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

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.

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The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0121.1 ◽

2016 ◽

Vol 55 (10) ◽

pp. 2263-2281 ◽

Cited By ~ 15

Author(s):

A. Wootten ◽

J. H. Bowden ◽

R. Boyles ◽

A. Terando

Keyword(s):

Puerto Rico ◽

Wrf Model ◽

Summer Precipitation ◽

Cumulus Parameterization ◽

Weather Research And Forecasting ◽

Grid Spacing ◽

Average Decrease ◽

Simulated Precipitation ◽

Projected Changes ◽

Horizontal Grid

AbstractThe sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales (<4 km) and grid nudging at non-convective-permitting scales (>4 km) improves the island average correlation of precipitation by 0.05–0.2 and reduces the island average RMSE by up to 40 mm on average over relying on the explicit microphysics at convective-permitting scales with grid nudging. Projected changes in summer precipitation between 2040–42 and 1985–87 using WRF to downscale CCSM4 range from a 2.6-mm average increase to an 81.9-mm average decrease, depending on the choice of CP scheme. The differences are only associated with differences between WRF configurations, which indicates the importance of CP scheme for projected precipitation change as well as historical accuracy.

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

Monthly Weather Review ◽

10.1175/mwr-d-15-0017.1 ◽

2015 ◽

Vol 143 (12) ◽

pp. 4997-5016 ◽

Cited By ~ 8

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.

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Simulation of the Summer Monsoon Rainfall over East Asia Using the NCEP GFS Cumulus Parameterization at Different Horizontal Resolutions

Weather and Forecasting ◽

10.1175/waf-d-13-00143.1 ◽

2014 ◽

Vol 29 (5) ◽

pp. 1143-1154 ◽

Cited By ~ 25

Author(s):

Kyo-Sun Sunny Lim ◽

Song-You Hong ◽

Jin-Ho Yoon ◽

Jongil Han

Keyword(s):

Summer Monsoon ◽

Monsoon Rainfall ◽

Asian Summer Monsoon ◽

Monsoon Season ◽

Wrf Model ◽

Horizontal Resolution ◽

Cumulus Parameterization ◽

Grid Spacing ◽

Simulated Precipitation ◽

Horizontal Grid

Abstract The most recent version of the simplified Arakawa–Schubert (SAS) cumulus scheme in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) (GFS SAS) is implemented in the Weather Research and Forecasting (WRF) Model with a modification of the triggering condition and the convective mass flux in order to make it dependent on the model’s horizontal grid spacing. The East Asian summer monsoon season of 2006 is selected in order to evaluate the performance of the modified GFS SAS scheme. In comparison to the original GFS SAS scheme, the modified GFS SAS scheme shows overall better agreement with the observations in terms of the simulated monsoon rainfall. The simulated precipitation from the original GFS SAS scheme is insensitive to the model’s horizontal grid spacing, which is counterintuitive because the portion of the resolved clouds in a grid box should increase as the model grid spacing decreases. This behavior of the original GFS SAS scheme is alleviated by the modified GFS SAS scheme. In addition, three different cumulus schemes (Grell and Freitas, Kain and Fritsch, and Betts–Miller–Janjić) are chosen to investigate the role of a horizontal resolution on the simulated monsoon rainfall. Although the forecast skill of the surface rainfall does not always improve as the spatial resolution increases, the improvement of the probability density function of the rain rate with the smaller grid spacing is robust regardless of the cumulus parameterization scheme.

