Abstract The Tomorrow.io microwave sounder (TMS) builds on a heritage of passive microwave sounders (PMWSs) that have well‐established value in numerical weather prediction (NWP). The TMS constellation of CubeSats uses a combination of sun‐synchronous and inclined orbits to fill gaps in current public PMWS data. This work establishes a data assimilation methodology for the TMS and demonstrates its real global weather forecast impacts with observing system experiments. Our assimilation methodology for the TMS encompasses all‐sky error modeling and quality control, and novel variational bias correction predictors. The all‐sky error model is based on techniques in earlier literature, and a new cloud impact filter enables more effective assimilation of surface‐sensitive TMS channels. TMSs in inclined orbits are exposed to a transient thermal environment that causes periodic biases in the current level 1 calibrated brightness temperatures. In response, we develop variational bias correction predictors related to the unique night–day cycle of each satellite. The new predictors partially mitigate the transient instrument biases and improve verification scores for upper‐level temperature forecasts. However, this is a partial solution, and remains an active area of investigation. In our research‐quality NWP setting, assimilating two TMS instruments in January 2025 achieves 6‐hr forecast accuracy improvements similar to two advanced technology microwave sounder instruments for water vapor, and 50% of two advanced technology microwave sounders for tropospheric temperature. Statistically significant forecast accuracy improvements from the TMS persist for water vapor up to 3 days, and for temperature and winds up to 2 days. The TMS provides complementary information content to public PMWSs. Additional efforts at operational forecasting centers are ongoing to conduct more complete investigations.
Guerrette et al. (Thu,) studied this question.