Current Research

South Baldy Thunderstorm Forecasts


Langmuir Laboratory


This page feature thunderstorm forecasts for use at the Langmuir Laboratory for Atmospheric Research.  Forecasts will be issued by me, Sunday through Thursday at 9pm Mountain Daylight Time (MDT), and will be valid for the following day. These forecasts will be based on top-of-the-hour, time-weighted output (that lends more credence to cycles nearer to times forecast) from two cycles of two convective-allowing, numerical weather prediction models; namely the High Resolution Rapid Refresh (HRRR) model, and the 3-km North American Mesoscale Model (NAM). These models output simulated composite radar reflectivity (SCR) and thermodynamic properties of the environments for which thunderstorm activity is situated[1]

To forecast thunderstorm activity, I will be using the following conditions: 1) an SCR threshold greater than 29 dBZ to determine whether heavy, presumably convective, precipitation is forecast; 2) a lifted condensation level temperature (LCLT) greater than -11 °C; and, 3) a Cloud Physics Thunder Parameter (CPTP) (see Bright et al (2005); and see Stull (2017) p. 569)[2]. The LCLTs and CPTPs will be from 1-hr before SCR output in an attempt to limit convective contamination. This seems reasonable since a Byers-Braham (1949) cell (see Stull (2017) p. 484-85) last on the order of 30-to-60-min. The LCLT and CPTP will be calculated from the model grid cell with the greatest SCR within 20-km (easting and northing) of South Baldy (since the 20-km range overlap with multiple model grid cells.) 

Since there are two models and two cycles of each model at every forecast time though, whether lightning is forecast to occur will depend on the sum of the time-weighted assignments (i.e., 0 or 1 times the weight) for every forecast time. Note: it is probably true that there is no "magic number" when it comes to weighting numerical model output; however, given the same or similar data streams (assuming no errors in initial conditions) and resolutions it is reasonable to assert that model cycles nearer to the time of the event (i.e., lightning) should provide more accurate (and precise) forecasts. With that in mind, my "time-weighted" scheme simply accounts for the time difference between model cycles; such that for longer range forecasts, the difference in time between one model cycle and the next becomes increasingly small relative to the forecast time.  

Forecast periods are 6-hr in length. If any forecast time (e.g., 15Z, 16Z, or 17Z over the 12-18Z period) within a 6-hr period is favorable for lightning, that (entire) period will be regarded as being favorable for lightning and will be assigned a "YES" in the table above under "Forecasts," accordingly. Additionally, probabilities from one time to the next within a 6-hr forecast period combine; in accordance with the binomial distribution[3]. In cases where conditions are favorable for lightning at the start of a 6-hr period, the last hour of the preceding (entire) 6-hr period will be regarded as being favorable for lightning (since deep, precipitating convection does not develop instantaneously in these models.) 

That said, forecasts for thunderstorm activity within 20-km (northing and easting) of South Baldy will be conveyed as a "YES" or "NO" for each 6-hr period in the table above under "Forecasts" as well as in once-daily email messages to Langmuir personnel. Verification of these forecasts will be done using data from the Langmuir Lightning Mapping Array[4]

  1. Stull (2017, p. 745-86) provides a succinct explanation of numerical weather prediction in case you are interested in learning more about the science (and numerics.)
  2. A flowchart of my decision making for one model cycle at one forecast time can he found here: SouthBaldyFlowchart.
  3. If I only need one lightning strike for a "YES" during a 6-hr period, and two of those 1-hr forecast times within a 6-hr period have an odds of 0.5 (50%), there is a 0.75 (75%) chance of there being a lightning strike sometime within the 6-hr period in question; which is sufficient for a "YES" (versus a 50% or lower which would yield a "NO") since 75% represent a better-than-odds chance (i.e., 50%.) This is the nature of the binomial distribution in terms of probability. 
  4. Tables used to store thunderstorm "YES" and "NO" forecasts from a series of scripted routines are provided, including forecast verifications; you are welcome to view those tables, however they are mostly intended for me: here.