Remote sensing and modeling of cloud contents and precipitation efficiency Chung-Hsiung Sui Institute of Hydrological Sciences National Central University Xiaofan Li Joint Center for Satellite Data Assimilation and NOAA/NESDIS/Office of Research and Applications On-going collaborations Ming-Dah Chou (National Taiwan Univ.) Ming-Jen Yang (IHS, NCU) Radar and regional modeling groups, IAP, NCU Chang-Hoi Ho (Seoul National Univ.)

Link of cloud contents to microphysics through satellite measurements and cloud models QPE Climate feedback processes  A 2D version of the Goddard Cumulus Ensemble Model TOGA COARE experiments  3D non-hydrostatic regional models, MM5 and WRF Typhoon simulations Cloud ratio IWP LWP LWP Rate ratio ([P DEP ]+[P SDEP ]+[P GDEP ]) [P CND ] [P CND ]CMPE P s ([P DEP ]+[P SDEP ]+[P GDEP ]+[P CND ]) ([P DEP ]+[P SDEP ]+[P GDEP ]+[P CND ])

Satellite measured and simulated cloud contents global oceanic tropics on March 2003 Microwave Surface and Precipitation NCEP/Global Forecast System (GFS) Products System (MSPPS) Products System (MSPPS)

~6-hourly data from three satellites (NOAA-15, 16, 17) are used in the MSPPS data whereas hourly data are analyzed in C. MSPPS C

Cloud and rate ratios A 2D version of the Goddard Cumulus Ensemble Model is forced by the vertical velocity derived from the Tropical Ocean Global Atmosphere Coupled Ocean– Atmosphere Response Experiment (TOGA COARE). Cloud ratio IWP LWP LWP Rate ratio ([P DEP ]+[P SDEP ]+[P GDEP ]) [P CND ] [P CND ]

cloud ratio =0.4 C SM cloud ratio = 1 rate ratio = 1 cloud ratio approaches 0 rate ratio approaches 0 cloud ratio >1 rate ratio >1 rate ratio = 0.1 C SM

Cloud ratio IWP LWP LWP Rate ratio ([P DEP ]+[P SDEP ]+[P GDEP ]) [P CND ] [P CND ]CMPE P s ([P DEP ]+[P SDEP ]+[P GDEP ]+[P CND ]) ([P DEP ]+[P SDEP ]+[P GDEP ]+[P CND ]) Stratiform Convective Mixed Cloud microphysical budgets in stratiform and convective regimes Stratiform (cloud ratio >1 or rate ratio>1) Convective (cloud ratio<0.4 or rate ratio<0.1)

Cloud ratio and microphysics

melting of graupel and accretion of cloud water by precipitation ice vapor deposition and condensation Rainfall and evaporation of rain Unit is h-1. rain rates 0.3 mm h -1

Column averaged water budgets =++ = – SI qv = [P CND ]+ [P DEP ]+ [P SDEP ]+ [P GDEP ] SO qv = [P REVP ]+ [P MLTG ]+ [P MLTS ], CMPE = LSPE = +  = = 1 – + – Precipitation efficiency

Short-term averaged budgets (hourly) What processes determine CMPE ? CONV c, vertical velocity, wind shear

Long-term averaged budgets (longer than daily) What factors determine CMPE ? Temperature, humidity (land vs ocean surface), CCN concentration, etc….

Realistic simulations 2D GCE COARE experiment The 3D cloud-resolving modeling is based on the MM5 simulation of Typhoon Nari (2001). processes

Simulated Track Simulated Intensity

(a) surface rainrate ( mm h -1 ) (b) LSPE

(c) CMPE (d) [SO qv ]/[SI qv ]

(e) positive [CONV c ]/[SI qv ] (f) negative [CONV c ]/[SI qv ]

118 km2 59 km 2 30 km 2 96 km 48 km 24 km

48 km 24 km 96 km 59 km 2 30 km km 2

48 km 24 km 96 km 59 km 2 30 km km 2

Factors Idealized experiments

Summary  Cloud ratio is statistically related to rate ratio  Time rate of change of rate ratio is derived as a function of three groups of microphysics processes Precipitation efficiency (short-term average)  E s + [CONV qv ] ~ SI qv regardless of the average area  LSPE ~ CMPE.  Function of (CONV C, P S ) (other processes ?) Precipitation efficiency (long-term average)  Function of T s (other factors ?)

 Statistic analysis (like probability distributions) of LWP, IWP, CR, CR tendency, and P s in different large-scale disturbances and environments using satellite data.  Evaluation of simulated LWP, IWP, CR, CR tendency, Ps, and precipitation efficiency in different models using explicit cloud micro- physics schemes against satellite derived quantities.  Investigations of responses of water vapor and clouds (LWP, IWP, CR, and CR tendency) to climate forcing anomalies. Further research – TRMM/GPM application

FDDA NESTED DOMAIN (30 km) OBS(NRA1) MOTHER DOMAIN (90 km)

Next steps 1. Further refinements: better spectral constraints (A ref ), reduce model physical and computational errors reduce model physical and computational errors 2. Finer resolution in the cyclogenesis region

Improve the prognostic cloud microphysics scheme in the atmosphere regional model to predict the formation and transport of hydrometeors. Combine radar and satellite measurements with the cloud (microphysics) model for cloud and rainfall estimate in severe weather

Remote sensing and modeling of cloud contents and precipitation efficiency Effect on weather and climate Cloud microphysics and cloud dynamics Mesoscale and large-scale circulation Parameterization and prediction Quantitative rainfall estimate  rain gauges (bucket, optical, acoustic): spatial resolution  radar: Z-R relations (microphysics), localized  satellite (IR, microwave): space and time sampling, micro-physics, cloud optical properties, radiative transfer  validation