evaluation of simulation results of a cloud-resolving model using satellite data and a satellite...

Evaluation of Simulation Results of a Cloud-Resolving Model

Using Satellite Data and a Satellite Simulator

Taro SHINODA(1), Hirohiko MASUNAGA(1), Munehisa K. YAMAMOTO(2,3), Masaya KATO(1), Atsushi HIGUCHI(2), Kazuhisa TSUBOKI(1),

and Hiroshi UYEDA(1)

(1) Hydrospheric Atmospheric Research Center (HyARC), Nagoya University(2) Center for Environmental Remote Sensing (CEReS), Chiba University

(3) Graduate School of Science, Kyoto University

National Central University @ Jhong-Li, TaiwanNovember 20, 2012

Introduction of Meteorological Laboratory, HyARC, Nagoya University

Taro Shinoda (Hydrospheric Atmospheric Research Center, Nagoya University)


Laboratory of Meteorology, HyARC, Nagoya UniversityHiroshi Uyeda, Professor.Kazuhisa Tsuboki, Professor.Taro Shinoda, Assistant professor.Tadayasu Ohigashi, Assistant Professor

Our research field is extreme mesoscale phenomena; such as typhoon, heavy rainfall/snowfall, gust wind/tornado using two X-band polarimetric Doppler radars and a cloud resolving model (CReSS).

X-band polarimetric Doppler radars Typhoon T0418 simulated by CReSS

Kin(Gold)

@HyARC@Gifu Univ.

Gin(Silver)

The HyARC X-band polarimetric Doppler radars were installed in November 2007.

HyARC polarimetric Doppler radars

Vertical polarization

Horizontal polarization

Polarization radar

・ Transmit horizontal and vertical polarizations as same power and phase.

Only horizontal polarization

Conventional radar

Conventional Radar vs. Polarimetric Radar

Vertical polarization

Horizontal polarization

Polarization radar

・ Transmit horizontal and vertical polarizations as same power and phase.

Polarimetric Parameters

Obtained parameters

* Radar reflectivity (Zh)

* Doppler velocity (V)

* Differential reflectivity (Zdr)

* Correlation coefficient between horizontal

and vertical polarization signals(ρhv)

* Differential propagation phase (Φdp)

* Specific differential phase (KDP)

For horizontal polarization・ Signal: large・ Phase delay: largeFor vertical polarization・ Signal: small・ Phase delay: small

Shape of precipitation particles vs polarimetric parameter (Zdr)

Houze (1993)

Snowflakes ： 0 ～ 0.5 dB

Graupels ： -0.5 ～ 0 dB

（ Vertical axis longer ）

Hailstones ： 0 dB（ Almost shpere ）

Raindrops ： 0 ～ 5.0 dBHorizontal scattered section is larger for the larger raindrops → positive Zdr

・ Particle identification was examined using Zh, Zdr, ρhv, KDP, and T.・ Large raindrops (D0 ~ 2.5 mm) exist below the ML.・ Dry and wet graupels (orange/red colors) exist around the ML.→ The melting of graupels should form large raindrops below the ML and caused a large amount of rainfall.

A sample of particle identification (Aug. 28, 2008)

Shinoda et al. MWR submitted

SE

NW

Particle identification in comparison with CReSS simulation

・ A large amount of graupel (blue contours) exists above the ML.・ These grauples fall with melting and form a large amount of large raindrops.・ As a large amount of cloud water intrudes (red contours), graupels should be formed by a riming process.

Shinoda et al. MWR submitted

Toward the confirmation of particle identification: HYVIS Obs.

The hydrometeor videosonde (HYVIS)・ In situ observation system to obtain images of cloud particles: such as phase, size, shape, and number concentration. ・ We conducted simultaneous observation using both the HYVIS and polarimetric radar in 2008, 2010, 2011, and 2012 (this year).

