Physics Experiments with the UFS Short-Range Weather Application for Prediction of an Extreme Heat Wave

Linlin Pan1,2,3, Evelyn Grell2,3,6, Evan Kalina1,2,3, Kathryn Newman3,4, LigiaBernardet1,3, Dom Heinzeller1,2,3, Jeff Beck1,3,5, Gerard Ketefian1,2,3, Michelle

Harrold3,4, Tracy Hertneky3,4, Nick Lybarger3,4, and Georg Grell11NOAA/GSL, 2University of Colorado/CIRES, 3Developmental Testbed Center,

4NCAR, 5Colorado State University/CIRA, 6NOAA/PSL

Motivation and Methods Study the impacts of different physics schemes on the heat wave simulation. Use the UFS Short-Range Weather (SRW) application Model Horizontal grid spacings: 25km, 13km, and 3km July 16, 2019 heat wave case

Verifications with GFS analysis, surface station observations and radiosondes using MET

Physics schemes used in the experimentsGFDL_MP GFS_V15.2 GFS_v16beta GSDv0 GSDv1 GSD_noah

Deep convection SA-SAS SA-SAS SA-SAS SA/AA-GF None SA/AA-GF

Shallowconvection

SA-MF SA-MF SA-MFMYNN-EDMF andSA-GF

None MYNN-EDMF andSA-GF

Microphysics GFDL GFDL GFDL AA-Thompson AA-Thompson AA-Thompson

Saturation adj. True True True False False False

PBL/Turbulence K-EDMF K-EDMF Moist SA-TKE-EDMF MYNN-EDMF MYNN-EDMF MYNN-EDMF

LSM Noah Noah Noah RUC RUC Noah

d4_bg 0.15 0.12 0.12 0.12 0.12 0.12vtdm4 0.075 0.02 0.02 0.02 0.02 0.02sponge 30 30 10 10 10 10tau 5 5 10 10 10 10hord_mt 6 6 5 5 5 5hord_vt 6 6 5 5 5 5hord_tm 6 6 5 5 5 5hord_dp -6 -6 -5 -5 -5 -5Ozone 2006 2015 2015 2015 2015 2015

Model-Analysis 25km

T2 12h FCST and BIAS

V16beta 25km V16beta 13km

V15.2 25km V15.2 13km

More detailed structures with higher resolution

Model-Analysis 25km

T2 bias at 25km

GFDL-MP 00h GFDL-MP 12h

GFSv15.2 12hGFSv15.2 00h

GFSv16beta 00h GFSv16beta 12h

Model-Observationat 00h and 12h FCST

Warm bias in West,cold bias in East

T2 bias at 25km

GFDL-MP 24h GFDL-MP 36h

GFSv15.2 36hGFSv15.2 24h

GFSv16beta 24h GFSv16beta 36h

Model-Observationat 24h and 36h FCST

Warm bias in West,cold bias in East

T2 bias at 25km

GSDv0 00h GSDv0 12h

GSDv1 12hGSDv1 00h

GSD_noah 00h GSD_noah 12h

Model-Observationat 00h and 12h FCST

Warm bias in West,cold bias in East

T2 bias at 25km

GSDv0 24h GSDv0 36h

GSDv1 36hGSDv1 24h

GSD_noah 24h GSD_noah 36h

Model-Observationat 24h and 36h FCST

Warm bias in West,cold bias in East

Domain Average of T2 Bias at 25km

CONUS West

Central East

Obvious diurnal cycle in T2 bias

Domain average bias changes with different resolutions

Obvious diurnal cycle in T2 bias

Sounding for Oakland, CA (OAK72493)

00h 12h

24h 36h

GFDL_MP GFSv15.2. GFSv16beta GSDv0 GSD_noahObs

T bias for grid spacing at 3km

GFDL_MP *GFSv15.2. ▽GFSv16beta ●GSDv0 ○GSDv1 ◻GSD_noah +

a) 00h

333333 333333

333333 333333

d) 36hc)24h

333333

b) 12h

Summary The impact of different physics schemes on the heat wave simulation was investigated

with the UFS Short-Range Weather (SRW) application. T2 biases of surface stations tend to have positive values in the western US, and negative

values in the eastern US. The impact of the diurnal cycle is obvious. The GFSv16beta forecasts have strong cold biases at the most of eastern stations.

Land surface model can have strong influences on the T2 bias pattern. Domain average sounding temperature bias show that there is almost no temperature bias

at the initial time, but it increases with time. The GFSv16beta forecasts have a larger temperature bias than the forecasts from GFSv15.2 at all horizontal grid spacing.

The bias pattern is similar for different horizontal grid resolutions. The magnitude of bias increases when the horizontal resolution increases.

Total cloud cover at 36h FCSTV15.2 v16beta V16beta-v15.2

Larger cloud cover in v16beta

RH bias at 36h FCST

V15.2 v16beta

25km 13km

3km