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NCAR Convection-permitting Physics Suite for WRF

 

1. Background

 

For years NCAR’s Mesoscale and Microscale Meteorology (MMM) Laboratory, which supports the WRF Model to the community, has run WRF for real-time forecasting focused on convective weather over the CONUS.  In this it has configured WRF with a particular combination of physics options, i.e., a physics suite.  This combination has been reliable and accurate and is now offered as the “NCAR convection-permitting suite”.  It consists of the following schemes.

 

       Thompson microphysics scheme                    (Thompson et al. 2008)

       MYJ PBL scheme                                           (Janjic 1994)

       Noah LSM                                                      (Chen et al. 1997)

       RRTMG shortwave radiation scheme             (Iacono et al. 2008)

       RRTMG longwave radiation scheme              (Iacono et al. 2008)

       Tiedtke cumulus scheme                                 (Tiedtke 1989, Zhang et al. 2011)

       MYJ surface layer scheme                              (Janjic 1994)

 

Note that while the suite has been used for convection-permitting forecasting in fully-explicit mode with a cumulus parameterization not activated, it does include a cumulus scheme for completeness and for applications with coarser grids.  An example is given below with the NCAR ensemble prediction system which involves both 15-km and 3-km grids.

 

The NCAR convection-permitting physics suite is documented here with references on its schemes, some history of its use in MMM WRF forecasting, and recent testing results.  As illustrated, the suite has been used reliably for years in various settings in MMM.  For WRF forecasting applications focused on mid-latitude, continental convection, it has been found to be skillful and robust.

 

2. History of Suite Use and Applications

 

a. NCAR Real-Time Forecasting

 

MMM began experimenting with real-time WRF forecasting in a convection-permitting mode in 2003 in the BAMEX (The Bow Echo and Mesoscale Convective Vortex Experiment) field campaign (Done et al. 2004) and has continued since.  Along the way there has been regular evaluation (both published and internal) of the forecasts reflecting the chosen physics configurations.  Weisman et al. (2008) assessed such real-time WRF forecasts and analyzed the physics scheme performance.  With respect to packages in the convection-permitting suite, they found that compared to the YSU PBL scheme, the MYJ PBL scheme consistently produced a cooler, moister boundary layer, which was more favorable for convection initiation.  And, the Thompson microphysics scheme tended to more accurately represent the stratiform regions behind squall lines than other options. 

 

From such yearly exercises and evaluations, the favored set of physics packages for applying WRF with convection-permitting grids has evolved.  By 2008, the MMM WRF suite had settled into the Noah LSM, MYJ PBL, Thompson microphysics, RRTM longwave radiation, and Goddard shortwave radiation options.  In 2012, the radiation schemes in the real-time configuration were updated to RRTMG, defining the current convection-permitting suite.

 

Since 2007 MMM has participated in the annual Spring Forecasting Experiment (SFE) organized by NOAA’s Storm Prediction Center (SPC) and NSSL by contributing real-time WRF forecasts to the model guidance pool.  This has allowed for a long-term examination of WRF, and its performance with the basic suite, by forecasters and scientists.  As an example from this setting, Coniglio et al. (2010) evaluated the forecasts from different configurations of WRF run by 2008 SFE modeling groups at convection-allowing scales (viz., 4-km and 3-km grids) and found that they outperformed the coarser, operational model forecasts with which they were compared in precipitation forecasts.  While they found a cold bias in surface and lower tropospheric temperatures and less-than-observed vertical wind shear and 850-mb moisture over the high plains in all the WRF runs, they suggested that the errors may be linked to the PBL parameterization and the lack of a shallow convection parameterization in the configurations considered.  Similarly, Schwartz et al. (2010) examined a number of WRF physics scheme combinations in convection-permitting forecasts from 2007, finding differences in precipitation biases, but with the MYJ and Thompson scheme pair showing reasonable biases for low-to-moderate accumulation thresholds.  Romine et al. (2013) further evaluated model physics choices in a continuously-cycled data assimilation system and showed that the set in the NCAR convection-permitting suite generally performed well.

