feasibility of data assimilation using documented weather record for reconstruction of historical...

Feasibility of data assimilation using documented weather record for reconstruction of

historical climateKei Yoshimura and Kinya Toride

AORI, Univ Tokyo

Yoshimura, Miyoshi, Kanamitsu, 2013Yoshimura, Miyoshi, Kanamitsu, 2014

Toride and Yoshimura, in prep.

H 18O

H

HD16O

Weather Reconstruction

Tree Ring

Sediment in Lake

Floods, Droughts

WeatherFamine

Records

Ice Core

3

Weather Records & Numerical Modeling

Cloud amount(Rain rate)

Global Spectral Model

4

Numerical simulation

Objective:To make clear what kinds of information could be derived by assimilating only cloud amount

Local Ensemble Transformed Kalman Filter(Miyoshi and Yamane, 2008)

• Not only the assimilated variables, but also other variables will be corrected to be a consistent field.

Experiments by using Reanalysis data

In order to validate this system, experiments were done by using NCEP-DOE reanalysis data

Ideal observations of cloud amount were made by adding error to reanalysis data

Earth System Research Laboratory

nature obs=nature + error

Standard error of cloud observation is 30%

Observations were taken once a day

7

Experiments settings

Global Spectral Model(GSM) was simulated from Jan.1 ,2005 to Jan.1, 2006 beforehand

Use the output on Jan.1, 2006 as an initial condition

Assimilate observations of cloud amount

No assimilation run is also done to examine the effects of assimilation

nature obsJan.1, 2005

Jan.1, 2006

GSM

No relationship!!

We don’t know! We know!8

Feb.1, 2006

Record Points

1600 1650 1700 1750 1800 1850 1900 19500

5

10

15

20

25

30

35

Number of records

Year

Official meteorological network started

Assume 18 Observation stations in Japan

Historical Weather Data Base http://hwdb.yamanashi.ac.jp/

[Yoshimura,2007]

The historical records are available on this website

91740 1870

Cloud Cover

RMSE(noobs) – RMSE(assim)

Red: Improved Blue: Worsened

Cloud AssimNo obsReanalysis

Cloud Assim No obs

>>>> Cloud is reproduced well!!

Correlation Coefficient

11

Time series @ observation station Blue: cloud assimRed: No obsBlack: Truth

Results improved by assimilating cloud!!

Specific humidity

12

Precipitation

Wind (Surface)

Wind (500hPa)

DistributionPrecipitation in Kyushu improved

13

Most of other variables clearly improved!

Surface Pressure

Precipitable water

Global impact:RMSE(noobs) – RMSE(assim)

Amount of cloud

Blue: Improved Red: Worsened

Surface Pressure

Japan

14

Jan1,2006 to Feb1,2006

Precipitable waterSpecific humidity(1000hPa)

Precipitation

Blue: ImprovedRed: Worsened

15

Data assimilation using Observed Cloud Data　　 from Japan Meteorological Agency

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 310

20

40

60

80

100

Chosi

ncep jma

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 310

20

40

60

80

100

Nemuro

ncep jma

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 310

20

40

60

80

100

Hamada

ncep jma

ChosiKofu

Utsunomiya

Okaya

maGifu

Fuku

oka

Nemuro

Kyoto

Nagasa

ki

Wak

ayam

aKoti

Morioka

Kagosh

ima

Aomori

Hamad

a

Kanaza

wa

Taka

da

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

R (JMA data & NCEP data)

Based on visual observation or equipment on ground

Correspondences with NECP data vary by observation point 17

Fig.8 Comparison with daily TCC obs.

Toride and Yoshimura, in prep

Blue: WorsenedRed: Improved

Fig.9 Comparison with 6-hourly SAT observation

Toride and Yoshimura, in prep

Blue: WorsenedRed: Improved

Summary and Conclusion

Developed cloud coverage data assimilation system.Reconstruction from reanalysis data shows good results from

various aspects.Reconstruction from observed data shows also good results.

However bias treatment would be the key.

Overall we apply this system for short term, long term effects should also be analyzed.

This system has high potential to derive some information just from analog data in old diaries!

Thank you!!