Lightning Probability - Warning Decision Training Division (WDTD)
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Products Guide
Lightning Probability
Short Description
Probability of cloud-to-ground (CG) lightning at a given location over the next 30 minutes and next 60 minutes.
Subproducts
Lightning Probability Next 30 minutes
Lightning Probability Next 60 minutes
Primary Users
NWS: WFO, CWSU, AWC, SPC
FAA: Tower, TRACON, ARTCC, ATCSCC, AFSS
Other: EM
Input Sources
Earth Network’s In-Cloud Lightning
Vaisala’s Cloud-to-Ground Lightning
MRMS products including Maximum Estimated Size of Hail (MESH) and Vertically Integrated Liquid (VIL)
Near-Storm Environmental Data
Resolution
Spatial Resolution: 0.01o Latitude (~1.11 km) x 0.01o Longitude (~1.01 km at 25oN and 0.73 km at 49oN)
Temporal Resolution: 2 minutes
Product Creation
The cloud-to-ground (CG) lightning probability product was created using "random forest" machine learning methodology. With storm-based inputs of Earth Network’s in-cloud lightning, Vaisala’s cloud-to-ground lightning, MRMS products including the Maximum Estimated Size of Hail (MESH) and Vertically Integrated Liquid (VIL), and near storm environmental data including lapse rate and CAPE, a random forest algorithm was trained to produce probabilities of CG lightning.
Technical Details
Latest Update: MRMS Version 12
References
Strengths
This product has proven more successful than the steady-state approach of simply advecting the lightning density field.
The major strength of using “Random forests” is that they can easily take large datasets and create decision trees. The decision tree is how the algorithm arrives at the “answer.”
Another strength of using random forest methodology in the creation of the lightning probability products is that it can show which inputs are most important to the algorithm by testing for statistical significance. The data inputs that were not statistically significant were removed from the algorithm, which makes the algorithm computationally more efficient by reducing the amount of data processing that needs to occur.
Limitations
Does not incorporate satellite-based data (GLM).
Quality Control
Applications
The lightning probability product is especially helpful in both tactical and short-term air traffic routing decisions, as well as with Impact-Based Decision Support Services, since it provides a lightning forecast, rather than just an observation
Example Images
Fig. 1: Cloud-to-Ground Lightning Probability over the next 30 minutes on 26 September 2018 at 1458Z.