 

b. NCAR Ensemble Prediction System

 

As described above, the suite options have been used in WRF setups for real-time forecasting by MMM, in large part in support of the annual Spring Forecasting Experiment, which has had a focus on prediction of convection in the central U.S.  In addition, in recent years the physics suite has also been heavily used by MMM in its real-time, convection-allowing ensemble prediction system (EPS).  The EPS’s configuration, operation, and performance have been documented in a number of publications (see below).  The system has two WRF components, one for analysis and one for forecasting, both of which use the suite.  The analysis component employs continuously-cycled ensemble adjustment Kalman filter (EAKF) data assimilation with 15-km horizontal grid spacing to produce analyses that initialize the forecast component’s high-resolution (3-km) ensemble predictions.  The physics suite is used in both parts, except the cumulus parameterization is turned off in the 3-km forecast component.

 

The suite has a history in EPS settings, such as its use in WRF in field campaigns (such as the Mesoscale Predictability Experiment (MPEX; 2013) (Schwartz et al. 2015a) and the Deep Convective Clouds and Chemistry Experiment (DC3; 2012) (Romine et al. 2014)) and (for ensembles for 20152017) the Spring Forecasting Experiment (Clark et al. 2012).  In addition to such applications, the EPS has been run for ongoing real-time WRF forecasting in MMM (Schwartz et al. (2015b); http://ensemble.ucar.edu).  In these EPS settings, the suite has demonstrated an ability to capture the evolutions and structures of organized convective storms (see, e.g., Powers et al. (2017), Fig. 3).

 

Schwartz et al. (2015a) first documented the convection-allowing EPS employing the suite, examining forecasts from MPEX over the central U.S.  While the WRF forecasts had a positive bias for rainfall (i.e., slight overprediction was seen for the suite and the ensemble setup), in comparisons with NCEP Stage IV observations, the (spatial) patterns of model precipitation demonstrated skill.  Overall, they found that the WRF configuration produced severe weather forecasts that were good based on subjective evaluations and objective statistics, despite noting some errors in convective timing and location.

 

Expanding on their initial work, Schwartz et al. (2015b) further demonstrated the skill of the WRF EPS using the suite in forecasting precipitation and in providing severe weather guidance.  Evaluating system forecasts for a 3-month period, they found that WRF in general correctly located precipitation and forecast reasonable amplitudes, although there was some overprediction for precipitation rates above 5.0 mm/hr and some underprediction for lower rates.  Regarding the simulation of convective structures, WRF using the suite was found to provide valuable guidance in strongly-forced MCS events, with lesser skill for weakly-forced events.

 

3. Recent Suite Testing

 

While the suite has a track record of use at NCAR, MMM has also done some targeted testing of the suite.  This primarily reflects runs using a convection-permitting grid over a spring period (MayJune 2016).  For this, WRF V3.9 was set up with a 3-km grid (736 x 676 points) over the central U.S. (Fig. 1), with 40 vertical levels up to 30 mb.  The forecasts were initialized at 0000 UTC and 1200 UTC (using the GFS as a first-guess) every three days for the period 17 May 2016 to 10 June 2016.  This domain and period were selected mainly because severe convective weather and substantial precipitation are common there in this time frame.

 

In addition to this testing, a single-case study of the WRF forecast sensitivity to microphysics scheme has been performed.  For this, one WRF run has the suite as defined and employing the Thompson microphysics (Thompson et al. 2008), while three others have a different packages: P3 (Morrison and Milbrandt 2015), Morrison (Morrison et al. 2009), and WSM6 (Hong and Lim 2006).  WRF is initialized at 0000 UTC 26 May 2016, and the forecasts are for 48 hours.  This case was selected because it involved large amounts of rainfall over a broad area (and regionally some very heavy rainfall).

