Atmospheric & Oceanic Science Theses and Dissertations

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OCEAN HEAT CONTENT CALCULATION IMPROVEMENTS FOR EARTH’S ENERGY IMBALANCE QUANTIFICATION
(2024) Boyer, Tim; Carton, James; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Earth’s Energy Imbalance, the difference between incoming and outgoing radiation at the top of the atmosphere, is stored in the atmosphere, land surface, cryosphere, and ocean, but is stored overwhelmingly (~90%) in the ocean on interannual and longer time scales. This imbalance, which is reflected in ocean heat uptake, is a primary indicator of the magnitude of change in energy the Earth’s system as well as an essential variable for understanding short-term variations and their effects on long-term regional and global climate change. The primary methods for calculating ocean heat content all depend on situ measurements of ocean subsurface temperature. The ocean subsurface temperature observing system as it is currently configured, with a substantial but not exclusive contribution from autonomous Argo profiling floats, is shown here to allow estimation of annual global ocean heat uptake with an uncertainty well below that possible with earlier ocean observing systems. It is also shown that maintenance and improvement of a global best quality ocean temperature profile database will lower uncertainty, both historically and for the current observing system and compensate to some extent for areas of sparse data in both direct calculation from observation and in data assimilation models. It is also shown that improvements to the methods used for mapping the inhomogeneous and anisotropic observations onto a regular grid spanning the global ocean will reduce uncertainty historically, currently, and into the future. On shorter monthly timescales regional changes in the Earth’s Energy Imbalance requires tracking the storage within the atmosphere, land, and cryosphere, and the heat transport within the ocean especially to depths where the energy is stored on longer time scales, in addition to ocean heat uptake. Monthly heat uptake estimates discussed here can be utilized with additional terms from atmosphere/land and ocean/sea ice reanalyses to provide Earth's Energy Imbalance estimates on these shorter time-scales in the future.
UNDERSTANDING CLIMATIC FACTORS DRIVING WILDFIRES IN THE WESTERN U.S.
(2024) ZHANG, LEI; Li, zhanqing; Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Wildfires have profound and catastrophic impacts on landscapes and human society and act as important agents in the transformation of ecosystems. Over the past decades, the western United States (WUS) has experienced a significant increase of large wildfires, with substantial rise of the economic and ecological costs. Considerable research efforts towards understanding climate change as a primary driver of larger and more severe wildfires, which exacerbates summer drought, reduces spring snowpack, etc. However, the physical relationships among wildfires in North America (NA) and regional feedback processes to changes in the large-scale circulation, global dryness, and linkages to global warming are still poorly understood. Our observational analyses of wildfire-climate relationships in North America were conducted using diverse independent observations and reanalysis data sets for the period 1984–2014. Results show that the WUS has experienced the most robust increase in burned area. In addition to warming, the WUS has been under the influence of multi-decadal trends in tropospheric relative humidity deficit, reduced cloudiness, increased surface net insolation, and enhanced adiabatic warming and drying from increased tropospheric subsidence, as well as drying from enhanced offshore low-level flow. These trends are found to be associated with a widening of the descending branch of the Hadley circulation, consistent with climate model projections under greenhouse gases warming. This work sheds new light on the underlying regional climate processes affecting wildfire trends in NA and linkages with climate change under global warming. My second work focuses on analyzing the causes of the exceptional 2020 fire season in the WUS. Our comprehensive examination shows this extraordinary year for fires in the WUS is the results of “perfect storm”, a combination of multiple climate and weather extremes events. Extreme fuel aridity in September serves as a compelling example of the critical significance of tropospheric subsidence to the surface and atmospheric RH deficit. The third study evaluates performance of the Canada and US fire indices over the various ecoregions of the WUS. My study also finds Haines index combined with current index further improves the performance of conditional frequency distribution and predictive skill of large fires, suggesting the importance and merit of input from atmosphere dryness and stability into current fire indices.
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.
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.
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.
Development and use of a Fast Response, Nitric Oxide Detector for Air Quality Monntoring and Eddy Correlation Flux Measurements
(1996-06-01) Civerolo, Kevin; Dickerson, Russell
High Resolution Remote Sensing Observations of Summer Sea Ice
(2022) Buckley, Ellen Margaret; Farrell, Sinéad L; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light.
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.
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.
