M.S. - Atmospheric Sciences

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https://hdl.handle.net/10125/50829

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Investigation of Weather-related Aviation Accidents in Hawai‘i from 2003 – 2022
(2023) Monte, Frederike Sara; Businger, Steven; Atmospheric Sciences
Flooding From the Ground Up: An Analysis of Rain and River Behavior on the North Shore of Kaua‘i
(University of Hawaii at Manoa, 2023) Sears, Mya; Nugent, Alison D.; Atmospheric Sciences
The windward side of Kauaʻi is prone to heavy rainfall events due to its topographical features and geographical location. Persistent northeasterly trade winds, coupled with steep changes in elevation, create an ideal environment for orographic precipitation on the north shore. In addition, Kaua‘i is located further north than the other main Hawaiian Islands, which makes it more likely to intercept midlatitude features, such as Kona lows, upper-level lows, and cold fronts, that frequently result in high rainfall and river discharge conditions. These cases of high river discharge can redistribute sediment along the rivers in Halele‘a, Kaua‘i, increasing uncertainty in watershed behavior and making flooding events more unpredictable. As such, it is important to update knowledge on the baselines of river gauge information and conduct studies that contextualize the observational data. This work focuses on using data from rain and river gauges in Halele‘a to understand the past and present characteristics of the Hanalei River and the Wainiha River. Three primary studies were conducted using these data: a statistical analysis of heavy rainfall and streamflow patterns, a case study of flooding events in Hanalei, and an analysis of atmospheric disturbances. The statistical analysis calculated the return levels of Halele‘a rainfall and streamflow, then used heavy and very heavy rainfall/streamflow definitions to highlight the seasonal and annual water patterns. The case study used rainfall data and streamflow time series from recent flooding events to understand the roles that antecedent conditions play in river behavior. The final study identified the atmospheric conditions that were responsible for the highest streamflow cases in Halele‘a. The findings that come from this project can improve the predictability of flooding events, shed light on the impacts of different atmospheric disturbances, and help community members to estimate when floodwaters may reach the town centers.
Assessing The Madden Julian Oscillation’s (MJO) Decay Rate And Frequency Using The Real-Time Multivariate MJO (RMM) Index
(University of Hawaii at Manoa, 2022) Cai, Lintong; Jin, Fei-Fei; Atmospheric Sciences
The Madden-Julian Oscillation (MJO), a dominant mode of variability on intraseasonal timescales, is a key phenomenon with pronounced global impacts and thus of great importance in the weather and climate continuum. In this study, we proposed the simplest linear stochastically forced damped oscillator model for MJO, by designating a complex MJO state variable with its real and imaginary parts by the widely used RMM index. With this 2-degree freedom conceptual MJO model, we developed a simple approach to assessing MJO’s decay rate and frequency as well as their seasonal and ENSO modulations, whereas these two key intrinsic properties of MJO have great implications for its predictability. Our approach gives a relatively robust estimation of MJO’s main decay rate, frequency, and their annual cycle modulations. We found that the decay rate is about −0.05 day^-1, and the frequency is about 2\pi/45 day^-1. The decay rate has a significant annual cycle modulation with an amplitude of 27% of its mean decay rate. Its minimum and maximum are in March and September respectively. We only found a modest ENSO modulation on the MJO frequency which is subject to large uncertainty with 40 years of data. The relative weak ENSO impacts on MJO inferred from the observed MJO index may be partly because this index itself may not adequately capture ENSO’s modulations on MJO patterns. Our approach may be further used to assess MJO’s two basic properties in comprehensive climate models and operational forecast models, which may be useful for quantifying MJO’s simulations and predictability. Further studies for a better understanding of MJO’s seasonal and ENSO modulations, may shield light on MJO’s fundamental dynamics, which remains an active research subject.
