Evaluating Global Precipitation Measurement Mission in the High Latitudes: A Case Study in Canada
Bibliographic record
Abstract
Precipitation is a vital component in the water cycle. Measuring accurate precipitation is significant because it is important to know when, where, and how much precipitation is occurring. Precipitation has commonly been measured using ground-based devices such as rain gauges and ground radars. Although these methods have worked well, their spatial coverage is restricted to land. With the development of satellite precipitation products, it is possible to obtain measurements at high spatial and temporal resolution. Satellite precipitation products can help get a better understanding of the Earth???s water distribution as well as improve the forecasting for extreme precipitation events. However, satellite products are prone to error, which make it necessary to evaluate their performances. The objective of the study is to assess the quality of the Global Precipitation Measurement (GPM) mission in the high latitudes. This study will use GPM Integrated Multi-satellitE Retrievals (IMERG), which is a data product that uses an algorithm that combines all passive-microwave instruments in the GPM satellite Constellation into half-hourly precipitation and daily estimates. The performance of GPM-IMERG is evaluated by comparing the estimates against ground-based observations over the high latitudes, mainly focusing on Canada from April 2014 to October 2017. For the evaluation, various statistical measures are applied in order to investigate the performances such as root mean square error, correlation coefficient, bias and other statistics derived from the contingency table.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".