Combining satellite and lidar measurements to investigate the sodium nightglow

2021 ◽  
Author(s):  
Julia Koch ◽  
Adam Bourassa ◽  
Chris Roth ◽  
Nicholas Lloyd ◽  
Titus Yuan ◽  
...  
Keyword(s):  
Measurement Techniques ◽  
Branching Ratio ◽  
State University ◽  
Excitation Mechanism ◽  
Satellite Measurements ◽  
Colorado State University ◽  
Atomic Sodium ◽  
Lidar Measurements ◽  
Ground Based Lidar ◽  
Mlt Region

<p>Using a combination of different measurement techniques is important to understand the numerous processes happening in the MLT-region. One of those processes is the excitation of atomic sodium by reaction with ozone which leads to emission of electromagnetic radiation: a phenomenon called Airglow. Although the sodium excitation mechanism was already proposed in 1939 by Sidney Chapman and further investigation was done by a great number of scientists, there are still some key parameters that are not well-known today. One of those parameters is the branching ratio f<sub>A</sub> which determines the amount of sodium in the excited state. Exact knowledge of this value would offer the opportunity to use Na-nightglow measurements to determine sodium profiles in the MLT-region. In this study we used both, satellite measurements and ground-based Lidar measurements to help approach a more reliable branching ratio f<sub>A</sub>. By comparing measurements that were made by the two instruments OSIRIS on Odin (Satellite) and the Lidar of the Colorado State University (ground-based) we found a branching ratio f<sub>A</sub> of 0.064 +- 0.028.</p>

scholarly journals Statistical Analysis of Sodium Doppler Wind–Temperature Lidar Measurements of Vertical Heat Flux

2008 ◽  
Vol 25 (3) ◽  
pp. 401-415 ◽  
Author(s):  
Liguo Su ◽  
Richard L. Collins ◽  
David A. Krueger ◽  
Chiao-Yao She
Keyword(s):  
Heat Flux ◽  
Photon Counting ◽  
Doppler Lidar ◽  
State University ◽  
Colorado State University ◽  
Lidar Measurements ◽  
Vertical Heat ◽  
Different Seasons ◽  
Flux Measurements ◽  
Vertical Heat Flux

Abstract A statistical study is presented of the errors in sodium Doppler lidar measurements of wind and temperature in the mesosphere that arise from the statistics of the photon-counting process that is inherent in the technique. The authors use data from the Colorado State University (CSU) sodium Doppler wind-temperature lidar, acquired at a midlatitude site, to define the statistics of the lidar measurements in different seasons under both daytime and nighttime conditions. The CSU lidar measurements are scaled, based on a 35-cm-diameter receiver telescope, to the use of large-aperture telescopes (i.e., 1-, 1.8-, and 3.5-m diameters). The expected biases in vertical heat flux measurements at a resolution of 480 m and 150 s are determined and compared to Gardner and Yang’s reported geophysical values of 2.3 K m s−1. A cross-correlation coefficient of 2%–7% between the lidar wind and temperature estimates is found. It is also found that the biases vary from −4 × 10−3 K m s−1 for wintertime measurements at night with a 3.5-m telescope to −61 K m s−1 for summertime measurements at midday with a 1-m telescope. During winter, at night, the three telescope systems yield biases in their heat flux measurements that are less than 10% of the reported value of the heat flux; and during summer, at night, the 1.8- and 3.5-m systems yield biases in their heat flux measurements that are less than 10% of the geophysical value. While during winter at midday the 3.5-m system yields biases in their heat flux measurements that are less than 10% of the geophysical value, during summer at midday all of the systems yield flux biases that are greater than the geophysical value of the heat flux. The results are discussed in terms of current lidar measurements and proposed measurements at high-latitude sites.

First International Workshop on Light Absorption by Aerosol Particles: Background, Activities, and Preliminary Results, 28 July–8 August 1980 Ft. Collins, Colo.

1981 ◽  
Vol 62 (9) ◽  
pp. 1321-1327 ◽  
Author(s):  
Hermann E. Gerber ◽  
Edward E. Hindman
Keyword(s):  
Light Absorption ◽  
Atmospheric Aerosol ◽  
International Workshop ◽  
Aerosol Particles ◽  
Measurement Techniques ◽  
State University ◽  
Absorption Properties ◽  
Colorado State University ◽  
Preliminary Results ◽  
Atmospheric Aerosol Particles

The amount of light absorbed by aerosol particles has not been determined with certainty because errors in measurement techniques have been difficult to quantify. To improve this situation, a workshop was conducted to establish experimentally the errors for the various techniques. The workshop was held between 28 July and 8 August 1980 at the Cloud Simulation and Aerosol Laboratory at Colorado State University. Preliminary results show that, for the same well-characterized aerosol particles, substantial differences exist between results from the various techniques. These differences can explain a fraction of the variations reported for the light absorption properties of similar types of atmospheric aerosol particles.

