Tuesday, May 31, 2016

Data Driven

Well, we have arrived at the fifth and final week of SFE 2016. By this point in the experiment, the facilitators are mostly used to the rhythm of the testbed, knowing what observational data and model guidance we'll go over each day. By the end of the week, participants are generally used to the fast pace of the experiment as well. However, the first day of each week provides some reminders as to how much we're throwing at the participants. I thought that tonight, I'd provide a brief rundown of what we consider when making our full period outlooks each day, which run from 16Z of any given day to 12Z the following day.

Observationally, we consider:
  • Current water vapor imagery
  • Surface-based CAPE
  • VAD wind profiles at 925mb, 850mb, and 500mb
  • Current radar
  • Precipitable water
  • Observed soundings
  • A radar slice at 9km (to see where strong updraft cores are located)
  • Analyzed CAPE with deep-layer shear vectors
  • Lightning flash frequency maps
  • Current watches and warnings
All of these things typically take between 15 and 20 minutes to consider, depending on how many areas within our domain we have to pay attention to. This is followed by a quick look at large scale deterministic guidance for our period:
  • 500mb heights and wind speeds from the GFS, NAM, and SREF mean
  • 850mb heights and wind speeds from the GFS, NAM, and SREF mean 
  • QPF and vertical velocity from each model
  • Soundings over our area of interest from the GFS and NAM
  • Calibrated severe probabilities from the SREF
  • SREF mean, minimum, and maximum STP forecasts
  • An in-house SPC significant tornado parameter
  • A SREF probability of co-located CAPE, shear, and convective precipitation
  • 4km NAM reflectivity, including a comparison to the present time to determine how well the model is capturing the current atmospheric state
This part of the discussion takes about another 20 minutes. The next thing we consider is the CAMs:
  • "Paintball" plots of reflectivity that show each member exceeding 40dBZ as a different color from the NCAR ensemble, SSEO, NSSL-WRF ensemble, and various subsets of the CLUE
  • Maximum updraft helicity tracks from the NCAR ensmeble, SSEO, NSSL-WRF ensemble, and CLUE subsets
  • Probabilities of updraft helicity exceeding 25 m2/s2 and exceeding 100 m2/s2
  • Maximum updraft speed
  • Probability of hail exceeding 1" and 2" from the HAILCAST and Thompson hail diagnostic methods from the NCAR ensemble, NSSL-WRF ensemble, and CLUE subsets
  • MPAS reflectivity fields
  • MPAS 500mb height fields
  • MPAS 24hr updraft helicity tracks
  • NCAR ensemble probabilities of UH exceeding 75m2/s2
  • NCAR ensemble member reflectivity 
  • NCAR ensemble member CAPE
  • NSSL-WRF ensemble probability of total severe with automated isochrones
  • HRRR reflectivity forecasts from the past three HRRR runs 
  • HRRR dewpoints from the past three HRRR runs
The CAMs take about 40 minutes to go over. So if you've been keeping track, we have about an hour and a half to take in all of this information before starting to make our full period outlook. Even with a team of about ten people, it can be hard to keep track of what's going on! This was particularly the case last week, when we had multiple boundaries to keep track of, most of which were adjacent to high-instability air and many of which had decently strong shear to work with.

After all of these fields are shown, we go back and take another look at the current satellite and radar, keeping us grounded in reality and the current boundary locations. By showing all of the information listed above, we hope to show forecasters at least one tool they haven't seen before and might find useful. We also hope to show researchers how difficult it is to assimilate huge amounts of data into a conceptual scenario of the day's weather, and be confident enough in that scenario to make a forecast. While some rare days show a consistent signal between all of the above guidance, more often this experiment the guidance has pointed in multiple directions and we have had to make a decision rooted in the observations and our forecast experience. By exposing forecasters and researchers to the challenges each face, we hope to generate new, innovative strategies for tackling the challenges of the atmosphere and big data. 

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