scholarly journals A new moisture tagging capability in the Weather Research and Forecasting Model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

2017 ◽  
Author(s):  
Damián Insua-Costa ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Great Lake ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Moisture Sources ◽  
Lake Effect ◽  
Snow Water ◽  
Weather Research

Abstract. A new moisture-tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with monthly long simulations over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50–60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socio-economic impacts.

scholarly journals A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

2018 ◽  
Vol 9 (1) ◽  
pp. 167-185 ◽  
Author(s):  
Damián Insua-Costa ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Great Lake ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Socioeconomic Impacts ◽  
Moisture Sources ◽  
Lake Effect ◽  
Weather Research

Abstract. A new moisture tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with a 1-month long simulation over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50 and 60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socioeconomic impacts.

scholarly journals Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements

2019 ◽  
Vol 20 (5) ◽  
pp. 847-862 ◽  
Author(s):  
Scott Havens ◽  
Danny Marks ◽  
Katelyn FitzGerald ◽  
Matt Masarik ◽  
Alejandro N. Flores ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Wrf Model ◽  
Temporal Distribution ◽  
Meteorological Variables ◽  
Weather Research And Forecasting ◽  
Atmospheric Flow ◽  
Temperature And Precipitation ◽  
Runoff Forecasting ◽  
Input Variables ◽  
Weather Research

Abstract Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins.

scholarly journals Humidity determines snowpack ablation under a warming climate

2018 ◽  
Vol 115 (6) ◽  
pp. 1215-1220 ◽  
Author(s):  
Adrian A. Harpold ◽  
Paul D. Brooks
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
Snow Water Equivalent ◽  
Critical Role ◽  
Radiant Energy ◽  
Snow Accumulation ◽  
Atmospheric Conditions ◽  
Atmospheric Humidity ◽  
Dry Conditions ◽  
Snow Water

Climate change is altering historical patterns of snow accumulation and melt, threatening societal frameworks for water supply. However, decreases in spring snow water equivalent (SWE) and changes in snowmelt are not ubiquitous despite widespread warming in the western United States, highlighting the importance of latent and radiant energy fluxes in snow ablation. Here we demonstrate how atmospheric humidity and solar radiation interact with warming temperature to control snowpack ablation at 462 sites spanning a gradient in mean winter temperature from −8.9 to +2.9 °C. The most widespread response to warming was an increase in episodic, midwinter ablation events. Under humid conditions these ablation events were dominated by melt, averaging 21% (202 mm/year) of SWE. Winter ablation under dry atmospheric conditions at similar temperatures was smaller, averaging 12% (58 mm/year) of SWE and likely dominated by sublimation fluxes. These contrasting patterns result from the critical role that atmospheric humidity plays in local energy balance, with latent and longwave radiant fluxes cooling the snowpack under dry conditions and warming it under humid conditions. Similarly, spring melt rates were faster under humid conditions, yet the second most common trend was a reduction in spring melt rates associated with earlier initiation when solar radiation inputs are smaller. Our analyses demonstrate that regional differences in atmospheric humidity are a major cause of the spatial variability in snowpack response to warming. Better constraints on humidity will be critical to predicting both the amount and timing of surface water supplies under climate change.

scholarly journals Mesocell Study Area Snow Distributions for the Cold Land Processes Experiment (CLPX)

2008 ◽  
Vol 9 (5) ◽  
pp. 957-976 ◽  
Author(s):  
Glen E. Liston ◽  
Christopher A. Hiemstra ◽  
Kelly Elder ◽  
Donald W. Cline
Keyword(s):  
Snow Water Equivalent ◽  
Distribution Patterns ◽  
Snow Accumulation ◽  
Meteorological Model ◽  
Meteorological Forcing ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Snow Water ◽  
Grid Points ◽  
Evolution Modeling

Abstract The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km × 30 km domains, enveloping the CLPX mesocell study areas (MSAs) in Colorado. The three MSAs have distinct topography, vegetation, meteorological, and snow characteristics. Simulations were performed using a 30-m grid increment and spanned the snow accumulation season (1 October 2002–1 April 2003). Meteorological forcing was provided by 27 meteorological stations and 75 atmospheric analyses grid points, distributed using a meteorological model (MicroMet). The simulations included a data assimilation model (SnowAssim) that adjusted simulated snow water equivalent (SWE) toward ground-based and airborne SWE observations. The observations consisted of SWE over three 1 km × 1 km intensive study areas (ISAs) for each MSA and a collection of 117 airborne gamma observations, each integrating area 10 km long by 300 m wide. Simulated SWE distributions displayed considerably more spatial heterogeneity than the observations alone, and the simulated distribution patterns closely fit the current understanding of snow evolution processes and observed snow depths. This is the result of the MicroMet/SnowModel’s relatively finescale representations of orographic precipitation, elevation-dependant snowmelt, wind redistribution, and snow–vegetation interactions.

