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Weekly Report________________________________________________________________________________________________________________________________________________________________RAMMB / CIRACooperative Research Program Division (CoRP)STAR/NESDISNational Oceanic and Atmospheric Administration (NOAA)________________________________________________________________________________________________________________________________________________________________Submitted by: Austin BoonePrepared by: RAMMB/CIRA contributorsDate of Submission: 29 January 2021Products and Applications Markov-like wind radii CLIPER model up and running: A Markov-like model to forecast Tropical Cyclone wind radii along the JTWC forecast based on climatology and persistence (i.e., CLIPER) that was delivered to NRL, Monterey in December has been successfully integrated into the Automated Tropical Cyclone Forecast system and is ready for transition to operations this next summer. This model was developed using satellite-only estimates of tropical cyclone size, which combined with intensity and motion forecasts are statistically related to wind radii. The model uses initial conditions derived from routine analysis of wind radii and successively makes forecasts using the forecasts of intensity and motion provided by the official forecasts that are blended with initial asymmetries in the wind radii. This new model makes forecasts that are more consistent with initial storm sizes and thus rectifies the shortcomings of the wind radii CLIPER model (also developed at CIRA/RAMMB), while producing forecasts that are nearly identical when initial storm size is close to climatology. A comparison forecast displayed on the ATCF is shown below. (POC: J. Knaff, John.Knaff@) Funding: U.S. Navy & STARFigure: Example of Markov-like wind radii CLIPER (purple) 34-knot wind radii forecast compared to the old wind radii CLIPER model (magenta) for Typhoon Chan-Hom (wp162020) initiated 6 October 2020 at 00 UTC. Chan-Hom was a rather large and Typhoon, one where the old CLIPER model’s relaxation to climatology resulted in small biases in large TCs. Notice how the wind radii are much larger for the Markov model, but still not large enough in this 5-day forecasts. The 34-kt wind radii from the working best track is at the bottom.Awards and Recognition PublicationsMedia and OutreachUK Met Office/NOAA: Chris Slocum along with other individuals involved with NCAI met with the UK Met Office Joint Centre for Excellence in Environmental Intelligence to discuss how to coordinate AI Community of Practices, lessons learned for workforce development, and share AI resources. (POC: C. Slocum, Christopher.Slocum@) Funding: STAR.Workshops, Conferences, and Meetings2nd NOAA Workshop on AI: Chris Slocum presented results “Forest-based Rapid Intensification Aid” model (FRIA) during the NOAA AI Workshop in a talk entitled “What can we learn from Random Forest in the context of the tropical cyclone rapid intensification problem?” The new model leverages existing model environmental parameters and geostationary satellite convective parameters including infrared window channel predictors and geostationary lightning data. (POC: C. Slocum, Christopher.Slocum@) Funding: STAR.2021 ESIP Winter Meeting: Yuhan Rao (CISESS) and Chris Slocum presented on “A Roadmap of Workforce Development for NOAA Center for Artificial Intelligence” during the ESIP Winter Meeting to discuss resources being developed for and released by the planned NOAA Center for AI. (POC: C. Slocum, Christopher.Slocum@) Funding: STAR.Improving NOAA’s Hurricane Intensity Forecasts Workshop: J. Knaff attended (as much as he could) the Improving NOAA’s Hurricane Intensity Forecasts Workshop hosted by The Global Ocean Monitoring and Observing (GOMO) Program. The meeting, held over 4 hours on three consecutive days, had presentations by researchers, modelers, observationalists, and forecasters discussing the needs of the future ocean observation capabilities focused on hurricane intensity change. It sounds like there will be a field experiment in 2022. (POC: J. Knaff, John.Knaff@) Funding: STARTraining and EducationNCAI Team Roles out Training Example: The NOAA Center for AI Team has released a demo Jupyter notebook to the NCAI Community of Practice that uses the Python programming language to predict the Ni?o 3.4 Index and to discuss basic machine learning principles and model training practices. The notebook can be found HERE (currently only visible to users with NOAA email addresses; make a copy to your Google Drive to run). The notebook is an example of NCAI’s workforce development plans where notebooks form the foundation of a curated training library with examples relevant to NOAA’s mission areas. (POC: C. Slocum, Christopher.Slocum@) Funding: STAR.JPSS Teletraining this week: J. Torres led Near-Constant Contrast (NCC) teletetraining on 25 January 2021; NWS WFO Anchorage, AK attended teletraining. J. Torres also led ‘JPSS/GOES Fire Monitoring Capabilities’ teletraining on 27 January 2021; NWS WFO San Antonio, TX attended teletraining. (POC: J. Torres, CIRA, Jorel.Torres@colostate.edu) Funding: JPSS.US Air Force Support of Satellite Liaison Blog: A US Air Force Meteorological Technician (Training) reached out expressing their appreciation of posts on the Satellite Liaison Blog. They requested permission to incorporate content of the blog posts into their forecast training materials. (POC: B. Line CoRP/RAMMB, bill.line@, Funding: PDRA)New Satellite Liaison Blog Post: A blog post titled “RGB Applications: Anticipating Convective Initiation Using the Nighttime Microphysics RGB” was recently published in collaboration with NWS Grand Forks, ND forecaster Carl Jones. The post provides an operational perspective on the use of GOES RGB imagery for detecting nighttime convective initiation. See Figure below. The link to the post can be found here. (POC: B. Line CoRP/RAMMB, bill.line@; Carl Jones NWS/FGF; Funding: PDRA)Figure: 19 Aug 2020 GOES-East Nighttime Microphysics RGB (left), 10.35 um IR (right). Blog post shows how the RGB can be used to identify nighttime convective initiation earlier than when using single-band IR imagery alone.The past 9 months have been VERY busy for CIRA’s International Training Group: The WMO-CGMS VLab/NOAA Regional Focus Group (RFG) of the Americas and Caribbean had a very busy and successful year in 2020. In a normal year, we conduct 12 monthly sessions, occasionally coordinate additional specific topic virtual sessions, and provide expertise and support for two in-person trainings. During 2020, CIRA coordinated with the WPC International Desks and WMO Centers of Excellence in Barbados, Costa Rica, Brazil, and Argentina, to conduct 12 regular bilingual (English and Spanish) sessions. We also coordinated with NOAA and many other international organizations to conduct 4 special topic sessions and convert 2 in-person workshops to virtual trainings. The RFG weather and climate briefings have connected instructors, researchers, forecasters, and weather enthusiasts and enabled them to view geostationary and low earth orbiting imagery and products, share information on seasonal weather patterns, hurricanes, severe weather, flooding, volcanic eruptions, and other significant events. The figure below shows the number of participants for the various sessions and events. The combined RFG and special sessions attracted over 1600 participants from 51 countries and an additional 82/113 people were engaged for the Costa Rica RA-IV / Ecuador RA-III training events. (POC: B. Connell, E. Sanders, CIRA, Bernie.Connell@colostate.edu, Erin.Sanders@colostate.edu, Funding: GOES, JPSS, and NWS)Figure 1. This graph shows attendance at sessions conducted by CIRA and collaborating partners in support of training offered through the WMO Virtual Laboratory from January through December 2020. Attendance at the RFG sessions (black blocks) was up from the pre-pandemic months of January through February, with many individuals participating from home due to COVID-19 restrictions. The additional virtual Special Topic Sessions and longer training events have also been very popular.OtherManuscript reviewed. J. Knaff reviewed a manuscript submitted to the Journal of Geophysical Research-Oceans. (POC: J. Knaff, John.Knaff@) Funding: STAR ................
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