1. Modifications to the MYNN PBL and Surface Layer Scheme for WRF-ARW Joseph Olson1,2 John M. Brown1 1NOAA-ESRL/GSD/AMB 2Cooperative Institute for Research in Environmental Sciences

2. Background on Mellor-Yamada- Nakanishi-Niino (MYNN) PBL Scheme
Implemented into WRF-ARW in 2008 (v3.0).
Main features of the MYNN include:
Turbulent kinetic energy (TKE)-based local mixing scheme (like MYJ)
Option to run at level 2.5 or 3.0 closure.
Liquid water potential temperature, θl (= θ - (θ/T)(Lv/cp)ql), and total water content, qw (= qv + ql), are used as thermodynamic variables.
Tuned to a database of LES simulations in order to overcome the typical biases associated with other MY-type schemes (insufficient growth of convective boundary layer and underestimated TKE).
More elaborate mixing length formulations to flexibly change behavior across the stability spectrum.
Initially adopted a very simple surface layer scheme, taken from an old version of the YSU surface layer scheme, with very little customization to the PBL scheme.

3. Known Problems with MYNN
warm-bias over desert/bare soil regions
production of negative TKE
high 10- and 60-m wind speed bias
excessive low-level clouds over the ocean/arctic
6-hr forecasts from MYNN and MYJ between
verified against rawinsonde data over CONUS region
Temperature Bias
Wind Vector RMSE
MYNN
MYJ
MYNN
MYJ

4. Recent changes to MYNN PBL scheme
Adjustment of closure constants to remove negative TKE problem (Canuto et al and Kitamura 2010) but also removes critical Richardson number.
Subsequent modifications to closure constants C2 and C3 to help reduce over-diffusive behavior
Further modifications to mixing length formulae (surface layer and buoyancy length scales) to compensate increased diffusivity (shown later).
after
before
-since implementation, many problems have been noted in the MYNN – some have fixes. For example...
-these mods require further tuning --- so this time, special attention will be paid to performance of low-level wind forecasting for wind energy application.

5. MYNN Mixing Length Formulation
The mixing length is designed such that the shortest length scale among, ls, lt, and lb will dominate:
Stable Conditions
where the surface layer length scale ls is a function of
the stability parameter(ζ=z/L; L in the M-O length):
ls = {
if 0 ≤ ζ ≤ 1
if ζ < 0
and the turbulent length scale lt is:
and the buoyancy length scale lb is:
Unstable Conditions
This is what makes the MYNN usinue
The smallest dominates the averaging
-ls varies with stability unlike other PBL scheme (most use l=kz) – cns controls its departure from kz in stable conditions
-lb also varies greatly wrt stability – a2 controls magnitude in stable conditions.
where qc is a turbulent velocity scale ~O(w*)

6. Changes to Surface Layer Length Scale (ls)
α4 = 100, cns = 2.7
α4 = 20, cns = 2.1
-The left panel shows how the original Ls varies with stability, with Ls ~O(z) at z/L =1, while much smaller than kz in stable conditions.
-The modified parameters (right) decrease the variation of Ls with stability.
At z/L = -1,
ls ~ 1.0z
At z/L = 1,
ls = kz/3.7
At z/L = -1,
ls ~ 0.75z
At z/L = 1,
ls = kz/3.1

7. Changes to MYNN surface layer scheme
Iterative - accurate solution of z/L, u* and θ*.
Updated thermal/moisture roughness lengths over land (Zilitinkevich (1995), Air Pollution III – Vol. I.)
Updates thermal/moisture roughness lengths over water, taken from the COARE 3.0 bulk algorithm (Fairall et al. 2003, J. of Climate).
Use of consistent flux-profile relationships with those used to formulate the surface layer length scale in the PBL scheme.
Relaxed some arbitrary limits, such as lower limit for u* (0.1  0.01).
-lot of similarities to Zili, but for a given u*, zt is larger at night and can be smaller during the day when HFX becomes very large.

8. 2 m Temperature Verification in RAP (all metars across CONUS region)
BIAS
RMSE
MYJ
MYNN
cold
warm
Afternoon warm bias reduced by up to 1oC in MYNN compared to MYJ
Both Biases and RMSEs are comparable at night.

9. 10 m Wind Speed Verification in RAP 9 hr forecasts – June 2012 verified against all metars across the CONUS region
MYJ
MYNN
α4=100
α4=20
OBS-MODEL
(negative means high wind speed bias)

10. . Evolution of the Low-Level Jet (3-km nest)
Loop was made from 1-hr output intervals between UTC 06 Sept 2011.
80-m wind speed (color)
WFIP-North Profilers .
Wind Speed (m s-1)
Wind speed and θ (Leeds, ND)
m s-1
-here’s the evolution of the 80 m wind speeds from UTC on the 06 Sept 2011.
- The red dots are the profilers, and I’ll be focusing on Leeds.
TKE and θ (Leeds, ND)
m2 s-2

11. Profile Evolution (Leeds, ND) – Lb Tests
MYNN
α2=0.53
MYJ
MYNN
α2=0.64
Not much sensitivity evident with different α2, compared to difference with MYJ!
All model profiles are taken from the 3 km simulations.
MYNN has stronger LLJ but too strong under 100 m AGL.
MYNN is improved with increased α2.
MYNN
α2=0.75

12. Mean profiles (LDS) 03-09 UTC (MYNN-lb tests)
α2 = 0.75 produces a weaker LLJ (~1.4 m s-1 weaker than α2=0.53).
α2 = 0.64 produces a weaker LLJ (~0.7 m s-1 weaker than α2=0.53).
Largest differences in TKE & lm are above the jet max.

