Stochastic modeling of rainfall processes: a Markov chain - mixed exponential model for rainfalls in different climatic conditions
Bibliographic record
Abstract
Watershed models simulating the physical process of runoff usually require daily or sub-daily rainfall time series data as input. However, even when rainfall records are available, they contain only limited and finite information regarding the historical rainfall pattern to adequately assess the response and reliability of a water resource system. This study is therefore concerned with the development of a stochastic rainfall model that can reliably generate many sequences of synthetic rainfall time series' that have similar properties to those of the observed data. The 'MCME' model developed is based on a combination of the rainfall occurrence (described using a Markov Chain process) and the distribution of rainfall amounts on wet days (represented by the Mixed-Exponential probability function). Various optimization methods were tested to best calibrate the model's parameters and the model was then applied to daily rainfall data from 3 different regions across the globe (Dorval, Quebec, Sooke Reservoir in British Columbia and Roxas City in the Philippines) to assess the accuracy and suitability of the model for daily rainfall simulation. The feasibility of the MCME model was also assessed using hourly rainfall data available at Dorval Airport in Quebec (Canada). In general, it was found that the proposed MCME model was able to adequately describe various statistical and physical properties of the daily and hourly rainfall processes considered. In addition, an innovative approach was proposed to combine the estimation of daily annual maximum precipitations (AMPs) by the MCME with those by the downscaled Global Circulation Models (GCMs). The combined model was found to able to provide AMP estimates that were comparable to the observed values at a local site. In particular, the suggested linkage between the MCME and downscaled-GCM outputs would be useful for various climate change impact studies involving rainfall extremes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".