Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method
Bibliographic record
Abstract
Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species.Cloud-to-ground (CG) lightning is the primary ignition source of wildfires.It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity.Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions.In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017.We recommended using Thompson 2-moment and Mellor-Yamada-Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra.Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9.We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra.Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".