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Capabilities and Limitations of Convection-Permitting WRF Simulations of Lake-Effect Systems over the Great Salt Lake

Weather and Forecasting ◽

10.1175/waf-d-15-0017.1 ◽

2015 ◽

Vol 30 (6) ◽

pp. 1711-1731 ◽

Cited By ~ 13

Author(s):

John D. McMillen ◽

W. James Steenburgh

Keyword(s):

Salt Lake ◽

Numerical Models ◽

Great Salt Lake ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Grid Spacing ◽

Convective Storm ◽

Lake Effect ◽

Object Based ◽

The Right

Abstract Although previous studies suggest that the Weather Research and Forecasting (WRF) Model can produce physically realistic banded Great Salt Lake–effect (GSLE) precipitation features, the accuracy and reliability of these simulations for forecasting applications remains unquantified. The ability of the WRF to simulate nonbanded GSLE features is also unknown. This paper uses subjective, traditional, and object-based verification to evaluate convection-permitting (1.33-km grid spacing) WRF simulations of 11 banded and 8 nonbanded GSLE events. In all simulations, the WRF was configured with the Thompson microphysics and the Yonsei University (YSU) planetary boundary layer parameterizations. Subjectively, a majority of the simulations of banded GSLE events produce physically realistic precipitation features. In contrast, simulations of nonbanded GSLE events rarely produce physically realistic precipitation features and sometimes erroneously produce banded precipitation features. Simulations of banded GSLE events produce equitable threat scores (ETSs) comparable to other convective-storm verification studies, whereas simulations of nonbanded events exhibit lower ETSs. Object-based verification shows that the WRF tends to generate precipitation to the right (relative to the flow) and downstream of observed. These results, although based on a specific WRF parameterization suite, suggest that deterministic prediction of GSLE using convection-permitting models will prove challenging in practice with current numerical models. In addition, identifying and addressing the causes of the rightward and downstream precipitation bias is necessary to achieve optimal performance from future probabilistic and/or deterministic high-resolution forecast systems.

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Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia from 1985 to 2009

International Journal of Wildland Fire ◽

10.1071/wf12048 ◽

2013 ◽

Vol 22 (6) ◽

pp. 739 ◽

Cited By ~ 10

Author(s):

Hamish Clarke ◽

Jason P. Evans ◽

Andrew J. Pitman

Keyword(s):

Initial Conditions ◽

Full Range ◽

Wrf Model ◽

Weather Research And Forecasting ◽

Fire Weather ◽

Grid Spacing ◽

Skill Scores ◽

Forest Fire Danger ◽

Environmental Prediction ◽

Weather Research

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

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A Comparison of Precipitation Forecast Skill between Small Convection-Allowing and Large Convection-Parameterizing Ensembles

Weather and Forecasting ◽

10.1175/2009waf2222222.1 ◽

2009 ◽

Vol 24 (4) ◽

pp. 1121-1140 ◽

Cited By ~ 154

Author(s):

Adam J. Clark ◽

William A. Gallus ◽

Ming Xue ◽

Fanyou Kong

Keyword(s):

Wrf Model ◽

Precipitation Forecast ◽

Convective Precipitation ◽

Weather Research And Forecasting ◽

Lead Times ◽

Grid Spacing ◽

Ensemble Forecasts ◽

Probabilistic Forecasts ◽

Central United States ◽

Statistical Consistency

Abstract An experiment has been designed to evaluate and compare precipitation forecasts from a 5-member, 4-km grid-spacing (ENS4) and a 15-member, 20-km grid-spacing (ENS20) Weather Research and Forecasting (WRF) model ensemble, which cover a similar domain over the central United States. The ensemble forecasts are initialized at 2100 UTC on 23 different dates and cover forecast lead times up to 33 h. Previous work has demonstrated that simulations using convection-allowing resolution (CAR; dx ∼ 4 km) have a better representation of the spatial and temporal statistical properties of convective precipitation than coarser models using convective parameterizations. In addition, higher resolution should lead to greater ensemble spread as smaller scales of motion are resolved. Thus, CAR ensembles should provide more accurate and reliable probabilistic forecasts than parameterized-convection resolution (PCR) ensembles. Computation of various precipitation skill metrics for probabilistic and deterministic forecasts reveals that ENS4 generally provides more accurate precipitation forecasts than ENS20, with the differences tending to be statistically significant for precipitation thresholds above 0.25 in. at forecast lead times of 9–21 h (0600–1800 UTC) for all accumulation intervals analyzed (1, 3, and 6 h). In addition, an analysis of rank histograms and statistical consistency reveals that faster error growth in ENS4 eventually leads to more reliable precipitation forecasts in ENS4 than in ENS20. For the cases examined, these results imply that the skill gained by increasing to CAR outweighs the skill lost by decreasing the ensemble size. Thus, when computational capabilities become available, it will be highly desirable to increase the ensemble resolution from PCR to CAR, even if the size of the ensemble has to be reduced.

