In the summer of 2021, I received a Ph.D. in atmospheric science from the University of Washington. I now work in climate risk modeling, at Jupiter Intelligence. As an industry scientist, I utilize weather and climate models to quantify the impact of climate change on the frequency and intensity of extreme weather events.
Outside of work, I enjoy playing team sports such as softball and soccer. I'm also an avid kayaker and cyclist. Some of my hobbies include working on my home server, crocheting, cooking, tinkering with my Raspberry Pi, playing video games, and spending time with Figaro.
As a transgender (trans) woman in science, I'm passionate about improving the representation and visibility of marginalized groups in STEM. This passion is informed by my own experience. As a graduate student, I struggled to see a future for myself in science, partly because I’d never met an openly trans scientist in my field.
Through my own visibility, I hope to address this lack of representation.
It is revolutionary for any trans person to choose to be seen and visible in a world that tells them that they should not exist. - Laverne Cox
Masters (M.A.) in Atmospheric Science May 2017
For my master’s work, I developed an android app (uWx) to retrieve measurements of atmospheric pressure from smartphones. Through this app, I demonstrated how precise measurements of pressure could be crowdsourced from smartphones without comprising battery life. To facilitate the use of smartphone pressures in numerical weather prediction, I developed a novel machine learning approach for bias correction.
Using bias-corrected pressure observations from smartphones and conventional pressure networks, I performed ensemble data assimilation experiments on the Microsoft Azure cloud. These cloud computing experiments revealed, for the first time, that smartphone pressure observations could enhance short-term forecasts of surface variables such as pressure, temperature, and moisture.
RMSE difference from the CNTRL simulation (no data assimilation). RMSE is plotted as a function of forecast hour (0-6 h) for sea level pressure (MSLP), temperature, moisture, and wind speed. Two assimilation experiments were performed using smartphone pressures before (NOQC) and after bias-correction (PHONE)
uWx Pressure app for Android
Doctorate (Ph.D.) in Atmospheric Science August 2021
For my doctoral research, I scaled up the machine learning bias correction approach, developed for uWx, to billions of smartphone pressure observations from The Weather Company (IBM). To address important privacy concerns, I developed a state-of-the-art framework for anonymizing smartphone pressures from IBM. My work demonstrated that anonymously retrieved smartphone pressures could produce more accurate pressure analyses than existing surface pressure networks (e.g. MADIS).
(a) Monthly median root mean squared error (RMSE) for gridded pressure analysis spanning the central and eastern U.S, Domain-average analysis (altimeter) error is shown for MADIS analyses, produced from conventional pressure observations (green), and smartphone pressure analyses, produced using uncorrected identifiable (UID)-SPOs (Grey), bias-corrected identifiable (UID)-SPOs (Blue), and bias-corrected anonymous (ANON)-SPOs (pink). (b) The distribution of the domain-averaged analysis (altimeter) RMSE, for each gridded pressure analysis over 2018.
Using gridded analyses of smartphone and conventional pressure observations I examined mesoscale pressure perturbations associated with precipitation. My findings revealed that smartphone pressures better resolved mesoscale pressure features associated with convection, such as wake-lows and meso-highs, than existing (conventional) surface pressure networks. An example from a squall line case is shown below.
Mesoscale pressure perturbations derived from gridded analysis of smartphone pressure observations (left) and conventional pressure observations (right), during the passage of a squall line at 0045 UTC on 4 April 2018.
Weather Scientist 2021 - Present
Jupiter Intelligence, Inc. (Seattle, WA)
As a member of a weather and climate modeling team, I analyze the regional impact of climate change on weather extremes by dynamically downscaling atmospheric models. In addition to numerical modeling, I collaborate with a team of software engineers and data scientists to create automated algorithms that can be rapidly deployed in the cloud and customized to meet the needs of individual customers
Graduate Research Assistant 2014 - 2021
University of Washington (Seattle, WA)
As a graduate student, I've performed research on the anonymization, bias-correction, and quality-control of smartphone pressure observations with machine learning and kriging. I've also worked to develop climatologies of mesoscale pressure phenomena, using both conventional and smartphone pressure observations, to evaluate whether smartphones can capture phenomena poorly observed by existing pressure networks.
Graduate Teaching Assistant 2016 - 2019
University of Washington (Seattle, WA)
In my capacity as a teaching assistant (T.A.) I developed quizzes, designed labs, and taught two to four discussion sections each week for introductory (non-major) courses in atmospheric science. I was also responsible for all course grades and the creation of midterms and final exams.
Hollings Scholar (Internship) 2013-2014
National Severe Storms Laboratory (Norman, OK)
Through the NOAA Hollings scholarship, I was granted a paid research internship at the National Severe Storms Laboratory (NSSL) during the summer of 2013-2014. During this internship I performed an analysis of the convective boundary layer, with a focus on turbulence, using a high spectral resolution lidar.
Undergraduate Research Assistant 2012 - 2014
Cooperative Institute for Mesoscale Meteorological Studies (Norman, OK)
As an undergraduate research assistant, I was responsible for the data collection, storage, and processing of lightning data collected by the Oklahoma Lightning Mapping Array (OKLMA). During my tenure, I developed software to fully automate the above processes which, until then, had been accomplished manually by retrieving hard drives from each station.
