Potential and problems of Potential and problems of cloud resolving model for cloud resolving model for

improvement of microwave improvement of microwave modeling and retrieval of snowfallmodeling and retrieval of snowfall

*Hisaki Eito and Kazumasa Aonashi

Meteorological Research Institute, Japan Meteorological Agency

55thth GGlobal Precipitation Measurement (Global Precipitation Measurement (GPMPM) International ) International Planning WorkshopPlanning Workshop

77––99 November 2005 Tokyo, JAPANNovember 2005 Tokyo, JAPAN

Background

Improvement of microwave snowfall retrieval is required for improvement of the accuracy of global precipitation measurement.Microwave radiative transfer in frozen precipitation clouds depends on various physical properties, other than surface precipitation rate. Observations of cloud physical properties of frozen precipitation clouds are limited in time and space.

Cloud Resolving Models (CRMs) with complicated cloud physical parameterization forecast various cloud physical variables with high resolution in time and space.

Microwave radiometer brightness temperatures (MWR TBs) are sensitive to water vapor, cloud liquid water, and precipitation, assimilation of MWR TBs to CRMs will be of great use.

Cloud physical validation of CRMs has not sufficiently been carried out due to lack of observations.

Background

field observations

CRM

retrieval algorism

MWR TBs

surface precipitation

rate

validation assimilation

cloud physical properties

validation assimilation

Design of CRM’s contribution to microwave snowfall retrieval

validation

Cloud resolving modelJMAJMA--NHM NHM (Saito (Saito et alet al., 2005)., 2005)

Basic equations : Fully compressiblePhysical processes : Cloud microphysics, Atmospheric radiation, Mixing in the PBL, etc

Cloud microphysics Cloud microphysics in JMAin JMA--NHMNHM

Explicit cloud microphysics scheme based on bulk methodThe water substances are categorized into 6 water species (water vapor, cloud water, rain, cloud ice, snow and graupel)Explicitly predicting the mixing ratios and the number concentrations

0.0 0.1 0.2 0.3 0.4 0.80.70.60.5

13JST 29 JAN. 2003 13JST 29 JAN. 2003

CRM simulation for snowfall clouds

Strong cold air outbreak from continent produces many convective clouds over the Sea of Japan in winter.

CRM successfully reproduces observed cloud features.

GMSGMS--5 : Visible5 : Visible--ImageImage CRM : Total condensed waterCRM : Total condensed water

•Large scattering index areas observed by AMSR-E well correspond to high radar reflectivity intensity areas observed by Radar.

MWR (AMSR-E) observation

JMA operational RadarJMA operational RadarReflectivity Intensity (Reflectivity Intensity (dBZdBZ)) AMSRAMSR--E 89 GHz PCT (K)E 89 GHz PCT (K)

13JST 29 JAN. 2003 13JST 29 JAN. 2003

Comparison of CRM with MWR

CRM well simulates a location of the area with large scattering index.A magnitude of high reflectivity index in CRM simulation is much larger than that

in AMSR-E observation, indicating that CRM overestimates an amount of frozen precipitation particles.

AMSRAMSR--EE 5km5km--CRM CRM

Precipitation particles in CRM

Most of CRM-simulated precipitation particles are snow particles. An amount of snow particles is large.There is a small amount of graupel and cloud water.

Total Snow Total Snow (kg m(kg m--22)) Total Total GraupelGraupel (kg m(kg m--22)) Total Cloud Water Total Cloud Water (kg m(kg m--22))

Total Total GraupelGraupel (kg m(kg m--22))

Sensitivity to CRM resolution

AMSRAMSR--EE 5km5km--CRMCRM

CRM with higher resolution has larger vertical velocities, a larger amount of cloud water and graupel and a smaller amount of snow.

A magnitude of high reflectivity index in simulation with higher resolution is also larger than observation, indicating that other problems remain in CRM simulation.

2km2km--CRMCRM

Problems of CRM in snowfall forecast and data requirementsCRM well reproduces dynamical structures of snow clouds. However, it has errors and bias in the forecast of cloud microphysical variables.

Observations focused on physical properties of frozen precipitation clouds are required for validation, tuning and improvement of cloud microphysics of CRM.Properties: particle type, particle density, fall velocity,

size distribution, IWC, etcTechnology: in-situ observation (aircraft),

remote sensing (GPM/DPR, CloudSat, ground based radars)

Summary• CRM with complicated cloud physical

parameterization has potential for improvement of microwave modeling and retrieval of snowfall.

• CRM has errors and bias in the forecast of cloud microphysical variables for frozen precipitation clouds.

• Observations focused on physical properties of frozen precipitation clouds are required for cloud physical validation and improvement of CRM.

Snow production in CRM

Production terms of snow Production terms of snow mixing ratio mixing ratio ((ss--11))

Snow production is almost responsible for depositional growth.Ice nucleation rate is large in ice supersaturation layer. Produced cloud ice is

instantly converted to snow by mainly depositional growth.Large depositional growth rate of snow is due to large snow number

concentration Increased by conversion from cloud ice.

Production terms of cloud ice Production terms of cloud ice number concentration number concentration (m(m--33ss--11))

0

1000

2000

3000

4000

5000

6000

7000

8000

-4.E-07 -2.E-07 0.E+00 2.E-07 4.E-07

(kg/kg/s)

Hei

ght (

m)

riming growth

depositionalgrowth

0

1000

2000

3000

4000

5000

6000

7000

8000

-20 -10 0 10 20

(/m/m/m/s)

Hei

ght (

m)

freezing of cloud water

collisions with snow

depositional and riminggrowth, and aggregationdeposition/sorptionnucleation