Narrow cold-frontal rain bands (NCFR) often produce short-duration and high-intensity precipitation that can lead to flooding and debris flow in California (CA). On 27 January 2021, an atmospheric river (AR) associated with an intense surface cyclone made landfall over coastal northern CA, which featured a prominent NCFR. This study uses high-resolution West WRF simulations to accurately resolve the gap and core structure of the NCFR and provides reliable precipitation estimations, compensating for limitations of radar and satellite observations. This NCFR was supported by robust synoptic-scale quasi-geostrophic (QG) forcing for ascent and frontogenesis. It propagated southward from Cape Mendocino to Big Sur in 12 hours before stalling and rotating counter-clockwise in central/southern CA due to upstream Rossby wave breaking and amplifying upper-tropospheric trough. With the lower to middle tropospheric flow backed considerably to the south-southwest over the NCFR, the increase of the vertical wind shear caused the transition from parallel to trailing stratiform precipitation. The stall and pivot of the AR and NCFR led to intense rainfall with a 2-day precipitation accumulation greater than 300 mm over central CA. In addition, under the potential instability and frontogenesis, a moist absolutely unstable layer between 850 hPa to 700 hPa was captured at the leading edge of the NCFR, which indicated slantwise deep layer lifting and high precipitation efficiency. This study reveals synoptic-scale and mesoscale drivers of rainfall outside orographic lifting and reaffirms the importance of high-resolution numerical modeling for the prediction of extreme precipitation and related natural hazards.

Atmospheric Rivers (ARs) intricately connect with diverse weather systems, spanning planetary-scale to mesoscale levels, influencing extreme weather events through the transportation of abundant moisture and the shaping of regional circulation patterns. In March 2019, a strong AR originating from the Gulf of Mexico fueled a record-breaking bomb cyclone in Colorado, resulting in widespread winter weather hazards across several states. Experimental model simulations and trajectory analysis indicate that mid-tropospheric latent heat release played a key role in the deepening of the cyclone. The latent heat release promoted the generation of a lower tropospheric positive potential vorticity (PV) anomaly and a stronger low-level cyclonic circulation, enhancing the cyclone, low-level jet stream, and associated water vapor transport. Additionally, it generated an upper tropospheric negative PV anomaly and strong upper-level anticyclonic circulation, influencing the structure of the trough-ridge couplet and the associated Rossby wave. Reductions in the initial intensity of the AR and disallowing latent heat release both weakened the cyclone. However, disallowing latent heat release significantly disturbed the synoptic-scale structure of the storm and embedded Rossby wave, resulting in a stronger impact. Thus, the reduction of diabatic PV generation, under the influence of AR activities, was crucial in the explosive intensification of the cyclone. Few studies have explored interactions between ARs and continental cyclones, and this paper highlights the need for further research on AR-associated extreme weather events inland.

Dropsonde data are high-accuracy, high-vertical-resolution data collected from dropwindsondes  deployed from reconnaissance research aircraft. Aircraft from Atmospheric River (AR) Reconnaissance campaign deploy dropsondes data in the upper troposphere or near the tropopause. These data include meteorological variables that are essential to observing and modeling atmospheric structures. Research showed that these dropsondes data can influence data assimilation via direct impact on the intial conditions and indirect impact on the model background and satellite bias correction.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of extreme winter weather in California, including ARs, SAWs and temperature extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1-4 lead. This methodology (i) uses dynamical model information considered most reliable, i.e., planetary/synoptic-scale atmospheric circulation, (ii) filters for dynamical model error/uncertainty at longer lead times, and (iii) increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.

Water years (WY) 2017 and 2023 were anomalously wet for California, each helping to terminate multiyear droughts. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier-than-normal winters. We analyze over seven decades of precipitation and snow records along with mid-tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical ENSO-precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO-independent weather patterns was a major cause of anomalous precipitation in WY2017, record-setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983,1998 and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal forecasts.

The results of prior work indicated that the Southeast United States (SEUS) region contains extreme Quantitative Precipitation Forecast (QPF) skill that is lower than other regions in the U.S. (e.g., the Western and Northeast US). A hypothesis is that this skill is influenced by a diversity of storm types such as Atmospheric Rivers (ARs), Mesoscale Convective Systems (MCS) and Tropical Cyclones (TCs) occurring within distinct synoptic patterns, which have resulted in significant floods such as in Nashville (2010) and Waverly (2021) Tennessee. Similarly, previous investigations have identified that synoptic patterns with higher integrated vapor transport (IVT) potentially have greater QPF skill than those with lower IVT. There is further opportunity to investigate pattern-wise contribution of storm types and QPF skill in SEUS. This study identified six synoptic patterns associated with heavy precipitation in Tennessee. These patterns exhibited distinct seasonality, with three patterns occurring in the cool season, two in the warm season, and one in the transition season. Approximately, 66 % of heavy precipitation in cool season and 47 % in transition season is associated with coincident ARs and MCS. Pattern-wise QPF skill derived from the GEFS Reforecast dataset illustrated that the cool season pattern with the highest IVT and largest fraction of ARs has better skill, whereas the warm season pattern with the highest CAPE and integrated water vapor has worse skill at multiple lead times. These results provide insights into the dynamical characteristics and predictability of heavy precipitation by storm type over the SEUS.