EXAMINATION OF TROPOSPHERIC OZONE AND ITS PRECURSORS WITHIN AN AIR QUALITY MODEL AND IMPLICATIONS FOR AIR QUALITY AND CLIMATE
(2021) Hembeck, Linda; Salawitch, Ross J; Canty, Timothy P; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Elevated levels of tropospheric ozone (O3) caused by emissions of NOx and VOCs negatively impact human health, crops, and ecosystems. Even if precursor emissions are reduced below current levels, predicted higher temperatures due to increased greenhouse gas emissions could impede resulting air quality benefits. Air quality models simulate the complex relationships that form O3 and are used to guide policy decisions directed at improving O3. The body of this work encompasses three projects related to improvements in the representation of O3 and precursors in air quality models. First, I examine the role of O3 and its precursors in air quality and climate change by evaluating ozone production efficiency (OPE) and O3 precursors within models. I modified a chemical mechanism and the emissions of NOx to accurately represent NOx, the reactivity of NOx with peroxy radicals, HCHO, isoprene, as well as organic and inorganic NOy reservoir species. Implementation of these modifications increased confidence in model simulations. Results indicate accepted inventories overestimated NOx emissions but underestimate total VOC reactivity and OPE. Second, I examined the dependence of surface O3 on temperature (climate penalty factor (CPF)) throughout a period of 11 years within an air quality model and measurements. Future increases in temperature could offset benefits from future reductions in the emission of O3 precursors. Determining and understanding the CPF is critical to formulating effective strategies to reduce future exceedances. I have demonstrated that the model can reproduce O3 sensitivity to temperature reasonably well. By controlling emissions specifically of NOx mankind has reduced its vulnerability. Third, I compare satellite-observed and modeled ammonia (NH3) under varying chemical environments over East Asia. Regulation of O3 precursor concentrations in the atmosphere has an indirect effect on NH3 concentrations. Air quality policy to reduce NOx and through that also nitric acid (HNO3) in the atmosphere can result in an increase in the concentration of NH3 because of its neutralizing ability. Therefore, a less acidic atmosphere sequesters less NH3. This preliminary work exposes different areas that need to be addressed to gain greater insight into NH3 emissions and chemistry.
A New Quantitative Framework for Application of Ensemble Forecast Sensitivity to Observations in NWP
(2021) Groff, David Neil; Kalnay, Eugenia; Chen, Tse-Chun; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Current global operational Numerical Weather Prediction (NWP) systems (e.g.the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS)) generally assimilate on the order of 10 million observations every 6 hours. Furthermore, there is substantial diversity in the sampling characteristics and associated error characteristics of the observation types assimilated. In this context, it is not feasible to obtain sufficiently detailed information for determining which available observations or observation types should be assimilated or rejected in NWP systems using traditional Observing System Experiment (OSE) approaches. Forecast sensitivity to observation impact (FSOI) based estimation techniques (Langland and Baker 2004) enable efficient estimation of forecast impacts due to assimilation of individual observations, and as such, represent a solution to this problem. The ensemble forecast sensitivity to observations (EFSO) (Kalnay et al. 2012)impact estimation technique uses ensembles of forecasts to perform linear mapping of innovations to forecast error changes. This mapping involves application of Kalman gain matrices consistent with the complete sets of observations assimilated during data assimilation cycles. As with the other forecast-sensitivity based observation impact estimation techniques there are two prominent “contextual” limitations for application of EFSO in NWP systems: i) the observation impacts are estimated with respect to simultaneously assimilating all other observations that contributed to an analysis, ii) EFSO calculations are relative to a background that includes information from all previously assimilated observations. To mitigate these “contextual” limitations in application of forecast-sensitivity based observation impact information, a new quantitative framework we call “EFSO-components” is developed by decomposing EFSO employed forecast errors and innovations into random and systematic components. Lorenz ’96 simple model experiments indicate that application of ”EFSO-components” provides potentially significant advantages in detection of specific observation flaws, and in further advancing the utility of EFSO-based PQC (Ota et al. 2013, Hotta et al. 2017a, Chen and Kalnay 2019, Chen and Kalnay 2020). As such, we explore how “EFSO-components” fundamentally addresses the aforementioned contextual limitations of forecast-sensitivity based observation impact estimation in a manner that explains the potential application advantages according to Lorenz ’96 simple model experiments. Additionally, a new technique we call predicted EFSO (PEFSO), which is astraightforward extension to EFSO, is introduced in this study. PEFSO represents a potential capability for estimating the hypothetical forecast impacts of unassimilated observations. We explore the potential application of PEFSO as a convenient low computational cost approach for comparing the efficiencies of observing systems in reducing forecast error using Lorenz ’96 simple model experiments.
