Bias correcting the precipitation dynamics of regional climate models via kernel-aware 2D convolutional-long short-term memory
Bibliographic record
Abstract
• Novel kernel-aware 2D Conv-LSTM network enhances spatial resolution via RCMs. • Model effectively captures adjacent precipitation impacts and temporal dependencies. • Pre-bias correction via Empirical Quantile Mapping improved quality of RCM data. • Project future precipitation to improve understanding of adjacent precipitation impacts. • Model led to RMSE and DC values of 14.74 mm and 0.85 under normal conditions. Some climate models face challenges covering the globe on a finer spatial scale. As a result, local studies are hindered by this limitation. This study introduces a novel spatial-based (kernel-aware) 2D Convolutional-Long Short-Term Memory (Conv-LSTM) network to enhance and bias correct spatial dynamics and generate precipitation products from Regional Climate Models (RCMs). The proposed network used 3 × 3 kernels, known as pixels that comprise nine grids for each specific point, which conduct convolutional layers to extract the features from the broad area (75 × 75 km), and LSTM networks for handling temporal dependencies. In this way, the RCM-based precipitation data were used as reference inputs, and gridded precipitation observation as target values. Since the precipitation products from the Coupled Model Intercomparison Project Phase 5 (CMIP5) of RCMs consisted of systematic biases, Empirical Quantile Mapping (EQM) was first used as the bias correction method as the pre-bias correction. This study applied 360 monthly observation precipitation and 460 bias-corrected RCM grid points covering Southern Alberta spanning from 1962 to 2006. Moreover, the proposed model was compared with the classical Feed Forward Neural Network (FFNN). Furthermore, the network’s capability spanned to the future, using Representative Concentration Pathway 4.5 till the end of this century. The results demonstrated that the proposed novel network could capture adjacent precipitation impacts on the target point and produce observation-like products with more precision by the Root Mean Squared Error (RMSE) and Determination Coefficient (DC) of 17.65 mm, 17.07 mm, 14.74 mm, and 0.60, 0.71 and 0.85 for high, low, and normal precipitation conditions, respectively.
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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 | 0.000 |
| 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".