HCMM satellite to take earth's temperature

The heat capacity mapping mission (HCMM), a low cost modular spacecraft built for the Applications Explorer Missions (AEM), was designed to allow scientists to determine the feasibility of using day/night thermal infrared remote sensor-derived data to: (1) discriminate various rock types and locate mineral resources; (2) measure and monitor surface soil moisture changes; (3) measure plant canopy temperatures at frequent intervals to determine transpiration of water and plant stress; and (4) measure urban heat islands. The design of the spacecraft (AEM-A), its payload, launch vehicle, orbit, and data collection and processing methods are described. Projects in which the HCMM data will be applied by 12 American and 12 foreign investigators are summarized

Internet-Enabled Co-Production: Partnering or Competing with Customers?

The Internet is democratizing commerce by turning economic models that were based on a strict separation between providers and consumers into models where this distinction is increasingly blurred. This implies significant opportunities and challenges for organizations, particularly with respect to the role that their customers play in the generation of economic value. Are customers partners or competitors? While firms typically strive to implement business models that leverage the customers as a resource (i.e., customer co-production), models in which customers are competitors (i.e., peer production) are frequently met with attempts to co-opt these customers (i.e., hybrid co-production). The purpose of this panel, presented at the 2006 International Conference on Information Systems, is to explore the range of Internet-enabled co-production models (i.e., customer and hybrid co-production) and the opportunities and challenges that they present for firms

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges

Methane Mitigation:Methods to Reduce Emissions, on the Path to the Paris Agreement

The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated-methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net-zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement

Rapid Temporal Changes of Midtropospheric Winds

The statistical distribution of the magnitude of the vector wind change over 0.25-, 1-, 2-. and 4-h periods based on data from October 1995 through March 1996 over central Florida is presented. The wind changes at altitudes from 6 to 17 km were measured using the Kennedy Space Center 50-MHz Doppler radar wind profiler. Quality controlled profiles were produced every 5 min for 112 gates, each representing 150 m in altitude. Gates 28 through 100 were selected for analysis because of their significance to ascending space launch vehicles. The distribution was found to be lognormal. The parameters of the lognormal distribution depend systematically on the time interval. This dependence is consistent with the behavior of structure functions in the f(exp 5/3) spectral regime. There is a small difference between the 1995 data and the 1996 data, which may represent a weak seasonal effect

The Impact Of Unmanned Aircraft System Observations On Convection Initiation Along A Boundary In Numerical Weather Prediction

Executing meteorological research experiments that utilize Small Unmanned Aircraft Systems (sUASs) is difficult due to regulatory limitations, and knowledge regarding weather impacts is limited. To overcome these challenges, an Observing System Simulation Experiment (OSSE) is used herein as a relatively inexpensive method to evaluate how these platforms could hypothetically improve the development, progression, and characteristics of simulated meteorological phenomena in Numerical Weather Prediction (NWP). This OSSE is part of a case study of an event that occurred in southwestern Oklahoma in May 2016 to examine how sUAS observations impact Convection Initiation (CI) along a boundary in NWP. Synthetic observations of dew point, temperature, wind speed and wind direction were collected by a simulated sUAS, and were ingested into the Weather Research and Forecasting (WRF) model via WRF Data Assimilation (WRFDA). The Three-Dimensional Variational Data Assimilation (3DVAR) technique was used and four sensitivity tests were conducted. These sensitivity tests included how the type of flight pattern, sampling frequency, background error covariance length scale, and assimilated observations impacted convection initiation along a dry line. Results showed that the type of flight pattern, background error covariance length scale, and type of observations assimilated significantly impacted CI and dry line characteristics

Biogeochemical cycling of carbon, nitrogen, and phosphorus across the greater Boston area

