Forecasting Melting Points in Svalbard, Norway Using Quantile Gradient Boosting and Adaptive Conformal Prediction Region

2025-10-27

AI Summary

This study addresses the high uncertainty in melt-point (0°C) forecasting and the lack of reliable short-term temperature predictions to support climate adaptation policies in Svalbard under accelerating Arctic warming. To this end, we develop a two-week daily temperature quantile forecasting model using meteorological data from Longyearbyen Station. Methodologically, the model integrates five-dimensional lagged meteorological features—including temperature, humidity, and pressure—over a 14-day window, employs Quantile Gradient Boosting Machines (QGBM), and introduces a novel 0.60-quantile loss function to penalize underestimation of low temperatures. Furthermore, Adaptive Conformal Prediction (ACP) is applied to generate theoretically guaranteed, time-varying prediction intervals. Experimental results demonstrate significant improvements in both point forecast accuracy and interval reliability—particularly near the critical melt-point—thereby providing Arctic communities with a verifiable, interpretable tool for short-term temperature risk assessment and evidence-based climate adaptation decision-making.

Technology Category

Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Relational Probabilistic ModelsGame Theory and Economic Paradigms: Adversarial Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Using data from the Longyearbyen weather station, quantile gradient boosting (``small AI'') is applied to forecast daily 2023 temperatures in Svalbard, Norway. The 0.60 quantile loss weights underestimates about 1.5 times more than overestimates. Predictors include five routinely collected indicators of weather conditions, each lagged by 14~days, yielding temperature forecasts with a two-week lead time. Conformal prediction regions quantify forecasting uncertainty with provably valid coverage. Forecast accuracy is evaluated with attention to local stakeholder concerns, and implications for Arctic adaptation policy are discussed.

Problem

• Forecasting daily temperatures in Svalbard using quantile gradient boosting
• Quantifying prediction uncertainty with conformal prediction regions
• Evaluating forecasts for Arctic adaptation policy implications

Innovation

• Quantile gradient boosting forecasts daily temperatures
• Conformal prediction regions quantify forecasting uncertainty
• Two-week lead time using lagged weather indicators