Observed response of microwave land surface emissivity to antecedent rainfall in Hainan Island
Bibliographic record
Abstract
Microwave emissivity is an important parameter for over-land retrieval of atmospheric variables such as water vapour, rainfall, and snowfall. However, it remains unclear for the dynamic response of multi-frequency microwave emissivity to prior rainfall over heterogeneous land surfaces. This study combined multi-frequency Microwave Land Surface Emissivity (MLSE) under all-weather conditions with hourly in-situ rainfall to investigate the response of MLSE to rainfall over different vegetation types in Hainan Island in China. Specifically, we explored the change of MLSE at 6.925, 10.65, 18.7, 23.8 and 36.5 Ghz to rainfall intensity in four rainfall cases, followed with statistical characteristics of MLSE to rainfall and dry duration (DD) across different vegetation types during 2003–2010. We found that the magnitude of decline in MLSE (0.004–0.06) is dependent on the rainfall intensity and duration, and the reduction caused by short-lasting heavy rain (daily rainfall >100 mm) was 2–6 times higher than that by light long-lasting rain (daily rainfall <80 mm). Among different microwave frequencies, the MLSE at 23.8 Ghz was most reduced by rainfall, while less reduction was found at low microwave frequencies such as at 6.925 and 10.65 Ghz. More importantly, the change of MLSE after rainfall was significantly different among vegetation types. Over woody savannas and forested lands, MLSE slightly decreased (about 0.004) in the first two hours of DD, whereas a rapid increase was observed in the first hour of DD over areas dominated with woody savanna, grassland, and cropland. These findings improve our understanding of the multi-frequency MLSE in response to rainfall over complex land surfaces, and benefit the microwave retrieval of rainfall over land.
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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".