Evaluating Stochastic Parameter Perturbations in Convection-Permitting Ensemble Forecasts of Lake-Effect Snow
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lake-effect snowstorms can produce large snowfall accumulations that are challenging to simulate and forecast. One source of forecast uncertainty for these events is the uncertain parameterization of subgrid processes, such as planetary boundary layer and surface layer (PBL/SL) turbulence and cloud and precipitation microphysics (MP), in numerical weather prediction models. One way to quantify this uncertainty is to design ensembles that use stochastic parameter perturbations (SPPs) to vary individual uncertain parameters within physics schemes. This research aims to evaluate and improve the utility of SPP for convection-permitting ensemble forecasts of lake-effect snow, with a focus on PBL/SL and MP parameterizations. We focus on a snowfall event observed during the Ontario Winter Lake-effect Systems (OWLeS) field campaign, which is simulated with 1-km horizontal grid spacing using the Weather Research and Forecasting Model. A suite of 20-member ensemble simulations are run, including ensembles where SPP is applied only to PBL/SL or MP, where SPP is applied to multiple schemes concurrently, where perturbations to initial and boundary conditions (ICs/BCs) are applied instead of SPP, and where SPP and IC/BC perturbations are applied together. SPPs produce substantial spread in simulated precipitation, despite having only modest impacts on the synoptic-scale flow. They accomplish this by modulating lake–atmosphere fluxes, boundary layer characteristics, precipitation growth processes, and hydrometeor terminal fall speeds. The spread and skill of simulated precipitation from an ensemble using SPP alone is comparable to that from ensemble that uses IC/BC perturbations alone. The physical pathways whereby SPPs generate spread are examined and discussed.
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it