₃ observations we assimilate are from the Solar Backscatter UltraViolet generation 2 (SBUV/2) satellite instrument (version 8) flown on the NOAA 16 and 17 satellites. The CO observations we assimilate are from the Measurements Of Pollution In The Troposphere (MOPITT) instrument (version 3) flown on the NASA TERRA satellite. We also develop a new observation operator for the assimilation of retrievals with the LETKF. We carry out numerical experiment for the period between 000UTC 1 July 2004 to 000UTC 15 August in the summer of 2004. The analysis and forecast impact of the assimilation of trace gas observations on the meteorological fields is assessed by comparing the analyses and forecasts to the high resolution operational NCEP GFS analyses and to radiosonde observations. The analysis and forecast impact on the trace gas fields is assessed by comparing the analyzed and predicted fields to observations collected during the Intercontinental Chemical Transport Experiment (INTEX-A) field mission. The INTEX-A field mission was conducted to characterize composition of pollution over North America, thus providing us with ozonesonde and aircraft based verification data. We find that adding the process of convective mixing to the parameterization package of the model and the assimilation of observations of the trace gases improves the analysis and forecast of the concentration of the trace gases. In particular, our system is more accurate in quantifying the concentration of O₃ in the troposphere than the original NCEP GFS. Also, our system is competitive with the state-of-the-art RAQMS atmospheric chemical model in analyzing the concentration of O₃ and CO throughout the full atmospheric model column. The assimilation of O₃ and CO observations has a mixed impact on the analysis and forecast of the meteorological fields. We find that most of the negative impact on the meteorological fields can be eliminated, without much reduction to the positive impact on the trace gas fields, by inflating the prescribed variance of the trace gas observations. The appendices of this dissertation reproduces two papers on related research. The first paper covers the northward front movement and rising surface temperatures in the great planes. The second paper covers the assessment of predictability with a Local Ensemble Kalman Filter.
Atlantic Multidecadal Variability: Surface and Subsurface Thermohaline Structure and Hydroclimate Impacts
(2014) Kavvada, Argyro; Nigam, Sumant; Ruiz-Barradas, Alfredo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The Atlantic Multidecadal Oscillation (AMO), a sea surface temperature mode of natural variability with dominant timescales of 30 -70 years and largest variations centered on the northern North Atlantic latitudes is one of the principal climate signals that have earned considerable attention in the recent decades, due to its multilateral impact on both local and remote weather and climate and its importance in predicting extreme events, such as drought development over North America. A 3-dimensional structure of the AMO is constructed based on observations and coupled, ocean-atmosphere 20th century climate simulations. The evolution of modeled, decadal-to-multidecadal variability and its hydroclimate impact is also investigated between two successive model versions participating in the CMIP3 and CMIP5 projects. It is found that both model versions underestimate low frequency variability in the 70-80 and 30-40 year ranges, while overestimating variability in higher frequencies (10-20 year range). In addition, no significant improvements are noted in the simulation of AMO's hydroclimate impact. A subsurface, vertically integrated heat content index (0-1000m) is proposed in an effort to capture the thermal state of the ocean and to understand the origin of AMO variability, especially its surface-subsurface link on decadal- to- multidecadal timescales in the North Atlantic basin. The AMO-HC index exhibits stronger oscillatory behavior and shorter timescales in comparison to the AMO-SST index, while leading the latter by about 5 years. A cooling of the North Atlantic subsurface is discernible in the recent years (mid-2000s -present), a feature that is almost absent at the ocean surface and could have tremendous implications in predicting future North Atlantic climate and in relation to the recent hiatus in the rise of global surface temperatures that was noted in the latest Intergovernmental Panel on Climate Change assessment report. Finally, AMO's decadal variability is shown linked to Gulf Stream's northward surges and the low-frequency NAO, as envisioned by Vinhelm Bjerknes in 1964. A cycle encompassing the low-frequency NAO, Gulf Stream's poleward excursions and the associated shifts in surface winds and SSTs over the subpolar North Atlantic is proposed as a possible mechanism for AMO's origin and a principal target for future research.
