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.

翻译：利用朗伊尔城气象站数据，应用分位数梯度提升（"小型人工智能"）方法预测挪威斯瓦尔巴群岛2023年日气温。0.60分位数损失函数对低估值的加权约为高估值的1.5倍。预测变量包含五个常规采集的气象指标，每个指标均滞后14天，从而生成提前两周的气温预测。共形预测区域通过可证明有效的覆盖度量化预测不确定性。预测准确性评估重点关注当地利益相关方的关切，并探讨了对北极适应政策的启示。