Comparing A Simple Stochastic Weather Generator With Two Common Statistical Techniques For Gap-filling Daily Rainfall In Hawaiʻi
(University of Hawaii at Manoa, 2022) Henry, Julie; Chu, Pao-Shin; Atmospheric Sciences
Considerable gaps and breaks of varying lengths are present in many historical daily rainfall records for Hawaiʻi. Countless gap-filling techniques exist, some more complex than others, and all are challenged by the spatially variable, complex terrain in Hawaiʻi. A stochastic weather generator (SWG) attempts to model the randomness and temporal persistence of rainfall and can be used as a gap-filling method. This study compares a simple two-state, first order Markov chain SWG for rainfall occurrence and amount, utilizing the mixed exponential distribution (MEXP), to two common gap-filling methods - quantile mapping (QM) and normal ratio (NR). QM and NR require at least one neighboring (“supporting”) station to gap-fill the target (“primary”) station. Whereas a single-site SWG uses the history of daily rainfall of the station itself, thus eliminating spatial bias. Test years were data masked to various percent coverages using gap types that exist in the record. Gap-filled series were evaluated for accuracy and analyzed for patterns according to station climatology.Overall results show QM is the best performing method when at least one supporting station is available. The SWG model had bias errors at least twice that of QM and NR, had a wider disparity between mean and median absolute error which is an indicator of model performance, and underpredicts monthly mean rainfall more than the other methods. However, it was the best performing method in a few instances, usually at dry stations or during the dry season. Moderate rainfall is possibly underpredicted due to parameters for the MEXP being estimated from only the data masked series. Calculating the transition probabilities and parameter estimations from the climatological record could yield more competitive results. Also, while the MEXP is not intended to model extreme events (avg daily rainfall > 100 mm), it is a good fit for the majority of non-zero daily rainfall. Incorporating an extreme value distribution into the SWG model may address the instances of heavy rainfall. Overall, the SWG method performed with sufficient skill for monthly occurrence and monthly mean rainfall to be considered a good model. If no nearby supporting station is available or missing substantial amounts of data the single-site SWG model is a viable method, but gap-filling any given station for temporal completeness may require a combination of methods to accurately capture the highly variable nature of rainfall in Hawaiʻi
Modeling Return Periods Of Tropical Cyclones In The Vicinity Of Hawai‘i
(University of Hawaii at Manoa, 2021) Zhang, Xinping; Chu, Pao-Shin; Atmospheric Sciences
The tropical cyclone is one of the most destructive natural disasters in Hawai’i. Tropical cyclones have caused a great number of damages on human lives and economies. The return periods of tropical cyclones in the vicinity of Hawai’i can give the important climate information to the government, insurance companies, engineers, and publics. This thesis developed a statistical model to estimate the return period of tropical cyclones in the vicinity. It contains the Poisson regression, the Gaussian-kernel density estimate, the extreme value distribution, and statistical resampling methods. Comparing the results in this model with the previous research based on the dataset from 1970 to 1995, high intensity of tropical cyclones’ return periods becomes much shorter indicating a higher risk from tropical cyclone induced winds in Hawai’i.
Cluster Analysis Of Eastern And Central North Pacific Tropical Cyclones And The Influences Of ENSO And MJO
(University of Hawaii at Manoa, 2021) Okun, Haley; Chu, Pao-Shin; Atmospheric Sciences
While the eastern and western North Pacific’s Tropical Cyclones (TCs) have been thoroughly studied, the central Pacific hurricanes are often overlooked, which poses a problem for those living in Hawaii. Using a mixture Gaussian model and an Expectation – Maximization (EM) algorithm, the Eastern and Central North Pacific TCs can be clustered into different track types. The best-track hurricane data from 1966 to 2019 has been sorted into four distinct tracks. Once separated, each track type is examined in terms of frequency, lifetime, accumulated cyclone energy, intensity, and maximum strength. Additionally, the relationships with El Niño Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), along with the environmental conditions, have been examined in order to gain a better understanding of regional TC activity over the Central North Pacific (CNP) and Eastern North Pacific (ENP). In particular, Central Pacific and Eastern Pacific El Niño events have been identified in an attempt to identify an evolving pattern of hurricanes in conjunction with changing sea surface temperature anomaly trends. The various phases of ENSO and MJO have been shown to influence which track type is more dominant, while the environmental conditions of a particular type can dictate the genesis location, maximum wind speed, intensity, and translation speed of TCs.
Characteristics of Yellow Sea Fog Under Varying Aerosol Conditions
(University of Hawaii at Manoa, 2021) Liang, Jiakun; Small Griswold, Jennifer D.; Atmospheric Sciences
Sea fog usually refers to a fog that occurs under the influence of the ocean, and the Yellow Sea is a region where sea fog regularly occurs. Fog occurrence and structure is impacted by aerosol concentration in the air where the fog forms. Along with industrial development, air pollution, and thus aerosol concentration has increased and become a serious environmental problem in Northeastern China. These higher pollution levels are confirmed by various satellite remote sensing instruments including Moderate Resolution Imaging Spectroradiometer (MODIS) aboard on the Aqua satellite, Ozone Monitoring Instrument (OMI) aboard on the EOS-Aura satellite, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite which observe aerosol, ozone and cloud properties. These observations show a clear influence of aerosol loading over the Yellow Sea region which can have an impact on the regional sea fog. High-resolution data sets from MODIS Aqua L2 are used to investigate the relationships between cloud properties, aerosol (AOD, aerosol optical depth), and SST features. The result of the bi-variate comparison method shows that for most of the cases, larger values of COT (Clout Optical Thickness) are related to both smaller ER (Droplet Effective Radius) and higher CTH (Cloud Top Height). However, for the cases where fog is relatively thinner with many zero values in CTH, the larger COT is related to both smaller ER and CTH. For the fog cases where AOD is dominated by smoke (e.g. confirmed fire activities in the East China Plain) the Semi Direct Effect/Cloud Burning of Soot likely plays a role in determining fog structure. The large amount of absorbing aerosol caused by fires can absorb sunlight and increasing the temperature of the air near surface, which can burn away clouds and result in a relationship where smaller ER corresponds with thinner fog and smaller COT values.
