toward improving representation of model microphysics errors in a convection-allowing ensemble:...

Toward Improving Representation of Model Microphysics Errors in a Convection-Allowing Ensemble: Evaluation and Diagnosis of mixed-

Microphysics and Perturbed Microphysics Parameter Ensembles in the 2011 HWT Spring

Experiment

Jeff Duda, Xuguang Wang, Fanyou Kong, Ming Xue

School of Meteorology and Center for Analysis and prediction of storms

University of Oklahoma, Norman, OK

Acknowledgements: Dan Dawson (NSSL), Kevin Thomas (CAPS), Keith Brewster (CAPS), Yunheng Wang (CAPS)

Warn-on-Forecast and High Impact Weather workshop, Norman, OK, Feb. 8-9, 2012

Introduction

• Sources of error in an NWP forecast:– IC/LBCs– Model error

• Dynamics• Physics

• Methods to account for microphysics errors– Multiple microphysics– Perturbed parameter within a single microphysics scheme

Background

• NOAA HWT 2011 spring experiment• 52 member storm-scale ensemble forecast system (SSEF) run

by CAPS at OU– ∆x = 4 km (no convective parameterization)

• Once-daily forecasts out to 36 hours– Initialized at 0000 UTC

• Use of 3DVAR and cloud analysis to assimilate radar data at initialization

• 35 forecasts from 27 April to 10 June

Setup

• Focus on two sub-ensembles– Mixed microphysics (six members)

• Thompson (control)• Ferrier+ (modified for NMMB)• Milbrandt-Yau (new for WRF v. 3.2)• Morrison• WDM-6• WSM-6

– Perturbed parameter (five members)• WSM-6 and four sets of perturbations

• N0r, N0g, and graupel/hail density perturbed

• Unperturbed values: N0r=8.0 x 106 m-4, N0g=4 x 106 m-4,

graupel density = 500 kg m-3

Name N0r N0g ρgraupel (kg m-3)

WSM6 (ctrl) 8 x 106 4 x 106 500

WSM6-M1 8 x 106 4 x 104 913

WSM6-M2 8 x 107 4 x 106 500

WSM6-M3 8 x 105 4 x 102 913

WSM6-M4 8 x 105 4 x 103 913

Outline of results

• Measures of skill for the two sub ensembles and their combination using various metrics

• Understand the difference among double moment microphysics schemes and single moment scheme with perturbed parameters for a case study using equivalent intercept parameters.

Spread

Rank histograms

Amount bias

RMSE

Frequency bias

Brier score

Fractions Brier score

Square neighborhood of radius 3 grid squares

Summary of measure of skill

• Perturbed parameter has larger spread, but performed worse than mixed-microphysics

• Both subensembles (and their pooled combination) under dispersive for precip; also have a slight positive bias

• Pooled ensemble better than both sub-ensembles for some metrics– Perhaps due only to larger ensemble size

• Appropriate choices of parameter values?

Equivalent intercept parameter for double moment schemes

• Double moment vs. single moment microphysics– Number concentration prognosed as well as mass mixing ratio– Given assumed particle size distribution (generally a gamma

distribution), can diagnose the value of the intercept parameter N0

• Loss of physical meaning for N0 for non-zero shape parameter distributions

• Testud et al. (2001): normalized intercept parameter

– For a fixed water species mass in a grid box, higher N0 smaller particles greater surface area more evaporation, stronger cold pools

M3/M4 Ctrl/M1 M2

T2m

1-hr precip

Conclusions

• Some consistency between N0r and cold pool strength, accumulated precip– More analysis needed on N0g

• Selected N0r values for perturbed parameter sub-ensemble seem appropriate

• Evidence of size-sorting of raindrops• Future work

– Evaluate other parameters• Storm propagation speed

– Evaluate other case studies– Include object-based evaluation of QPF (MODE)