Multi-site precipitation downscaling via an expanded conditional density network
Bibliographic record
Abstract
Abstract Statistical downscaling models are used to estimate weather data at a station or stations based on atmospheric circulation data defined at a coarser resolution, for example gridded outputs from a Global Climate Model (GCM). Downscaled data can be used as inputs to environmental models that require finer-scale climate fields than are currently available from GCMs. Maintaining realistic downscaling relationships between sites and variables is particularly important in hydrological models, as streamflow depends strongly on the spatial distribution of precipitation in a watershed and on interactions with temperature that determine whether precipitation falls as rain or snow. More generally, precipitation is a difficult variable to downscale because of its non-normal distribution and its spatial and temporal patchiness.A downscaling algorithm for daily precipitation series at multiple stations is presented. The expanded conditional density network (ECDN) models the conditional density of the Poisson-gamma distribution via an artificial neural network. ECDN is capable of (1) specifying the conditional distribution of precipitation at each site; (2) modeling occurrence and amount of precipitation simultaneously; (3) reproducing observed spatial relationships between sites; (4) randomly generating synthetic precipitation series; and (5) predicting precipitation amounts in excess of those in the observational record. The ECDN model is applied to two downscaling problems: the first is a benchmark precipitation downscaling task previously evaluated by other modeling groups; the second is a multi-site precipitation dataset from British Columbia, Canada. Results suggest that the ECDN approach is capable of generating spatially and temporally realistic precipitation series that are suitable for use as inputs to hydrological models or to spatial interpolation schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".