West Coast Forecast Challenges and Development of Atmospheric River Reconnaissance

Ralph, F. Martin; Cannon, Forest; Tallapragada, Vijay; Davis, Christopher A.; Doyle, James D.; Pappenberger, Florian; Subramanian, Aneesh; Wilson, Anna M.; Lavers, David A.; Reynolds, Carolyn A.; Haase, Jennifer S.; Centurioni, Luca; Ingleby, Bruce; Rutz, Jonathan J.; Cordeira, Jason M.; Zheng, Minghua; Hecht, Chad W.; Kawzenuk, Brian; Monache, Luca Delle

doi:10.1175/bams-d-19-0183.1

Cited by 41 publications

(30 citation statements)

References 67 publications

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“…http://cw3e.ucsd.edu/arrecon_overview/ Ralph et al (2020) Radiosondes Radiosonde observations are made throughout the country by the National Weather Service and compiled and made available by the University of Wyoming. Several other research groups, such as CW3E at Scripps, also record radionsonde data.…”

Section: Snow Level Radarsmentioning

confidence: 99%

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Hatchett

Cao

Dawson

et al. 2020

Earth and Space Science

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Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐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: (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 identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

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Section: Snow Level Radarsmentioning

confidence: 99%

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Hatchett

Cao

Dawson

et al. 2020

Earth and Space Science

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“…A crucial advancement of late has been the development of an observational campaign called AR Reconnaissance (AR Recon) in the northeast Pacific 43 . The aim of AR Recon is to improve the science and forecasts of landfalling ARs to help better inform decision-makers on water management and flooding in the western United States through improving NWP models.…”

Section: Atmospheric Riversmentioning

confidence: 99%

“…1a). The locations to sample are largely determined using both adjoint and ensemble methods to estimate the forecast sensitivity of west coast United States precipitation to moisture and wind conditions over the eastern Pacific before AR landfall 43 . These regions can include the strong IVT filament in the AR and upper-level circulations seen as potential vorticity anomalies.…”

Section: Atmospheric Riversmentioning

confidence: 99%

Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding

Lavers

Ralph

Richardson

et al. 2020

Commun Earth Environ

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Atmospheric rivers lie behind many extreme precipitation and flood episodes in the mid-latitudes. Better forecasts of atmospheric rivers and their impacts could help with preparedness. Here we argue that a comprehensive and systematic observational campaign could help advance numerical weather prediction, and thereby provide a path towards much improved forecasts of atmospheric rivers. We envision an interdisciplinary European–American observational campaign in the North Atlantic to identify and address numerical weather prediction errors in atmospheric rivers, and the associated extratropical cyclones. Insights gained could be applied in other regions. With improved understanding of the physiography of river basins and insights into their flood response to extreme precipitation, the impacts of atmospheric rivers can also be forecast more reliably.

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“…Advances in research on initial conditions are being made through the AR Reconnaissance (AR-Recon) program which targets ARs using airborne instruments with emphasis on in situ dropsondes (Ralph et al 2020). Choosing optimal aircraft tracks and dropsonde locations, however, is difficult because it is not always clear where additional observations are most likely to improve the analyses and subsequent forecasts.…”

Section: Introductionmentioning

confidence: 99%

The Circulation Response of a Two-Dimensional Frontogenetic Model to Optimized Moisture Perturbations

Demirdjian

Rotunno

Cornuelle

et al. 2021

Journal of the Atmospheric Sciences

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An analysis of the influence and sensitivity of moisture in an idealized two-dimensional moist semigeostrophic frontogenesis model is presented. A comparison between a dry (relative humidity, RH=0%) and moist (RH=80%) version of the model demonstrates that the impact of moisture is to increase frontogenesis, strengthen the transverse circulation (??????,??), generate a low-level potential-vorticity anomaly and develop a low-level jet. The idealized model is compared to a real case simulated with the full-physics three-dimensional Coupled Ocean-Atmospheric Mesoscale Prediction System (COAMPS) model establishing good agreement and thereby confirming that the idealized model retains the essential physical processes relevant for improving understanding of midlatitude frontogenesis. Optimal perturbations of mixing ratio are calculated to quantify the circulation response of the model through the computation of singular vectors, which determines the fastest-growing modes of a linearized version of the idealized model. The vertical velocity is found to respond strongly to initial-condition mixing-ratio perturbations such that small changes in moisture lead to large changes in the ascent. The progression of physical processes responsible for this nonlinear growth is (in order): jet/front transverse circulation → moisture convergence ahead of the front → latent heating at mid-to-low elevations → reduction in static stability ahead of the front → strengthening of the transverse circulation, and the feedback cycle repeats. Together, these physical processes represent a pathway by which small perturbations of moisture can strongly impact a forecast involving midlatitude frontogenesis.

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West Coast Forecast Challenges and Development of Atmospheric River Reconnaissance

Abstract: Capsule Atmospheric River Reconnaissance is a multi-year research and operations partnership to evaluate the potential of targeted airborne observations over the Northeast Pacific to improve forecasts of atmospheric river impacts on the U.S. West Coast at lead times of less than five days.

Cited by 41 publications

References 67 publications

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Observations of an Extreme Atmospheric River Storm With a Diverse Sensor Network

Improved forecasts of atmospheric rivers through systematic reconnaissance, better modelling, and insights on conversion of rain to flooding

The Circulation Response of a Two-Dimensional Frontogenetic Model to Optimized Moisture Perturbations

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