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Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-22-1095-2018 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1095-1117 ◽

Cited By ~ 38

Author(s):

Ila Chawla ◽

Krishna K. Osuri ◽

Pradeep P. Mujumdar ◽

Dev Niyogi

Keyword(s):

Land Surface ◽

Heavy Rainfall ◽

Wrf Model ◽

Extreme Rainfall ◽

Weather Research And Forecasting ◽

Grid Spacing ◽

Ganga Basin ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Heavy Rainfall Events

Abstract. Reliable estimates of extreme rainfall events are necessary for an accurate prediction of floods. Most of the global rainfall products are available at a coarse resolution, rendering them less desirable for extreme rainfall analysis. Therefore, regional mesoscale models such as the advanced research version of the Weather Research and Forecasting (WRF) model are often used to provide rainfall estimates at fine grid spacing. Modelling heavy rainfall events is an enduring challenge, as such events depend on multi-scale interactions, and the model configurations such as grid spacing, physical parameterization and initialization. With this background, the WRF model is implemented in this study to investigate the impact of different processes on extreme rainfall simulation, by considering a representative event that occurred during 15–18 June 2013 over the Ganga Basin in India, which is located at the foothills of the Himalayas. This event is simulated with ensembles involving four different microphysics (MP), two cumulus (CU) parameterizations, two planetary boundary layers (PBLs) and two land surface physics options, as well as different resolutions (grid spacing) within the WRF model. The simulated rainfall is evaluated against the observations from 18 rain gauges and the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42RT version 7 data. From the analysis, it should be noted that the choice of MP scheme influences the spatial pattern of rainfall, while the choice of PBL and CU parameterizations influences the magnitude of rainfall in the model simulations. Further, the WRF run with Goddard MP, Mellor–Yamada–Janjic PBL and Betts–Miller–Janjic CU scheme is found to perform best in simulating this heavy rain event. The selected configuration is evaluated for several heavy to extremely heavy rainfall events that occurred across different months of the monsoon season in the region. The model performance improved through incorporation of detailed land surface processes involving prognostic soil moisture evolution in Noah scheme compared to the simple Slab model. To analyse the effect of model grid spacing, two sets of downscaling ratios – (i) 1 : 3, global to regional (G2R) scale and (ii) 1 : 9, global to convection-permitting scale (G2C) – are employed. Results indicate that a higher downscaling ratio (G2C) causes higher variability and consequently large errors in the simulations. Therefore, G2R is adopted as a suitable choice for simulating heavy rainfall event in the present case study. Further, the WRF-simulated rainfall is found to exhibit less bias when compared with the NCEP FiNaL (FNL) reanalysis data.