Zdr

2mm

0.5mm

Column type ice crystals were observed at 5800 m (-5℃)

Sample of a HYVIS observation

0.08 dB

ρhv 0.996

Zh 24.8 dBZe

Kdp 0.41º/km

X: HYVIS

positive

positive

E

NN

The Philippines

Palau

60 km

60 km

80 km

Aimeliik

HYVIS Observation

Aimeliik

Ngarchelong

From Houze (2010)

Typhoon

Vortex:D ~ 10 km

Vortex:D ~ 100

㎞

E

Vortex: D = 300 ~ 500 ㎞

Pressure drop and enhancement of vorticity

ＶＨＴ

A plan of our field experiment in Palau (in 2013 and 2014)

ObservationMay & June 2013December 2013May & June 2014

Palau

Courtesy of Prof. Uyeda

Our own cloud-resolving model (CReSS)

・ CReSS develop from 1997 by Prof. Tsuboki and Mr. Sakakibara. ・ CReSS has non-hydrostatic, compressible equation system and terrain-following coordinate system. ・ CReSS has a bulk cold rain scheme (Qv, Qc, Qr, Qi, Qs, Qg). ・ CReSS is optimized for parallel computers. ・ The basic framework of CReSS resembles the ARPS model.

Niigata heavy rainfall case: July 28-30, 2011

22 JSTJuly 29

06 JST July 30

01 JST July 30

09 JSTJuly 30

Niigata heavy rainfall case: Total rainfall amount (Obs. vs Sim.)

・ We have simulated Typhoon Morakot (T0908) using the CReSS to test the framework of the daily simulation for the SoWMEX2010.

Typhoon Morakot (T0908) simulation: 2.5-km horizontal resolution

・ Maximum accumulated rainfall amount during this simulation reaches over 4300 mm around the CMR over Taiwan Island.

Typhoon Morakot (T0908) simulation: 2.5-km horizontal resolution

Gifu heavy rainfall case: July 15, 2010 (JMA radar vs CReSS)

Observation (JMA-Radar) CReSS

Maximum rainfall350 mm / 6 h

No significant rainfall region!

Effect of the simple Data Assimilation Run

Observation (JMA-Radar)CReSS: Radar data nudging(Qr and Qv)

Maximum rainfall350 mm / 6 h

Heavy rainfall area is reproduced well by radar data nudging!

Additional processes Current status

CReSS-NOHES (1) Non-hydrostatic ocean model Completed

MIROC-CReSSMultiscale Modeling Framework (MMF) and super parameterization

Completed

AFES-CReSSMultiscale Modeling Framework (MMF) and super parameterization

Completed

CReSS-3DVARThree-dimensional variational data assimilation system (3DVAR)

Completed

CReSS-SDSU* Evaluation using a satellite simulator Testing

CReSS-Lightning (2) Lightning simulator Testing

CReSS-MSTRN Cloud Radiation Testing

CReSS-wbin Warm bin cloud microphysics Testing?

CReSS-SiBUC Land-surface model Constructing

CReSS-SPRINTARS Aerosol-cloud interactionConstructing

CReSS-SDM Super droplet model Constructing?

Recent expansion of CReSS

CReSS

NHOES

SST

UfricHeat &Water fluxes

Wave

U10

SurfaceCurrent

Skin stress +Dissipation stress

Skin stress +Wave stress

Surfaceroughness

・ NHOES: NonHydrostatic Ocean model for Earth Simulator ・ NHOES is developed by Dr. Aiki of JAMSTEC.・ We have started to develop of CReSS-NHOES coupling since 2010 and to conduct daily forecasting from last June.

A regional atmosphere-ocean coupled model (CReSS-NHOES)

Typhoon simulation (T1204) using CReSS-NHOES

Coupled Sim. – Uncoupled Sim. at 54 hours

SST difference with or without 3D ocean coupled model

・ Large SST difference (> 1℃) appear between 1D and 3D ocean coupled result along the typhoon track and along the warm/cold currents region.

Coupled Sim. – Uncoupled Sim. at 54 hours


・ Sea temperature above a depth of 30 m (around the bottom of mixed layer: 50 m) decrease (increase) by vertical mixing induced by high wind of the typhoon.

A: EPT (color) across wind (coutor)O: Temperature difference from initial condition(color)

雷雲の電気的半生のモデル化 Modeling of a lightning process in thunderclouds

(1)

(2) (3)

(1) Accumulation of electric charge 　　 by hydrometeor particle charging. (2) Extension of lightning channels. (3) Charge neutralization.Lightning process was followed by MacGoman et al. (2001).

