scholarly journals Impact of Coupling an Ocean Model to WRF Nor’easter Simulations

2015 ◽  
Vol 143 (12) ◽  
pp. 4997-5016 ◽  
Author(s):  
Stephen D. Nicholls ◽  
Steven G. Decker
Keyword(s):  
Storm Track ◽  
Wrf Model ◽  
Ocean Model ◽  
Weather Research And Forecasting ◽  
Model Coupling ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Simulated Precipitation ◽  
Ocean Atmosphere ◽  
The Impact

Abstract The impact of ocean–atmosphere coupling and its possible seasonal dependence upon Weather Research and Forecasting (WRF) Model simulations of seven, wintertime cyclone events was investigated. Model simulations were identical aside from the degree of ocean model coupling (static SSTs, 1D mixed layer model, full-physics 3D ocean model). Both 1D and 3D ocean model coupling simulations show that SSTs following the passage of a nor’easter did tend to cool more strongly during the early season (October–December) and were more likely to warm late in the season (February–April). Model simulations produce SST differences of up to 1.14 K, but this change did not lead to significant changes in storm track (<100 km), maximum 10-m winds (<2 m s−1), or minimum sea level pressure (≤5 hPa). Simulated precipitation showed little sensitivity to model coupling, but all simulations did tend to overpredict precipitation extent (bias > 1) and have low-to-moderate threat scores (0.31–0.59). Analysis of the storm environment and the overall simulation failed to reveal any statistically significant differences in model error attributable to ocean–atmosphere coupling. Despite this result, ocean model coupling can reduce dynamical field error at a single level by up to 20%, and this was slightly greater (1%–2%) with 3D ocean model coupling as compared to 1D ocean model coupling. Thus, while 3D ocean model coupling tended to generally produce more realistic simulations, its impact would likely be more profound for longer-term simulations.

scholarly journals Influence of Bulk Microphysics Schemes upon Weather Research and Forecasting (WRF) Version 3.6.1 Nor'easter Simulations

2016 ◽  
Author(s):  
Stephen D. Nicholls ◽  
Steven G. Decker ◽  
Wei-Kuo Tao ◽  
Stephen E. Lang ◽  
Jainn J. Shi ◽  
...  
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Large Variability ◽  
Mixing Ratios ◽  
Simulated Precipitation ◽  
Single Moment ◽  
Double Moment ◽  
Order Of Magnitude ◽  
The Impact ◽  
Weather Research

Abstract. This study evaluated the impact of five, single- or double- moment bulk microphysics schemes (BMPS) on Weather Research and Forecasting (WRF) model (version 3.6.1) winter storm simulations. Model simulations were integrated for 180 hours, starting 72 hours prior to the first measurable precipitation in the highly populated Mid-Atlantic U.S. Simulated precipitation fields were well-matched to precipitation products. However, total accumulations tended to be over biased (1.10–2.10) and exhibited low-to-moderate threat scores (0.27–0.59). Non-frozen hydrometeor species from single-moment BMPS produced similar mixing ratio profiles and maximum saturation levels due to a common parameterization heritage. Greater variability occurred with frozen microphysical species due to varying assumptions among BMPSs regarding ice supersaturation amounts, the dry collection of snow by graupel, various ice collection efficiencies, snow and graupel density and size mappings/intercept parameters, and hydrometeor terminal velocities. The addition of double-moment rain and cloud water resulted in minimal change to species spatial extent or maximum saturation level, however rain mixing ratios tended higher. Although hydrometeor differences varied by up to an order of magnitude among the BMPSs, similarly large variability was not upscaled to mesoscale and synoptic scales.

scholarly journals The Contribution of Lake Enhancement to Extreme Snowfall within the Chicago–Milwaukee Urban Corridor during the 2011 Groundhog Day Blizzard

2017 ◽  
Vol 145 (6) ◽  
pp. 2405-2420 ◽  
Author(s):  
Nathan D. Owens ◽  
Robert M. Rauber ◽  
Brian F. Jewett ◽  
Greg M. McFarquhar
Keyword(s):  
Surface Temperature ◽  
Wrf Model ◽  
Lower Atmosphere ◽  
Weather Research And Forecasting ◽  
Structure Stability ◽  
Sea Level Pressure ◽  
Dewpoint Temperature ◽  
The Impact ◽  
Urban Corridor ◽  
The City