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Evaluating Warm and Cold Rain Processes in Cloud Microphysical Schemes Using OLYMPEX Field Measurements

Monthly Weather Review ◽

10.1175/mwr-d-19-0092.1 ◽

2020 ◽

Vol 148 (5) ◽

pp. 2163-2190

Author(s):

Aaron R. Naeger ◽

Brian A. Colle ◽

Na Zhou ◽

Andrew Molthan

Keyword(s):

Wrf Model ◽

Field Measurements ◽

Weather Research And Forecasting ◽

Flow Conditions ◽

Surface Precipitation ◽

Particle Properties ◽

Warm Rain ◽

Simulated Precipitation ◽

Rain Fall ◽

Western Washington

Abstract Field observations from the Olympic Mountain Experiment (OLYMPEX) around western Washington State during two atmospheric river (AR) events in November 2015 were used to evaluate several bulk microphysical parameterizations (BMPs) within the Weather Research and Forecasting (WRF) Model. These AR events were characterized by a prefrontal period of stable, terrain-blocked flow with an abundance of cold rain over the lowland region followed by less stable, unblocked flow with more warm rain, and a shift in the largest precipitation amounts to over the windward Olympic slopes. Our WRF simulations underpredicted the precipitation by 19%–36% in the Morrison (MORR) and Thompson (THOM) BMPs and 10%–23% in the predicted particle properties (P3) BMP, with the largest underpredictions over the windward slopes during the more convective, unblocked flow conditions. Several important processes related to the BMPs led to the differences in simulated precipitation. First, the prognostic single ice category parameterization in the P3 scheme promoted a more realistic evolution of rimed particles and larger cold rain production, which led to the lowest underpredictions in precipitation among the schemes. Second, efficient melting processes associated with the production of nonspherical ice and snow in the P3 and THOM BMPs, respectively, promoted a more realistic transition to rain fall speeds within the warm layer compared to the spherical snow assumption in MORR. Last, all BMPs underpredict the contribution of warm rain processes to the surface precipitation, particularly during the unblocked flow period, which may be partly explained by too weak condensational and collisional growth processes due to the neglect of turbulence parameterizations within the schemes.

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

10.5194/gmd-2016-151 ◽

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.

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Hurricane Vortex Dynamics during Atlantic Extratropical Transition

Journal of the Atmospheric Sciences ◽

10.1175/2007jas2488.1 ◽

2008 ◽

Vol 65 (3) ◽

pp. 714-736 ◽

Cited By ~ 59

Author(s):

Christopher A. Davis ◽

Sarah C. Jones ◽

Michael Riemer

Keyword(s):

Vertical Structure ◽

Wrf Model ◽

Vertical Wind Shear ◽

Weather Research And Forecasting ◽

Synoptic Scale ◽

Grid Spacing ◽

Extratropical Transition ◽

Varying Environments ◽

Atlantic Hurricanes ◽

Moving Storm

Abstract Simulations of six Atlantic hurricanes are diagnosed to understand the behavior of realistic vortices in varying environments during the process of extratropical transition (ET). The simulations were performed in real time using the Advanced Research Weather Research and Forecasting (WRF) model (ARW), using a moving, storm-centered nest of either 4- or 1.33-km grid spacing. The six simulations, ranging from 45 to 96 h in length, provide realistic evolution of asymmetric precipitation structures, implying control by the synoptic scale, primarily through the vertical wind shear. The authors find that, as expected, the magnitude of the vortex tilt increases with increasing shear, but it is not until the shear approaches 20 m s−1 that the total vortex circulation decreases. Furthermore, the total vertical mass flux is proportional to the shear for shears less than about 20–25 m s−1, and therefore maximizes, not in the tropical phase, but rather during ET. This has important implications for predicting hurricane-induced perturbations of the midlatitude jet and its consequences on downstream predictability. Hurricane vortices in the sample resist shear by either adjusting their vertical structure through precession (Helene 2006), forming an entirely new center (Irene 2005), or rapidly developing into a baroclinic cyclone in the presence of a favorable upper-tropospheric disturbance (Maria 2005). Vortex resiliency is found to have a substantial diabatic contribution whereby vertical tilt is reduced through reduction of the primary vortex asymmetry induced by the shear. If the shear and tilt are so large that upshear subsidence overwhelms the symmetric vertical circulation of the hurricane, latent heating and precipitation will occur to the left of the tilt vector and slow precession. Such was apparent during Wilma (2005).

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