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  • 1
    Online Resource
    Online Resource
    Evaluation of Atmospheric River Predictions by the WRF Model Using Aircraft and Regional Mesonet Observations of Orographic Precipitation and Its Forcing
    American Meteorological Society ; 2018
    In:  Journal of Hydrometeorology Vol. 19, No. 7 ( 2018-07-01), p. 1097-1113
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 19, No. 7 ( 2018-07-01), p. 1097-1113
    Abstract: Accurate forecasts of precipitation during landfalling atmospheric rivers (ARs) are critical because ARs play a large role in water supply and flooding for many regions. In this study, we have used hundreds of observations to verify global and regional model forecasts of atmospheric rivers making landfall in Northern California and offshore in the midlatitude northeast Pacific Ocean. We have characterized forecast error and the predictability limit in AR water vapor transport, static stability, onshore precipitation, and standard atmospheric fields. Analysis is also presented that apportions the role of orographic forcing and precipitation response in driving errors in forecast precipitation after AR landfall. It is found that the global model and the higher-resolution regional model reach their predictability limit in forecasting the atmospheric state during ARs at similar lead times, and both present similar and important errors in low-level water vapor flux, moist-static stability, and precipitation. However, the relative contribution of forcing and response to the incurred precipitation error is very different in the two models. It can be demonstrated using the analysis presented herein that improving water vapor transport accuracy can significantly reduce regional model precipitation errors during ARs, while the same cannot be demonstrated for the global model.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    URL: Article
    DOI: 10.1175/JHM-D-17-0098.1
    DOI: 10.1175/JHM-D-17-0098.s1
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2018
    detail.hit.zdb_id: 2042176-X
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  • 2
    Online Resource
    Online Resource
    West-WRF 34-Year Reforecast: Description and Validation
    American Meteorological Society ; 2023
    In:  Journal of Hydrometeorology ( 2023-09-6)
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, ( 2023-09-6)
    Abstract: This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) model, tailored for the prediction of extreme hydrometeorological events over the Western U.S. (West-WRF) spanning 34 cool seasons (1 December to 31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering Western North America and the Eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast compared to GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin-scale, the reforecast can improve MAP compared to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coastal Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high resolution ( 〈 25 km) regional models. The applications of this high-resolution multi-year reforecast include process-based studies, assessment of model performance, and machine learning applications.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    DOI: 10.1175/JHM-D-22-0235.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2023
    detail.hit.zdb_id: 2042176-X
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  • 3
    Online Resource
    Online Resource
    Object-based Verification of Atmospheric River Predictions in the Northeast Pacific
    American Meteorological Society ; 2021
    In:  Weather and Forecasting ( 2021-07-13)
    Details
    In: Weather and Forecasting, American Meteorological Society, ( 2021-07-13)
    Abstract: Accurate forecasts of atmospheric rivers (ARs) provide advance warning of flood and landslide hazards, as well as greatly aid effective water management. It is therefore critical to evaluate the skill of AR forecasts in numerical weather prediction (NWP) models. A new verification framework is proposed leveraging freely available software and metrics previously used for different applications. Specifically, AR detection and statistics are computed for the first time using the Method for Object-based Diagnostic Evaluation (MODE). In addition, the measure of effectiveness (MoE) is introduced as a new metric for understanding AR forecast skill in terms of size and location. The MoE provides a quantitative measure of the position of an entire forecasted AR compared to observation, regardless of whether the AR is making landfall. In addition, the MoE can provide qualitative information about the evolution of a forecast by lead time with implications about the predictability of an AR. We analyze AR forecast verification and skill using 11 years of cold season forecasts from two NWP models, one global and one regional. Four different thresholds of integrated vapor transport (IVT) are used in the verification revealing differences in forecast skill based on the strength of an AR. In addition to MoE, AR forecast skill is also addressed in terms of intensity error, landfall position error, and contingency table metrics.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    DOI: 10.1175/WAF-D-20-0236.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2025194-4
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  • 4
    Online Resource
    Online Resource
    A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events
    Wiley ; 2021
    In:  Hydrological Processes Vol. 35, No. 1 ( 2021-01)
    Details
    In: Hydrological Processes, Wiley, Vol. 35, No. 1 ( 2021-01)
    Abstract: Soil moisture is a key modifier of runoff generation from rainfall excess, including during extreme precipitation events associated with Atmospheric Rivers (ARs). This paper presents a new, publicly available dataset from a soil moisture monitoring network in Northern California's Russian River Basin, designed to assess soil moisture controls on runoff generation under AR conditions. The observations consist of 2‐min volumetric soil moisture at 19 sites and 6 depths (5, 10, 15, 20, 50, and 100 cm), starting in summer 2017. The goals of this monitoring network are to aid the development of research applications and situational awareness tools for Forecast‐Informed Reservoir Operations at Lake Mendocino. We present short analyses of these data to demonstrate their capability to characterize soil moisture responses to precipitation across sites and depths, including time series analysis, correlation analysis, and identification of soil saturation thresholds that induce runoff. Our results show strong inter‐site Pearson's correlations ( 〉 0.8) at the seasonal timescale. Correlations are strong ( 〉 0.8) during events with high antecedent soil moisture and during drydown periods, and weak ( 〈 0.5) otherwise. High event runoff ratios are observed when antecedent soil moisture thresholds are exceeded, and when antecedent runoff is high. Although local heterogeneity in soil moisture can limit the utility of point source data in some hydrologic model applications, our analyses indicate three ways in which soil moisture data are valuable for model design: (1) sensors installed at 6 depths per location enable us to identify the soil depth below which evapotranspiration and saturation dynamics change, and therefore choose model soil layer depths, (2) time series analysis indicates the role of soil moisture processes in controlling runoff ratio during precipitation, which hydrologic models should replicate, and (3) spatial correlation analysis of the soil moisture fluctuations helps identify when and where distributed hydrologic modelling may be beneficial.
