A Regionalized Stochastic Rainfall Model for the Generation of High Resolution Data in Peninsular Malaysia
Bibliographic record
Abstract
<p>High resolution rainfall data is an important input for studies on hydrological systems. Often time synthetic data has to be generated in the absence of historical data. The stochastic Neyman Scott Rectangular Pulse (NSRP) model has been developed to produce synthetic data of high resolution. To take into account the spatial and temporal nature of rainfall, an extra domain had been added producing the Spatial Temporal NSRP (ST-NSRP) model. However these models require the repeat estimation of all the model parameters for each location. This work develops the Regionalized ST-NSRP model which produces a single model for a region. The regionalization approach is carried out by the station-year method using ten years (2001-2010) of records from sixteen stations. The statistical characteristics used in estimating the model’s parameters are hourly and daily coefficient of variation, hourly and daily lag one auto correlation, hourly cross correlation between sites and hourly skewness of rainfall series. Simulation process is conducted at two independent stations with distinctly different rainfall profile. The Regionalized ST-NSRP model produced statistical characteristics of the ungauged sites which matched those of the observed series fairly well even though there is a tendency to underestimate hourly covariance and lag one autocorrelation. Considering that spatial variability of rainfall is high within the studied region, this model is sufficiently robust in producing reasonable synthetic hourly rainfall series which captured the characteristics and pattern of observed rainfall series. Therefore the model has shown a promising potential in generating high resolution synthetic data for any ungauged site within the region.</p>
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".