13. Profile Evolution (Leeds, ND) – Ls Tests
MYNN
cns=2.7
MYJ
MYNN
cns=2.1
Not much sensitivity evident with different α2, compared to difference with MYJ!
All model profiles are taken from the 3 km simulations.
Parameter cns has a powerful impact on the LLJ strength.
MYNN is improved with decreased cns..
MYNN
cns=1.5

14. Mean profiles (LDS) 03-09 UTC (MYNN-ls tests)
cns = 1.5 produces a weaker LLJ (~1.8 m s-1 weaker than cns = 2.7).
cns = 2.1 produces a weaker LLJ (~0.9 m s-1 weaker than cns = 2.7).
Large differences in TKE & lm are below the jet max.

15. . Evolution of the Low-Level Jet (3-km nest)
Loop was made from 1-hr output intervals between UTC 10 June 2012.
80-m wind speed (color)
WFIP-North Profilers
MYNN PBL scheme configured with: α2 = 0.64, cns = 2.1, and α4 = 20. .
Wind speed and θ (Ainsworth, NE)
m s-1
-here’s the evolution of the 80 m wind speeds from UTC on the 10 June 2012.
- The red dots are the profilers, and I’ll be focusing on Leeds.
TKE and θ (Ainsworth, NE)
Wind Speed (m s-1)
m2 s-2

16. Profile Evolution (Ainsworth, NE)
MYNN
Profiler Winds
MYJ
Hard to see differences here – move to back

17. Relative Humidity Bias Relative Humidity RMSE
6-hr forecasts from RAP with MYNN and MYJ (including all modifications) between verified against rawinsonde data over CONUS region
Wind Speed Bias
Temperature Bias
Relative Humidity Bias
MYJ
MYNN
MYJ
MYNN
Wind Vector RMSE
Temperature RMSE
Relative Humidity RMSE
Hard to see differences here – move to back

18. Summary
Important modifications were made to improve the MYNN PBL scheme: (1) modified lb (α2), (2) modified ls (cns) , and (2) modified ls (α4).
A small change to all 3 parameters can collectively reduce the high wind speed bias in the lowest 100 m, while maintaining better forecast skill of wind speed in the rest of the PBL.
Many improvements to the surface layer scheme reduced the daytime warm bias, while remaining competitive with the MYJ at night.
Too much tuning to improve the low-level wind speeds at 13 km grid spacing may degrade wind forecasts at higher resolution (or other altitudes). Cliff Mass will discuss this in the next talk.

19. Not Shown
Hybrid PBLH: uses θ-based PBLH in neutral/unstable conditions and TKE-based PBLH in stable conditions.
TKE budgets are available output fields by configuring Registry.EM.
Future Work
Make 3D TKE-budgets output fields dependent on namelist option, to reduce memory useage when not needed.
Implement changes into v3.4.1.

20. Extra Slides

21. Background on Rapid Refresh (RAP)
Hourly data assimilation system which uses the WRF-ARW as the model forecast component (Weygandt et al., 2.1).
Parent model of the High-Resolution RAP (HRRR).
Testing different parameterizations within the WRF-ARW against our current configuration.
For wind energy application, comparison of current PBL scheme, Mellor-Yamada-Janjic (MYJ) to Mellor-Yamada-Nakanishi-Niino (MYNN). Main features of the MYNN include:
Turbulent kinetic energy (TKE)-based local mixing scheme (like MYJ)
Option to run at level 2.5 or 3.0 closure.
Tuned to a database of LES simulations in order to overcome the typical biases associated with other MY-type schemes (insufficient growth of convective boundary layer and underestimated TKE).
More elaborate mixing length formulations to flexibly change behavior across the stability spectrum.

22. Hybrid PBLH in MYNN Coastal Jet Case
Wind speed parallel to the barrier (color), potential temperature (red contours) and TKE (black contours).
22 UTC
23 UTC
00 UTC
ziTKE
zihybrid + 0.3zihybrid
Mt. Fairweather
Mt. Fairweather
Mt. Fairweather
zihybrid
ziθv
If we take the maximum wind speed of the jet as the PBL top, then ziθv significantly underestimates the PBL height.
ziTKE overestimates the PBL height, especially during the period of elevated mixing associated with the strong vertical wind shear on the outer edge of the jet.
The hybrid PBLH, zihybrid, best follows the level of maximum wind speeds, but is also shallow-biased prior to the period enhanced mixing.
The height, zihybrid + 0.3zihybrid, is used as the level for which the turbulent length scale is integrated to (between the surface and zihybrid + 0.3zihybrid). This allows the TKE within the “entrainment layer” to be accounted for when determining the turbulent length scale.

23. 2 m Temperature Verification (8-day means of 9 hr forecasts – valid at 21 UTC)
MYJ
MYNN
East Bias: 2.41
East MAE: 3.03
West Bias: 1.33
West MAE: 2.38
East Bias: 1.78
East MAE: 2.69
West Bias: 0.22
West MAE: 2.33