    Type of Medium: Online Resource
    ISSN: 0885-6087 , 1099-1085
    URL: Issue
    URL: Article
    DOI: 10.1002/hyp.v35.1
    DOI: 10.1002/hyp.13998
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1479953-4
    SSG: 14
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  • 5
    Online Resource
    Online Resource
    The Hydrometeorological Observation Network in California’s Russian River Watershed: Development, Characteristics, and Key Findings from 1997 to 2019
    American Meteorological Society ; 2020
    In:  Bulletin of the American Meteorological Society Vol. 101, No. 10 ( 2020-10-01), p. E1781-E1800
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 101, No. 10 ( 2020-10-01), p. E1781-E1800
    Abstract: The Russian River Hydrometeorological Observing Network (RHONET) is a unique suite of high-resolution in situ and remote sensing observations deployed over 20 years to address both scientific and operational gaps in understanding, monitoring, and predicting weather and water extremes on the United States’ West Coast. It was created over many years by diverse organizations ranging from universities to federal, state, and local government agencies and utilities. Today, RHONET is a hybrid network with diverse observation sets aimed at advancing scientific understanding of physical processes driving extreme precipitation and runoff in the region. Its development is described, including the specific goals that led to a series of network enhancements, as well as the key characteristics of its sensors. The hydroclimatology of the Russian River area is described, including an overview of the hydrologic extremes and variability driving the scientific and operational needs in the region, from atmospheric river behavior and orographic precipitation processes to hydrologic conditions related to water supply and flooding. A case study of Lake Mendocino storage response to a landfalling atmospheric river in 2018 is presented to demonstrate the network’s performance and hydrologic applications during high-impact weather events. Finally, a synopsis of key scientific findings and applications enabled by the network is provided, from the first documentation of the role of landfalling atmospheric rivers in flooding, to the occurrence of shallow nonbrightband rain, to the buffering influence of extremely dry soils in autumn, and to the development of forecast-informed reservoir operations for Lake Mendocino.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0253.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 6
    Online Resource
    Online Resource
    Observations and Predictability of a High-Impact Narrow Cold-Frontal Rainband over Southern California on 2 February 2019
    American Meteorological Society ; 2020
    In:  Weather and Forecasting Vol. 35, No. 5 ( 2020-10-01), p. 2083-2097
    Details
    In: Weather and Forecasting, American Meteorological Society, Vol. 35, No. 5 ( 2020-10-01), p. 2083-2097
    Abstract: Short-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, specific contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance field campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24 h later, are used to demonstrate that a configuration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numerical weather prediction and instill confidence in the utility of WRF as a forecast guidance tool for short- to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.
    Type of Medium: Online Resource
    ISSN: 0882-8156 , 1520-0434
    URL: Article
    DOI: 10.1175/WAF-D-20-0012.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2025194-4
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  • 7
    Online Resource
    Online Resource
    Skill of Rain–Snow Level Forecasts for Landfalling Atmospheric Rivers: A Multimodel Assessment Using California’s Network of Vertically Profiling Radars
    American Meteorological Society ; 2020
    In:  Journal of Hydrometeorology Vol. 21, No. 4 ( 2020-04), p. 751-771
    Details
    In: Journal of Hydrometeorology, American Meteorological Society, Vol. 21, No. 4 ( 2020-04), p. 751-771
    Abstract: The partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming.
    Type of Medium: Online Resource
    ISSN: 1525-755X , 1525-7541
    URL: Article
    DOI: 10.1175/JHM-D-18-0212.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 2042176-X
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  • 8
    Online Resource
    Online Resource
    Reaping Rewards from River and Rain: Applications of California’s Russian River Hydrometeorological Observing Network (RHONET)
    American Meteorological Society ; 2021
    In:  Bulletin of the American Meteorological Society Vol. 102, No. 8 ( 2021-08), p. 737-742
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 8 ( 2021-08), p. 737-742
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0253.A
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
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  • 9
    Online Resource
    Online Resource
    Modeled diurnally varying sea surface temperatures and their influence on surface heat fluxes
    American Geophysical Union (AGU) ; 2014
    In:  Journal of Geophysical Research: Oceans Vol. 119, No. 7 ( 2014-07), p. 4101-4123
    Details
    In: Journal of Geophysical Research: Oceans, American Geophysical Union (AGU), Vol. 119, No. 7 ( 2014-07), p. 4101-4123
    Abstract: Diurnal warming is important to seasonal sea surface temperature variability Surface heat fluxes are underestimated without diurnal variability of SSTs Modeled diurnal cycles are sensitive to wind, heat flux, and precipitation
    Type of Medium: Online Resource
    ISSN: 2169-9275 , 2169-9291
    URL: Issue
    URL: Article
    DOI: 10.1002/jgrc.v119.7
    DOI: 10.1002/2013JC009489
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2014
    detail.hit.zdb_id: 2016804-4
    detail.hit.zdb_id: 161667-5
    detail.hit.zdb_id: 3094219-6
    SSG: 16,13
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