A document by Xun Zou. Click on the document to view its contents.

A novel method for estimating spatial-temporal modes of time varying data containing complex non-linear interacting multivariate fields, called the entropy field decomposition (EFD), is applied to the problem of characterizing the formation and intensification of atmosphere rivers (ARs), and reveals two novel findings. First, analysis of global time-varying interacting wind (w) and specific humidity (q) fields produces spatiotemporal modes consistent with observed global distribution of ARs detected by Integrated Water Vapor Transport (IVT). Secondly, space-time information trajectories (STITs) generated from coupled w-q EFD modes, representing optimal (in the sense of maximum entropy) parameter pathways, reveal a clear connection between ARs and planetary-scale circulation with structure similar to Rossby waves and reveal that ARs appear to be dynamically linked with the outflow region of the wave troughs. These findings provide an automated quantitative method to examine impacts of interacting multiscale dynamics on AR formation and activities.

Atmospheric rivers can provide as much as 50% of the total annual rainfall to the U.S. West Coast via orographic precipitation. Dust is thought to enhance orographic precipitation via the “seeder-feeder”; mechanism, in which ice particles from a high cloud fall through a lower orographic cloud, seeding precipitation in the low cloud. Using the Weather Research and Forecasting model, we vary dust concentrations in simulations of two-dimensional flow over a mountain. This idealized framework allows us to test the sensitivity of the precipitation-dust response to a variety of different dust concentrations and initial conditions. The model is run using an ensemble of 60 radiosondes collected from Bodega Bay, CA in 2017-2018, clustered based on their vertical moisture profile into “deep moist”, “shallow moist”, and “subsaturated” clusters. The principle impact on precipitation is to increase the ratio of precipitation falling as snow. This produces a “spillover” effect, decreasing precipitation upwind of the peak and increasing precipitation downwind of the peak. The largest impacts on the snow/rain ratio occur at the end of the event, during cold front passage. The ensemble mean does not produce a significant seeder-feeder response, however in individual cases with favorable initial conditions there is a significant increase in precipitation throughout the domain due to dust effects on the seeder-feeder mechanism. These findings afford an opportunity to build a more comprehensive understanding for the conditions under which dust aerosol can have a significant impact on precipitation during atmospheric rivers, with implications for future developments in forecasting.

The elevation of the mountain rain-snow transition is critical for short-term hazard forecasting and longer-term water supply considerations. Despite the transition’s importance, direct in-situ observations are rare. Here we present two new methods that utilize “anomalous” snow observations to detect rainfall during rain-on-snow: (1) a mass fluctuation at snow pillow sites, and (2) inflated remotely sensed snow grain sizes. Using auxiliary data, we show snow pillows respond to rain-on-snow with distinct perturbations that appear as pulses, collapses and declines within the snow water equivalent. We use these responses to identify mountain-scale rain-snow transitions across California’s Sierra Nevada. We also show how a threshold approach (>200 mm) for remotely sensed snow grain size can identify rain-on-snow as snow grain sizes artificially inflate due to a liquid water film. While the methods are not predictive, if paired retroactively with hydrometeorological models, these new methods have the potential to improve predictive streamflow capabilities.

Atmospheric rivers (ARs) are responsible for the vast majority (approximately 88%) of flood damage in the Western U.S, an annual average of USD$1.1 billion. Here, using historical flood insurance data, we investigate the genesis characteristics of damaging ARs in the Western U.S. ARs exceeding USD$20 million in damage (90th percentile), are shown to travel further across the Pacific Ocean, with median genesis locations 8° to 27° longitude further westward compared to typical ARs. Identifying regions of preferential genesis of damaging ARs elicit important implications for AR observation campaigns, highlighting distant regions not currently considered for AR reconnaissance. The genesis of damaging ARs is associated with elevated upper-level zonal wind speeds along with deeper cyclonic anomalies, most pronounced for Washington and Oregon ARs. Linking AR dynamics and lifecycle characteristics to economic damage provides an opportunity for impact-based forecasting of ARs prior to landfall, supporting efforts to mitigate extreme flood damages.