# Presentation: 490069

## SOO/STRC WRF Environmental Modeling System

- Robert Rozumalski
- National SOO Science and Training Resource CoordinatorNOAA/NWS/OCWWS/Training Division/FDTB

- Simulated Radar Reflectivity

## Simulated Reflectivity

- WRF Simulated Reflectivity

**Notes:**
A comparison of observed Vs Simulated reflectivity – NMM core

Red line represents cross section for next slide

## Simulated Reflectivity

- Cross Section of Simulated Reflectivity

**Notes:**
A cross section of simulated reflectivity

## Simulated Reflectivity

**Notes:**

Cross section of the four (4) microphysical species used to derive simulated reflectivity

Dark Blue = Cloud ice mixing ratio
White = Snow mixing ratio
Light Blue = Cloud water Mixing ratio
Green = Rain water mixing ratio

## Simulated Reflectivity

**Information provided by Koch et al.** *

**Lake effect snowbands**

**Narrow Cold Frontal Rainband**

**Notes:**
A comparison between simulated reflectivity (ARW core) on left and observed on right.

Note that the model is able to simulate the narrow frontal cold bands and regions of lake effect snow

## Simulated Reflectivity

- Precipitation Bands

- 3-hourly Total precipitation

**Information provided by Koch et al.** *

**Notes:**

A demonstration of the utility of the simulated reflectivity fields compared to traditional precipitation fields

Here is 3-hourly accumulated precipitation wherein rain bands are evident

## Simulated Reflectivity

- Gravity Waves
- Precipitation Bands

**Information provided by Koch et al.** *

**Notes:**

A demonstration of the utility of the simulated reflectivity fields compared to traditional precipitation fields

Here is simulated reflectivity wherein both  rain bands and gravity waves are evident

## Simulated Reflectivity

- Precipitation Vs. Simulated Reflectivity

- 03 UTC 1 March 2005

**Information provided by Koch et al.** *

**Notes:**
A comparison of ARW core simulated precipitation, model reflectivity, and observed reflectivity for cold season event

## Simulated Reflectivity

- ARW

- NMM

**Information provided by Koch et al.** *

**Notes:**
A comparison of ARW and NMM core simulated precipitation and model reflectivity

## Simulated Reflectivity

- ARW – 4km

- Note: NMM reflectivity does not exceed > 52 dBZ

- ARW – 2km

- NMM – 4km

**Notes:**
A comparison of ARW and NMM core simulated reflectivity.

 Note the simulated hook echoes

Also, note that NMM core reflectivity does not exceed 52 dBZ in current release

## Simulated Reflectivity

- Easier to visualize relationships between cloud and precipitation bands
- Many mesoscale phenomena are more readily revealed, especially during the cold season
- Convective storm structures, such as supercells, are more clearly identified
- Possibility of drawing comparisons to observed radar imagery for model verification

- Simulated reflectivity offers several advantages over customary accumulated precipitation products

**Information provided by Koch et al.** *

## Simulated Reflectivity

- It is not possible to make strict comparisons between predicted vertical profiles of reflectivity and observed radar imagery because:
- Radar resolution degrades with distance from transmitter
- Information is lost at low levels due to earth curvature
- Ground clutter, AP, and other issues
- Bright Banding

- Simulated reflectivity – Caveat Emptor!

**Information provided by Koch et al.** *

## SOO/STRC WRF Environmental Modeling System

- The End

- Simulated Radar Reflectivity

## Simulated Reflectivity

- The difference in the reflectivity product appearance between the WRF-NMM and ARW models is largely attributed to the differences in model microphysics -primarily the snow intercept parameter and the size distributions for snow

**Information provided by Koch et al.** *

## Simulated Reflectivity

- Both cores use single-moment microphysics schemes that assume the Marshall-Palmer exponential DSD:
*N*s(*D*s) = *N*osexp(-s*D*s)
- Where:
*N*s(*D*s) = The number of snow particles per unit size range per unit volume
*N*os = Snow intercept parameter
*D*s = The diameter of the snow particles
**s = The slope factor for snow
- Equivalent reflectivity is computed using the (*ND**6*) method assuming Rayleigh scattering and spherical hydrometeors

**Information provided by Koch et al.** *

## Simulated Reflectivity

- ARW Core Microphysics
- WSM5 – 5 class scheme
- WSM6 – 6 class scheme (WSM5 + graupel)
- In WRF Postprocessor
- Cloud water and ice are combined
- Rain and snow are also combined
- Fixed intercept *N*o used in computing reflectivity even though WSM5/6 uses a temperature dependent *N*o

**Information provided by Koch et al.** *

## Simulated Reflectivity

-  Cloud liquid
-  Cloud Ice
-  Mixed Ice (Snow, graupel, sleet)
-  Rain
- In WRF Postprocessor uses same temperature dependent *N**o*, which is consistent with Ferrier scheme

**Information provided by Koch et al.** *

## Method for computing simulated reflectivity 

- Equivalent reflectivity factor for M-P distribution
*Z**e** = ****(7)N**o****-7*
- After accounting for non-solid ice particles and the larger dielectric constant for liquid compared to ice (Kl/Ki) = 5.3:
*Z**e** = 0.224****(7)N**o****-7**(****s**/****i**)**2*
- The slope factor for snow () depends on the snow mixing ratio
-  = ((***N**o****s*)/(***a**q**s*))1/4

- Simulated Reflectivity

**Information provided by Koch et al.** *

## Method for computing simulated reflectivity 

- Simulated Reflectivity

- It can be shown that the equivalent reflectivity factor for snow can be written in terms of the snow mass content *M**s** *and the snow number concentration *n**s** *instead of the slope factor and intercept parameter as follows:
*Z**e** = 1.634 x 10**4** (M**s**2**/n**s**)*
- Thus, for the same snow mass content, differences in radar reflectivity will scale with differences in parameterized snow number concentrations; e.g., for a value of *ns *= 0.1 g m-3:
*dBZ**eS*=31.1+17.5log*M**S   *= 13.6  - Fixed intercept ARW
*dBZ**eS*=38.6+17.5log*M**S *= 21.1  - WSM-5
*dBZ**eS*=30.0+10log*M**S    *= 20.0  - NMM Ferrier

**Information provided by Koch et al.** *

## Simulated Reflectivity

- The Ferrier (NMM) method predicts higher reflectivity values than the Fixed-intercept (ARW) method for all  values of snow number concentrations ns < 0.713gm-3
- For convective storms, the NMM reflectivity values are restricted to a value of 52 dBZ, which is primarily the result of the following imposed restrictions:
- The mean size of raindrops is assumed fixed at 0.45mm for rain contents > 1 gm-3
- The maximum number concentration of precipitating ice is fixed, and thus the intercept parameter is fixed

- Method for computing simulated reflectivity*** ***

**Information provided by Koch et al.** *