→

(1)

Separation of updraft, gravity (by density of particle)

Modeling of a lightning process in thunderclouds

Reproduction of thunderclouds


(1)

(2) (3)


→

(1)


(1)

(2) (3)


→

(1)



Reproduction of charge distributions




(1)

(2) (3)


→

(1)







Reproduction of Lightning

Reproduction of Lightning

Lightning simulation result in comparison with the LLS observation

Simulated CG LightningTotal ：　　　　　　　 10644 Positive polarity: 3522 Negative polarity: 7122

Lightning simulation result in comparison with the LLS observation

Simulated CG LightningTotal ：　　　　　　　 10644 Positive polarity: 3522 Negative polarity: 7122

Observed CG LightningTotal: 2588Positive polarity: 651Negative polarity: 1937

Evaluation of Simulation Results of a Cloud-Resolving Model

Using Satellite Data and a Satellite Simulator

Taro SHINODA(1), Hirohiko MASUNAGA(1), Munehisa K. YAMAMOTO(2,3), Masaya KATO(1), Atsushi HIGUCHI(2), Kazuhisa TSUBOKI(1),

and Hiroshi UYEDA(1)

(1) Hydrospheric Atmospheric Research Center (HyARC), Nagoya University(2) Center for Environmental Remote Sensing (CEReS), Chiba University

(3) Graduate School of Science, Kyoto University


Cloud Resolving Models (CRMs)

・ Cloud Resolving Models (CRMs) explicitly resolve convective clouds, so they are useful tools to analyze the structure of precipitation systems.

・ We have a CRM named Cloud Resolving Storm Simulator (CReSS).

・ CRMs have many uncertainties in cloud microphysical processes.

・ To confirm the accuracy of CRMs, it is useful to compare the results of simulations with those of satellite observations.

・ The physical parameters simulated by the CRM were compared with those retrieved by satellite observations. ・ The retrieved physical parameters could contain their own biases due to uncertainties in the inversion algorithms.→ It is difficult to make an evaluation of the CRM using satellite-derived physical parameters.

Evaluation of CRM simulations using satellite data

Masunaga et al. (2010, BAMS)

A satellite simulator

・ Several satellite simulators are developed in recent several years.・ It estimates satellite-consistent radiances from the CRM outputs using radiative transfer calculations (forward model).・ Direct satellite measurements (radiances) should have less uncertainties than retrieved physical parameters.

Radiative transfer calculations

Masunaga et al. (2010, BAMS)

Satellite Data Simulator Unit (SDSU)

・ SDSU is developed to compute synthetic satellite data from CRM output by Dr. Masunaga.

・ SDSU is designed to simulate * thermal infrared brightness temperature, * microwave brightness temperature, * radar reflectivity, * visible and near-infrared radiances.

・ Input parameters * P, PT, Qv, Qc, Qr, Qi, Qs, Qg, Ni, Ns, Ng, z * SST, Surface winds

http://precip.hyarc.nagoya-u.ac.jp/sdsu/sdsu-main.html

Brief summary of the comparison method

Framework of the simulationTaiwan-Okinawa 2010 Taiwan-Okinawa 2008

The Cloud Resolving Model CReSS ver2.3 CReSS ver2.2

Surface Parameterization

（Murakami 1990, Murakami et al. 1994）With cloud ice (Qi) sedimentation Without cloud ice sedimentation

Radiative ParameterizationHorizontal : 700×600 Horizontal : 360×336Vertical : 50 Layers (up to 25.0 km) Vertical : 42 Layers (up to 20.6 km)Horizontal : 2.5 km Horizontal : 4.0 kmVertical : Lowest 100 m, stretching Vertical : Lowest 100 m, stretching3.0 sec. (non-sonic term） 5.0 sec. (non-sonic term）1.5 sec. （sonic term） 1.0 sec. （sonic term）

GSM (Horizontal resolution ～120 km)

→ MM5 (20 km)Initial Time and Integrated Time 36 hours from 18 Z 36 hours from 00 Z

Simulation Period Apr. 27 ～ Jun. 30, 2010 May 14 ~ Jun. 30, 2008Analysis Period May 14 ～ Jun. 16, 2010 May 14 ~ Jun. 24, 2008

Lower Boundary ConditionsSST data provided from JMA (MGDSST)GTOPO30 (Horizontal resolution ～ 1 km)USGS land-use categories

Turbulent Parameterization1.5-TKE (Klemp and Wilhelmson 1978)Mellor-Yamada Level 2 (Mellor and Yamada 1974)Bulk method (Louis et al. 1981)1-moment for liquid and 2-moment for 3 ice categories bulk cold rain

