A Heuristic Approach for Precipitation Data Assimilation: Effect of Forecast Errors and Assimilation of NCEP Stage IV Precipitation Analyses
Bibliographic record
Abstract
Abstract Recently, Pérez Hortal et al. introduced a simple data assimilation (DA) technique named localized ensemble mosaic assimilation (LEMA) for the assimilation of radar-derived precipitation observations. The method constructs an analysis by assigning to each model grid point the information from the ensemble member that is locally closest to the precipitation observations. This study explores the effects of the forecasts errors in the performance of the method using a series of observing system simulation experiments (OSSEs) with different magnitudes of forecast errors employing a small ensemble of 20 members. The ideal experiments show that LEMA is able to produce forecasts with considerable and long-lived error reductions in the fields of precipitation, temperature, humidity, and wind. Nonetheless, the quality of the analysis deteriorates with increasing forecast errors beyond the spread of the ensemble. To overcome this limitation, we expand the spread of the ensemble used to construct the analysis mosaic by considering states at different times and states from forecasts initialized at different times (lagged forecasts). The ideal experiments show that the additional information in the expanded ensemble improves the performance of LEMA, producing larger and long-lived improvements in the state variables and in the precipitation forecast quality. Finally, the potential of LEMA is explored in real DA experiments using actual Stage IV precipitation observations. When LEMA uses only the background members, the quality of the precipitation forecast shows small or no improvements. However, the expanded ensemble improves the LEMA’s effectiveness, producing larger and more persistent improvements in precipitation forecasts.
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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 | 0.001 |
| 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".