Feasibility of data assimilation using documented weather record for reconstruction of historical...
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Transcript of 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!!