With a burgeoning population, increasing land area, and the emergence of new megacities, urban areas have the ability to alter biogeochemical cycles across great scales. Though cities are hotspots of pollution, these concentrated population centers present an opportunity to reduce the human footprint and provide a model of sustainability. Creating sustainable cities requires an understanding of urban biogeochemical cycles of nutrients, such as carbon (C), nitrogen (N), and phosphorus (P). Studies in urban areas, however, often include measurements at only a few sites, either in an urban-rural comparison or as an anchor along an urban-rural gradient. In my dissertation work, I deployed a network of sites across the greater Boston area to measure several key biogeochemical processes: 1) rates of carbon dioxide (CO2) efflux through soil respiration, 2) atmospheric inputs and soil solution concentrations of N, P, and organic C, and 3) rates of N mineralization and nitrification in soils. I found that urban soil respiration is driven by landowner management and that respiration from urban residential soils produces almost 75% of the CO2 as fossil fuel emissions in these areas during the growing season. I also found that mean fluxes of inorganic N in throughfall are double rural rates and vary more than threefold throughout the urban area, exhibiting rates at some urban sites which are as low as rural rates. These rates are driven by vehicular N emissions and local fertilizer inputs, and are decoupled from rates of soil biogeochemical cycling of C and N. Finally, I found atmospheric fluxes of organic N equaling almost 40% of total atmospheric N inputs, atmospheric inputs of organic C on par with rural rates, atmospheric inputs of P similar to rates of P in parking lot runoff, and an enhancement of nutrient inputs to urban ecosystems by the urban tree canopy. My dissertation work highlights the need for a more thorough understanding of biogeochemical fluxes in cities, provides further impetus for the development of a more holistic, multifaceted understanding of urbanization, and suggests that urban areas should be studied as systems unto themselves, rather than strictly in comparison to rural areas

Factors Impacting Observation-Based Estimates of Urban Greenhouse Gas Emissions

Urban areas are responsible for a large and increasing fraction of anthropogenic greenhouse gas emissions. Accurate methods for quantifying and monitoring those emissions are needed to suggest and evaluate mitigation policies, as well as for fundamental carbon cycle science as anthropogenic carbon dioxide emissions become a dominant source of uncertainty in closing the global carbon budget. I present investigations into several factors that can impact our ability to characterize urban greenhouse gas emissions using observations in the atmosphere. An automated method is developed for estimating the mixing depth, a key meteorological variable affecting the sensitivity of mole fraction observations to emissions fluxes, using optical remote sensing instruments. In a long time series of mixing depth estimates in Pasadena, California, day-to-day variability is shown to be large in comparison to seasonal trends. Significant mixing depth biases are demonstrated in meteorological models, and the likely impacts on emissions estimation are discussed. Optimized estimates of methane emissions in the South Coast Air Basin, California, are made using several flux inversion or regularization methods, with four sources of meteorological information, and with all or some of the mole fraction observations taken at nine within-basin observing sites associated with the LA Megacities Carbon Project. Using the full observational dataset in a geostatistical inversion, the capability to detect seasonal and event-driven emissions changes is demonstrated with generic meteorology, opening the door to near-real-time monitoring. Differences in absolute methane emissions flux magnitude according to the source of driving meteorological information are shown to be largely removable by calibration to a trusted model. The choice of inversion or regularization method is shown to have substantial impacts both on the estimated emissions time series and on the capacity to detect emissions changes, especially when the observational constraint is reduced.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145986/1/johnware_1.pd

Current gust forecasting techniques, developments and challenges

Gusts represent the component of wind most likely to be associated with serious hazards and structural damage, representing short-lived extremes within the spectrum of wind variation. Of interest both for short range forecasting and for climatological and risk studies, this is also reflected in the variety of methods used to predict gusts based on various static and dynamical factors of the landscape and atmosphere. The evolution of Numerical Weather Prediction (NWP) models has delivered huge benefits from increasingly accurate forecasts of mean near-surface wind, with which gusts broadly scale. Techniques for forecasting gusts rely on parametrizations based on a physical understanding of boundary layer turbulence, applied to NWP model fields, or statistical models and machine learning approaches trained using observations, each of which brings advantages and disadvantages.Major shifts in the nature of the information available from NWP models are underway with the advent of ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields. This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts on the performance of gust forecasting using current methods. The transition to probabilistic prediction using ensembles at the regional level means that considerations such as these must also be carried through to the aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.</p