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https://arxiv.org/abs/physics/0410053 [physics/0410053] Probabilistic temperature forecasting: a comparison of four spread-regression... Abstract page for arXiv paper physics/0410053: Probabilistic temperature forecasting: a comparison of four spread-regression models temperature forecasting physics probabilistic https://www.sintef.no/en/publications/publication/0198cc93744a-9d1d42bb-0836-44e2-aade-7efdb0a88720/ Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Regime - SINTEF temperature forecasting synthetic data enhancing indoor https://www.mdpi.com/1996-1073/17/5/1124 Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research... machine learning https://www.preprints.org/manuscript/202503.0067 A New Transformer Network for Short-Term Global Sea Surface Temperature Forecasting: Importance of... Short-term sea surface temperature (SST) forecasts are crucial for operational oceanology. This study introduced a specialized Transformer model... https://www.frontiersin.org/journals/sports-and-active-living/articles/10.3389/fspor.2023.1277070/full Frontiers | Forecasting individual exercise sweat losses from forecast air temperature and energy... Introduction: Recent success in predicting individual sweat losses from air temperature and energy expenditure measurements suggests potential for forecastin... https://observablehq.com/@ceenell/forecasting-stream-temperature Forecasting stream temperature / Cee Nell | Observable Sep 7, 2022 - Predicting temperature exceedance How likely is it that stream temperature will exceed 75F? Ideas: show how forecasts change with different # of ensemble... forecasting stream temperature cee nell https://www.whoi.edu/press-room/news-release/forecasting-with-fins/ Forecasting with Fins: Sharks can improve ocean temperature predictions, new study finds - Woods... Research demonstrates how animal-borne sensors can work in tandem with traditional tools to improve ocean predictions