STAR-ESDM: A New Bias Correction Method for High-Resolution Station- and Grid-Based Climate Projections
Bibliographic record
Abstract
The Seasonal Trends and Analysis of Residuals (STAR) Empirical-Statistical Downscaling Model (ESDM) is a new bias correction and downscaling method that employs a signal processing approach to decompose observed and model-simulated temperature and precipitation into long-term trends, static and dynamic annual climatologies, and day-to-day variability. It then individually bias-corrects each signal, using a nonparametric Kernel Density Estimation function for the daily anomalies, before reassembling into a coherent time series. Comparing the performance of this method in bias-correcting daily temperature and precipitation relative to 25km high-resolution dynamical global model simulations shows significant improvement over commonly-used ESDMs in North America for high and low quantiles of the distribution and overall minimal bias acceptable for all but the most extreme precipitation amounts (beyond the 99.9th quantile of wet days) and for temperature at very high elevations during peak historical snowmelt months. STAR-ESDM is a MATLAB-based code that minimizes computational demand to enable rapid bias-correction and spatial downscaling of multiple datasets. Here, we describe new CMIP5 and CMIP6-based datasets of daily maximum and minimum temperature and daily precipitation for nearly 10,000 weather stations across North and Central America, as well as gridded datasets for the contiguous U.S., Canada, and globally. In 2022, we plan to extend the station-based downscaling globally as well, since point-source projections can be of use in assessment of climate impacts in many fields, from urban health to water supply. The projections have furthermore been translated into a series of impact-relevant indicators at the seasonal, monthly, and daily scale including multi-day heat waves, extreme precipitation events, threshold exceedences, and cumulative degree-days for individual RCP/ssp scenarios as well as by global mean temperature thresholds as described in Hayhoe et al. (2018; U.S. Fourth National Climate Assessment Volume 1 Chapter 4). In this presentation we describe the methodology, briefly highlight results from the evaluation and comparison analysis, and summarize available and forthcoming projections using this computational framework.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".