A MODEL FOR DEVELOPING HIGH-QUALITY ONLINE COURSES: INTEGRATING A SYSTEMS APPROACH WITH LEARNING THEORY

2008 ◽  
Vol 12 (3) ◽  
Author(s):  
Maria Jean Puzziferro ◽  
Kaye Shelton
Keyword(s):  
Online Education ◽  
Design Theory ◽  
Systems Approach ◽  
Online Courses ◽  
Process Models ◽  
State University ◽  
High Quality ◽  
Colorado State University ◽  
Online Instructional Design ◽  
Online Course Development

As the demand for online education continues to increase, institutions are faced with developing process models for efficient, high-quality online course development. This paper describes a systems, team-based, approach that centers on an online instructional design theory (Active Mastery Learning) implemented at Colorado State University-Global Campus.

scholarly journals Preface: Modern Heterocycle Synthesis and Functionalization

Synlett ◽  
2021 ◽  
Vol 32 (02) ◽  
pp. 140-141
Author(s):  
Louis-Charles Campeau ◽  
Tomislav Rovis
Keyword(s):  
Active Pharmaceutical Ingredient ◽  
Associate Professor ◽  
Columbia University ◽  
State University ◽  
Heterocycle Synthesis ◽  
Colorado State University ◽  
University Of Toronto ◽  
Process Chemistry ◽  
The University ◽  
Small Molecule Therapeutics

obtained his PhD degree in 2008 with the late Professor Keith Fagnou at the University of Ottawa in Canada as an NSERC Doctoral Fellow. He then joined Merck Research Laboratories at Merck-Frosst in Montreal in 2007, making key contributions to the discovery of Doravirine (MK-1439) for which he received a Merck Special Achievement Award. In 2010, he moved from Quebec to New Jersey, where he has served in roles of increasing responsibility with Merck ever since. L.-C. is currently Executive Director and the Head of Process Chemistry and Discovery Process Chemistry organizations, leading a team of smart creative scientists developing innovative chemistry solutions in support of all discovery, pre-clinical and clinical active pharmaceutical ingredient deliveries for the entire Merck portfolio for small-molecule therapeutics. Over his tenure at Merck, L.-C. and his team have made important contributions to >40 clinical candidates and 4 commercial products to date. Tom Rovis was born in Zagreb in former Yugoslavia but was largely raised in southern Ontario, Canada. He earned his PhD degree at the University of Toronto (Canada) in 1998 under the direction of Professor Mark Lautens. From 1998–2000, he was an NSERC Postdoctoral Fellow at Harvard University (USA) with Professor David A. Evans. In 2000, he began his independent career at Colorado State University and was promoted in 2005 to Associate Professor and in 2008 to Professor. His group’s accomplishments have been recognized by a number of awards including an Arthur C. Cope Scholar, an NSF CAREER Award, a Fellow of the American Association for the Advancement of Science and a ­Katritzky Young Investigator in Heterocyclic Chemistry. In 2016, he moved to Columbia University where he is currently the Samuel Latham Mitchill Professor of Chemistry.

scholarly journals 260 Genetic prediction for first-service conception rate in Angus heifers using a random regression model

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 197-198
Author(s):  
Miguel A Sánchez-Castro ◽  
Milt Thomas ◽  
Mark Enns ◽  
Scott Speidel
Keyword(s):  
Conception Rate ◽  
Random Regression ◽  
State University ◽  
Base Function ◽  
Genetic Prediction ◽  
Colorado State University ◽  
Random Regression Model ◽  
Service Conception ◽  
Available Information ◽  
Angus Heifers

Abstract First-service conception rate (FSCR) can be defined as the probability of a heifer conceiving in response to her first artificial insemination (AI). Given the binary nature of its phenotypes, FSCR has been typically evaluated using animal threshold models (ATM). However, susceptibility of these models to the extreme-category problem (ECP) limits their ability to use all available information to calculate Expected Progeny Differences (EPD). Random regression models (RRM) represent an alternative method to evaluate binary traits, and they are not affected by ECP. Nevertheless, RRM were originally developed to analyze longitudinal traits, so their usefulness to evaluate traits with singly observed phenotypes remains unclear. Therefore, objectives herein were to evaluate the feasibility of a RRM genetic prediction for heifer FSCR by comparing its resulting EPD and genetic parameters to those obtained with a traditional ATM. Breeding and ultrasound records of 4,334 Angus heifers (progeny of 354 sires and 1,626 dams) collected between 1992 to 2019 at the Colorado State University Beef Improvement Center were utilized. Observations for FSCR (1, successful; 0, unsuccessful) were defined by fetal age at pregnancy inspections performed approximately 130 d post-AI. Traditional FSCR evaluation was performed using a univariate BLUP threshold animal model, whereas an alternative evaluation was performed by regressing FSCR on age at AI using a linear RRM with Legendre Polynomials as the base function. Heritability estimates were 0.03 ± 0.02 for the ATM and 0.005 ± 0.001 for the average age at AI with the RRM, respectively. Pearson and rank correlations between EPD obtained with each method were 0.63 and 0.60, respectively. The regression coefficient of RRM predictions on those obtained with the ATM was 0.095. In conclusion, these results suggested that although a RRM genetic prediction for FSCR was feasible, a considerable degree of re-ranking occurred between the two methodologies.

The Coaxium® Wheat Production System: A New Herbicide-Resistant System for Annual Grass Weed Control and Integrated Weed Management

2021 ◽  
Vol 32 (4) ◽  
pp. 151-157
Author(s):  
Raven A. Bough ◽  
Phillip Westra ◽  
Todd A. Gaines ◽  
Eric P. Westra ◽  
Scott Haley ◽  
...  
Keyword(s):  
Weed Management ◽  
Production System ◽  
Integrated Weed Management ◽  
State University ◽  
Wheat Production ◽  
Colorado State University ◽  
Herbicide Resistant ◽  
System A ◽  
Grass Weed ◽  
Global Food

The authors discuss the importance of wheat as a global food source and describe a novel multi-institutional, public-private partnership between Colorado State University, the Colorado Wheat Research Foundation, and private chemical and seed companies that resulted in the development of a new herbicide-resistant wheat production system.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

2014 ◽  
Vol 14 (16) ◽  
pp. 8389-8401 ◽  
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Warm Cloud ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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