Radiation measurements from polar and geosynchronous satellites Semiannual report, 1 Oct. 1970 - 31 Mar. 1971

Radiation measurements from polar and synchronous satellites applied to problems of atmospheric circulation and energetic

Annual report on studies of space/time variability of marine boundary layer characteristics

March 1988.Includes bibliographical references.Contract #N00014-886-C-0459

Meteorological measurements, Satellite PL Quarterly report, 1 Jun. - 31 Aug. 1968

Development of balloon-borne miniature radio altimeter and investigation of multiple array infrared imaging from synchronous satellite

Defining Safe Training Datasets for Machine Learning Models Using Ontologies

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain

The Financial Analysis and the Application of U.S. GAAP Principles

This thesis follows eleven case studies examining various accounts of U.S. GAAP financial statements. Each case study represents a specific area of the financial statements, and each case uses a different company to analyze the accounts. Through the cases, commonly misstated items are explored. The purpose of this thesis is to determine the proper U.S. GAAP treatment of these items. The thesis explores all areas of the financial statements including accounts from both the income statement and balance sheet. The thesis was written during the Honors Accy 420 class during the academic year 2015-2016. The class allowed me to gain a deeper knowledge of the accountancy methods I learned in my junior level accountancy classes. The cases challenged me to think beyond the simple answers and engage in thought about complex accountancy issues. I learned to research using accountancy software, a skill I will take to my future career. In addition to the technical knowledge I gained, I honed my writing skills. In my future profession, I will be expected to write reports about my suggestion for accountancy treatments and this thesis has allowed me to practice this skill. The issues this thesis covers are complex, but learning to work through these problems has given me a deeper hands on education

The Go Clean Cup

Single-serve coffee machines and pods have gained popularity in recent years for ease of use. While these machines can save energy and water in the long run, especially in business settings, the consequences include billions of disposable coffee pods being thrown into landfills around the world each year. Although companies, such as Keurig®, have made reusable pods to fix this problem, the current designs are incompatible with many machines and they use hard plastic, making them difficult to clean. According to a survey conducted by the Go Clean Cup project, 63% of consumers would buy a new reusable single-serve coffee pod of equal price as the others if it was easier to clean. The Go Clean Cup is a reusable single-serve coffee pod that is flexible and eversible for easy cleaning. The pod can be partially or completely everted for coffee grounds to be wiped out without hassle and will be universally compatible with single-serve beverage machines. With a pending utility patent on the design, the Go Clean Cup project is currently focused on developing the best manufacturing method, including molding and laser drilling, to get the product on retail shelves for the lowest cost

Remote sensing of water vapor over land using the advanced microwave sounding unit

Includes bibliographical references.Water vapor is a fundamentally important variable in the atmosphere for making accurate forecasts. Its global distribution is a challenge to determine and can change rapidly in both space and time. Several ground and space based methods are currently employed to determine its spatial and temporal variability. The microwave spectrum is very useful for remote sensing due to its ability to penetrate through clouds at most frequencies. Microwave satellite sensors have been used to retrieve atmospheric state parameters for several decades, however the retrievals of certain parameters have not been performed satisfactorily over land thus far. Retrievals rely on the ability to extract the atmospheric state from the upwelling radiation, most of which comes from emission from the surface. Knowing the surface emissivity to a high degree of accuracy is essential for calculating the land surface temperature, however it is also important because this emission must be removed in order to retrieve the atmospheric parameters desired. Land type, vegetation, snow, ice, rain, urbanization effects, and many other factors have an effect on the aggregate emission within each viewing scene and results in a strong sensitivity and variability of microwave emissivity on small scales. A physically based iterative optimal estimation retrieval has been implemented to retrieve atmospheric parameters from the Advanced Microwave Sounding Unit (AMSU). This retrieval is based on the method of Engelen and Stephens (1999). The retrieval uses a first guess of water vapor and temperature profiles (currently from radiosondes, but will soon be from GDAS), and uses a first guess of emissivity at each of five frequencies (from the MEM). The retrieval was run with a highly accurate first guess in order to detect bias, and the total precipitable water amounts were validated against a radiosonde match-up dataset. The match-up showed fair agreement between the radiosondes and the retrieval (within 20%), however a systematic bias was detected due mostly to coastline contamination. Data from the Global Positioning System (GPS) was also used to validate the total precipitable water, however the results showed less agreement than the radiosonde results (variations of ~20-35%). Most of this disagreement stemmed from geographical co-location differences. The analytical Jacobian was also examined to determine the sensitivities of all channels to the state vector parameters. This enables any retrieval user to pick a channel configuration that gives the desired sensitivities. Vertical profiles of water vapor sensitivities at four varying emissivities were investigated. Sensitivities of water vapor to emissivity were also examined at three distinct atmospheric pressure levels. The Jacobian determined that water vapor is able to be detected throughout a vertical column with adequate skill, although problematic areas occurred between 600 and 800 mb as the emissivity approached unity (e>0.99) for a wet atmospheric case. These results give confidence that AMSU can detect TPW over land for both weather forecasting and for climate studies. The current capabilities may be improved further once bias sources are dealt with satisfactorily.Research was supoprted in part by Cloud Sat at NASA-Goddard under Contract Agreement NAS5-99237, the DoD Center for Geosciences/Atmospheric Research at Colorado State University under the Cooperative Agreement DAAD19-02-2-0005 with the Army Research Lab, and by the Joint Center for Satellite Data Assimilation (JCSDA) Program via NOAA grant NA17RJ1228#15 under CIRA's Cooperative Agreement with NOAA

