Evaluating the effects of topographical factors on the precipitation simulated by kilometer-scale versus quarter-degree dynamical downscaling models in eastern China
Bibliographic record
Abstract
Topography as well as its attributes are fundamental factors during precipitation generation. Various models with different complexity have been established to interpret the topography–precipitation relationship. In this study, the topography–precipitation relationships simulated by two dynamical downscaling models (DDMs) at the kilometer-scale and traditional quarter-degree resolution in eastern China are evaluated by utilizing multi-scale geographically weighted regression with station precipitation observations as reference. The precipitation simulated by the kilometer-scale DDM had a higher agreement with observations than the quarter-degree simulation. For the effects of topography on precipitation, observations revealed a dominant role played by the topographical relief in the precipitation distribution at most stations in the study region. The kilometer-scale DDM generally reflected this dominant role of topographical relief. However, the quarter-degree DDM showed an excessive dependency of the precipitation distribution on the topographical elevation. This research highlights the key role of underground sub-grid variations on the precipitation in eastern China, which implies a potential way forward for precipitation simulation improvements. 摘要 与传统的1/4度 (≈25-30 km) 动力降尺度模拟相比, 公里尺度模拟的降水空间分布与观测结果更为接近. 为了研究这一差异原因, 本研究以华东地区为例, 探究了地形因子在观测和模拟的降水中的作用. 为了更好地体现地形因子对降水分布非均匀性的影响, 以及不同地形因子作用的尺度差异, 本研究采用多尺度地理加权回归模型, 对五个主要地形因子与公里尺度和1/4度分辨率模拟的降水的关系进行了评估. 基于观测数据的研究结果显示地形起伏度, 地形高程和离海岸线距离对华东地区降水分布的非均匀性都有重要影响, 其中地形起伏度在研究区大部分站点降水分布中起主导作用; 公里尺度模拟结果基本反映了地形起伏度的主导作用; 而1 / 4度模拟结果表现出降水对地形高程的过度依赖. 本研究揭示了公里尺度地形分布对中国东部降水的非均匀分布的关键作用, 研究结果可以为改进降水模拟提供新的思路.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".