Impact Of COVID-19 Lockdowns On Aerosols In India During 2020
(University of Hawaii at Manoa, 2021) Yu, Shuangge; Griswold, Jennifer JG; Atmospheric Sciences
Aerosols can affect the Earth’s radiation balance, visibility, and human health. They play an important role in the natural environment and human society. At the end of 2019, the appearance of COVID-19 and the following pandemic caused worldwide impacts on society and resulted in a noticeable change in air quality over India. This study provides a comparison between the monthly climatological mean aerosol optical depth (AOD) from 2000 to 2020 and the monthly average AOD in 2020. Results show daily AOD values are significantly reduced after lockdowns were implemented in India. After reopening AOD increased back to climatological levels suggesting the decrease was the result of the COVID-19 lockdowns. The anomalies of Global Precipitation Measurement (GPM), Southern Oscillation Index (SOI), and Multivariate ENSO Index (MEI) in 2020 indicate that precipitation and ENSO do not influence the reduction of AOD. The trends of daily PM10 and PM2.5 in Mumbai and Delhi are similar to the trends seen in daily AOD values over all of India. The combined satellite and station data lend credibility to the observed AOD reduction being related to the lockdown. Although COVID-19 is harmful to human health, its appearance temporarily reduced aerosol loading and thus likely improved human health in India during the lockdown.
High Resolution Modeling of Hurricane Impacts in the Hawaiian Islands under Present and Future Climate Conditions: A Case Study of 2018 Hurricane Lane
(University of Hawaii at Manoa, 2021) van der Veken, Jan Jozef; Karamperidou,, Christina; Atmospheric Sciences
Hurricanes and tropical cyclones, in general, are of great interest to weather and climate scientists alike due to their destructive tendency. Though Hawaii has historically avoided being hit directly by hurricanes, it is certain that the islands are uniquely vulnerable to cyclone impacts given their location and geographic isolation from major emergency management resources afforded to the mainland US. Thus, it is important to ask if the vulnerability of Hawaii could be further exploited in the future due to changes in tropical cyclones that may arise from anthropogenic global warming. Previous studies have used various GCM downscaling approaches and hybrid models to simulate these future conditions. However, these methods have inherent biases in TC simulation that can affect the ability to assess whether localized impacts will change. This study uses a Pseudo-Global Warming (PGW) technique, whereby changes in background conditions (SST, wind, geopotential height, pressure, mean sea level pressure, soil moisture, two meter temperature, and relative humidity) are imposed on the forcing fields of a historical simulation of a hurricane with the Weather Forecasting and Research (WRF) model to simulate changes in its trajectory and impacts under a global warming scenario. First, an ensemble of historical simulations of Hurricane Lane, a recent non-landfalling hurricane with significant impacts on the islands, was performed in order to identify the optimal parameterization schemes. To quantify the performance of each scheme in simulating accumulated precipitation across a number of stations, a novel application of a categorical agreement metric, Cohen’s kappa, was employed. The optimal simulation chosen was one that used the WSM 3-class microphysics scheme, RRTM Longwave radiation scheme, CAM Shortwave radiation scheme, had the cumulus scheme turned off for inner domain, and spectral nudging turned on. Then, PGW experiments were performed in order to assess the response of Lane to changing background conditions under a high emissions scenario. The simulation (WSM3) responded significantly to changes in background conditions enacted by the PGW technique. The result was a Lane that tracked more South of the Islands, creating areas in excess of 200mm drier over the 9 day period considered compared to the historical simulation. This study demonstrates the usefulness of a new statistical method for comparing station data to gridded model output, validating further use for extended topics. This research also illustrates the utility of PGW technique in assessing the impact of climate change on localized impacts of hurricanes in Hawaii.
A Diagnostic Study Of Rainfall Evolution In A Weak Landfalling Tropical Cyclone Over East China
(University of Hawaii at Manoa, 2021) Tang, Lichun; Wang, Yuqing; Atmospheric Sciences
Tropical storm Rumbia (2018) made landfall over East China on 16 August 2018 with a moderate intensity but led to long-lasting and heavy landfall, causing causality and tremendous economic loss to East China. In this study, the fifth generation European Centre for MediumRange Weather Forecasting (ECMWF) reanalysis (ERA-5) data, the best-track tropical cyclone data, and rainfall observations from China Meteorological Administration (CMA) were used to diagnose the rainfall evolution of Rumbia after its landfall. Resultsshowed that when it approached and made landfall over East China, Rumbia was embedded in an environment with a deep-layer (300–850hPa) southwesterly vertical wind shear (VWS), heavy rainfall mostly occurring downshear-left in its inner-core region and downshear-right in the outer-core region. The translation of Rumbia also contributed to the rainfall distribution to some extent. The strong southwesterly-southeasterly summer monsoon flow transported water vapor from the tropical ocean and the East China Sea to the storm’s core region, providing moisture and convective instability conditions in the mid-lower troposphere for the sustained rainfall even after Rumbia moved well inland. The low-level convective instability and the deep-layer environmental VWS played an important role in deepening the inflow boundary layer and the development of the secondary circulation and the heavy rainfall in the northeast quadrant of Rumbia. It is concluded that the environmental VWS and the storm translation are key to the asymmetric rainfall distribution, and the southwesterly-southeasterly summer monsoon flow transported warm and moist air from the tropical oceans and provided the moisture and convective instability conditions to the sustained rainfall of Rumbia after its landfall.

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