09.11.2017: Anna Hodshire: “Nucleation and growth under high OH conditions: Using an oxidation flow reactor and the TOMAS box model to learn about new particle formation”

09.18.2017: Rui Zhang: “Source Regions Contributing to Excess Reactive Nitrogen Deposition in the Greater Yellowstone Area”

09.25.2017: **(on main campus, LSC room 276-78)** Dom Spracklen: “Natural aerosol, air quality and climate”

10.02.2017: ** **Jack Kodros: “Aerosols in the Arctic, particulate matter in your lungs”

10.09.2017: **cancelled **

10.16:2017: AAAR meeting (no group meeting this week)

10.23.2017: Derek Weber **(at 2 pm):** “Volatile Organic Compound Concentrations and the Impacts of Future Oil and Natural Gas Development in the Colorado Northern Front Range”** **

10.30.2017: **cancelled **

11.06.2017: **cancelled **

11.13.2017: Tom Moore: “Applying Regulatory Emissions Inventories for Air Quality Planning”

11.20.2017: Thanksgiving break (no group meeting)

11.27.2017: **cancelled**

12.04.2017: Kate Smits

]]>08.25.2016:

09.01.2016:

09.08.2016:

09.15.2016:

09.22.2016: Jack Kodros, presenting “What I wish I could tell Hillary Clinton: Estimating the aerosol indirect effect with and without prognostic aerosol microphysics, A cost/benefit analysis of regional cookstove improvement scenarios, and Forecasting summer PM2.5 in the Western United States”

09.29.2016: Week of IGAC Conference in Breckenridge.

10.06.2016: **Ruhi Humphries, Aerosols in the Southern Ocean and Antarctic sea ice region **

10.13.2016: Sailaja Eluri, presenting “Modeling and constraining the production and composition of secondary organic aerosol from a diesel engine using parameterized and semi-explicit chemistry and thermodynamic models”; Ali Akherati, presenting “Simulating the Combined Effect of Volatility, Multigenerational Chemistry, Unspeciated Precursors and Vapor Wall-Losses on Ambient Organic Aerosol in 3-D Air Quality Model”

10.20:2016: AAAR meeting (NO group meeting this week)

10.27.2016: Emily Bian, presenting “Secondary organic aerosol formation in biomass-burning plumes: Theoretical analysis of lab studies and ambient plumes” (practice for smoke symposium)

11.03.2016: Anna Hodshire, presenting “The role of MSA in particle growth and the aerosol direct and indirect effects” and “Using an oxidation flow reactor and the TOMAS box model to constrain the volatility distribution of ambient pine forest air ”

11.10.2016: Bonne Ford, presenting “How future fire activity will affect mid-century air quality over United States” (presenting for Maria Val Martin) and “Who, what, when, where? Determining the health implications of wildfire smoke exposure”; Steve Brey, presenting “There’s the smoke, where’s the fire? A regional analysis of smoke transport pathways based on 8 years of HMS smoke and fire location data” (all talks are practice talks for the smoke symposium)

11.17.2016: NO group meeting

11.24.2016: Thanksgiving!!

12.01.2016: Derek Weber and Jakob Lindaas, titles TBA (practice talks)

12.08.2016:

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- Make sure to give a
**summary paragraph for the editor**. This should include an overview of what the paper does and what you learned. It should also include a recommendation*e.g.*publish after minor revisions, publish after major revisions, not recommended for publication. For all the above options give detailed reasons as to why. Note if you are not qualified to understand something presented in the analysis. You want to point this out so the editor has an idea if at least one of the reviewers has the expertise to make an assessment on a specific component of the analysis. **Major Comments:**This section includes bigger questions for the authors. Did you think about how XYZ may have affect your results? I would have expected the analysis to account for X. Is there a disconnect between the conclusions drawn and the methods? Are the conclusions overstepping? Suggest useful papers to cite or consult to strengthen the analysis. Don’t just suggest they site your work, this is a total uncool move.**Minor Comments:**This is where you should bring up any typos, spelling or grammar mistakes.

When writing a review make sure to use language that makes it about the paper not the authors.

]]>Outside people looking to join, please do! Feel free to sign up for open weeks (listed below) by email sjbrey (at) rams.colostate.edu. These meetings will typically occur in ATS 209 at 11am.

**4.28.2016:** Ilana Pollack. Double checking NOx instrument calibration methods.

**5.4.2016:** Steven Brey. Double checking my logic in assigning pairs for satellite fire detections. Also, does this HYSPLIT ensemble look sensible?

