Validation of Solar Radiation Surfaces from MODIS and Reanalysis Data over Topographically Complex Terrain
Bibliographic record
Abstract
Abstract The magnitude and distribution of incoming shortwave solar radiation (SW↓) has significant influence on the productive capacity of forest vegetation. Models that estimate forest productivity require accurate and spatially explicit radiation surfaces that resolve both long- and short-term temporal climatic patterns and that account for topographic variability of the land surface. This paper presents a validation of monthly average total (SW↓t) and diffuse ( SW↓df ) incoming solar radiation surfaces taken from North American Regional Reanalysis (NARR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a mountainous region of the Pacific northwestern United States and Canada. A topographic solar radiation model based on a regionally defined clearness index was used to downscale the 32-km NARR SW↓t surfaces to 1 km, resulting in surfaces that better matched the spatial resolution of MODIS, as well as accounted for elevation and terrain effects including shadowing. Validation was carried out using a series of ground station measurements (n = 304) collected in 2003. The results indicated that annually, the NARR and MODIS SW↓t surfaces were both in strong agreement with ground measurements (r = 0.98 and 0.97), although the strength and bias of the relationships varied considerably by month. Correlations were highest in winter, early summer, and fall and lowest in spring. The NARR and MODIS SW↓df surfaces displayed poorer agreement with ground measurements (r = 0.89 and 0.79), the result of some months having negative correlations. The correlation and spatial structure between NARR and MODIS SW↓t surfaces was enhanced by topographic correction, resulting in more consistent input radiation surfaces for use in broad-scale forest productivity modeling.
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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 | 0.000 |
| 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) | 0.000 | 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 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".