The application of object-oriented programming and other advances in computer science to the atmospheric sciences has in turn led to advances in modeling and analysis tools and methods. The open-source language Python has been at the forefront of the application of such advances, through general science packages such as scipy and matplotlib, as well as atmospheric science-specific projects such as PCMDI's CDAT and ESG end-user tools and NCAR's PyNGL, resulting in a robust computing environment for all kinds of atmospheric science, including (but not limited to): modeling, time series analysis, air quality data analysis, satellite data processing, in-situ data analysis, GIS, visualization, gridding, model intercomparison, workflow integration, and very large (petabyte) dataset manipulation and access.

Still, to many atmospheric scientists, object-oriented programming in general, and Python in particular, seems mysterious and remote, and as a result, find the idea of learning Python to be daunting. Additionally, while a number of tutorials and other curricula exist to introduce a newcomer to Python, few are geared to the specific needs of atmospheric scientists. This course provides a gentle introduction to Python for the atmospheric scientist, specialized to the needs of the field. While we expect all participants will have basic programming experience—including basic knowledge of variables (integers, floats, strings), loops, conditionals (if/then), and functions—no other exposure to Python or object-oriented programming is assumed. If you are a moderately experienced Python programmer, this course will be a poor fit for you.

All attendees will need to bring a laptop (with power adapter) that has Python installed on it. Instructions will be emailed to registered attendees before the course begins on how to install Python. Because the course is two days, to maximize learning value for students, there will be optional homework assigned at the end of day one that will be discussed the next day.

The Community Earth System Model (CESM) version 2, released in June 2018 is a state-of-the-art Earth System Model that can be used for prediction and understanding of a wide variety of aspects of the earth system.  Moreover, CESM2 is freely available for use by the climate research community making it an ideal tool for earth system research and education. The capabilities within CESM2 range from idealized models of the atmosphere to comprehensive coupled simulations for leading edge research in climate science.  This workshop will provide an introduction to the capabilities within CESM2 and provide attendees with the basic building blocks to start their research with it. This will be achieved through a hands-on interactive tutorial in running CESM on the Amazon Web Service (AWS) Cloud.

Radar polarimetry with multi-parameter measurements has matured to the point that it has been implemented on the national network of WSR-88D Doppler radars. While the technology of radar polarimetry has matured, and polarimetric radar data (PRD) are available nationally and worldwide, radar polarimetry is still in its initial stages for operational usage. There is a lot of room for research and development, especially in using PRD. Phased array technology has recently been introduced to the weather community to increase data update rates to lengthen the lead-time of weather hazard warnings. Polarimetric phased array radar is desirable for future weather observations and multi-mission capabilities. This short course will provide the background information on weather radar polarimetry and polarimetric phased array radar (PPAR) and its applications, and will introduce the latest advances in research and development of a PPAR that can serve the multiple functions (e.g., weather and aircraft surveillance).

This short course will provide information on the status of post launch activities for NOAA-20 and the status of NOAA-20 and Suomi NPP instruments.  Background will be provided on JPSS’ improved observational capabilities, methods for accessing JPSS data and data products, and on-line resources available for training.  The course will focus on instructing and demonstrating how JPSS data products can be applied to a variety of operational forecasting scenarios.  Participants will have an opportunity for hands-on experiences using data from both Suomi-NPP and NOAA-20 to observe and forecast major environmental events.

In 2018, the University Corporation for Atmospheric Research (UCAR) developed a 1-day diversity, equity and inclusion (DEI) training for scientists, based on the highly successful 4-month UCAR/NCAR Equity & Inclusion (UNEION) program[1]. The 1-day program, called UCAR/NCAR Inclusion Training Experience (UNITE) has been developed and tested specifically to take the most impactful pieces of UNEION and create a training experience that can be run in a single day. The proposed workshop brings UNITE to AMS members, and aims to help people from all backgrounds explore their identities, the way power differentials operate across identity and professional roles, aspects of gender in the workplace, and how race and ethnicity are socialized and affect our experiences. The final 1.5 hours of the workshop will be invested in exploring effective ways to broaden competencies around DEI and bring these ideas to others in our field.

 

No prior experience with DEI conversations is expected, but participants in the workshop will be required to do around 2-3 hours of pre-reading in order to ensure that we all start on the same page. The workshop is designed to enable participants from a range of levels and backgrounds and prior experience to fully participate, as it largely focuses on understanding our own identities and the ways that this can affect how we show up in our workplaces. The only requirements for participation are an open mind, a willingness to be challenged, and to consider alternative viewpoints. For those with marginalized identities, please be aware that this is a learning space, and while we will do everything we can, we cannot guarantee a safe space in this environment.

 

[1]: https://hbr.org/2018/03/5-things-we-learned-about-creating-a-successful-workplace-diversity-program

With GOES-16 now serving as GOES-East and GOES-17 soon-to-be taking over in the West we are continually learning more about the capabilities of the next generation of geostationary weather satellites. Bringing a new format to 2019, this course will showcase a brief overview of the Advanced Baseline Imager (ABI) 16 channels and the Geostationary Lightning Mapper (GLM). The course will immerse students into 3 different forecasting challenge topics, i.e. Aviation Forecasting, Fire Weather, and Convective Weather with each covering specific GOES-R Series products and applications with hands on case studies and lab exercises.

The use of the Python programming language has grown immensely over the past decade and has become an essential tool within education, research, and industry within the atmospheric sciences. This course aims to go beyond a basic Python introduction and help attendees advance their ability to apply Python to practical problems in meteorology. This includes topics such as remote data access, calculation of derived quantities, and plotting of these quantities on map projections. As a more intermediate workshop, this workshop assumes a basic knowledge of Python syntax and some familiarity with scientific Python libraries like NumPy and Matplotlib.

The goal of the course is to have attendees learn how to apply Python to practical meteorology problems through use of the MetPy library. They will gain experience accessing remote datasets, using MetPy to calculate derived quantities, and plotting these quantities on weather maps, including station plots. Synoptic meteorology serves as a backdrop for these activities, with motivating examples for case studies such as visualization of satellite imagery, quasigeostrophic/isentropic analysis, and soundings.

This course is extensively hands-on through the use of Jupyter notebooks and will consist of one day of interactive lecture sessions with incorporated exercises that will be completed during the short course. In addition, the afternoon sessions will be aimed at developing Jupyter notebooks that will launch each attendee to bring something tangible home from the course. The instructors for the course are:

Dr. Ryan May, UCAR/Unidata, Dr. John Leeman, UCAR/Unidata, and Dr. Kevin Goebbert, Valparaiso University.

The AMS Short Course: Machine Learning in Python for Environmental Science Problems will be held on Sunday January 6, 2019 preceding the 99th AMS Annual Meeting in Phoenix, Arizona.

Interest in artificial intelligence, machine learning, and deep learning in the environmental sciences has grown rapidly in conjunction with the increased presence of AI in our daily lives. Many people now want to apply machine learning to their own data and problems but do not know where to start. This short course will enable participants to learn how to use Python machine learning and deep learning libraries to process their data, train a variety of machine learning models and generate predictions, and evaluate and interpret their models for physical understanding of what the models learned. Participants will interact with real-world data and the machine learning pipeline through a series of Jupyter notebooks that will enable thorough exploration of the data and methods.