Abstract This paper examines the impact of the Laurentian Great Lakes (GL) on atmospheric structure, stability, and precipitation within the Chicago–Milwaukee urban corridor during the passage of the 1–2 February 2011 extratropical cyclone. This storm produced the third largest snowfall [53.8 cm (21.2 in.)] recorded in a 130-yr period in the city of Chicago. Two simulations of the storm using the Weather Research and Forecasting (WRF) Model are described: the first with the GL present, and the second with the lakes replaced with land having characteristics of adjacent shores. The GL were found to alter the surface temperature and moisture fields in their lee during cyclone passage. The changes were limited to the layer below the frontal inversion, but were significant enough to reduce the mean sea level pressure in some locations by 2.0–2.5 hPa, and raise the surface temperature and dewpoint temperature by 2°–4°C across several states downwind. In the Chicago–Milwaukee metropolitan corridor where the heavy snow occurred, the surface temperature and dewpoint temperature increased from +3° to +6°C as a result of heating and moistening of the lower atmosphere by the GL. Enhanced convergence also occurred along the downwind shoreline. Despite these changes, the areal impact on precipitation was surprisingly small, with liquid equivalent precipitation increases exceeding 5 mm limited to a small area over metropolitan Chicago late in the storm. The reason for the limited impact appeared to be the shallow nature of the cold air mass below the frontal inversion. Nevertheless, over metropolitan Chicago, as much as 20% of the snowfall could be attributed to the presence of the GL.

scholarly journals The Impact of Cumulus Parameterization Schemes on the Simulation of Tropical Cyclone “Roanu” Over the Bay of Bengal Using WRF Model

2020 ◽  
Vol 68 (1) ◽  
pp. 87-94
Author(s):  
Saifullah ◽  
Md Idris Ali ◽  
Ashik Imran
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Wrf Model ◽  
Absolute Error ◽  
Sensitivity Study ◽  
Cumulus Parameterization ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Cumulus Parameterization Schemes ◽  
The Impact

A sensitivity study has been made on cumulus parameterization (CP) schemes of Weather Research and Forecasting (WRF) model for the simulation of tropical cyclone Roanu which formed over Bay of Bengal during May 2016. The model was run for 72 hours with different CP schemes such as Kain–Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell–Freit as Ensemble (GFE), Grell 3D Ensemble (G3E) and Grell–Devenyi (GD) Ensemble schemes to study the variation in track, intensity. The landfall position error is minimum for BMJ scheme but the time delayed only 1.5-5 hours for all schemes except GD scheme. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of minimum sea level pressure and maximum wind speed is smaller for BMJ, GFE, GD schemes. The RMSE-MAE of rainfall is minimum for BMJ and G3E schemes. Except GD scheme all the other schemes give the better result. Dhaka Univ. J. Sci. 68(1): 87-94, 2020 (January)

scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

Greenland melting signatures in model simulations and oceanic observations

2021 ◽  
Author(s):  
Sophie Stolzenberger ◽  
Roelof Rietbroek ◽  
Claudia Wekerle ◽  
Bernd Uebbing ◽  
Jürgen Kusche
Keyword(s):  
North Atlantic ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Ocean Model ◽  
Freshwater Flux ◽  
Model Simulations ◽  
The North ◽  
Sea Level Anomalies ◽  
The North Atlantic ◽  
The Impact

<p>The impact of Greenland freshwater on oceanic variables in the North Atlantic has been controversially discussed in the past. Within the framework of the German research project GROCE (Greenland Ice Sheet Ocean Interaction), we present a comprehensive study using ocean modelling results including and excluding the Greenland freshwater flux. The aim of this study is whether signatures of Greenland ice sheet melting found in ocean model simulations are visible in the observations. Therefore, we estimate changes in temperature, salinity, steric heights and sea level anomalies since the 1990s. The observational database includes altimetric and gravimetric satellite data as well as Argo floats. We will discuss similarities/differences between model simulations and observations for smaller regions around Greenland in the North Atlantic. As these experiments are available for two different horizontal resolutions, we will furthermore be able to assess the effects of an increased model resolution.</p>

scholarly journals A Study of the Interaction between Typhoon Francisco (2013) and a Cold-Core Eddy. Part I: Rapid Weakening

2020 ◽  
Vol 77 (1) ◽  
pp. 355-377 ◽  
Author(s):  
Zhanhong Ma
Keyword(s):  
Structural Changes ◽  
Surface Wind ◽  
Wrf Model ◽  
Ocean Model ◽  
Atmospheric Environment ◽  
Weather Research And Forecasting ◽  
Eye Size ◽  
Convective Bursts ◽  
Cold Core

Abstract Typhoon Francisco (2013) experienced unusually rapid weakening (RW) with its maximum surface wind decreasing by 45 kt (1 kt ≈ 0.51 m s−1) over 24 h as measured from the satellite-derived advanced Dvorak technique (ADT) dataset, which is more than twice the weakening rate defined as RW by DeMaria. The mechanisms leading to the extreme RW event of Francisco are examined based on observational analysis and simulations by coupling the Weather Research and Forecasting (WRF) Model, version 3.7, with the Stony Brook Parallel Ocean Model (sbPOM). The RW of Francisco took place in a relatively favorable atmospheric environment while passing over detrimental oceanic conditions, dominated by the presence of a cold-core eddy. The passages of two prior typhoons apparently intensified the cold-core eddy, contributing to a major role of eddy feedback on RW for Francisco. The structural changes in Francisco accompanying eddy interaction are characterized by a substantially enlarged eye size, which evolved from ~20 to ~100 km in diameter, as indicated from satellite images. Numerical simulations suggest that the eddy is prominent in weakening the intensity of Francisco during the storm–eddy interaction, with its role less significant but still comparable to that of the cold wake. Both the cooler water and stronger upward motion in the eddy lead to a larger sea surface temperature decrease induced by Francisco, which results in a nearly 50% decrease of surface enthalpy flux, suppressed convective bursts, and a 50% reduction in latent heat release. These results underscore the potential importance of open-ocean, cold-core eddies in contributing to the RW of tropical cyclones.

