A novel approach to systematically analyze the error structure of precipitation datasets using decision trees
Bibliographic record
Abstract
<div>Multiple environmental factors influence the error structure of precipitation datasets. The conventional precipitation evaluation method over-simply analyzes how the statistical indicators vary with one or two factors via dimensionality reduction. As a result, the compound influences of multiple factors are superposed rather than disassembled. To overcome this deficiency, this study presents a novel approach to systematically and objectively analyze the error structure within precipitation products using decision trees. This data-driven method can analyze multiple factors simultaneously and extract the compound effects of various influencers. By interpreting the decision tree structures, the error characteristics of precipitation products are investigated. Three types of precipitation products (two satellite-based: ‘top-down’ IMERG and ‘bottom-up’ SM2RAIN-ASCAT, and one reanalysis: ERA5-Land) are evaluated across CONUS. The study period is from 2010 to 2019, and the ground-based Stage IV precipitation dataset is used as the ground truth. By data mining 60 binary decision trees, the spatiotemporal pattern of errors and the land surface influences are analyzed.</div><div> </div><div>Results indicate that IMERG and ERA5-Land perform better than SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the ten years of data analyzed. The conventional bias evaluation finds that ERA5-Land and SM2RAIN-ASCAT underestimate in summer and winter, respectively. The decision tree method cross-assesses three spatiotemporal factors and finds that underestimation of ERA5-Land occurs in the eastern part of the rocky mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine physical variables, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover classification and the topography position index are two relatively weak factors.</div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".