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Christman Field Latest Observations
Select Units
Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2023-03-25 16:40 32.6 25.0 0.6 20.4 289 29.2 289 24.679 108.0 0.00
2023-03-25 16:35 32.6 25.8 1.3 22.7 294 27.5 283 24.676 111.0 0.00
2023-03-25 16:30 32.7 26.6 2.1 22.3 305 31.4 289 24.673 113.3 0.00
2023-03-25 16:25 32.9 23.9 -0.0 22.7 296 30.0 286 24.672 116.7 0.00
2023-03-25 16:20 33.1 25.0 1.1 17.3 313 24.1 303 24.671 120.8 0.00
2023-03-25 16:15 33.3 25.9 2.0 22.0 306 30.9 307 24.667 134.6 0.00
2023-03-25 16:10 33.6 22.7 -0.6 19.2 300 23.8 283 24.666 169.5 0.00
2023-03-25 16:05 33.8 23.1 -0.1 23.7 286 29.5 299 24.664 214.2 0.00
2023-03-25 16:00 34.0 24.5 1.4 22.8 290 33.3 275 24.663 252.5 0.00
2023-03-25 15:55 34.1 23.9 0.9 21.1 293 27.3 287 24.662 233.4 0.00
2023-03-25 15:50 34.1 24.3 1.3 22.2 288 34.8 290 24.659 242.0 0.00
2023-03-25 15:45 34.0 25.1 1.9 20.0 282 29.4 295 24.657 232.6 0.00
2023-03-25 15:40 33.9 26.4 2.9 20.4 300 25.7 290 24.655 177.1 0.00
2023-03-25 15:35 34.0 25.7 2.4 18.6 286 23.7 287 24.654 174.3 0.00
2023-03-25 15:30 34.1 24.5 1.5 21.7 292 30.1 300 24.651 183.4 0.00
2023-03-25 15:25 34.5 24.9 2.2 20.9 290 27.2 278 24.648 217.6 0.00
2023-03-25 15:20 35.1 25.1 2.8 19.2 298 27.0 284 24.647 295.5 0.00
2023-03-25 15:15 35.0 27.1 4.5 16.6 285 22.7 271 24.645 364.3 0.00
2023-03-25 15:10 34.7 23.5 1.1 20.1 291 24.6 269 24.644 346.9 0.00
2023-03-25 15:05 34.5 25.8 2.9 22.8 279 31.2 285 24.642 210.2 0.00
2023-03-25 15:00 34.9 23.2 0.9 22.7 295 29.1 286 24.640 217.2 0.00
2023-03-25 14:55 35.7 25.8 3.9 23.0 288 29.9 286 24.639 308.7 0.00
2023-03-25 14:50 36.2 24.3 3.1 20.2 287 29.1 280 24.637 484.9 0.00
2023-03-25 14:45 36.7 21.5 0.9 16.1 295 23.0 295 24.638 604.4 0.00
2023-03-25 14:40 36.2 23.9 2.7 19.5 264 24.7 271 24.637 607.5 0.00
2023-03-25 14:35 35.9 21.6 0.3 21.5 270 26.9 273 24.638 600.5 0.00
2023-03-25 14:30 35.8 23.8 2.3 23.2 273 30.2 268 24.636 608.1 0.00
2023-03-25 14:25 35.9 22.5 1.2 16.3 275 24.4 278 24.635 489.1 0.00
2023-03-25 14:20 35.4 22.5 0.8 19.1 286 28.3 280 24.634 423.8 0.00
2023-03-25 14:15 35.6 23.4 1.8 19.2 296 24.8 280 24.633 387.3 0.00
Date Time
MST
Temp
°C
RH
%
DewPt
°C
Wind
m/s
Dir
°
Gust
m/s
Dir
°
Press
hPa
Solar
W/m^2
Prec
mm
2023-03-25 16:40 0.3 25.0 -17.4 9.1 289 13.1 289 835.71 108.0 0.00
2023-03-25 16:35 0.3 25.8 -17.1 10.1 294 12.3 283 835.63 111.0 0.00
2023-03-25 16:30 0.4 26.6 -16.6 10.0 305 14.1 289 835.51 113.3 0.00
2023-03-25 16:25 0.5 23.9 -17.8 10.2 296 13.4 286 835.49 116.7 0.00
2023-03-25 16:20 0.6 25.0 -17.2 7.8 313 10.8 303 835.44 120.8 0.00
2023-03-25 16:15 0.7 25.9 -16.7 9.8 306 13.8 307 835.33 134.6 0.00
2023-03-25 16:10 0.9 22.7 -18.1 8.6 300 10.6 283 835.28 169.5 0.00
2023-03-25 16:05 1.0 23.1 -17.8 10.6 286 13.2 299 835.21 214.2 0.00
2023-03-25 16:00 1.1 24.5 -17.0 10.2 290 14.9 275 835.19 252.5 0.00
2023-03-25 15:55 1.1 23.9 -17.3 9.4 293 12.2 287 835.14 233.4 0.00
2023-03-25 15:50 1.2 24.3 -17.0 9.9 288 15.6 290 835.05 242.0 0.00
2023-03-25 15:45 1.1 25.1 -16.7 8.9 282 13.1 295 834.98 232.6 0.00
2023-03-25 15:40 1.1 26.4 -16.1 9.1 300 11.5 290 834.93 177.1 0.00
2023-03-25 15:35 1.1 25.7 -16.4 8.3 286 10.6 287 834.87 174.3 0.00
2023-03-25 15:30 1.2 24.5 -17.0 9.7 292 13.4 300 834.76 183.4 0.00
2023-03-25 15:25 1.4 24.9 -16.5 9.3 290 12.2 278 834.68 217.6 0.00
2023-03-25 15:20 1.7 25.1 -16.2 8.6 298 12.1 284 834.63 295.5 0.00
2023-03-25 15:15 1.7 27.1 -15.3 7.4 285 10.1 271 834.58 364.3 0.00
2023-03-25 15:10 1.5 23.5 -17.2 9.0 291 11.0 269 834.53 346.9 0.00
2023-03-25 15:05 1.4 25.8 -16.1 10.2 279 14.0 285 834.46 210.2 0.00
2023-03-25 15:00 1.6 23.2 -17.3 10.2 295 13.0 286 834.41 217.2 0.00
2023-03-25 14:55 2.0 25.8 -15.6 10.3 288 13.3 286 834.37 308.7 0.00
2023-03-25 14:50 2.3 24.3 -16.0 9.0 287 13.0 280 834.32 484.9 0.00
2023-03-25 14:45 2.6 21.5 -17.3 7.2 295 10.3 295 834.33 604.4 0.00
2023-03-25 14:40 2.3 23.9 -16.3 8.7 264 11.1 271 834.31 607.5 0.00
2023-03-25 14:35 2.1 21.6 -17.6 9.6 270 12.0 273 834.33 600.5 0.00
2023-03-25 14:30 2.1 23.8 -16.5 10.4 273 13.5 268 834.28 608.1 0.00
2023-03-25 14:25 2.2 22.5 -17.1 7.3 275 10.9 278 834.24 489.1 0.00
2023-03-25 14:20 1.9 22.5 -17.3 8.5 286 12.7 280 834.20 423.8 0.00
2023-03-25 14:15 2.0 23.4 -16.8 8.6 296 11.1 280 834.16 387.3 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

