Performance of TRMM satellite data over the rain-gauge observations in Bangladesh
Bibliographic record
Abstract
Many rainfall estimation techniques and algorithms are developed for a particular region and for very different time-space scales. Instantaneous rain rates may vary from fractions of mm to over 100mm per hr. and the rainfall intensity with duration varies from region to region. We need to understand the errors structure for a variety of instruments and algorithms that are in use today or will be in use tomorrow over different regions. Bangladesh is the country that suffers from flooding in most of the year because of highly intensive rainfall within and outside of the country. The performance of satellite rainfall is an important issue for hydro-meteorological application in Bangladesh. In this study, the first space-borne Precipitation Radar (PR) launched by Tropical Rainfall Measuring Mission (TRMM) satellite data is used, which produces rain/no rain flag, vertical rain rate profile, near surface rain etc. However, only those gauge stations are considered in this study that falls inside the instantaneous field of view of particular TRMM observations. The preliminary result shows that Bangladesh is distinct from the other region in USA. Passive Microwave calibrated IR (3B41RT) performs better than TMI-2A12 rain product over Bangladesh. The main reason could be summer rain in Bangladesh that comes mainly from extensive mid-level stratiform clouds. We could also observe from PR reflectivity profile using contoured frequency by altitude display (CFAD), higher detection error are those areas where stratiform rain is dominant, or constitute a significant proportion.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".