Verification of Solid Precipitation Forecasts from Numerical Weather Prediction Models in Norway
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Assessing the quality of precipitation forecasts requires observations, but all precipitation observations have associated uncertainties making it difficult to quantify the true forecast quality. One of the largest uncertainties is due to the wind-induced undercatch of solid precipitation gauge measurements. This study discusses how this impacts the verification of precipitation forecasts for Norway for one global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and one high-resolution, limited-area model [Applications of Research to Operations at Mesoscale (MEPS)]. First, the forecasts are compared with high-quality reference measurements (less undercatch) and with more simple measurement equipment commonly available (substantial undercatch) at the Haukeliseter observation site. Then the verification is extended to include all Norwegian observation sites: 1) stratified by wind speed, since calm (windy) conditions experience less (more) undercatch; and 2) by applying transfer functions, which convert measured precipitation to what would have been measured with high-quality equipment with less undercatch, before the forecast–observation comparison is performed. Results show that the wind-induced undercatch of solid precipitation has a substantial impact on verification results. Furthermore, applying transfer functions to adjust for wind-induced undercatch of solid precipitation gives a more realistic picture of true forecast capabilities. In particular, estimates of systematic forecast biases are improved, and to a lesser degree, verification scores like correlation, RMSE, ETS, and stable equitable error in probability space (SEEPS). However, uncertainties associated with applying transfer functions are substantial and need to be taken into account in the verification process. Precipitation forecast verification for liquid and solid precipitation should be done separately whenever possible.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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