Thunderstorm Forecasts

Short Term Forecast

South Baldy Extended Forecast

South Baldy Thunderstorm Forecast Methodology

Short Term Forecasts

Short term (i.e., 9-to-33-hr) thunderstorm forecasts for use at the Langmuir Laboratory for Atmospheric Research.  Forecasts will be issued by me, Sunday through Thursday at 9pm Mountain Time, and will be valid for the following day[1]. These forecasts will be based on top-of-the-hour (e.g., 00, 01, 02Z), time-weighted output (that lends more credence to initializations nearer to times forecast) from the 18Z and 00Z initializations of two convective-allowing, numerical weather prediction models; namely the High Resolution Rapid Refresh (HRRR) model, and the 3-km North American Mesoscale Model (3-km NAM). These models output simulated composite radar reflectivity (SCR) and thermodynamic properties of the environments for which thunderstorm activity is situated[2]. 

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) greater than zero [dimensionless] (see Bright et al (2005); and see Stull (2017) p. 569). 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) lasts on the order of 30-to-60-min. The LCLT and CPTP will be calculated from the model gridcell with the greatest SCR within 20-km (easting and northing) of South Baldy. A 30-m resolution map of the "20-km South Baldy Domain" using data from NASA's Shuttle Radar Topography Mission, converted from elevation above mean sea level to surface (station) pressure using (1976) U.S. Standard Atmosphere conditions, is shown in Fig. 1 to give you, the reader, a sense of the mountainous, low-pressure (high-altitude) environment the Domain is situated. 

Figure 1. The "20-km South Baldy Domain" as indicated by 30-m resolution, NASA Shuttle Radar Topography Mission, data. Elevation data was converted to surface (station) pressure using (1976) U.S. Standard Atmosphere conditions. Mission data provided by NASA. (1976) U.S. Standard Atmosphere conditions provided by NOAA. Pertinent calculations and mapping done in Python3.  

Since there are two models and two initializations 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 initializations 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 initializations; such that for longer range forecasts, the difference in time between one model initialization and the next becomes increasingly small relative to the forecast time. Fig. 2 indicates the time weights used. 

Figure 2. Time weights used for HRRR and 3-km NAM model output. The time weighting scheme lends more credence to initializations nearer to times forecast. Tables plotted in Python3.

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 "Short Term Forecast," 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).  Fig. 3 indicate an application of the time-weighting scheme for three afternoon forecasts; two afternoons when thunderstorm activity was forecasted (i.e., a "YES" forecast), and one afternoon when no thunderstorm activity was forecast (i.e., a "NO" forecast).

Figure 3.  A decision making example for three afternoon forecasts valid on different dates. Cells shaded in yellow indicate a favorable simulated composite reflectivity and a favorable thermodynamic parameter space for thunderstorm activity. The forecast valid for 07/23/25 and 08/29/25 result in a "YES" for thunderstorm activity for the 18Z through 23Z periods (i.e., afternoon), and the forecast valid for 10/10/25 result in a "NO" for thunderstorm activity for the 18Z through 23Z periods (i.e., afternoon). Tables plotted in Python3. 

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 (simple) table above under "Short Term Forecast". Verification of these forecasts will be done using data from the Langmuir Lightning Mapping Array. Occasionally, I will post verification style graphs in figure plots under the "South Baldy Thunderstorm Forecast Verification" section below just to see how these forecasts are panning out (and as a means to improve these forecasts). The latest verification style plot is indicated in Fig. 4 in the "Short Term Forecast" subsection of the "South Baldy Thunderstorm Forecast Verification" section. 

Extended Forecasts

Extended (i.e., 33-to-105-hr) thunderstorm forecasts for use at the Langmuir Laboratory or Atmospheric Research.  Forecasts will be issued by me, Sunday through Thursday at 9pm Mountain Time, and will be valid for 2-to-4 days out[1]. These forecasts will be based on top-of-the-hour, space-weighted output (that lends more credence to the higher resolution model of the two in terms of gridcell spacing) from the 18Z initialization of two medium range numerical weather prediction models: the Global Forecast System (GFS) model, which runs at approximately 28-km (or exactly 1-Decimal Degree (DD) in terms of longitude and latitude) horizontal resolution out to 384-hr from the time of initialization; and, the North American Mesoscale Model (NAM), which runs at 12-km horizontal resolution out to 84-hr from the time of initialization. Similar to HRRR and 3-km NAM, the GFS and NAM models output SCR and thermodynamic properties of the environments for which thunderstorm activity is situated[2]. However, in contrast to HRRR and 3-km NAM, the GFS and NAM are coarser resolution with respect to space and time and are poorer at being able to capture the space and time occupied by (individual) Byers-Braham (1949) cells (see Stull (2017) p. 484-85). As such, neither the GFS or NAM are convective-allowing (which is typical for medium or longer range models because of the coarser resolutions). So, instead of output being available every hour at 3-km (horizontal) resolution as is the case with the HRRR and 3-km NAM, the GFS and NAM are only available at 28-km and 12-km (horizontal) resolution, respectively, every three-hours (e.g., 00, 03, 06Z, and so on). Additionally, since the aim is for a 2-to-4 day forecast here, it is worth noting that only one of these models, namely the GFS, offer solutions to cover later times in the forecast beyond 84-hr from the time of initialization. So, beyond 84-hr from the time of model initialization, only the GFS is available for forecasts. Nevertheless, the model time steps for GFS and NAM are identical and each model has a convective parameterization scheme that can be used to identify deep, precipitating convection. 

