Numerical Modeling Laboratory Yonsei University A new ice microphysical processes for a commonly used bulk parameterization of cloud and precipitation Song-You Hong (Yonsei Univ) Jimy Dudhia (NCAR) Shu-Hua Chen (U.C. Davis)

Numerical Modeling Laboratory Yonsei University List of presentation Background A revised cloud scheme Idealized case experiment Heavy rainfall case experiment Ice cloud – radiation interaction Conclusion A tip for the MRFPBL

Numerical Modeling Laboratory Yonsei University The highest level for the PBL is the half of the total number of vertical layers KLPBL = KL/2 (currently in WRF & MM5)  The PBL mixing is ill-posed with many layers near the surface as done for the air pollution application Correction : In the “mrfpbl.F”, change KLPBL = 1 (modified one)

Numerical Modeling Laboratory Yonsei University WRF (Weather and Research Forecasting Model) Community model: NCAR, NCEP, FSL, AFWA, NSSL, and University communities Real time fcsts : NCAR (22km, 10km), NSSL(34km), AWFA(45km), Italy (20km)  MRF PBL, Kain-Fritsch cumulus  RRTM, Dudhia Radiation  Lin or NCEP simple ice microphysics

Numerical Modeling Laboratory Yonsei University NCEP Cloud Microphysics - Hong et al. (1998), NCEP RSM cloud physics - NCEP cloud microphysics v1.0 (Hong et al. 1998, with some modifications) - NCEP cloud microphysics v1.1 (Jimy’s bug fix in computing Vr, Vs) - > solves the too much precip. - NCEP cloud microphysics v1.2 (Hong et al. 2002, the new scheme)

Numerical Modeling Laboratory Yonsei University A bug fix in computing sedimentation of precipitation ( ) Domain-total precip V1.1 V1.0 V1.1 V1.0

Numerical Modeling Laboratory Yonsei University NCEP (Hong) Cloud schemes NCEP CLOUD 3 (simple ice) and CLOUD 5 (mixed phase) (qci,qrs)(qc,qi,qr,qs) qv Modifications after Dudhia (1989) and Rutledge and Hobbs (1983)

Numerical Modeling Laboratory Yonsei University Background Lin et al. (1983) and Rutledge and Hobbs (1983) -> core part of microphyscs A typical problem -> too much cirrus due to Ni from Fletcher Different assumptions in microphysics ( Meyers et al. 1992, Kruger et al. 1995, Reisner et al. 1998, Rotstayn et al. 2000, Ryan 2001 ) Sedimentation of ice crystals (Manning and Davis, 1997, Wang 2001)

Numerical Modeling Laboratory Yonsei University Fletcher Reisner Rotstyan MM5 simple ice Ice number concentration, Ni

Numerical Modeling Laboratory Yonsei University Ice crystal property (Mass, Diameter, Mixing ratio, Ice number)

Numerical Modeling Laboratory Yonsei University Ice number concentration (Ni) vs. cloud ice (den*qi)

Numerical Modeling Laboratory Yonsei University Ryan 1996 Rotstayn 2000 Ryan 2000 Observed and formulated Ni

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University Ni0 Ni0, number concentrations for ice nucleation

Numerical Modeling Laboratory Yonsei University Cloud ice amount for initial generation Fletcher This study 1gm gm-3

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University Slope parameter of the size distributions for snow

Numerical Modeling Laboratory Yonsei University - qicrit has small range of T : 0.1 and 1 gkg-1 for –27 and –32C Fletcher : D89, RH83 - qicrit=0.18gkg-1, at T=-40C, P=300 mb This study

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University Comparison of deposition rate of water vapor onto ice as a function of cloud temperature, with the assumption that cloud ice mixing ratio is 0.1 gkg-1 and the air is supersaturated with respect to ice by 10 %. RH83,D89 This study

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University TC80 Kessler TC80 rc=8

Numerical Modeling Laboratory Yonsei University -A limit of microphysical processes Some microphysical processes are bounded to a value at the half of supersaturation amount

Numerical Modeling Laboratory Yonsei University LW radiation : RRTM SW radiation : Dudhia Vertical diffusion : MRF Cumulus scheme : Kain-Fritsch Microphysics : NCEP (HONG) simple ice Grid size : 45 km, 15 km Time step : 120 s, 60 s Initial time : 1200 UTC 23 June 1997 Integration : 48 hrs Initial and BDY : NCEP GDAS WRF version 1.1-beta

Numerical Modeling Laboratory Yonsei University Sensitivity Experiments Exp1 : Dudhia microphysics (OLD) Exp2 : Dudhia + sedimentation of qi Exp3 : New microphysics Exp4 : New + sedimentation of qi (NEW)

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University Cloud and Precipitation after 30 min. qci qrs HDC3 Lin

Numerical Modeling Laboratory Yonsei University Fig. 3. Profiles of domain-averaged (a) cloud/ice water and (b) snow/rain water mixing ratio (gkg-1) for the Exp1 (thin solid line), Exp2 (dotted line), Exp3 (dashed line), and Exp4 (thick solid line) experiments. Exp1 Exp3 Exp2 Exp4 qci Exp1, 2 Exp3,4 qrs

