Regional ML Weather Prediction Models

• Regional MLWP models are data-driven frameworks that use multi-site meteorological data to predict localized weather with enhanced accuracy.
• Integrated machine learning methods like random forest and extra trees achieve up to a 35% RMSE reduction compared to single-site approaches.
• These models deliver rapid, energy-efficient forecasts that are scalable for deployment on low-cost and mobile hardware.

Regional machine learning weather prediction (MLWP) models are data-driven approaches that employ machine learning algorithms to forecast atmospheric variables over sub-global spatial domains. In contrast to traditional numerical weather prediction (NWP) models, which rely on numerical integration of complex physical equations and require extensive computational resources, regional MLWP frameworks exploit historical and real-time meteorological data (often from distributed sensor networks) to learn statistical or dynamical relationships governing local weather evolution. The regional focus allows these models to capture fine-scale interactions and heterogeneities, address specific forecasting challenges, and potentially run on resource-constrained hardware with reduced energy consumption.

1. Data Integration and Representation

Regional MLWP models incorporate multi-source meteorological data to construct feature-target pairs for training. The primary data sources and processing strategies include:

• Historical Weather Station Data: High-resolution, multivariate time series from local networks are aggregated across the region (e.g., temperature, humidity, wind variables), as in the Tennessee paper with ten city weather stations.
• Spatially Augmented Features: Each feature vector at time $t$ incorporates observations from all sites (not only the target), capturing spatial correlations and regional atmospheric coupling.
• One Hot Encoding and Scaling: Categorical variables (e.g., wind direction, weather condition) are encoded as one-hot vectors prior to train–test split, ensuring consistent feature dimensions, while continuous variables undergo mean scaling ($x \leftarrow (x - \mu)/\sigma$) to normalize the input distribution and enhance convergence.
• Handling Missing Data: Records with missing/incomplete station measurements are omitted to maintain data integrity and eliminate artifacts arising from inconsistent feature sets.

This regional, multi-station approach is crucial for exploiting cross-site dependencies and enhances model skill by leveraging the interrelatedness of local weather signals.

2. Machine Learning Methodologies

A variety of regression algorithms have been applied to the regional MLWP paradigm:

Model	Core Mechanism	Notable Remarks
Random Forest	Ensemble of decision trees; bagging	Superior RMSE with multi-city data
Extra-Tree	Randomized tree splits; ensemble	Low RMSE; comparable to RF
Support Vector Reg	Kernel-based regression; regularized	Good relative RMSE; stable to overfit
Ridge Regression	Linear regression with L2 penalty	Higher RMSE; less suited to nonlinearity
Multi-Layer Perceptron (MLP)	2-layer NN (100+50 units)	Higher error vs. ensembles

The target variable is typically the future value (e.g., next-day temperature in Nashville) with the RMSE metric,

$$\mathrm{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i} - y_{i})^2}$$

serving as both the loss function (for regression models) and the principal evaluation statistic. Regressor selection is driven by the balance between complexity, generalization, and computational tractability.

3. Experimental Evaluation and Performance Analysis

The evaluation protocol emphasizes the influence of training dataset configuration and cross-site feature inclusion:

• Regional vs. Single-site Models: Exclusive use of the target site (e.g., Nashville) produces substantially higher RMSE. Expanding the feature vector to include all neighboring sites yields a nearly 35% RMSE reduction (from $\sim$4.5 to $\sim$3.0 for RFR, ETR).
• Temporal Training Range: Model performance is sensitive to the choice of training window—using more than eight weeks of data may introduce regime shifts and increase forecast error.
• Residual Analysis: Residual distributions plotted for single-city versus multi-city models show tighter error clustering with the regional approach, indicating improved error variance control and unbiased predictions.

These experiments demonstrate that cross-site (regional) coupling increases predictive skill, justifying the adoption of spatially augmented features in operational settings.

4. Computational Efficiency and Deployment

A core motivation for regional MLWP is resource efficiency relative to physics-based NWP frameworks:

• Hardware Requirements: Traditional NWP models necessitate hundreds of HPC nodes and consume significant energy. The described MLWP models are lightweight and suitable for execution on CPUs, low-cost hardware, and even mobile devices.
• Run-time Benefits: The reduction in model complexity translates into rapid forecast generation—enabling near-real-time web service deployment and interactive applications for local users.
• Energy Consumption: While numerical figures are not specified, the qualitative assessment underscores a substantial reduction in energy and hardware cost.

This strategic advantage enables democratized access to forecasting in resource-limited or spatially remote settings.

5. Regional Nowcasting: Tennessee Case Study

In the Tennessee application, the regional model forecasts next-day hourly temperatures for Nashville using prior day features from a ten-city network:

• Dataset: Two months of records for training; one week of test data (Sept 1–7, 2018).
• Findings: RFR and ETR trained on full regional data achieve best forecast accuracy (RMSE $\sim$3.0). Models restricted to local data perform significantly worse (RMSE up to 4.5).
• Practical Utility: The real-time and low-cost properties of the approach position it as a viable supplement to state-of-the-art NWP, especially for regions with highly variable, non-stationary weather dynamics.

This regional modeling approach also serves as a pathway for integrating local knowledge and infrastructure in the weather forecasting process.

6. Forward Directions and Research Challenges

The paper identifies several avenues for advancing regional MLWP:

• Integration of IoT Sensors: Addition of low-cost local sensors promises higher spatiotemporal resolution and more granular “hyper-local” forecasting.
• Extension to Multiple Targets: While the current application focuses on temperature, methods are broadly extensible to humidity, wind, rainfall, and other surface variables.
• Edge and Mobile Computing: Future deployments are envisioned on edge devices (mobile/field instruments) exploiting the lightweight inference requirements.
• Enriched Feature Sets: Combining MLWP with traditional NWP outputs, or assimilating novel sensor modalities, may further improve performance via “multi-source data fusion.”

Rigorous benchmarking against ensemble NWP models and assessment under rapidly changing weather regimes remain important for validating generalizability and robustness.

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In summary, regional MLWP models—exemplified by the multi-city regression approach for temperature forecasting in Tennessee—demonstrate that simple but carefully designed machine learning algorithms trained on spatially distributed historical station data can match or supplement state-of-the-art forecasting techniques, particularly in regimes where traditional NWP models are computationally prohibitive or insufficiently granular. This methodology offers a robust, scalable, and resource-efficient pathway for democratizing regional weather prediction and lays the groundwork for further integration of dense sensor networks and advanced feature engineering.