To forecast thunderstorm activity, I have chosen to use an algorithm similar to what I use for short term forecasts: 1) an SCR threshold greater than 29 dBZ to determine whether heavy, convective, precipitation is forecast; 2) LCLT greater than -11 °C; and, 3) a CPTP greater than zero [dimensionless] (again, see Bright et al 2005; and see Stull (2017) p. 569). However, in contrast to short term forecasts, the LCLTs and CPTPs will now be from 3-hr before SCR output in an attempt to limit convective contamination. While I am have some valid concerns regarding this long of a leap in time (i.e., 3-hr), mainly because, again, a (typical) Byers-Braham (1949) cell (only) lasts on the order of 30-to-60-min, I am not convinced that the alternative is much better. This is because the "alternative" would involve using a best-fit line to interpolate dewpoint and temperature up to 1-hr in advance of model storm activity using output valid from 2-hr prior; effectively generating a 2-hr forecast of dewpoint and temperature on top of what is already being forecast (i.e., lightning). That said, LCLT and CPTP will be calculated from the model gridcell with the greatest SCR within 20-km (easting and northing) of South Baldy, similar to short term forecasts. 

As previously indicated, since the GFS model run at 28-km horizontal resolution and the NAM model run at 12-km horizontal resolution, there are approximately 5.4 times as many NAM gridcells as GFS gridcells within the domain. So, given these differences in terms of the number of gridcells between GFS and NAM, whether lightning is forecast to occur will depend on the sum of the space-weighted assignments (i.e., 0 or 1 times the weight) for every forecast time wherever there is overlap.  My "space-weighted" scheme weighs NAM output 5.4 times as much as GFS output for any given hour. Again, because there are 5.4 times as many NAM gridcells as GFS gridcells in the domain, and because I am trying to resolve something (i.e., a thunderstorm) that is finer in scale than the (horizontal) space resolution of each model. However, since the GFS offer solutions farther out than NAM, all of the weight is shifted to the GFS beyond 84-hr from the time of model initialization (i.e., 18Z, everyday) since the NAM only runs out to 84-hr from the time of its initialization. That said, note: similar to the time-weight scheme used in short term forecasts, it is probably true that there is no "magic number" here either when it comes to weighting numerical model output; however, given the same or similar data streams (assuming no errors in initial conditions), it is reasonable to assert that the model nearer to the scale of a thunderstorm in this case should provide more accurate (and precise) forecasts of a thunderstorm. 

Forecast periods are 12-hr in length versus the 6-hr in length periods used in my short term forecasts. I decreased the resolution of the forecast periods to roughly match the decreased resolutions of the models being used in extended forecasts relative to short term forecasts. Activities at Langmuir Laboratory also tend to be grouped as being either "daytime" or "nighttime," so use of a 12-hr period seems appropriate if the beginning and end of periods are set to be within a few hours of sunrise and sunset (i.e., 6am and 6pm, Mountain Time). That said, if any forecast time (e.g., 12Z, 15Z, 18Z, or 21Z over the 12-00Z period) within a 12-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 "Extended Forecast," accordingly. Additionally, probabilities from one time to the next within a 12-hr forecast period combine; in accordance with the binomial distribution[4]. In cases where conditions are favorable for lightning at the start of a 12-hr period, the 3-hr preceding the (entire) 12-hr period will be regarded as being favorable for lightning (since deep, precipitating convection does not develop instantaneously in these models, even though it is not explicitly resolved in the case). 

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 12-hr period in the (simple) table above under "Extended Forecast".  Verification of these forecasts will be done using data from the Langmuir Lightning Mapping Array. Occasionally, I will post verification style graphs in figure plots to "Extended Forecasts" under the "South Baldy Thunderstorm Forecast Verification" section below just to see how these forecasts are panning out (and as a means for improving forecasts).

South Baldy Thunderstorm Forecast Verification

Short Term Forecasts

Figure 4. Percent accurate forecast, over forecast, under forecast, and unverifiable forecast, for each forecast period from 06/10/24 - 10/17/25. Data verified using the Langmuir Lightning Mapping Array. Percentages calculated in Google Sheets. Plotting done in Python3.