Determining Cloud Type

https://digitalcommons.cwu.edu/government_posters/1057/thumbnail.jp

Sea State Photographs for Determining Wind Speed

https://digitalcommons.cwu.edu/government_posters/1058/thumbnail.jp

The impact of climate change and urban growth on urban climate and heat stress in a subtropical city

Urban residents face increasing risk of heat stress due to the combined impact of climate change and intensification of the urban heat island (UHI) associated with urban growth. Considering the combined effect of urban growth and climate change is vital to understanding how temperatures in urban areas will change in the future. This study investigated the impact of urban growth and climate change on the UHI and heat stress in a subtropical city (Brisbane, Australia) in the present day (1991–2000) and medium term (2041–2050; RCP8.5) during summer. A control and urban growth scenario was used to compare the temperature increase from climate change alone with the temperature increase from climate change and urban growth. Average and minimum temperatures increased more with climate change and urban growth combined than with climate change alone, indicating that if urban growth is ignored, future urban temperatures could be underestimated. Under climate change alone, rural temperatures increased more than urban temperatures, decreasing the effect of the UHI by 0.4 °C at night and increasing the urban cool island by 0.8 °C during the day. With climate change, the number of hot days and nights doubled in urban and rural areas in 2041–2050 as compared to 1991–2000. The number of hot nights was higher in urban areas and with urban growth. Dangerous heat stress, defined as apparent temperature above 40 °C, increased with climate change and occurred on average 1–2 days every summer during 2041–2050, even in shaded conditions. There was higher temperature increases with urban growth and climate change than with climate change alone, indicating that reducing the effect of the UHI is vital to ensuring urban growth does not increase the heat stress risks that urban residents will face in the future

Prediction and Analysis of Ground Stops with Machine Learning

A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common type of delays. Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops (GS), issued at affected airports. Ground Stops are implemented to control air traffic volume to specific airports where the projected traffic demand is expected to exceed the airports’ acceptance rate over a short period of time due to conditions such as inclement weather, volume constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic Management Initiative (TMI), particularly because all flights destined to affected airports are grounded until conditions improve. Efforts have been made over the years to reduce the impact of Traffic Management Initiatives on airports and flight operations. However, these efforts have largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP), due to their frequency and duration compared to Ground Stops. Limited work has also been carried out on Ground Stops because of the limited amount of time that traffic management personnel often have between planning and implementing Ground Stops and external factors that influence decisions of traffic management personnel. Consequently, this research primarily focuses on the prediction of weather-related Ground Stops at Newark Liberty International (EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors that influence their occurrence. It is expected that this research will provide stakeholders with further insights into factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by benchmarking Machine Learning algorithms in order to identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning algorithms to predict the occurrence of weather-related Ground Stops. The performance of these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR and LGA. Further analysis also revealed that model performance is significantly better when using balanced datasets compared to imbalanced datasets

Precocious Natural Mummification in a Temperate Climate (Western Cape, South Africa)

The general process and pattern of decomposition is well-documented and understood. However, specific environmental conditions may alter this pattern and prematurely terminate the decay process. An example of this is natural mummification – a preservative process characterized by desiccation, brittleness and shrinkage of the skin and body tissues. It is important to understand how, when, and where such variations may occur, and for this reason environmentally-specific studies of decay are required. The aim of the present study was the establish baseline data on soft-tissue decomposition in two terrestrial habitats in the Western Cape. A total of 16 pig carcasses serving as analogues for humans were deployed in these habitats during two successive winters and summers between 2014 and 2016. The rate and pattern of decomposition were assessed via measurement of weight loss over time and scoring the decomposition process using Megyesi et al. (2005) Total Body Score system and study-specific criteria for mummification. Carcasses typically followed the expected pattern of decay with a few exceptions, most notably instances of rapid natural mummification. Natural mummification, as defined by Megyesi et al. (2005), was observed to occur as early as 17 days postmortem, with five carcasses mummifying in less than one month. The timing of natural mummification varies widely, from a few days to several years, averaging around three months in temperate regions. Natural mummification occurring in less than one month is termed precocious mummification and is rarely observed in temperate regions. With only three reports globally, this study’s findings are globally significant, highlighting the importance of regionally-specific decomposition studies. Two local forensic cases wherein precocious mummification has been observed are also presented and, considered together with the study’s results, a possible mechanism driving this process is proposed

Natural archives of long-range transported contamination at the remote lake Letšeng-la Letsie, Maloti Mountains, Lesotho

Naturally accumulating archives, such as lake sediments and wetland peats, in remote areas may be used to identify the scale and rates of atmospherically deposited pollutant inputs to natural ecosystems. Co-located lake sediment and wetland cores were collected from Letšeng-la Letsie, a remote lake in the Maloti Mountains of southern Lesotho. The cores were radiometrically dated and analysed for a suite of contaminants including trace metals and metalloids (Hg, Pb, Cu, Ni, Zn, As), fly-ash particles, stable nitrogen isotopes, polycyclic aromatic hydrocarbons (PAHs) and persistent organic pollutants such as polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs) and hexachlorobenzene (HCB). While most trace metals showed no recent enrichment, mercury, fly-ash particles, high molecular weight PAHs and total PCBs showed low but increasing levels of contamination since c.1970, likely the result of long-range transport from coal combustion and other industrial sources in the Highveld region of South Africa. However, back-trajectory analysis revealed that atmospheric transport from this region to southern Lesotho is infrequent and the scale of contamination is low. To our knowledge, these data represent the first palaeolimnological records and the first trace contaminant data for Lesotho, and one of the first multi-pollutant historical records for southern Africa. They therefore provide a baseline for future regional assessments in the context of continued coal combustion in South Africa through to the mid-21st century

Soil quality regeneration by grass-clover leys in arable rotations compared to permanent grassland: Effects on wheat yield and resilience to drought and flooding

Intensive arable cropping depletes soil organic carbon and earthworms, leading to loss of macropores, and impaired hydrological functioning, constraining crop yields and exacerbating impacts of droughts and floods that are increasing with climate change. Grass and legume mixes traditionally grown in arable rotations (leys), are widely considered to regenerate soil functions, but there is surprisingly limited evidence of their effects on soil properties, resilience to rainfall extremes, and crop yields. Using topsoil monoliths taken from four intensively cropped arable fields, 19 month-old grass-clover ley strips in these fields, and from 3 adjacent permanent grasslands, effects on soil properties, and wheat yield in response to four-weeks of flood, drought, or ambient rain, during the stem elongation period were evaluated. Compared to arable soil, leys increased earthworm numbers, infiltration rates, macropore flow and saturated hydraulic conductivity, and reduced compaction (bulk density) resulting in improved wheat yields by 42–95 % under flood and ambient conditions. The leys showed incomplete recovery compared to permanent grassland soil, with modest gains in soil organic carbon, total nitrogen, water-holding capacity, and grain yield under drought, that were not significantly different (P > 0.05) to the arable controls. Overall, grass-clover leys regenerate earthworm populations and reverse structural degradation of intensively cultivated arable soil, facilitating adoption of no-tillage cropping to break out of the cycle of tillage-driven soil degradation. The substantial improvements in hydrological functioning by leys will help to deliver reduced flood and water pollution risks, potentially justifying payments for these ecosystem services, especially as over longer periods, leys increase soil carbon sequestration