Numerical Modeling Laboratory Yonsei University A heavy rainfall case : (a)(b) A

Numerical Modeling Laboratory Yonsei University UTC UTC UTC (a) ) (b) (c) (d) ) (e) ) (f))

Numerical Modeling Laboratory Yonsei University Fig. 6. Vertical profiles of (a) the ice/cloud water mixing ratios and snow/rain water mixing ratios averaged over Korea (33-40 N, E) at 0000 UTC 25 June 1997, obtained from the Exp1 (thin solid), Exp2 (dotted), Exp3 (dashed), and Exp4 (thick solid) experiments. Domain-averaged qci (left) and qrs (right) over Korea at 36hr fcst Exp1 Exp2 Exp3 Exp4 Exp1,2,3 Exp4

Numerical Modeling Laboratory Yonsei University 45-km experiment : 24-hr precipitation (mm) EXP1 EXP3 EXP4 OBS EXP2 > 90 mm

Numerical Modeling Laboratory Yonsei University EXP1 EXP3 EXP2 EXP4 OBS 15-km experiment : 24-hr precipitation (mm)

Numerical Modeling Laboratory Yonsei University Implicit rain Explicit rain Exp1 Exp4 Exp1

Numerical Modeling Laboratory Yonsei University Cloudiness at 36-h fcst (0000UTC 25 June) Exp1 Exp4

Numerical Modeling Laboratory Yonsei University Exp1 Exp2 Exp3 Exp4 Exp1 Exp2 Exp3 Exp4 ANAL Volume-averaged qci Domain averaged 300 hPa T

Numerical Modeling Laboratory Yonsei University q T Exp1,2,3 Exp4 Exp1,2,3 Bias of domain averaged T & q at 46-hr fcst time

Numerical Modeling Laboratory Yonsei University Exp1 Exp2 Exp3 Exp4 Exp1 Exp2 Exp3 Exp4 ANAL Volume-averaged qci Domain averaged 300 hPa T Impact of Vi in the Lin ’ s scheme Lin without Vi Lin with Vi Lin without Vi Lin with Vi

Numerical Modeling Laboratory Yonsei University EXP1 Lin ’ s-no Vi EXP4 Lin ’ s-Vi

Numerical Modeling Laboratory Yonsei University Exp1 : Dudhia microphysics (too much cloud ice -> warm bias) NORA : Exp1 but without radiation feedback due to ice cloud) NOLW : Exp1 but without LW radiation feedback due to ice cloud) NOSW : Exp1 but without SW radiation feedback due to ice cloud) Ice cloud - radiation feedback

Numerical Modeling Laboratory Yonsei University EXP1 NOSW NOLW NORA

Numerical Modeling Laboratory Yonsei University Volume-averaged qciDomain averaged 300 hPa T EXP1 NORA NOLW NOSW NORA NOLW ANAL NOSW EXP1

Numerical Modeling Laboratory Yonsei University EXP1 NOSW NOLW NORA EXP1 NOSW NOLW NORA Domain average rain Implicit rain Explicit rain

Numerical Modeling Laboratory Yonsei University NOSW EXP1 NORA NOLW NOSW EXP1 NORA NOLW q T Bias of domain averaged T & q at 46-hr fcst time

Numerical Modeling Laboratory Yonsei University Ice cloud - radiation feedback More cloud ice Less SW heating More LW heating Tropospheric cooling Less SFC buoyancy Upper level heating Less cloud ice Less explicit rain Less implicit rain Less Precipitation, Warmer Troposphere

Numerical Modeling Laboratory Yonsei University Concluding Remarks New scheme produces better cloudiness (remove high cloud bias) New scheme alleviates the discontinuity problem of small and large ice particles Reduction of ice clouds induces more surface precipitation Combined effects of improved microphysics and the inclusion of sedimentation of ice crystals are attributed to the improvement of precipitation, cloudiness, and large-scale features Sedimentation of HD1990 dominates the effects of detailed ice- microphysical processes

Numerical Modeling Laboratory Yonsei University Severe weather Regional climateSeasonal predictionClimate mechanism NWP

Numerical Modeling Laboratory Yonsei University OLD NEW

Numerical Modeling Laboratory Yonsei University ANAL ANAL (GDAPS)

Numerical Modeling Laboratory Yonsei University NEW OLD ANAL

Numerical Modeling Laboratory Yonsei University

Numerical Modeling Laboratory Yonsei University (a)) (b) (c) (d)) (e)) (f)) UTC UTC UTC

Numerical Modeling Laboratory Yonsei University YOURS The YOnsei University Regional prediction System

Numerical Modeling Laboratory Yonsei University The WRF model : current status and an evaluation for a heavy rainfall over Korea Hong, Song-You and Jung-Ok Lim Dept. Atmospheric Sciences, Yonsei University Acknowledgements: Yoo-Jeong Noh (YSU/FSU) Jimy Dudhia (NCAR) Shu-Hua Chen (U.C. Davis)

Numerical Modeling Laboratory Yonsei University