Estimation and correction of beam mismatch of the precipitation Radar after an orbit boost of the tropical rainfall measuring mission Satellite
Bibliographic record
Abstract
The Tropical Rainfall Measuring Mission (TRMM) satellite changed its altitude from 350 to 402.5 km in August 2001. As a result, the level-1 algorithm for the new orbit of the Precipitation Radar (PR) onboard the TRMM has to correct the "beam mismatch" resulting from the altitude change. Since the PR uses fixed transmission-reception timing, an altitude change of 50 km corresponds to a delay of return signals of 1 pulse repetition interval (PRI). This is not a serious problem if the angle bin of the next pulse is the same as the angle bin of the current pulse. Otherwise, return signals arrive at the PR when the antenna direction shifts to the next angle bin. This is called a "beam mismatch." It affects one pulse sample out of 32 averaged pulse samples. In other words, one "beam mismatch" pulse sample and 31 normal pulse samples are averaged at the onboard processor of the PR. A new algorithm was added to the PR's level-1 algorithm, 1B21, to eliminate this mismatch sample at the 402.5-km altitude. In this paper, the effect of beam mismatch is estimated for both rain echo and surface echo in terms of the received power and the incident angle dependency. The basic function of the beam mismatch correction algorithm is to estimate the received power of the mismatched pulse. Theoretically, the effective round-trip antenna pattern of a mismatched pulse has a peak right in the middle of the transmission and reception directions with a gain reduction of 6 dB. The new 1B21 algorithm uses the average of the received power of successive angle bins with a 6-dB gain reduction as the power from the mismatched pulse. The effectiveness of the correction algorithm was evaluated using high angular resolution data obtained during external calibration observations and the statistics of the normalized radar cross section of the earth's surface (/spl sigma//sup 0/), which is thought to be unchanged. The estimated error was less than 0.2 dB for the rain echo, and a large error of up to 0.5 dB was found at the boundary of the surface based on the error estimation using high angular resolution data. The difference in /spl sigma//sup 0/ and its angle dependency is explained using a simple surface model. The model results indicate the correction error reaches up to 0.8 dB at the skirts of the surface echo.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".