Recent satellite techniques have uncovered detailed tropospheric water vapor isotope patterns on a daily basis, yet the significance of water isotopes on weather forecasting has remained largely unknown. Here, we perform a proof‐of‐concept observing system simulation experiment to show that mid‐tropospheric water isotopes observed by the Infrared Atmospheric Sounding Interferometer (IASI) can substantially improve weather forecasts through non‐local impacts on the convective heating structure and large‐scale circulation. Assimilating IASI isotopes can improve wind, humidity, and temperature fields by more than 10% at mid‐troposphere compared to only assimilating conventional non‐isotopic observations. These improvements are about two‐thirds of assimilating simultaneous IASI water vapor observations. The improvements can be attributed more to thermodynamic (phase change) effects than dynamic (transport) effects of water isotopes. Furthermore, isotopic observations produce additional 3%–4% improvements to the fields constrained by the conventional observations and simultaneous IASI water vapor observations, demonstrating the unique characteristics of water isotopes.

The concept of probable maximum precipitation (PMP) is widely used for the design and risk assessment of water resource infrastructure. Despite its importance, past attempts to estimate PMP have not investigated the realism of design maximum storms from a meteorological perspective. This study investigates estimating PMP with realistically maximized storms in a Pacific Northwest region dominated by atmospheric rivers (ARs) using numerical weather models (NWMs). The moisture maximization and storm transposition methods used in NWM-based PMP estimates are examined. We use integrated water vapor transport as a criterion to modify water vapor only at the modeling boundary crossing the path of ARs, whereas existing methods maximize relative humidity at all initial/boundary conditions. It is found that saturation of the entire modeling boundaries can produce unrealistic atmospheric conditions and does not necessarily maximize precipitation over a watershed due to storm structure, stability, and topography. The proposed method creates more realistic atmospheric fields and more severe precipitation. The simultaneous optimization of moisture content and location of storms is also considered to rigorously estimate the most extreme precipitation. Among the 20 most severe storms during 1980–2016, the AR event during 5–9 February 1996 produces the largest 72-h basin-average precipitation when maximized with our method (defined as PMP of this study), in which precipitation is intensified by 1.9 times with a 0.7° shift south and a 30% increase in AR moisture. The 24-, 48-, and 72-h PMP estimates are found to be at least 70 mm lower than the Hydrometeorological Reports estimates regardless of duration.

The Madden‐Julian oscillation (MJO) modulates atmospheric river (AR) activity along the West Coast of North America. The forecast skill of ARs based solely on the MJO phase is limited, motivating the identification of other predictive factors. Here we show that low‐frequency variability of the Pacific/North American (PNA) pattern is mostly independent of the MJO, and the monthly PNA pattern influences the MJO‐AR relationship much more robustly than other large‐scale patterns such as the El Niño Southern Oscillation and the quasi‐biennial oscillation. During PNA+ conditions, when the Pacific jet exit region is located further east than average, baroclinic wave packets extend downstream toward North America. The clear contrast in wave packets' zonal scale results in partitioning active and inactive AR phases. The PNA influence is most significant over the Pacific Northwest region of North America, where AR landfall is most frequent, with implied forecasting skill beyond 20 days.