**5.12.2016: **Jared Brewer. Hey guys does this MORRIS METHOD code look right?

**5.19.2016: **

**5.26.2016:**

**6.2.2016: **Zitely. Lets improve some IDL code! Shout out to people who write IDL code, please come to this meeting.

**Main points:**

- Having scientists bet of whether they think a study is reproducible has shown success at finding which studies have results that are less likely to be reproduced
- “If you believe the result will be replicated, you buy the contract, which increases the price. If you don’t believe in a study, then you can short-sell it.”
- “The prediction market correctly called nearly three-quarters (71 percent) of the attempted replications”
**Possible solution:**“For example, a journal might decide to use markets to vet studies before publication. If the market says, ‘Yeah, that’s cute, but that’s probably bogus,’ then that’s probably not something you should publish. Since it’s too costly and complicated to replicate every study, another approach might be to enter 100 studies into a prediction market and then select 10 at random to replicate. Presumably, the threat of a replication would create an incentive for researchers to be more careful.”

1/30: **Juliet Zhu****: **What can TES observations tell us about PAN in free troposphere?** **

2/6: **Wenxiu Sun: **The impact of local meteorology and large scale dynamics on surface ozone in the U.S.

2/13: **Derek Weber:** VOC concentrations and the Impacts of Future Oil and Natural Gas Operations in the Front Range** **

2/20:** Yixing Shao: **Mobile Measurements of Ammonia in the Front Range

2/27: **(Main campus: Room 374 LSC) Shantanu Jathar: **Investigating Diesel Engines as an Atmospheric Source of Isocyanic Acid in Urban Areas

3/6: **cancelled due to faculty interviews **

3/13: **Spring Break!! **

3/20: **Arsineh Hecobian:** Emission rates of methane and VOCs from oil and natural gas activities in Colorado** **

3/27: **(Main campus: Room 374 LSC) Andrew Abeleira: **Investigating ozone and ozone precursors in the Front Range of Colorado

4/3: **Jakob Lindaas **

4/10: **Jack Kodros:** 4 Million people died from exposure to cookstove smoke this year. Or maybe it was 2 million. I can’t be sure.

4/17: **William Lassman **

4/24: **(Main campus: Room 312 LCS) Ryan Gan:** An Introduction to Epidemiology for Atmospheric Scientists** **

5/1: **Tom Hill **

R-Bloggers article that lead me to the Nature paper. Demos R and Jupyter

- “Free, open-source software package called IPython, which helps researchers to keep a detailed lab notebook for their computational work.”
- “Students [researchers etc.] write explanatory text and intersperse it with raw code and the charts and figures that their algorithms generate.”
- “Designed to make data analysis easier to share and reproduce, the IPython notebook is being used increasingly by scientists who want to keep detailed records of their work, devise teaching modules and collaborate with others.”
- “Some researchers are even publishing the notebooks to back up their research papers — and Brown, among others, is pushing to use the program as a new form of interactive science publishing.”
- “Pérez and Granger saw that data scientists found it hard to share detailed but understandable descriptions of their raw code that would allow others to build on their research. That is partly because many scientists in computation-intensive fields write code in an iterative and piecemeal fashion as each analysis reveals new insight and spins off multiple lines of inquiry. Keeping track of the different versions of code that produce various figures, and linking those files with explanatory notes, is a headache. And what gets published is usually not detailed enough for the reader to follow up on. “In my own computational physics work,” says Granger, “a high-level description of the algorithm that goes into the paper is light years away from the details that are written in the code. Without those details, there is no way that someone could reproduce it in a reasonable time scale.
The IPython notebook addresses both issues by helping scientists to keep track of their work, and by making it easy to share and for others to explore the code.”

By Dan Navarro

http://health.adelaide.edu.au/psychology/ccs/docs/lsr/lsr-0.5.pdf

- Chapter 1: Why do we learn statistics? Psychology and statistics. Statistics in everyday life. Some examples where intuition is misleading, and statistics is critical.
- Chapter 2: A brief introduction to research design Basics of psychological measurement. Reliability and validity of a measurement. Experimental and non-experimental design. Predictors versus outcomes.

