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Rutz J.J.,National Weather Service - NWS | James Steenburgh W.,University of Utah | Martin Ralph F.,University of California at San Diego
Monthly Weather Review | Year: 2015

Although atmospheric rivers (ARs) typically weaken following landfall, those that penetrate inland can contribute to heavy precipitation and high-impact weather within the interior of western North America. In this paper, the authors examine the evolution of ARs over western North America using trajectories released at 950 and 700 hPa within cool-season ARs along the Pacific coast. These trajectories are classified as coastal decaying, inland penetrating, or interior penetrating based on whether they remain within an AR upon reaching selected transects over western North America. Interior-penetrating AR trajectories most frequently make landfall along the Oregon coast, but the greatest fraction of landfalling AR trajectories that eventually penetrate into the interior within an AR is found along the Baja Peninsula. In contrast, interior-penetrating AR trajectories rarely traverse the southern "high" Sierra. At landfall, interior-penetrating AR trajectories are associated with a more amplified flow pattern, more southwesterly (vs westerly) flow along the Pacific coast, and larger water vapor transport (qν). The larger initial qν of interior-penetrating AR trajectories is due primarily to larger initial water vapor q and wind speed ν for those initiated at 950 and 700 hPa, respectively. Inland- and interior-penetrating AR trajectories maintain large qν over the interior partially due to increases in ν that offset decreases in q, particularly in the vicinity of topographical barriers. Therefore, synoptic conditions and trajectory pathways favoring larger initial qν at the coast, limited water vapor depletion by orographic precipitation, and increases in ν over the interior are keys to differentiating interior-penetrating from coastal-decaying ARs. © 2015 American Meteorological Society.

Ten Hoeve J.E.,National Weather Service - NWS | Augustine J.A.,Earth Systems Research Laboratory
Geophysical Research Letters | Year: 2016

Previous studies of the second aerosol indirect (lifetime) effect on cloud cover have estimated the strength of the effect without correcting for near-cloud contamination and other confounding factors. Here we combine satellite-based observations with a multiyear ground-based data set across five rural locations in the United States to more accurately constrain the second indirect aerosol effect and quantify aerosol effects on radiative forcing. Results show that near-cloud contamination accounts for approximately 40% of the satellite-derived aerosol-cloud relationship. When contamination is removed and the effect of meteorological covariation is minimized, a strong physical aerosol effect on cloud cover remains. Averaged over all stations and after correcting for contamination, the daytime solar and total (solar + IR) radiative forcing is -52 W/m2 and -19 W/m2, respectively, due to both direct and indirect aerosol effects for aerosol optical depths (τ) between 0 and 0.3. Averaged diurnally, the average total radiative forcing is +16 W/m2. © 2016. American Geophysical Union. All Rights Reserved.

Data suggest that the arrival of winter's permanent snowpack impacts daily high and low surface temperatures in Fairbanks, Alaska. Given temperatures at 850 hPa ranging from 0 °C to - 5 °C in October, high temperatures on days with snow on the ground are 4.9 °C colder than high temperatures on days with no snow on the ground. The difference for low temperatures is 7.3 °C. While the exact date the snowpack is established varies from year to year, standardizing this date in time as "S-Day" reveals that the drop in daily high and low temperatures through the period from 5 days before S-Day to 5 days after S-Day is from 5 to 7 °C greater than the gradual cooling associated with the change of seasons from fall to winter.

Alcott T.I.,National Weather Service - NWS | Steenburgh W.J.,University of Utah
Monthly Weather Review | Year: 2013

Although several mountain ranges surround the Great Salt Lake (GSL) of northern Utah, the extent to which orography modifies GSL-effect precipitation remains largely unknown. Here the authors use observational and numerical modeling approaches to examine the influence of orography on the GSL-effect snowstorm of 27 October 2010, which generated 6-10mmof precipitation (snow-water equivalent) in the Salt Lake Valley and up to 30cm of snow in the Wasatch Mountains. The authors find that the primary orographic influences on the event are 1) foehnlike flow over the upstream orography that warms and dries the incipient low-level air mass and reduces precipitation coverage and intensity; 2) orographically forced convergence that extends downstream from the upstream orography, is enhanced by blocking windward of the Promontory Mountains, and affects the structure and evolution of the lake-effect precipitation band; and 3) blocking by the Wasatch and Oquirrh Mountains, which funnels the flow into the Salt Lake Valley, reinforces the thermally driven convergence generated by the GSL, and strongly enhances precipitation. The latter represents a synergistic interaction between lake and downstream orographic processes that is crucial for precipitation development, with a dramatic decrease in precipitation intensity and coverage evident in simulations in which either the lake or the orography are removed. These results help elucidate the spectrum of lake-orographic processes that contribute to lake-effect events and may be broadly applicable to other regions where lake effect precipitation occurs in proximity to complex terrain. © 2013 American Meteorological Society.

Herr H.D.,National Weather Service - NWS | Krzysztofowicz R.,University of Virginia
Journal of Hydrology | Year: 2010

The problem is to provide a short-term, probabilistic forecast of a river stage time series {H1,...,HN} based on a probabilistic quantitative precipitation forecast. The Bayesian forecasting system (BFS) for this problem is implemented as a Monte-Carlo algorithm that generates an ensemble of realizations of the river stage time series. This article (i) shows how the analytic-numerical BFS can be used as a generator of the Bayesian ensemble forecast (BEF), (ii) demonstrates the properties of the BEF, and (iii) investigates the sample size requirements for ensemble forecasts (produced by the BFS or by any other system). The investigation of the ensemble size requirements exploits the unique advantage of the BFS, which outputs the exact, analytic, predictive distribution function of the stochastic process {H1,...,HN}, as well as can generate an ensemble of realizations of this process from which a sample estimate of the predictive distribution function can be constructed. By comparing the analytic distribution with its sample estimates from ensembles of different sizes, the smallest ensemble size M* required to ensure a specified expected accuracy can be inferred. Numerical experiments in four river basins demonstrate that M* depends upon the kind of probabilistic forecast that is constructed from the ensemble. Three kinds of forecasts are constructed: (i) a probabilistic river stage forecast (PRSF), which for each time n(n=1,...,N) specifies a predictive distribution function of Hn; (ii) a probabilistic stage transition forecast (PSTF), which for each time n specifies a family (for all hn-1) of predictive one-step transition distribution functions from Hn-1=hn-1 to Hn; and (iii) a probabilistic flood forecast (PFF), which for each time n specifies a predictive distribution function of max{H1,...,Hn}. Overall, the experimental results demonstrate that the smallest ensemble size M* required for accurate estimation (or numerical representation) of these predictive distribution functions is (i) insensitive to experimental factors and on the order of several hundreds for the PRSF and the PFF and (ii) sensitive to experimental factors and on the order of several thousands for the PSTF. The general conclusions for system developers are that the ensemble size is an important design variable, and that the optimal ensemble size M* depends upon the purpose of the forecast: for dynamic control problems (which require a PSTF), M* is likely to be larger by a factor of 3-20 than it is for static decision problems (which require a PRSF or a PFF). © 2010 Elsevier B.V.

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