BRED VECTORS IN THE NASA NSIPP GLOBAL COUPLED MODEL AND THEIR APPLICATION TO COUPLED ENSEMBLE PREDICTIONS AND DATA ASSIMILATION
(2005-04-27) Yang, Shu-Chih; Yang, Shu-Chih; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The theme of my thesis research is to perform breeding experiments with NASA/NSIPP coupled general circulation model (CGCM) in order to obtain ENSO-related growing modes for ensemble perturbations. We show for the first time that the breeding method is an effective diagnostic tool for studying the coupled ENSO-related instabilities in a coupled ocean-atmosphere general circulation model that includes physical and dynamical processes of many different time scales. We also show for the first time that it is feasible to utilize the coupled bred vectors (BV) as a way to construct perturbations for ensemble forecasts for ENSO prediction using an operational coupled climate prediction model. The results of the thesis research show that coupled breeding can detect a precursor signal associated with ENSO events. Bred vectors are characterized by air-sea coupled features and they are very sensitive to ENSO phases and background season. This indicates that bred vectors can effectively project on the seasonal-to-interannual instabilities by growing upon the slowly varying coupled instability. These results are robust: bred vectors obtained from both the NASA and NCEP coupled systems exhibit similarities in many fields, even in atmospheric teleconnected regions. We show that bred vectors have a structure similar to the one-month forecast error (analysis increment). The BV growth rate and the one-month forecast error show similar low frequency variations. Both of their subsurface temperatures have large-scale variability near the depth of thermocline. Evidence shows that bred vectors capture the eastern movement of the analysis increment (one-month forecast error) along the equatorial Pacific during 1997-1998 El Niño evolution. The results suggest that one-month forecast error in NSIPP CGCM is dominated by dynamical errors whose shape can be captured by bred vectors, especially when the BV growth rate is large. These results suggest that bred vectors should be effective coupled perturbations for ensemble ENSO predictions, compensating for the lack of coupled ENSO-related perturbations in current operational ensembles. The similarity between the bred vectors and the one month forecast errors suggests that bred vectors can capture "errors of the month" and could also be applied to improve oceanic data assimilation by providing information on the month-to-month background variability.
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.
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.
Carbon cycle data assimilation using a coupled atmosphere-vegetation and the Local Ensemble Transform Kalman Filter
(2009) Kang, Ji-Sun; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
We develop and test new methodologies to best estimate CO2 fluxes on the Earth's surface by assimilating observations of atmospheric CO2 concentration, using the Local Ensemble Transform Kalman Filter. We perform Observing System Simulation Experiments and assimilate simultaneously atmospheric observations and atmospheric carbon observations, but no surface fluxes of carbon. For the experiments, we modified an atmospheric general circulation model to transport atmospheric CO2 and coupled this model with a dynamical terrestrial carbon model and a simple physical land model. The state vector of the model prognostic variables was augmented by the diagnosed carbon fluxes CF, so that the carbon fluxes were updated by the background error covariance with other variables. We designed three types of analysis systems: a C-univariate system where CF errors are coupled only with CO2, a multivariate system where all the error covariances are coupled, and a one-way multivariate analysis where the wind is included in the carbon error covariance, but there is no feedback on the winds. With perfect model experiments, the one-way multivariate analysis has the best results in CO2 analysis. For the imperfect model experiments, we applied techniques of model bias correction and adaptive inflation. With those, we obtained a high-quality analysis of surface CO2 fluxes. Furthermore, the adaptive inflation technique also provides a good estimate of observation errors. A new approach in the multivariate data assimilation with "variable localization", where the error correlations between unrelated variables are zeroed-out further improved the multivariate analyses surface CO2 fluxes. We note that with the simultaneous assimilation of winds and carbon variables, we are able to transport atmospheric CO2 with winds as well as, for the first time, couple their error covariances. As a result, the multivariate systems perform well, and do not require any kind of a-priori information that should be pre-calculated by independent observations or model simulations. The many new techniques that we developed and tested put us on a solid basis to tackle the assimilation of real atmospheric and CO2 observations, a project being carried out collaboratively by Dr. Junjie Liu under the direction of Prof. Inez Fung at UC Berkeley.
The Caribbean low-level jet: Its structure and interannual variability
(2007-11-21) Munoz, Ernesto; Busalacchi, Antonio J; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The Caribbean region shows maxima in easterly winds greater than 12 m/s at 925 hPa in July and February referred herein as the summer and winter Caribbean low-level jet (CBNLLJ), respectively. The purpose of this study is to identify the mechanisms of the CBNLLJ formation and variability and their association to the regional hydroclimate. To study the climatological aspects of the CBNLLJ, climatological fields are calculated from 1979 to 2001. It is observed that the low-level (925-hPa) zonal wind over the Caribbean basin has a semi-annual cycle. The semi-annual cycle has peaks in February and July that are regional amplifications of the large-scale circulation by means of a meridional pressure gradient. High mountains to the south of the Caribbean Sea influence the air temperature meridional gradient providing a baroclinic structure that favors a stronger easterly wind. The boreal summer strengthening of the CBNLLJ is associated with subsidence over the subtropical North Atlantic from the May-to-July evolution of the Central American monsoon. Additionally, the mid-summer minimum of Caribbean precipitation is related to the Caribbean LLJ through greater moisture flux divergence. The CBNLLJ has interannual variability with greater standard deviation during boreal summer. The summer interannual variability of the CBNLLJ is due to variability of the meridional pressure gradient across the Caribbean basin influenced by sea surface temperature (SST) gradients between the tropical Pacific and the tropical Atlantic. To determine the inter-decadal changes statistical diagnostics of summer CBNLLJ anomalies are analyzed for different periods, in particular 1958-1978 and 1979-2001. Also analyzed were inter-basin SLP and SST gradient indices. The CBNLLJ showed more intense events and greater persistence during 1979-2001 than during 1958-1978 as a result of more extreme SLP and SST inter-basin gradients during the more recent decades. These are due to changes in the relation between the Pacific and the Atlantic after the late 1970's. The results of the present study are important in the context of understanding an intrinsic component of the Caribbean climate, i.e., the Caribbean low-level jet.

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