The Impact of Observation Localization on South Plains Convective Forecasts

6th EnKF Workshop May 2014Brock Burghardt, Brian AncellAtmospheric Science Group

Dept. of Geosciences

Why do this?

1. Optimize forecast accuracy of South Plains convective events for ESA investigation– Improve warm-season convective forecast performance

in TTU RT EnKF2. Assess localization cutoff impact using an object-

based approach to verify rainfall– Preliminary step in verifying convective-scale details– Implement into RT EnKF system for precipitation

verification

Motivation• Limited literature addressing optimal localization

covariance cutoff in atmospheric modeling.– Would expect some correlation with model grid spacing

and length scale of phenomena (Hamill et al. 2001)– Sobash and Stensrud (2013) find localization cutoff

values on order of 10 km yield most accurate forecast solutions in convective OSSEs of an MCS over Oklahoma (assimilating simulated radar data)

– Initial testing of radii during TTU EnKF system development showed some differences in verification metrics (Ancell)

Methodology• Select significant convective events places across the

Southern Plains (occurring within innermost domain)• Vary localization radius (half-width) cutoff radius in

inner two domains– Using a Gaspari-Cohn distance cutoff function– Optimize D2 vary D3 (traditional & object statistics)– Focusing on day 1 (0-24h) forecastHalf-width cutoff localization radiusr = 200 kmr = 300 km (original)r = 400 km

Model Domain and Configuration • WRF-ARW v3.5.1

– GFS analysis/forecasts used for D1 ICs/LBCs– Hourly state output

d01: dx=36 kmd02: dx=12 kmd03 : dx=4 km

38 vert. levels

Ensemble Background• DART EAKF system (Anderson 2001, et al. 2009)– 50 members (all domains) utilizing adap. inflation– Initialized using WRF-VAR v3.5.1 background error

covariance climatology (cv3)– MADIS obs filtered into 6 h forecast background– Gaspari-Cohn cutoff function for localization covariance

t= 0 h t= 24/30 ht= -30 ht= -54 h

Cycle D1 Cycle all domains

Integrate full forecastNest

down D2, D3

MADIS data obtained from: http://madis.noaa.gov

Case 1

Verification D3 (varied D2 radius)

r=200 km 2.643 K

r=300 km 2.564 K

r=400 km 2.576 K

Time mean RMSE

r=200 km 2.140 m/s

r=300 km 2.099 m/s

r=400 km 2.095 m/s

Time mean RMSE

Verification D2

r=200 km 2.670 K

r=300 km 2.688 K

r=400 km 2.680 K

Time mean RMSETime mean RMSEr=200 km 2.211 m/s

r=300 km 2.235 m/s

r=400 km 2.223 m/s

Object-based verification

Group values into objects

Insert original values

Double mask values 1) Orig. field ≥ 15 mm

2) Smoothed field ≥ 10 mm

Apply Gaussian smoothing operator (r=2; sigma=1)

Fit Stage IV rainfall to D3 grid • Follows work of Davis et al. 2006 a,b, Burghardt et al. 2014

• Using total forecast time rainfall

• Matching is based on lowest distance and area difference error• Objects constrained to max

distance error of 500 km

Stage IV rainfall data obtained from:www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4

Rainfall objects without time domain

Rainfall objects for case 1

Member 30 (best net member) from D2 r=300 km

Object-based verification

Half radius (D2) localization [km]

Accumulated matching error [km2]

Accumulated distance error [km]

Accumulated area difference [km2] POD FAR bias CSI

r=200 30272504.3 73191.7 -1302080 0.731 0.162 0.949 0.609r=300 23668216.1 60664.7 -1710320 0.711 0.141 0.882 0.609r=400 25718677.3 72155.0 -2622160 0.818 0.190 1.084 0.648

Up next…Case 2

Continuing work and ideas• More simulations of varied r values.• More cases (~10)• Look at impact of localization radius on ESA (do

meso-beta –gamma -scale flow features exhibiting sensitivity diminish with decreasing radius?)

• Using 1-h time tracking object algorithm• Applying algorithm to archived TTU RT EnKF

precipitation forecasts

Takeaway points• Some evidence larger localization radius values

(relative to initial values on D2) improve mesoscale and convective-scale forecast details.

• Tendency for model to produce too many net rainfall objects that are smaller than observed.

Any suggestions, recommendations?

Email: brock.burghardt@ttu.edu