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.

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.

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.

Observational networks enhance real-time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multi-tiered observational network in California provided insights into hydrometeorological processes and impacts during a three-day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows for: 1) the validation of record cool season precipitable water observations over southern California, 2) the identification of phenomena that produce natural hazards and present difficulties for short-term weather forecast models, such as extreme precipitation amounts and snow level variability, 3) the use of soil moisture data to improve hydrologic model forecast skill in northern California’s Russian River basin, and 4) the combination of meteorological data with seismic observations to “observe” a large avalanche on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of diverse observations to characterize climatological context and advance understanding of processes modulating extreme weather.

In Feb. 2017, a five-day sequence of atmospheric river storms in California, USA, resulted in extreme inflows to Lake Oroville, the state’s second-largest reservoir. Damage to the reservoir’s spillway infrastructure necessitated evacuation of 188,000 people; subsequent infrastructure repairs cost $1 billion. We assess the atmospheric conditions, snowmelt, and runoff against major historical events. The event generated exceptional runoff volumes (second-largest in a 30 year record) partially at odds with the event precipitation totals (ninth-largest). We explain the discrepancy with observed record melt of deep antecedent snowpack, heavy rainfall extending to unusually high elevations, and high water vapor transport during the atmospheric river storms. An analysis of distributed snow water equivalent indicates that snowmelt increased water available for runoff watershed-wide by 37% (25-52% at 90% confidence). The results highlight an acute flood risk to public safety and infrastructure projected to increase in severity in a warmer and more variable climate.

Traditional post-processing methods have relied on point-based applications that are unable to capture complex spatial precipitation error patterns. With novel ML methods using convolution to more effectively identify and reduce spatial biases, we propose a modified U-Net convolutional neural network (CNN) to post-process daily accumulated precipitation over the US west coast. For training, we leverage 34 years of deterministic Western Weather Research and Forecasting (West-WRF) reforecasts. On an unseen 4-year data set, the trained CNN yields a 12.9-15.9% reduction in root mean-square error (RMSE) over West-WRF for lead times of 1-4 days. Compared to an adapted Model Output Statistics baseline, the CNN reduced RMSE by 7.4-8.9% for all events. Effectively, the CNN adds more than a day of predictive skill when compared to West-WRF. The CNN outperforms the other methods also for the prediction of extreme events, highlighting a promising path forward for improving precipitation forecasts.

Satellites provide the primary dataset for monitoring the earth system and constraining analyses in numerical models. A challenge for utilizing satellite radiances is the estimation of their biases. High-accuracy non-radiance data are typically employed to anchor radiance bias corrections. This study provides the first assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program that samples ARs over the Northeast Pacific on the radiance assimilation using the Global Forecast System (GFS) and the Global Data Assimilation System. Including this dropsonde dataset has provided better anchoring for bias corrections and improved model background, leading to an increase of ~5-10% in the amount of assimilated microwave radiance in the lower/middle troposphere over the Northeast Pacific and North America. The impact on tropospheric infrared radiance is small but also beneficial. This result points to the usefulness of dropsondes, along with other conventional data, in the assimilation of satellite radiance.

Robust and reliable projections of future streamflow are essential to create more resilient water resources, and such projections must first be bias corrected. Standard bias correction techniques are applied over calendar-based time windows and leverage statistical relations between observed and simulated data to adjust a given simulated datapoint. Motivated by a desire to connect the statistical process of bias correction to the underlying dynamics in hydrologic models, we introduce a novel windowing technique for projected streamflow wherein data are windowed based on hydrograph-relative time, rather than Julian day. We refer to this method as ‘seasonally anchored’. Four existing bias correction methods, each using both the standard day-of-year and the novel windowing technique, are applied to daily streamflow simulations driven by 10 global climate models across a diverse subset of six watersheds in California to investigate how these methods alter the model climate change signals. Among the methods, only PresRat preserves projected annual streamflow changes, and does so for both windowing techniques. The seasonally anchored window PresRat reduces the ensemble bias by a factor of two compared to quantile mapping (Qmap), cumulative distribution function transform (CDFt), and equidistant quantile matching (EDCDFm) methods. For wet season flows, PresRat with seasonally anchored windowing best preserves the original model change over the entire distribution, particularly at the highest quantiles, and the other three methods show improved performance using the novel windowing method. Concerning temporal shifts in seasonality, PresRat and CDFt preserve the original model signals with both the novel and standard windowing methods.