 

From the seasonal testing, Fig. 2 compares forecast precipitation from the MayJune 2016 WRF suite runs with Stage IV observations.  The model output and Stage IV data are put on the same grid and the precipitation accumulations compared.  The upper left and right panels show average gridpoint accumulations for the 48 hours of the 0000 UTC (left) and 1200 UTC (right) forecast initializations.  After a spin-up period of 612 hours in which WRF (expectedly) underforecasts the precipitation, there is good agreement with the observations for both initialization times.  Note that model performance (for precipitation) is better in the first 30 forecast hours for the 1200 UTC initializations than for the 0000 UTC initializations.  This may reflect the fact that the diurnal cycle (the precipitation signal of which is also seen in Fig. 2) has more precipitation occurring near 0000 UTC, so the 0000 UTC run output will miss more of this due to being earlier in the spin-up phase than that of the 1200 UTC runs.  For both initializations, as the lead time increases, the influence of initial condition wanes.  Lastly, the bottom panel in Fig. 2 shows the average gridpoint precipitation amount bias (modelobserved), with close-to-zero bias after the spin-up period.

The results from the case comparison (26 May 2016) of the convective suite with its base (Thompson) microphysics scheme and other microphysics schemes are presented in Figs. 3 and 4.  Figure 3 shows the spatial distributions of precipitation across the simulations for a 24-hour period after a model spin-up period of 12 hours.  Thus, the accumulations correspond to hours 1236 of the forecasts (1200 UTC 26 May1200 UTC 27 May 2016).  The microphysics scheme for each run is noted in the tops of the panels, with the Stage IV data in the bottom panel.  The model patterns of accumulation overall are consistent with the observations.  However, the center of heaviest precipitation in southeastern Texas along the Gulf Coast is shifted northward relative to that observed in all the simulations except the WSM6 experiment, where the center is located more inland compared to the observations.  Similarly, with the observed precipitation center in the Midwest, the model counterpart is shifted northward relative to the observations in all four experiments.   

 

Figure 4 shows the bias and fractions skill score (FSS) metrics for the 24-hr period examined above.  The FSS neighborhood was set at 60 km.  The Thompson microphysics selection of the suite generally shows better bias and FSS scores than the P3 scheme, but not as good as Morrison and WSM6.  All four schemes perform better for lighter precipitation.  For heavy precipitation, the convective suite’s Thompson scheme underestimates the frequency, while the other three schemes overestimate it.

 

Note that these microphysics comparison results are for a single case for which the Stage IV observations indicate local precipitation (in eastern Texas) was up to 452.9 mm/24 hr for the  1200 UTC 26 May1200 UTC 27 May period.  It is recognized that such heavy rainfall makes for a forecast challenge for WRF.  However, the microphysics scheme comparison was done to provide some basic indication of the performance of the target suite relative to variations of it, and an exhaustive inter-scheme comparison is not the purpose here.  Systematic experiments of various cases/scenarios would be necessary to provide a comprehensive picture of the relative performance of the different microphysics schemes within the convection-permitting suite.


 

Figures

Fig. 1: WRF domain used for testing of suite for Spring 2016 period.  Grid spacing: 3 km.  Terrain shaded (m, MSL); scale at bottom.

 

 

 

Fig. 2: Comparisons of WRF suite test period runs (red) with Stage IV precipitation observations (blue) across forecast hours 048.  Upper left panel: Average gridpoint accumulation (mm) across model domain for forecasts initialized at 0000 UTC.  Upper right panel: Average gridpoint accumulation (mm) across model domain for forecasts initialized at 1200 UTC.  Bottom panel: Difference (mm) of average gridpoint accumulations for WRF suite runs (red) and Stage IV observations (blue).

 

 

Fig. 3: 24-hr accumulations of precipitation from WRF runs for case of 26 May 2016.  Period plotted: forecast hours 1236, 1200 UTC 26 May1200 UTC 27 May 2016.  Stage IV analysis for the period shown in bottom panel.  Precipitation amounts in mm/24 hr, scale at bottom.


 

 

 

Fig. 4: Bias (left) and fractions skill score (FSS) (right) metrics for the 24-hr forecast period from WRF runs for the case of 26 May 2016.  Period plotted: forecast hours 1236, 1200 UTC 26 May1200 UTC 27 May 2016.  The FSS neighborhood was set at 60 km. 

