Master of Science

thesisThe Tug Hill Plateau of upstate New York rises approximately 500 m above Lake Ontario, observes frequent (often heavy) lake-effect snowfall, and is one of the snowiest regions in the eastern United States. This work presents a climatology of lake-effect precipitation created using data from the KTYX WSR-88D radar situated atop the plateau. Base reflectivity imagery was manually examined to identify lake-effect periods (LEPs) during each cool season (16 Sep - 15 May) from 16 Sep 2001 - 15 May 2014. The most active months for lake effect in this region are December and January. There is a tendency for LEPs to begin within a few hours before and after sunset in the spring and fall, with no such diurnal signal observed in the winter. Correspondingly, lake effect is slightly more frequent at night than during the day. Overall, the diurnal variability is weaker than found over smaller bodies of water such as the Great Salt Lake of Utah. Classification of events by morphological type revealed that broad coverage and long-lake-axis-parallel (LLAP) account for ~72% and ~24% of lake-effect hours, respectively. The diurnal signal for broad coverage (LLAP) events was less (more) pronounced than for LEPs in general. The near-shore areas south and east of Lake Ontario receive the most frequent lake-effect precipitation. The Tug Hill Plateau produces a strong orographic signal, with an echo frequency maximum on the western (typically windward) slope. Data from cooperative observer (COOP) sites and the Snow Data Assimilation System (SNODAS) corroborate these radar-derived results. The ‘broadening' of high echo frequency over the Tug Hill Plateau, as well as the existence of the lake-orographic morphology, may point to inland/orographic intensification and generation of precipitation during some LEPs

Validation of Aeolus wind products over the tropical Atlantic using radiosondes

Since its launch by the European Space Agency in 2018, the Aeolus satellite has been using the first Doppler wind lidar in space to acquire three-dimensional atmospheric wind profiles around the globe. Especially in the tropics, these observations compensate for the currently limited number of other wind observations, making an assessment of the quality of Aeolus wind products in this region crucial for numerical weather prediction. To evaluate the quality of the Aeolus L2B wind products across the tropical Atlantic Ocean, 20 radiosondes corresponding to Aeolus overpasses were launched from the islands of Sal, Saint Croix and Puerto Rico during August-September 2021 as part of the Joint Aeolus Tropical Atlantic Campaign. During this period, Aeolus sampled winds within a complex environment with a variety of cloud types in the vicinity of the Inter-tropical Convergence Zone and aerosol particles from Saharan dust outbreaks. On average, the validation for Aeolus Rayleigh-clear revealed a random error of 3.8 – 4.3 m s–1 between 2–16 km and 4.3 – 4.8 m s–1 between 16–20 km, with a systematic error of -0.5±0.2 m s–1. For Mie-cloudy, the random error between 2–16 km is 1.1 – 2.3 m s–1 and the systematic error is -0.9 ±0.3 m s–1. It is therefore concluded that Rayleigh-clear winds do not satisfy the random error requirement of the mission, whereas Mie-cloudy winds do so, when considering the standard error. Below clouds or within dust layers, the quality of Rayleigh-clear observations are degraded when the useful signal is reduced. In these conditions, we also noticed an underestimation of the L2B estimated error. Gross outliers which we define with large deviations from the radiosonde but low error estimates account for less than 5% of the data. These outliers appear at all altitudes and under all environmental conditions; however, their root-cause remains unknown. Finally, we confirm the presence of an orbital-dependent bias observed with both radiosondes and European Centre for Medium-Range Weather Forecasts model equivalents. The results of this study contribute to a better characterization of the Aeolus wind product in different atmospheric conditions and provide valuable information for further improvement of the wind retrieval algorithm

Doctor of Philosophy

dissertationLake- and sea-effect snowstorms can produce some of the highest snowfall rates and contribute to some of the deepest seasonal snowpacks observed on Earth, including 30.5 cm of snow in 1 hr at Copenhagen, NY and an 8 m deep snowpack at Mt. Gassan, Japan. Though the enhancement of snowfall is often large, the lowland-upland snowfall distribution within individual storms is highly variable, with heavy snowfall often crippling lowland areas as well. The factors affecting this distribution and enhancement are poorly understood, yet critical for accurate weather forecasting and future climate projections. Thus this work examines the factors affecting the inland and orographic enhancement in lake- and sea-effect snowfall in two geographically and climatologically diverse regions, the Tug Hill Plateau east of Lake Ontario and the Hokuriku Region on the west coast of the Japanese island of Honshu. The speed and direction of the flow are important in both regions, with a stronger flow yielding greater precipitation rates, a maximum displaced further inland, and a greater enhancement over the terrain relative to the shoreline. The direction of the flow is also important, with small changes in the flow yielding drastic changes in the precipitation distribution in the Hokuriku region as flow is either deflected by or surmounts the high terrain. The CAPE induced by the air-water temperature difference is also important, with greater values yielding greater precipitation rates, a maximum displaced further inland, and a greater enhancement over the terrain iv relative to the shoreline. Though only evaluated over Tug Hill, the mode of the convection is also important, with banded (nonbanded) periods seeing greater precipitation rates, a maximum displaced closer to the shoreline (further inland), and a lesser (greater) enhancement over the terrain relative to the shoreline. Collectively, these results represent a significant enhancement in our understanding of the interaction of lake- and sea-effect precipitation with downwind topography, and will be helpful in future cross-disciplinary precipitation researc

Validation of Aeolus L2B products over the tropical Atlantic using radiosondes

International audienceSince its launch by the European Space Agency in 2018, the Aeolus satellite has been using the first Doppler wind lidar in space to acquire three-dimensional atmospheric wind profiles around the globe. Especially in the tropics, these measurements compensate for the currently limited number of other wind observations, making an assessment of the quality of Aeolus wind products in this region crucial for numerical weather prediction. To evaluate the quality of the Aeolus L2B wind products across the tropical Atlantic Ocean, 20 radiosondes corresponding to Aeolus overpasses were launched from the islands of Sal, Saint Croix and Puerto Rico during August-September 2021 as part of the Joint Aeolus Tropical Atlantic Campaign. During this period, Aeolus sampled winds within a complex environment with a variety of cloud types in the vicinity of the Inter-tropical Convergence Zone and aerosol particles from Saharan dust outbreaks. On average, the validation for Aeolus Raleigh-clear revealed a random error of 3.8-4.3 ms-1 between 2-16 km and 4.3-4.8 ms-1 between 16-20 km, with a systematic error of-0.5±0.2 ms-1. For Mie-cloudy, the random error between 2-16 km is 1.1-2.3 ms-1 and the systematic error is-0.9 ±0.3 ms-1. Below clouds or within dust layers, the quality of Rayleigh-clear measurements can be degraded when the useful signal is reduced. In these conditions, we also noticed an underestimation of the L2B estimated error. Gross outliers which we define with large deviations from the radiosonde but low error estimates account for less than 5% of the data. These outliers appear at all altitudes and under all environmental conditions; however, their root-cause remains unknown. Finally, we confirm the presence of an orbital-dependent bias of up to 2.5 ms-1 observed with both radiosondes and European Centre for Medium-Range Weather Forecasts model equivalents. The results of this study contribute to a better characterization of the Aeolus wind product in different atmospheric conditions and provide valuable information for further improvement of the wind retrieval algorithm