Included only the solar radiation absorption by cloud

Microphysical Parameterization

Grid Numbers

Grid Spacing

Time Step

Initial and Lateral Boundary Conditions GSM (Horizontal resolution ～50 km)

TBB-IR distributions (MTSAT vs CReSS-SDSU: May 29, 2010)

・ MTSAT obs.: Well-developed MCSs develop over southeast and southwest far from Taiwan Island.・ The location and minimum TBB of the southeastern MCS are well reproduced in CReSS-SDSU.・ The cloud cover is seen over the almost all of the simulation region in the MTSAT obs. and CReSS-SDSU.

May 15 20 25 30 June 04

09 14 19 24 29

Time series of cloud fraction (MTSAT vs CReSS-SDSU) in 2010

・ The variation of the cloud fraction (CF) is well reproduced.・ Difference of CF is small ( ～ 10%), sometimes over 30%.

※ Definition of the cloud column: The column whose difference in temperature between the SST and IR TBB is greater than 15 K.

TBB-IR distributions (MTSAT vs CReSS-SDSU: June 4, 2008)

・ MTSAT obs.: MCSs develop along the Meiyu/Baiu front from southwest of Taiwan to south of Okinawa. ・ Low TBB area expands broadly over the north of the Meiyu/Baiu front in CReSS-SDSU. ・ At this time, the cloud cover in CReSS-SDSU is close to that in the MTSAT observation.

Time series of cloud fraction (MTSAT vs CReSS-SDSU) ): in 2008

・ In 2008, the simulated cloud fraction (CF) is larger than the observed one, thus is not reproduced.

May 15 20 25 30 June 04 09 14 19 24

Compared PDF/CPDF of TBB-IR (MTSAT vs CReSS-SDSU) in 2010

・ Frequency of UC (TBB < 240 K) of the CReSS-SDSU is larger. ・ Frequency of MC/LC (TBB > 250 K) of the CReSS-SDSU is lesser.

・ The shape of the cumulative PDF in 2008 is quite same that in 2010. * Frequency of UC (MC/LC) of the CReSS-SDSU is larger (lesser). ・ However, the difference of the frequency of UC in 2010 is reduced a little that in 2008.・ This should be attributed to the inclusion of cloud ice sedimentation.

Compared PDF/CPDF of TBB-IR (MTSAT vs CReSS-SDSU): in 2008

TBB-MW 89GHz distributions (AMSR-E vs CReSS-SDSU)

・ This frequency is sensitive to ice particles in the upper troposphere.・ If large amount of ice particle exists, TBB shows small values.・ Low TBB areas are seen in the southeast and southwest MCSs. ・ Minimum TBB in the simulation is quite lower. → This suggests the excessive existence of solid hydrometeors.・ The area of moderate TBB in the simulation is quite smaller.→ The extension of dense stratiform region cannot be reproduced.

Reflectivity distributions (TRMM-PR vs CReSS-SDSU)

Height: 2 km

Comparison of CFAD (TRMM-PR vs CReSS-SDSU)

・ Distributions of reflectivity below the melting level is reproduced very well in the simulation. ・ Large reflectivity above the melting level should be simulated by the excessive existence of graupels.

Comparison of PDF of reflectivity (TRMM-PR vs CReSS-SDSU)

・ Above the ML (7 km): Frequency (> 24 dBZ) is large quite large in the simulation. → Excessive existence of hydrometeors in the narrow area (convective core). ・ Around the ML (5 km): Frequency (23~25 dBZ) is less in the simulation. → Less extension of stratiform region. ・ Below the ML (2 km): Frequency is quite same, but that (> 28 dBZ) is less. → Less extension of stratiform region.

Reflectivity cross-sections (CloudSat-CPR vs CReSS-SDSU)

Comparison of CFAD (CloudSAT-CPR vs CReSS-SDSU)

・ The arc-like structure (Masunaga et al. 2008) is seen. ・ Peak reflectivity of the simulation in the upper (middle) troposphere above 10 km (between 5 and 10 km) in the simulation greater (lesser) than that of the observation. → Excessive (less) mixing ratio of Qi (Qs) or unrealistic particle size distribution of Qi (Qs) are caused of the inconsistency.

