Improving surface-based precipitation phase determination through air mass boundary identification

Feiccabrino, James; Lundberg, Angela; Gustafsson, David

doi:10.2166/nh.2012.060b

Cited by 20 publications

(19 citation statements)

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“…Feiccabrino et al [48] found TS and TR values for a group of cold air mass boundaries to be 1 °C warmer than that of all other observations. When these observation groups were analyzed separately, total misclassified precipitation was reduced by 23% in the temperature range −1 to 5 °C.…”

Section: Resultsmentioning

confidence: 84%

“…This was most likely caused by many precipitation events occurring on a sub-daily timescale (most common exception warm frontal light stratiform precipitation) which often ranges from tens of minutes (intense cumuliform precipitation) to several hours in duration [48]. Since temperatures change diurnally with a maximum temperature in the afternoon and a minimum temperature in the morning, a decrease in the temperature sampling time step closer to the time scale to which precipitation events occur would result in a more representative temperature for precipitation phase determination.…”

Section: Resultsmentioning

confidence: 99%

“…This theory worked in a Central United States continental climate [48] but did not improve precipitation phase identification in the maritime mountain environment of the Scandinavian Peninsula [19]. In a maritime or island environment surface air in the winter is modified by the warm ocean temperatures making all but the strongest frontal passages advect warm air over the land.…”

Section: Resultsmentioning

confidence: 99%

“…Cold air mass boundaries were found to have TS and TR values 1 °C warmer than all other precipitation observations [48]. When treated separately, total misclassified precipitation was reduced from 7.0% to 5.4% for the temperature range −1°C to 5 °C [48].…”

Section: Air Mass Boundary Schemementioning

confidence: 89%

“…Harder and Pomeroy [29] showed that a daily TA had double the temperature range with 5% or more misclassified precipitation than an hourly time-step. A shorter time step decreases the uncertainty of actual temperature during precipitation caused by e.g., diurnal temperature change [30], and air mass boundaries [48]. However, reducing the time step further to 15 min gave similar misclassified precipitation as one hour [29].…”

Section: Time Step Dependencementioning

confidence: 99%

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Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

et al. 2015

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An accurate precipitation phase determination-i.e., solid versus liquid-is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer OPEN ACCESSHydrology 2015, 2 267 temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.

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Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Air Mass Boundary Schemementioning

confidence: 89%

Section: Time Step Dependencementioning

confidence: 99%

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Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

et al. 2015

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Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods

Magnusson¹,

Gustafsson

Hüsler

et al. 2014

Water Resources Research

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In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model.

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Snow and frost: implications for spatiotemporal infiltration patterns – a review

Lundberg

Ala‐aho

Eklo

et al. 2015

Hydrological Processes

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Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well as on the fate of pollutants. Therefore, snow and frost need to be central inherent elements of risk assessment and management schemes. However, snow and frost are often neglected or treated summarily or in a simplistic way by groundwater modellers. Snow deposition is uneven, and the snow is likely to sublimate, be redistributed and partly melt during the winter influencing the mass and spatial distribution of snow storage available for infiltration, the presence of ice layers within and under the snowpack and, therefore, also the spatial distribution of depths and permeability of the soil frost. In steep terrain, snowmelt may travel downhill tens of metres in hours along snow layers. The permeability of frozen soil is mainly influenced by soil type, its water and organic matter content, and the timing of the first snow in relation to the timing of sub-zero temperatures. The aim with this paper is to review the literature on snow and frost processes, modelling approaches with the purpose to visualize and emphasize the need to include these processes when modelling, managing and predicting groundwater recharge for areas exposed to seasonal snow and frost.

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Improving surface-based precipitation phase determination through air mass boundary identification

Cited by 20 publications

References 0 publications

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models

Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods

Snow and frost: implications for spatiotemporal infiltration patterns – a review

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