scholarly journals The OWLeS IOP2b Lake-Effect Snowstorm: Mechanisms Contributing to the Tug Hill Precipitation Maximum

2017 ◽  
Vol 145 (7) ◽  
pp. 2461-2478 ◽  
Author(s):  
Leah S. Campbell ◽  
W. James Steenburgh
Keyword(s):  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Land Breeze ◽  
Eastern Shore ◽  
Model Simulations ◽  
Lake Effect ◽  
Precipitation Enhancement ◽  
Weather Research

Lake-effect storms frequently produce a pronounced precipitation maximum over the Tug Hill Plateau (hereafter Tug Hill), which rises 500 m above Lake Ontario’s eastern shore. Here Weather Research and Forecasting Model simulations are used to examine the mechanisms responsible for the Tug Hill precipitation maximum observed during IOP2b of the Ontario Winter Lake-effect Systems (OWLeS) field program. A key contributor was a land-breeze front that formed along Lake Ontario’s southeastern shoreline and extended inland and northeastward across Tug Hill, cutting obliquely across the lake-effect system. Localized ascent along this boundary contributed to an inland precipitation maximum even in simulations in which Tug Hill was removed. The presence of Tug Hill intensified and broadened the ascent region, increasing parameterized depositional and accretional hydrometeor growth, and reducing sublimational losses. The inland extension of the land-breeze front and its contribution to precipitation enhancement appear to be unidentified previously and may be important in other lake-effect storms over Tug Hill.

scholarly journals Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

2013 ◽  
Vol 10 (3) ◽  
pp. 3629-3664
Author(s):  
G. A. Artan ◽  
J. P. Verdin ◽  
R. Lietzow
Keyword(s):  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Status ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Snow Accumulation ◽  
Meteorological Model ◽  
Model Input ◽  
Snow Water ◽  
Colorado Basin

Abstract. We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.

scholarly journals On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

2016 ◽  
Vol 10 (3) ◽  
pp. 1021-1038 ◽  
Author(s):  
Luc Charrois ◽  
Emmanuel Cosme ◽  
Marie Dumont ◽  
Matthieu Lafaysse ◽  
Samuel Morin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Meteorological Model ◽  
Modis Data ◽  
Modeling Uncertainties ◽  
Snow Water ◽  
Mean Square Errors ◽  
Reflectance Data

Abstract. This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multilayer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms.Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. In snowpack modeling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates.Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45 % the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works.

Evaluation of a Hydro-Meteorological Model System for Flood Forecasting of a Mediterranean Basin in Turkey

2020 ◽  
Author(s):  
Berina M. Kilicarslan ◽  
Eren Duzenli ◽  
Heves Pilatin ◽  
Ismail Yucel ◽  
M. Tugrul Yilmaz
Keyword(s):  
Heavy Rainfall ◽  
Hydrological Model ◽  
Hydrologic Model ◽  
Model System ◽  
Model Systems ◽  
Weather Research And Forecasting ◽  
Meteorological Model ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Weather Research

<p>Floods, which are considered as one of the most destructive extreme weather events, are being more severe issues with changing climate, and they are threatening both human life and property. To address flood hazard issues, this study evaluates the application of a hydro-meteorological model system as an early warning system approach. The Weather Research and Forecasting Hydrological model system (WRF-Hydro), a fully-distributed, multi-physics, multi-scale hydrologic model, has the capability of accurately capturing the flood hydrographs in terms of shape and peak time corresponding to storm precipitation. WRF-Hydro model system is implemented with meteorological forcing data obtained from the Weather Research and Forecasting (WRF) atmospheric model. WRF/WRF-Hydro model system is operated in uncoupled mode. The study area is the Oymapinar Basin in Southern Turkey has complex topographic characteristics, and in the upstream basin area, the river network originates from mountainous region. Five heavy rainfall events occurred between January 2015 and May 2015 in the basin selected to assess the model performance of simulating flood hydrograph. The model calibration process is performed by covering three heavy rainfall events, while two of them are used for validation of the model system. This study provides an initial evaluation for possible coupled atmospheric-hydrological model simulations between WRF and WRF-Hydro model systems for future applications.</p>

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