Conclustion: Take home message

・ We can find out some biases on our CRM using satellite data and a satellite simulator.

・ For our microphysical scheme:* The sedimentation process of cloud ice (fall velocity) should be improved (using IR TBB). * Excessive existence of graupel and less extension of stratiform region should be improved by revisiting the conversion process from snow to graupel or warm rain process (using MW TBB and TRMM-PR). * Mixing ratio and/or particle size distributions of cloud ice and snow should be rechecked using CloudSat-CPR.

・ The method is quite useful for evaluating cloud microphysical processes (both in bulk and bin schemes).

Thank you for your kind attention!!Thank you for your kind attention!!

Welcome to your questions and comments!Welcome to your questions and comments!

Input parameters ・ Zh, Zdr , ρhv, Kdp: obtained by a polarimetric radar

・ T (temperature): obtained by an upper-air sounding

Output hydrometeor categories

1)Drrizle 2)Rain

3)Wet (Melting) Snow 4)Dry Snow

5)Ice Crystal

6)Dry Graupel 7)Wet (Melting) Graupel

8)Small Hail 9)Large Hail 10)Rain and Hail

Particle identification

Typhoon T1112 case: Total precipitation and track of the Typhoon

Typhoon T1112 case: Total rainfall amount (Obs. vs Sim.)

CReSS Observation


・ Large SST difference appears between 1D and 3D ocean coupled result, especially, in the Kuroshio (warm current) and Oyashio (cold current) region. ・ SST difference should play an important role under typhoons.

Tracing of radioactive materials from Fukushima Nuclear Power Plants

・ We tried to reproduce the tracing of radioactive matrials released from the Fukushima Nuclear Power Plant on March 15, 2011, using the prototype of CReSS-SPRINTARS.

・ The tsunami generated by the massive earthquake that occurred east of Japan on March 11, 2011 caused serious damages of the Fukushima Nuclear Power Plant on its cooling facilities for nuclear reactors. ・ Hydrogen and vapor blasts that occurred at 06 and 09 JST on March 15 outside of the reactors led to the emission of radioactive materials into the air. ・ We show a transport process from the plant to the northern Japan using the prototype of CReSS-SPRINTARS. ・ SPRINTARS is the aerosol transport model developed for AGCM.・ In this simulation, only the sub-cloud scavensing process in SPRINTARS is installed into the CReSS model.・ Color shades show the decreasing ratio from the initial aerosol particles released from the plant.

Cloud Resolving Models (CRMs)

・ Cloud Resolving Models (CRMs) explicitly resolve convective clouds, so they are useful tools to analyze the structure of precipitation systems.

・ We have a CRM named Cloud Resolving Storm Simulator (CReSS).

Cold rain parameterization in CReSS (2-moment bulk method)

The purpose of this study

The purpose of this study is to develop an evaluation method by comparison in brightness temperatures (TBBs) and reflectivities calculated using SDSU applied to the daily simulation results by CReSS with those obtained by satellite observations around the Taiwan/Okinawa region during the Meiyu/Baiu season in 2008 and 2010.

Satellite observation data used in this study

Satellite observation data used in this study are as follows:・ Infrared (Ch-1, 10.8 μm): by the MTSAT Horizontal resolution: 4 km × 4 km: Cloud-top height and cloud fraction

・ Microwave (6 channels): by the AMSR-E/Aqua 89.0 GHz (5.9 km × 3.5 km) Sensitivity for ice cloud (IWP)

36.5 GHz (14.4 km × 8.2 km) Sensitivity for liquid water (LWP)23.8 GHz (32 km × 18 km) Sensitivity for water vapor (TPW)18.7 GHz (27 km × 16 km) Sensitivity for heavy rain10.65 GHz (51 km × 29 km) Sensitivity for heavy rain6.925 GHz (75 km × 43 km)

・ Radar Reflectivity: by the TRMM-PR 13.8 GHz (4.3 km × 4.3 km × 0.25 km) Reflectivity (Precipitation)

・ Radar Reflectivity: by the CloudSAT-CPR 94.0 GHz (2 km × 1 km × 0.5 km) Reflectivity (Cloud particles)

Reflectivity cross-sections (CloudSat-CPR vs CReSS-SDSU)

evaluation of simulation results of a cloud-resolving model using satellite data and a satellite...

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