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Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy. 

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events. 

Abstract Extreme heat events are becoming more frequent and intense. In cities, the urban heat island (UHI) can often intensify extreme heat exposure, presenting a public health challenge across vulnerable populations without access to adaptive measures. Here, we explore the impacts of increasing residential air-conditioning (AC) adoption as one such adaptive measure to extreme heat, with New York City (NYC) as a case study. This study uses AC adoption data from NYC Housing and Vacancy Surveys to study impacts to indoor heat exposure, energy demand, and UHI. The Weather Research and Forecasting (WRF) model, coupled with a multilayer building environment parameterization and building energy model (BEP–BEM), is used to perform this analysis. The BEP–BEM schemes are modified to account for partial AC use and used to analyze current and full AC adoption scenarios. A city-scale case study is performed over the summer months of June–August 2018, which includes three different extreme heat events. Simulation results show good agreement with surface weather stations. We show that increasing AC systems to 100% usage across NYC results in a peak energy demand increase of 20%, while increasing UHI on average by 0.42 °C. Results highlight potential trade-offs in extreme heat adaptation strategies for cities, which may be necessary in the context of increasing extreme heat events. 

Abstract As a consequence of the warm and humid climate of tropical coastal regions, there is high energy demand year-round due to air conditioning to maintain indoor comfort levels. Past and current practices are focused on mitigating peak cooling demands by improving heat balances by using efficient building envelope technologies, passive systems, and demand side management strategies. In this study, we explore city-scale solar photovoltaic (PV) planning integrating information on climate, building parameters and energy models, and electrical system performance, with added benefits for the tropical coastal city of San Juan, Puerto Rico. Energy balance on normal roof, flush-mounted PV roof, and tilted PV roof are used to determine PV power generation, air, and roof surface temperatures. To scale up the application to the whole city, we use the urbanized version of the Weather Research and Forecast (WRF) model with the building effect parameterization (BEP) and the building energy model (BEM). The city topology is represented by the World Urban Database Access Portal Tool (WUDAPT), local climate zones (LCZs) for urban landscapes. The modeled peak roof temperature is maximum for normal roof conditions and minimum when inclined PV is installed on a roof. These trends are followed by the building air conditioning (AC) demand from urbanized WRF, maximum for normal roof and minimum for inclined roof-mounted PV. The net result is a reduced daytime Urban Heat Island (UHI) for horizontal and inclined PV roof and increased nighttime UHI for the horizontal PV roof as compared with the normal roof. The ratio between coincident AC demand and PV production for the entire metropolitan region is further analyzed reaching 20% for compact low rise and open low rise buildings due to adequate roof area but reaches almost 100% for compact high rise and compact midrise buildings class, respectively. 

This work in progress paper presents an overview of the Hispanic Alliance for the Graduate Education and the Professoriate (H-AGEP) program. H-AGEP is working on developing and implementing a new model to improve the preparation and transition of Hispanic STEM doctoral students into community college faculty positions. The partnership is a collaborative effort between the City College of New York (CCNY) (lead institution) and The University of Texas at El Paso (UTEP) along with a group of partner community colleges: LaGuardia Community College, Queensborough Community College, and El Paso Community College. The H-AGEP model consists of three main elements: (1) a training and mentoring program for effective STEM teaching at community colleges; (2) a training program for effective mentoring of community college students in STEM research; and (3) a professional development program to address career preparation, transitioning, and advancement at academic careers in community colleges. H-AGEP research goals are: (1) to consider the collected evaluation and research data to determine what intervention activities are most impactful, and (2) to better understand the career-decision making process of Hispanic STEM doctoral students regarding whether they will seek employment at community colleges and other two-year institutions. An interesting aspect of the partnership is that the institutions in El Paso, Texas, serve primarily a Mexican-American student population while the New York institutions serve primarily a Hispanic population of Caribbean origin. This provides the unique opportunity to compare Hispanic students from both groups. The program evaluation: (1) documents and provides feedback on H-AGEP activities and model implementation; and (2) assesses the extent to which H-AGEP is achieving its intended outcomes. Assessment results on the first cohort of students in the program show the value of including community college faculty as career and teaching mentors in the program. Furthermore, the effect of model interventions in students from the first cohort show positive advances in improving teaching skills, increasing student professional networks, and increasing interest and awareness in careers at community college.