 

 

References

 

Chen, F., Z. Janjic, K. Mitchell, 1997: Impact of atmospheric surface layer parameterization in the new land-surface scheme of the NCEP Mesoscale Eta numerical model.  Bound.-Layer Meteor.185, 391421.

 

Clark, A. J., and Coauthors, 2012: An Overview of the 2010 Hazardous Weather Testbed

Experimental Forecast Program Spring Experiment.  Bull. Amer. Meteor. Soc., 93, 55–74.

doi:10.1175/BAMS-D-11-00040.1

 

Coniglio, M.C., K.L. Elmore, J.S. Kain, S.J. Weiss, M. Xue and Weisman, M. L., 2010: Evaluation of WRF model output for severe weather forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment.  Wea. Forecasting, 25, 408–427.  doi: 10.1175/2009WAF2222258.1

 

Done, J., C. A. Davis, and M. L. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecast (WRF) model.  Atmos. Sci. Lett., 5, 110–117. doi:10.1002/asl.72.

 

Hong, S.–Y., and J.–O. J. Lim, 2006: The WRF single–moment 6–class microphysics scheme (WSM6).  J. Korean Meteor. Soc., 42, 129–151.

 

Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gasses: Calculations with the AER radiative transfer models.  J. Geophys. Res., 113, D13103.  doi: 10.1029/2008JD009944.

 

Janjic, Z., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945.

 

Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one– and two–moment schemes.  Mon. Wea. Rev., 137, 991–1007.

 

Morrison, H., and J. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests.  J. Atmos. Sci., 72, 287311.

 

Powers, J. G., and Co-Authors, 2017: The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions.  Bull. Amer. Meteor. Soc., 98 (in press).

 

Romine, G. S., C. S. Schwartz, C. Synder, J. L. Anderson, and M. L. Weisman, 2013: Model bias in a continuously cycled assimilation system and its influence on convection-permitting forecasts.  Mon. Wea. Rev., 141, 1263–1284.  doi:10.1175/MWR-D-12-00112.1

 

Romine, G. S., C. S. Schwartz, J. Berner, K. R. Fossell, C. M. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system.  Mon. Wea. Rev., 142, 4519–4541.  doi:10.1175/MWR-D-14-00100.1

 

Schwartz, C. S., J. S. Kain, S. J. Weiss, M. Xue, D. R. Bright, F. Kong, K. W. Thomas, J. J. Levit, M. C. Coniglio, and M. S. Wandishin, 2010: Toward improved convection-allowing ensembles: Model physics sensitivity and optimizing probabilistic guidance with small ensemble membership.  Wea. Forecasting, 25, 263–280.  doi:10.1175/2009WAF2222267.1

 

Schwartz, C. S., G. S. Romine, M. L. Weisman, R. A. Sobash, K. R. Fossell, K. W. Manning, and S. B. Trier, 2015a: A real-time convection-allowing ensemble prediction system initialized by mesoscale Ensemble Kalman Filter analyses.  Wea. Forecasting, 30, 1158–1181.  doi:10.1175/WAF-D-15-0013.1

 

Schwartz, C. S., G. S. Romine, R. A. Sobash, K. R. Fossell, and M. L. Weisman, 2015b: NCAR’s experimental real-time convection-allowing ensemble prediction system.  Wea. Forecasting, 30, 1645–1654.   doi:10.1175/WAF-D-15-0103.1

 

Thompson, G., P. R. Field, R. M. Rasmussen, W. D. Hall, 2008: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization.  Mon. Wea. Rev., 136, 5095–5115.

 

Tiedke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models.  Mon. Wea. Rev.117, 1779–1800.

 

Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0-36-h explicit convective forecasts with the WRF-ARW model.  Wea. Forecasting, 23, 407–437.   doi:10.1175/2007WAF2007005.1.

 

Zhang, C., Y Wang, and K. Hamilton, 2011: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme.  Mon. Wea. Rev.139, 3489–3513.  doi:10.1175/MWR-D-10-05091.1.

 

 

 



 
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