Incorporating satellite derived cloud climatologies to improve high resolution interpolation of daily precipitation
Bibliographic record
Abstract
Wilson, Adam M., Benoit Parmentier, Brian McGill, Robert Guralnick, and Walter Jetz. 2013. “Incorporating Satellite Derived Cloud Climatologies to Improve High Resolution Interpolation of Daily Precipitation.” In 6th International Conference. Miami, FL: International Biogeography Society. http://www.biogeography.org/html/Meetings/2013/. Abstract: Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the factors that control the spatial distribution of species and this has become even more important in the face of climate change. However, at global scales there can be enormous uncertainty in environmental data used to model species distributions. Even ‘simple’ metrics such as mean annual precipitation are difficult to estimate in areas with few weather stations and available data sets do not quantify uncertainty in these surfaces. We are developing a global, 1km resolution, daily meteorological dataset for 1970-2010 by leveraging relatively high quality station observations with spatially continuous but indirect satellite observations. While several satellite derived precipitation products exist, all are relatively coarse (≥0.25deg) to estimate other cloud parameters that are more directly related to precipitation. Precipitation will fall when cloud particles achieve sufficient mass to overcome updraft winds and when the clouds have sufficient vertical extent to facilitate the growth of these particles. The MODIS Cloud Product (MOD06) includes estimates of effective radius and optical thickness at 1km resolution. These parameters are theoretically related to precipitation and we are exploring their utility in the interpolation of station precipitation observations. Improving high resolution estimates of precipitation will facilitate analysis of geographic and/or environmental shifts in species distributions in response to global climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".