University of Washington Seattle, WA
Doctorate (Ph.D.) in Atmospheric Science................................................................................. August 2021
Masters (M.A.) in Atmospheric Science........................................................................................... May 2017
Mobile App Development
As part of my Master's work, I developed two android apps capable of retrieving atmospheric pressure from smartphones: uWx and Aeolus. Sample code for Aeolus, which is a pared-down version of uWx, is available here. For more information about uWx check out the official app webpage
AMS AI Course Instructor
In 2021 and 2022 I volunteered to serve as an instructor for the AMS short course on Machine Learning in Python for Environmental Science Problems. As an instructor, I was responsible for teaching the section of the course on tree-based supervised learning. The curriculum I designed for the course is available here.
Climate Modelling and Game Development
During quarantine, I started a hobby project to incorporate weather and climate data from a global climate model into a popular space-flight simulation game: Kerbal Space Program. The result was Kerbal Weather Project (KWP). In KWP, weather and climate data from the Model for Prediction Across Scales (MPAS) was incorporated into KSP gameplay through a C# plugin. The science behind the mod was presented during a University seminar and at the American Meteorological Society (AMS) annual meeting in the form of a student poster presentation.
Public Science Communication
As a board member of UW Engage, a graduate student sci-comm organization, I've had the opportunity to communicate my research to the public at Townhall Seattle. I've also been privileged to lead and facilitate workshops on communicating with science-averse audiences at the American Association for the Advancement of Science (AAAS) and at Science Talk.
AMS 19th and 20th Conference on Artificial Intelligence
1st place student oral presentation
Microsoft Azure Research Award
NOAA Hollings Scholar
McNicholas, C., & Mass, C. F. (2022). A Comparison of Mesoscale Pressure Features Observed with Smartphones and Conventional Observations, Weather and Forecasting, 37(5), 659-680. https://doi.org/10.1175/WAF-D-21-0166.1
McNicholas, C., & Mass, C. F. (2021). Bias Correction, Anonymization, and Analysis of Smartphone Pressure Observations Using Machine Learning and Multi-Resolution Kriging, Weather and Forecasting, 36(5), 1867-1889. https://doi.org/10.1175/WAF-D-20-0222.1
Hintz S. K., C. McNicholas, R. Randriamampianina, H. T.P. Williams, B. Macpherson, M. Mittermaier, J. Onvlee-Hooimeijer, S. Balazs (2021): Crowd-sourced observations for short-range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019. Atmos Sci Lett. 2021; 22:e1031. https://doi.org/10.1002/asl.1031
Hintz et al. (2019): Collecting and utilizing crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4-5 December 2018. Atmos Sci Lett. 2019; 20:e921. https://doi.org/10.1002/asl.921
McNicholas, C., & Mass, C. F. (2018). Impacts of Assimilating Smartphone Pressure Observations on Forecast Skill during Two Case Studies in the Pacific Northwest, Weather and Forecasting, 33(5), 1375-1396. https://doi.org/10.1175/WAF-D-18-0085.1
McNicholas, C., and C. Mass, (2018): Smartphone pressure collection and bias correction using machine learning. J. Atmos. Oceanic Technol., 35, 523–540. https://doi.org/10.1175/JTECH-D-17-0096.1
McNicholas, C., and D. D. Turner, (2014): Characterizing the convective boundary layer turbulence with a high spectral resolution lidar. J. Geophys. Res. Atmos., 119, 12 910–12 927. https://doi.org/10.1002/2014JD021867
McNicholas, C. (2022): A Comparison of Mesoscale Pressure Features Observed with Smartphones and Conventional Observations. 31st Conference on Weather Analysis and Forecasting (WAF), Virtual, Amer. Meteor. Soc., 10.ii.6, https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/397728
McNicholas, C. (2021): Anonymizing Smartphone Pressure Observations for Machine Learning Bias Correction. 20th Conference on Artificial Intelligence, Virtual, Amer. Meteor. Soc., 10.ii.6, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/384354
McNicholas, C. (2021): Learning Atmospheric Science With MPAS and Kerbal Space Program. 20th Annual Student Conference, Virtual, Amer. Meteor. Soc., 79, Soc., https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/385746
McNicholas, C. (2020): Smartphone Pressure Analysis with Machine Learning and Kriging. 19th Conference on Artificial Intelligence, Boston, MA., Amer. Meteor. Soc., 1B.2, Soc., https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/368678
McNicholas, C. (2017): Surface Pressure Observations from Smartphones: Current Status and Future Promise. 28th Conference on Weather Analysis and Forecasting, Seattle, WA., Amer. Meteor. Soc., J6.3, Soc., https://ams.confex.com/ams/97Annual/webprogram/Paper303017.html
Thesis and Dissertations
McNicholas, C. (2021): Characterizing Mesoscale Pressure Features with Bias Corrected Smartphone Pressures, University of Washington, 128 pp, https://www.proquest.com/docview/2594572279?pq-origsite=gscholar&fromopenview=true
McNicholas, C. (2017): Advanced Approaches for the Collection, Quality Control, and Bias Correction of Smartphone Pressure Observations and Their Application in Numerical Weather Prediction, Dept. Atmospheric Science, University of Washington, 78 pp, https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/39940/Callie_Masters_Thesis.pdf?sequence=5
Kerbal Weather Project