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Mo Mode del Parame meter Estima mation n with h Da Data Assi Assimilation on usi sing g NICAM AM-LE LETKF Sh Shunji Ko Kotsuki 1 , Y. Sato 2 , K. Terasaki 1 , H. Yashiro 1 H. Tomita 1 , M. Satoh 3 , and T. Miyoshi 1 1. RIKEN Center

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SLIDE 1

Mo Mode del Parame meter Estima mation n with h Da Data Assi Assimilation

  • n usi

sing g NICAM AM-LE LETKF

7th ISDA (ISDA2019), Jan. 22, 2019 @RIKEN-CCS, Kobe

  • 1. RIKEN Center for Computational Science, Japan
  • 2. Graduate School of Engineering, Nagoya University, Japan
  • 3. Atmosphere and Ocean Research Institute, U. Tokyo, Japan

Sh Shunji Ko Kotsuki1, Y. Sato2, K. Terasaki1, H. Yashiro1

  • H. Tomita1, M. Satoh3, and T. Miyoshi1
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SLIDE 2

NICAM-LETKF

NICAM Big Data LETKF

Local Ensemble Transform Kalman Filter (Hunt et al. 2007)

Goal: Look for effective use of precipitation measurements.

(JAXA)

(Terasaki et al., 2015; SOLA)

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SLIDE 3

Near-real-time NICAM-LETKF :: NEXRA

Kotsuki et al. (2019, , SOLA)

Running o

  • n J

JAXA’s Supercomputer ( (JSS2)

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SLIDE 4

Model Parameter r Estimation wi with th Data Assimilati tion

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SLIDE 5

Experimental setting

  • Num

umerical Mode del

– NICAM (Satoh et al. 2008, 2014)

  • Horizontal : GL6 (approx. 110 km resolution)
  • Vertical : 38 layers up to approx. 40 km
  • Cumulus Parameterization : Arakawa and Shubert (1974)
  • La

Large Scale Condensati tion : Berry (1967)

  • Observations

ns

– State estimation: PREPBUFR, AMSU-A, GSMaP – Parameter e estimation: ???

???

  • Da

Data Assimilation

– LETKF (Hunt et al. 2007) with 40 members

  • Localization: 400 km (horizontal) & 0.4 lnp (vertical)
  • Inflation by RTPS (α = 0.90)
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SLIDE 6

Parameter Estimation in NICAM-LETKF

On Online!

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SLIDE 7

: mean : spread Es Esti timated Parameter De Defaul ult P Parameter

Estimated Parameter (large scale condensation)

ｓ

Berry (1967)’s LSC sche heme

2

1 2 3

c

B l P N B B l r r = +

ρ : air density P: precipitation rate l : cloud water mixing ratio Nc: total # of cloud droplet

w/ w/o Par aram ameter DA w/ w/ Par aram ameter DA OBS OBS (GS GSMaP_Ga Gauge)

(Kotsuki et al., 2018; JGR)

2014/06/16/00UTC mm/6h

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SLIDE 8

OLR bias increased

6-h fcst OLR BIAS (vs. ERA-Interim)

CT CTRL (B1=0 =0.10) w/ w/ Parameter DA

ç to too cloudy

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SLIDE 9

Ho How can we e improve e radiation bias s with parameter r DA?

1.

  • 1. Es

Estimating ng B1 pa parameter with h LWP (f (from G

  • m GCOMW/AM

AMSR2)

  • 2. Estimating B1 parameter spatially
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SLIDE 10

Parameter Estimation impact on LWP bias

CT CTRL (B (B1=0.10) Pa Parameter DA (Local) Gl Glob

  • bal Parameter Estimation
  • n

OBS OBS (GCOM OMW/AM AMSR-2) 2) LWP improved

Tropi pics: good Ex Extra-tropi pics: unde underestimated

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SLIDE 11

6-h fcst OSR BIAS (NICAM − CERES)

[W/m2]

CT CTRL (B1=0 =0.10) Globa bal Parameter DA

OSR R improved significantly

201501-201512 OSR: Outgoing Short Wave Radiation cl clou

  • udierè

ç le less clo loudy

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SLIDE 12

Qu Question: Is spatially-va varying parameter r beneficial?

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SLIDE 13

Global and Local Parameter Estimations

Global Parameter r Estimation By By ETKF

  • bservation

Local Parameter r Estimation By By LETKF

lo localiz alizatio ion lo localiz alizatio ion

LET LETKF LET LETKF

・Estimate a global constant parameter ・no localization ・Estimate spatially-varying parameter ・w/ localization

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SLIDE 14

Pa Parameter Estimation Re Results Name Name of Exp. DA DA me method Ob Obs. s. LW LWP OS OSR CT CTRL (B (B1=0.1) / / Ov Overp rproduced La Largely

  • v
  • verestimated

Ko Kotsuki et al. (2018, , JGR) ET ETKF GS GSMaP Ov Overp rproduced / Gl Glob

  • bal Con
  • nstant

Pa Parameter DA ET ETKF LW LWP Good Good Im Improved Lo Local (S (Spa patial) ) Pa Parameter DA LE LETKF (σ=2 =200km) LW LWP

? ?

Experiments

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SLIDE 15

Estimated Parameter Field (Berry’s B1) de defaul ult value ue = = 0.1 .1

faster r conversion è ç slower r conversion

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SLIDE 16

Estimated Parameter Field (Berry’s B1) de defaul ult

time

Pr Promising seasonality off the coast of California (s (shallow convection in summer)

global estimates local estimates global estimates local estimates

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SLIDE 17

Parameter Estimation impact on LWP bias

CT CTRL Pa Parameter DA (Local) Gl Glob

  • bal Parameter Estimation
  • n

OBS OBS (GCOM OMW/AM AMSR-2) 2)

Tr Tropics: good Ex Extra-tr tropics: u underesti timated

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SLIDE 18

LOCAL Parameter Estimation impact on LWP bias

CT CTRL Lo Local P Parameter E Estimation Gl Glob

  • bal Parameter Estimation
  • n

OBS OBS (GCOM OMW/AM AMSR-2) 2)

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SLIDE 19

[W/m2]

Globa bal Parameter DA Lo Local P Parameter D DA

lo local al par aram ameter DA seems benefic ficial ial in in shallo allow-co convection regions

6-h fcst OSR BIAS (NICAM − CERES)

201501-201512 OSR: Outgoing Short Wave Radiation cl clou

  • udierè

ç le less clo loudy

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SLIDE 20

Summary

Pa Parameter Estimation Re Results Name Name of Exp. DA DA me method Ob Obs. s. LW LWP OS OSR CTRL (B1=0.1) / / Overproduced Overestimated Kotsuki et al. (2018, JGR) ETKF GSMaP Overproduced / Gl Glob

  • bal Con
  • nstant

Pa Parameter DA ET ETKF LW LWP Good Good im improved Lo Local (S (Spa patial) ) Pa Parameter DA LE LETKF (σ=2 =200km) LW LWP Be Better im improved

Kotsuki S., Terasaki K., Yashiro H., Tomita H., Satoh M. and Miyoshi T . (2018): Online Model Parameter Estimation with Ensemble Data Assimilation in the Real Global Atmosphere: J. Geophys. Res. Atmos., 123, 7375-7392.