Predictability Studies of Atmospheric Rivers Using Adjoint Sensitivity

The atmosphere is so remarkably complex that determining factors that influence storm behavior can be extremely difficult. One way to attack this challenging problem is to examine it in a linear framework. Tangent linear and related adjoint versions of numerical forecast models allow us to do that. Adjoint-based tools can provide valuable insight into how distinct aspects of the initial state impact storm intensity. Here we explore initial condition sensitivity and predictability aspects of the series of high-impact atmospheric river (AR) storms that hit the US west coast in early 2017. These storms were associated with both widespread flooding and wind damage. The copious amount of precipitation associated with these storms was enough to end the severe drought conditions over much of California. The adjoint, tangent linear, and nonlinear models for the atmospheric portion of the nonhydrostatic Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) are applied to the sequence of these AR storms. Because the storms resulted in impactful wind and precipitation, we consider both the sensitivity of precipitation and sensitivity of near-surface kinetic energy to changes in the initial state. In agreement with previous studies of mid-latitude cyclones, the adjoint diagnostics indicate that the intensity of both winds and rainfall in these storms is especially sensitive to perturbations in the moisture and temperature fields and to a lesser degree the wind fields. In particular, the adjoint sensitivity results underscore the importance of the low- and mid-level moisture distribution, particularly in filaments of the water vapor fields embedded in ARs. While both winds and precipitation are most sensitive to low-level moisture, there does appear to be systematic differences in the sensitivity fields. For example, the initial sensitivity regions associated with forecast precipitation occur further upstream than the initial sensitivity regions associated with forecast winds. In addition to relating the sensitivity patterns to the AR structure, the area-averaged sensitivity fields will be used to quantify how changes to mean temperature and moisture profiles can impact precipitation and damaging winds. Sensitivity related specifically to snow will also be examined to understand potential impacts of climate change on snow pack. These findings will be used to estimate how changes to the mean state in future climate scenarios may impact storm intensity.