Diurnal cycle of tropical deep convection examined using high space and time resolution satellite data

September 1997.Includes bibliographical references.Infrared (IR) and visible (VIS) satellite data from GMS-4 with 5-km spatial and 1-hr temporal resolution was used to examine the diurnal cycle of deep convection over a sector of the tropical west Pacific warm pool (WP) bounded by 140°-180°E, 0°-20°N. Data were analyzed for 45 days of summer from 22 June 1994 - 5 August 1994 (JJA) and for 65 days of winter between 28 November 1994 – 31 January 1995 (NDJ). The synoptic backdrop for JJA was characterized by the monsoon trough, oriented northwest to southeast through the WP. Convection was largely focused along the trough. During NDJ, convection was concentrated within 5° latitude of the intertropical convergence zone (ITCZ) which was oriented east to west near the equator. December 1994 was characterized by an active phase of the intraseasonal oscillation (ISO) while January 1995 coincided with an inactive phase. Deep convective cloud was identified in IR imagery using brightness temperature (TBB) threshold techniques. Cloud forms associated with deep convection showed two distinct diurnal modes representing deep convection (TBB ≤ -60°C) and stratiform cirrus (-52°C ≤ TBB ≤ -23°C). Clouds with TBB warmer than -60°C and colder than -53°C comprised a mixed deep convection and cirrus anvil regime from the satellite's perspective with a diurnal cycle reflecting both modes of variability. The diurnal variation of cloud in these regimes was consistent for all time periods and for two tropical storms which occurred in the WP during December 1994. Based on these results and on previous studies, a -65°C cloud-top TBB threshold was chosen to isolate pixels containing active, deep convection. Spectral analysis of time series constructed from hourly cold cloud (≤ -65°C) pixel counts revealed a powerful diurnal cycle of deep convection significant at the 95% confidence level during JJA and NDJ. Composited hourly statistics of fractional areal cloud cover documented a 0500-0600 LST maximum with a 1500-1900 LST minimum of convection for both seasons. The ratio of maximum to minimum areal cold cloud coverage was greater than 2: I. A significant bi-diurnal cycle was evident in both JJA and January 1995. The bi-diurnal peak was strongest in the near-equatorial region during JJA. No semi-diurnal (spectral) peak occurred during either season. This suggests that semi-diurnal atmospheric tides do not strongly influence convection in the WP. Three objective analysis techniques were developed to analyze the relation of tropical cloud cluster structure to the daily spatial and temporal variation of deep convection. The first technique identified cold cloud intervals, called line clusters, in each image. These line clusters represented a characteristic horizontal dimension for cloud clusters of various sizes. Results showed that the diurnal cycle of convective rainfall with an early morning maximum was disproportionately dominated by the largest ~ 10% of clusters for each time period. While the number of large clusters increased only slightly throughout nocturnal hours, the area of cold cloud associated with these systems expanded dramatically. An algorithm called threshold initiation showed that all scales of organized, intensifying deep convection existed at all times of day and night. In addition, the early morning peak was composed primarily of building convection. Conditional recurrence probabilities of line clusters were computed at 24 and 48 hour intervals. Results for JJA and December 1994 revealed that when early morning convection occurred at any location, the same region contained convection the next morning nearly half the time. Convection was less likely at the 48 hour point. These results do not support diurnal theories based on sea surface heating, afternoon initiation of convection and nocturnal evolution of mesoscale convective systems. Findings indicate that the diurnal cycle of deep convective cloud is driven by the internal variation of large clusters. Clusters that exist into or form during the night, grow spatially larger and more intense. Some results support direct radiative forcing of clouds and large scale clear region radiative destabalization as possible contributors to diurnal convective variability. However, all findings are consistent with the work of Gray and colleagues that emphasizes the role of day-night variations in net tropospheric cooling in clear and longwave cooling in cloudy versus clear regions as an explanation of the observed daily variation of tropical convective rainfall.Research supported under the Center for Geosciences, Phase II at CIRA/CSU by DoD grant no. DAAH04-94-G-0420