Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data
Bibliographic record
Abstract
Accurate estimation of surface energy fluxes is essential for various hydrological, meteorological, agricultural, and ecological applications. Over the years, a wide variety of instrument systems and estimation methodologies have been developed to measure and estimate surface fluxes. Comparisons of various scale field experimental data and different model estimates show a large degree of scatter with a wide range of root mean square error. We explore and evaluate analytically the error property of the traditionally used energy balance residual method for latent heat flux estimation in an attempt to identify the possible existence of an irreducible error bound for latent heat flux measurement and estimation over large areas. Our analysis shows that the error is typically on the order of 10%–20% or larger for surface sensible and latent heat fluxes. A simplified remote sensing latent heat flux estimation approach is proposed and its error properties are evaluated. Results suggest that a similar or better error bound can be achieved using primarily remotely sensed data over large areas for the estimation of latent heat flux using this alternative approach.
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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 | 0.018 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".