Katherine Haynes

Dr. Katherine Haynes


Job Title:

Research Scientist II

Email Address:

Phone Number:


970-491-2560

Mailing Addresss:


Cooperative Institute for Research in the Atmosphere
Colorado State University
1375 Campus Delivery
Fort Collins, CO 80523 USA

Office Location:


CIRA 114

About Me:


For my undergraduate degrees, I studied at Indiana University, where I received two B.S. degrees: one in mathematics and one in music performance with an outside field in computer science.  During the summer after my junior year, I was fortunate to participate in the NASA Summer Institute on Atmospheric and Hydrospheric Sciences at NASA Goddard Space Flight Center.  I loved the research project I worked on, which was focused on optimizing satellite CO2 retrievals.  Following this, I decided to continue my studies and attended Colorado State University, where I received both a M.S. and Ph.D. in atmospheric science, focusing on the carbon cycle and land-atmosphere interactions.

Since I was a kid, I always loved to travel, and again I was fortunate to obtain a research scientist position at the Commonwealth Scientific and Industrial Research Organisation (CSIRO).  While living in Melbourne, Australia, I assisted in developing their global climate model.  After two years, I moved back to Fort Collins, CO, to continue work as a research scientist at CSU in the Department of Atmospheric Science.  Continuing to focus on land-atmosphere interactions and the carbon cycle, my primary role was to update a complex land surface model, the Simple Biosphere Model (SiB).  I designed, developed, and documented SiB4, which is a mechanistic, prognostic land surface model that integrates heterogeneous land cover, environmentally responsive prognostic phenology, dynamic carbon allocation, and cascading carbon pools.  SiB4 is available on GitLab, and global hourly, daily, and monthly output for 2000-2018 is archived on the NASA ORNL DAAC.  For more information on SiB4, please see:

 

While working at CSU, I took advantage of my employee benefits to obtain a Graduate Certificate in Business Intelligence in 2019.  This rekindled my enjoyment with machine learning, and I decided to obtain a Masters Degree in Computer Science (M.C.S.) in 2020.  For class projects, I enjoyed applying machine learning to environmental science tasks, and in 2021 I started working at CIRA with the hopes of combining my computer science skills with my atmospheric science background.    Since being at CIRA, I have applied machine learning to a wide array of projects:

  • Detecting low clouds
  • Improving vertical profiles of temperature and moisture
  • Automatic detection of gravity waves
  • Creating synthetic microwave imagery
  • Developing a new ensemble global genesis index for tropical cyclones

 

As a researcher who understands the importance of reliable datasets, ranging from in situ measurements to tower-based eddy covariance data to aircraft measurements to satellite data, I have recently been involved in a project aimed at developing AI-ready data standards, tools, and benchmarks.  Using the TC PRIMED dataset, I have been helping to assess this dataset while providing feedback to the NOAA Center for AI (NCAI) data readiness sub-team on the AI-ready data guidelines.  I am also helping to develop Jupyter notebooks providing training material on how to apply TC PRIMED to machine-learning applications.

 

I am also a collaborator with the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES).  In working towards developing trustworthy AI for weather and climate, I have recently been enjoying working on incorporating skillful uncertainty estimates into machine learning predictions.  A team of us here at CIRA recently published a paper on how to create and evaluate uncertainty estimates with neural networks, which is accompanied by a series of Jupyter notebooks demonstrating the methods.  I am also working with a risk communication team on how to effectively visualize and communicate uncertainty estimates, particularly for images.