Multi-modal transmission forecasting for the Vera C. Rubin Observatory

In the age of large automated surveys conducted at facilities like the NSF-DOE Vera C. Rubin Observatory, delivering precision science goals demands highly efficient scheduling strategies that adapt to changing environments on the summit. On site, a variety of telemetry streams can be used to inform a scheduler that is incentivized to find the best next pointing given atmospheric transmission. At Rubin, live upward-facing monitoring is provided by the Dutch Rubin Enhanced Atmospheric Monitor (DREAM) all-sky camera. Currently, dynamic scheduling uses short-term predictive algorithms, such as tracking cloud cross-correlations, to anticipate movement at short (< 10 min) scheduling timescales. However, many of Rubin’s core science objectives require survey strategies to commit to observations 20–40 min in advance (pair mode), which remains out of reach because clouds at those timescales can arrive from beyond the horizon. Here, we report on a novel multimodal strategy that folds asynchronous data from both the DREAM camera and weather satellite imagery to forecast multi-horizon sky transmission at 90 s, 5 min, 20 min, and 30 min in advance. We demonstrate that at 20-minute timescales, our model reduces transmission error by 21% and false-clear misses by 22% versus Rubin’s frozen-flow tracker, and reduces false-clear misses by 26% versus persistence. We also demonstrate empirically, through ablations, that the DREAM all-sky camera anchors the short-horizon predictive power while satellite imagery provides long-horizon predictive power. We estimate the optimistic order-of-magnitude gain for LSST survey efficiency improvement to be between 0.1% and 1%.