scholarly journals Comparison of Simulated Precipitation over East Asia in Two Regional Models with Hydrostatic and Nonhydrostatic Dynamical Cores

2016 ◽  
Vol 144 (10) ◽  
pp. 3579-3590 ◽  
Author(s):  
Jihyeon Jang ◽  
Song-You Hong
Keyword(s):  
Wrf Model ◽  
Local Maximum ◽  
Weather Research And Forecasting ◽  
Model Program ◽  
Grid Spacing ◽  
Dynamical Core ◽  
Simulated Precipitation ◽  
Precipitation Area ◽  
Hydrostatic Limit ◽  
Regional Forecasts

This study examines the characteristics of a nonhydrostatic dynamical core compared to a corresponding hydrostatic dynamical core in the Regional Model Program (RMP) of the Global/Regional Integrated Model system (GRIMs), a spectral model for regional forecasts, focusing on simulated precipitation over Korea. This kind of comparison is also executed in the Weather Research and Forecasting (WRF) finite-difference model with the same physics package used in the RMP. Overall, it is found that the nonhydrostatic dynamical core experiment accurately reproduces the heavy rainfall near Seoul, South Korea, on a 3-km grid, relative to the results from the hydrostatic dynamical core in both models. However, the characteristics of nonhydrostatic effects on the simulated precipitation differ between the RMP and WRF Model. The RMP with the nonhydrostatic dynamical core improves the local maximum, which is exaggerated in the hydrostatic simulation. The hydrostatic simulation of the WRF Model displaces the major precipitation area toward the mountainous region along the east coast of the peninsula, which is shifted into the observed area in the nonhydrostatic simulation. In the simulation of a summer monsoonal rainfall, these nonhydrostatic effects are negligible in the RMP, but the simulated monsoonal rainfall is still influenced by the dynamical core in the WRF Model even at a 27-km grid spacing. One of the reasons for the smaller dynamical core effect in the RMP seems to be the relatively strong horizontal diffusion, resulting in a smaller grid size of the hydrostatic limit.

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

Impact of Soil Moisture Initialization on Temperature Extreme Detection in the context of Regional Climate Modeling

2020 ◽  
Author(s):  
Matilde García-Valdecasas Ojeda ◽  
Juan José Rosa-Cánovas ◽  
Emilio Romero-Jiménez ◽  
Patricio Yeste ◽  
Sonia R. Gámiz-Fortis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Climate Modeling ◽  
Regional Climate ◽  
Wrf Model ◽  
Regional Climate Modeling ◽  
Weather Research And Forecasting ◽  
Temperature Extremes ◽  
Temperature Extreme ◽  
The Impact

<p>Land surface-related processes play an essential role in the climate conditions at a regional scale. In this study, the impact of soil moisture (SM) initialization on regional climate modeling has been explored by using a dynamical downscaling experiment. To this end, the Weather Research and Forecasting (WRF) model was used to generate a set of high-resolution climate simulations driven by the ERA-Interim reanalysis for a period from 1989 to 2009. As the spatial configuration, two one-way nested domains were used, with the finer domain being centered over the Iberian Peninsula (IP) at a spatial resolution of about 10 km, and nested over a coarser domain that covers the Euro-CORDEX region at 50 km of spatial resolution.</p><p>The sensitivity experiment consisted of two control runs (CTRL) performed using as SM initial conditions those provided by ERA-Interim, and initialized for two different dates times (January and June). Additionally, another set of runs was completed driven by the same climate data but using as initial conditions prescribed SM under wet and dry scenarios.</p><p>The study is based on assessing the WRF performance by comparing the CTRL simulations with those performed with the different prescribed SM, and also, comparing them with the observations from the Spanish Temperature At Daily scale (STEAD) dataset. In this sense, we used two temperature extreme indices within the framework of decadal predictions: the warm spell index (WSDI) and the daily temperature range (DTR).</p><p>These results provide valuable information about the impact of the SM initial conditions on the ability of the WRF model to detect temperature extremes, and how long these affect the regional climate in this region. Additionally, these results may provide a source of knowledge about the mechanisms involved in the occurrence of extreme events such as heatwaves, which are expected to increase in frequency, duration, and magnitude under the context of climate change.</p><p><strong>Keywords</strong>: soil moisture initial conditions, temperature extremes, regional climate, Weather Research and Forecasting model</p><p>Acknowledgments: This work has been financed by the project CGL2017-89836-R (MINECO-Spain, FEDER). The WRF simulations were performed in the Picasso Supercomputer at the University of Málaga, a member of the Spanish Supercomputing Network.</p>

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