REMOTE SENSING OF AEROSOL AND THE PLANETARY BOUNDARY LAYER, AND EXPLORING THEIR INTERACTIONS
(2022) SU, Tianning; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Aerosol-planetary boundary layer (PBL) interaction (API) is an important mechanism affecting the thermodynamics and convection in the lower atmosphere. API plays a critical role in the formation of severe pollution events and the development of convective clouds. Despite the progress made in understanding these processes, their magnitude and significance still have large uncertainties, varying significantly with aerosol distribution, aerosol optical property, and meteorological conditions. This study attempts to develop advanced remote sensing algorithms to retrieve information about the PBL and the aerosols contained within it. These remote sensing techniques are further used to elucidate the mechanisms governing API, enhancing our ability to predict air quality and model convective clouds, as well as understand the impact of aerosols on the climate system.In particular, we develop algorithms to improve the retrieval accuracy of aerosols and the PBL from satellite sensors and a ground-based lidar. For aerosol remote sensing, we use the deep neural network (DNN) to construct surface reflectance relationships (SRR) between different wavelengths. We then incorporate the DNN-constrained SRR into a traditional dark-target algorithm to retrieve the aerosol optical depth (AOD) using information from a current-generation geostationary satellite, i.e., Himawari-8, as input. As a result, the performance of AOD retrievals over East Asia is significantly improved. For PBL remote sensing, we explore different techniques for retrieving the PBL height (PBLH) from both a space-borne lidar (i.e., the Cloud-Aerosol Lidar with Orthogonal Polarization) and a ground-based lidar. We further develop a new method that combines lidar-measured aerosol backscatter with a stability-dependent model of PBLH diurnal variation. The new method circumvents or alleviates an inherent limitation of lidar-based PBLH detection when a residual layer of aerosols does not change in phase with the evolving thermodynamics. By separately considering surface-cloud coupling regimes, this method also offers high-quality retrievals of PBLH under cloudy conditions. Utilizing the enhanced retrievals of PBLH and synergistic measurements, we can also address some scientific questions concerning API, including the influencing factors of API and the role of aerosol vertical distributions. The correlation between the PBLH and the concentration of particulate matter with aerodynamic diameters less than 2.5 microns is generally negative. However, the magnitude, significance, and even the sign of their relationship vary greatly, depending on location and meteorological and aerosol conditions. In particular, API is considerably different under three aerosol vertical structure scenarios (i.e., well-mixed, decreasing and increasing with height). The vertical distribution of aerosol radiative forcing differs dramatically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in aerosol radiative forcing leads to different aerosol effects on atmospheric stability and entrainment processes. Absorbing aerosols are much less effective in stabilizing the lower atmosphere when aerosols decrease with height than in an inverted structure scenario.
An evaluation of convection-allowing ensemble forecast sensitivity to initial conditions
(2021) Schwartz, Craig; Poterjoy, Jonathan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation aims to advance understanding of initial conditions (ICs) for convection-allowing ensembles (CAEs). To do so, experiments with 80-member limited-area ensemble Kalman filters (EnKFs) were performed over the entire conterminous United States for a 4-week period. The EnKF data assimilation systems differed in terms of their cycling strategies (continuous or partial cycling) and horizontal grid spacings (15- or 3-km horizontal grid spacing). EnKF analyses initialized 36-h, 3-km, 10-member CAE forecasts that were evaluated with a focus on precipitation, providing insights about CAE forecast sensitivity to ICs. Additionally, EnKF analyses were leveraged to isolate CAE forecast sensitivity to resolution of both IC perturbations and central initial states about which IC perturbations were centered. A “blending” approach was also used to produce new sets of CAE ICs by combining small scales from continuously cycling EnKF analyses with large scales from Global Ensemble Forecast System (GEFS) ICs using a low-pass filter. Key results are as follows:• CAE forecasts initialized from continuously cycling 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and continuously cycling 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized forecasts were better than forecasts initialized from continuously cycling EnKFs. Blended 3-km ICs led to ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts while preserving short-term forecast benefits of unblended continuously cycling 3-km EnKF analyses. • Continuously cycling EnKF analyses initialized ~1–18-h forecasts that were comparable to or somewhat better than those with partial cycling EnKF ICs. Conversely, ~18–36-h forecasts with partial cycling EnKF ICs were comparable to or better than those with unblended continuously cycling EnKF ICs. However, blended ICs yielded ~18–36-h forecasts that were statistically indistinguishable from those with partial cycling ICs. • It is more important for central initial states than for IC perturbations to possess convection-allowing horizontal grid spacing for short-term CAE forecasting applications. These collective findings have important implications for model developers working on next-generation CAEs and suggest paths toward potentially saving computing resources, streamlining processes for improving CAE ICs, and unifying short-term and next-day CAE forecasting systems.

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