- Chapter 3: Getting started with R. Getting R and Rstudio. Typing commands at the console. Simple calculations. Using functions. Introduction to variables. Numeric, character and logical data. Storing multiple values asa vector.
- Chapter 4: Additional R concepts. Installing and loading packages. The workspace. Navigating the file system. More complicated data structures: factors, data frames, lists and formulas. A brief discussion of generic functions.

- Chapter 5: Descriptive statistics. Mean, median and mode. Range, interquartile range and standard deviations. Skew and kurtosis. Standard scores. Correlations. Tools for computing these things in R. Brief comments missing data.
- Chapter 6: Drawing graphs. Discussion of R graphics. Histograms. Stem and leaf plots. Boxplots. Scatterplots. Bar graphs.
- Chapter 7: Pragmatic matters. Tabulating data. Transforming a variable. Subsetting vectors and data frames. Sorting, transposing and merging data. Reshaping a data frame. Basics of text processing. Reading unusual data files. Basics of variable coercion. Even more data structures. Other miscellaneous topics, including floating point arithmetic.
- Chapter 8: Basic programming. Scripts. Loops. Conditionals. Writing functions. Implicit loops.

- Prelude. The riddle of induction, and why statisticians make assumptions.
- Chapter 9: Introduction to probability. Probability versus statistics. Basics of probability theory. Common distributions: normal, binomial, t, chi-square, F. Bayesian versus frequentist probability.
- Chapter 10: Estimating unknown quantities from a sample. Sampling from populations. Estimating population means and standard deviations. Sampling distributions. The central limit theorem. Confidence intervals.
- Chapter 11: Hypothesis testing. Research hypotheses versus statistical hypotheses. Null versus alternative hypotheses. Type I and Type II errors. Sampling distributions for test statistics. Hypothesis testing as decision making. p-values. Reporting the results of a test. Effect size and power. Controversies and traps in hypothesis testing.

- Chapter 12: Categorical data analysis. Chi-square goodness of fit test. Chi-square test of independence. Yate’s continuity correction. Effect size with Cramer’s V. Assumptions of the tests. Other tests: Fisher exact test and McNemar’s test.
- Chapter 13: Comparing two means. One sample z-test. One sample t-test. Student’s independent sample t-test. Welch’s independent samples t-test. Paired sample t-test. Effect size with Cohen’s d. Checking the normality assumption. Wilcoxon tests for non-normal data.
- Chapter 14: Comparing several means (one-way ANOVA). Introduction to one-way ANOVA. Doing it in R. Effect size with eta-squared. Simple corrections for multiple comparisons (post hoc tests). Assumptions of one-way ANOVA. Checking homogeneity of variance using Levene tests. Avoiding the homogeneity of variance assumption. Checking and avoiding the normality assumption. Relationship between ANOVA and t-tests.
- Chapter 15: Linear regression. Introduction to regression. Estimation by least squares. Multiple regression models. Measuring the fit of a regression model. Hypothesis tests for regression models. Standardised regression coefficient. Assumptions of regression models. Basic regression diagnostics. Model selection methods for regression.
- Chapter 16: Factorial ANOVA. Factorial ANOVA without interactions. Factorial ANOVA with interactions. Effect sizes, estimated marginal means, confidence intervals for effects. Assumption checking. F-tests as model selection. Interpreting ANOVA as a linear model. Specifying contrasts. Post hoc testing via Tukey’s HSD. Factorial ANOVA with unbalanced data (Type I, III and III sums of squares)

- Chapter 17: Bayesian statistics. Introduction to Bayesian inference. Bayesian analysis of contingency tables. Bayesian t-tests, ANOVAs and regressions.
- Chapter 18: Epilogue. Comments on the content missing from this book. Advantages to using R.
- References. Massively incomplete reference list.

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http://www.kunc.org/post/whos-making-ozone-cu-analysis-says-its-hard-pin-down

]]>The 15 semester hour requirement does not mean that you need to take 15 hours of classes! That would be miserable, and you would probably never graduate. What you need to do is top of registration by registering for ATS 699U-001 and adjust the number of credits so that you are at 15 total. ATS 699U-001 is tropospheric chemistry thesis credits under Emily Fischer. Look for this class when topping off registration.

**Why do we register for 15 credits instead of 9? **

1) The university facility fee goes up by $95 dollars and thus someone gets more money.

2) This increases the number of faculty credit hours for teaching credit purposes with the University. Basically, it is a more accurate acknowledgement (in terms of credits taken per student per faculty advisor) of the effort of students and faculty in regards to effort in the program.

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