Exploring multi-scale and model-error treatments in ... · Experiments with the T30L7 SPEEDY model...
Transcript of Exploring multi-scale and model-error treatments in ... · Experiments with the T30L7 SPEEDY model...
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Takemasa Miyoshi1,3*, S. Otsuka1, and K. Kondo1,2 1RIKEN Advanced Institute for Computational Science
2University of Tsukuba
3University of Maryland
October 8, 2013, WMO DA Symposium, College Park, MD
Exploring multi-scale and model-error treatments in ensemble data assimilation
With many thanks to
UMD Weather-Chaos group, Data Assimilation Research Team, CREST members (K. Bessho, S. Satoh, T. Ushio, H. Tomita, Y. Ishikawa, H. Seko)
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With more powerful computers…
• Higher resolution
• More precise physics
• Large ensemble simulations
– Multi-model ensemble
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Motivation for multi-scale approach
Localization plays an essential role
in an EnKF to cope with limited
ensemble size.
Higher resolution requires more
localization, limiting the use of
observations.
Localized
No localization
We look for better use of
observations by separating the
scales.
Analysis increment from a single
profile observation (20 members)
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Scale-separated analysis increments
We will construct analysis increments at high (h) and low (l)
resolutions separately.
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Longer-range covariance
Full-range (T30) analysis increment Analysis increment from reduced-
resolution (T21) ensemble perturbations
Motivated by Buehner (2012), we apply spatial smoothing to the
ensemble perturbations to reduce noise in longer-range covariance.
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Larger-scale localization Applying a 1000-km (larger scale) localization.
Full-range (T30) analysis increment Analysis increment from reduced-
resolution (T21) ensemble perturbations
Noisier in distance
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Smaller-scale structure Applying a 500-km (smaller scale) localization.
More structure in short range
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Merging the two scales Original covariance with 500-km
(smaller scale) localization
Large-scale covariance with 1000-km
(larger scale) localization
Removing the short-rage structure Preserve the smaller-scale
structure in short range
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Merged analysis increment
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Review: the algorithm
1. Compute the analysis increment
regularly
(with smaller-scale localization)
2. Compute the analysis increment with
smoothed ensemble perturbations
(with larger-scale localization)
3. Compute the analysis increment with
smoothed ensemble perturbations
(with smaller-scale localization)
4. Take the difference between 2 and 3
5. Add 1 and 4
1
2
3
4
5
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Results are promising.
Experiments with the T30L7 SPEEDY model (Molteni, 2003)
Regular localization (700 km)
Dual localization (600-900 km)
Mid-level U Low-level T
Near-surface Q Surface pressure
Global-average RMSE
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Improved almost everywhere
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Vertical structure
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Localization parameter sensitivities
Relatively insensitive.
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Summary and future plans
• Dual-localization LETKF analysis (with single
resolution forecasts) showed promising results.
– LETKF computations are tripled for this approach.
• Future plans
– Applying to higher-resolution models
• Multi-scale considerations are more important with higher
resolutions.
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Motivation for multi-model approach
• Multi-model ensemble is an approach to dealing
with the model-error problem.
Previous studies (e.g., Meng and Zhang
2007) prescribed the distribution.
Goal: adaptive estimation
0
Ense
mb
le S
ize
0Physics1 Physics2 Physics3 Physics4 Physics1 Physics2 Physics3 Physics4
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Approach: a discrete Bayesian filter
0Ense
mb
le S
ize
0
Prior Obs Posterior
Prior Obs Posterior
t = t0
t = t0 + Δt
×
×
=
=
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An idea to find the obs PDF
Model 1
Model 2
Obs
Extended
forecasts Farther
from obs,
lower prob.
Closer to obs, higher prob.
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Results with the Lorenz-96 model
Assimilation steps
En
sem
ble
siz
e
True model: F=8
Multi-model ensemble: F=6, 7, 8, 9, 10 (including truth)
The system finds the true model quickly.
Converge quickly to
F=8 (truth)
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With imperfect models True model: F=8
Multi-model ensemble: F=6, 7, 9, 10 (imperfect)
En
sem
ble
Siz
e
Converge quickly to
F=7 and 9
The system finds a better combination.
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Optimal RMSE is obtained. Optimal: brute-force parameter tuning (F6:F7:F9:F10=0:17:3:0)
Uniform: F6:F7:F9:F10=1:1:1:1 (5 members each)
Adaptive: the proposed approach
With F=8 (perfect), RMSE=0.189
Manual tuning
F6 = F10 = 0
F7:F9 = 17:3
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Toward next 20 years of DA
High-resolution simulation High-resolution obs
More computational power Advanced obs technology
Enabling effective use
Big Data Big Data
“Big Data Assimilation” Era Throughput
~10 Exabytes/day
Exploding data
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Next-generation geostationary satellite
(Courtesy of JMA)
Full Disk
Super Rapid Scan
every 30 seconds
Himawari 8 will be launched in 2015.
Himawari 9 will be launched in 2017.
10 min. 2.5 min.
Rapid Scan
30 sec. Super Rapid Scan
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Rapid-Scan images from MTSAT
A heavy rainfall event on
July 28, 2013, 10am-1pm
Vis High-resolution every 5 min.
(half of next-generation Himawari)
Vis Low-resolution every 30 min.
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Rapid scan effective for convections
Time (min.)0 10 15 20 25 30
Height
5km
10km
Typical lifetime of a convective system ~30 min.
Satellite imagery can capture
developing convections.
Radar can capture rain particles
after the developing stage.
(may be too late…)
Chisholm, A. J. and Renick, J. H. (1972)
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フェーズドアレイレーダーによる3次元立体観測(10~30秒)
パラボラアンテナによる3次元立体観測(5~10分)
Phased Array Radar
(courtesy of NICT)
~15 scan angles
Every 5-10 minutes
~100 scan angles
Every 10-30 seconds
Conventional Radar Phased Array Radar
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Conventional Radar (every 5 min.)
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Phased Array Radar (every 30 sec.)
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New data: can we use live-camera images?
1. Assimilation of reduced/extracted information (e.g., weather type,
visibility)
(challenge) Automated image processing technology
2. Simulating images from model outputs (i.e., having observation
operators of live cameras) Direct assimilation
(challenge) precise 3-dimensional radiation model
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Towards “Big Data Assimilation”
Improving simulations
“Big Data Assimilation”
High-resolution simulation
High-resolution observation
Combination of
next-generation technologies
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An idea of a super-rapid 30-sec. cycle
①30-sec Ensemble Forecast Simulations
2 PFLOP
②EnsembleData Assimilation
2 PFLOP
Himawari500MB/2.5min
シミュレーションデータ
シミュレーションデータ
Ensemble Forecasts200GB
Phased Array Radar1GB/30sec/2 radars
シミュレーションデータ
シミュレーションデータ
Ensemble Analyses200GB
A-1. Quality ControlA-2. Data Processing
B-1. Quality ControlB-2. Data Processing
Analysis Data2GB
③30-minForecast Simulation
1.2 PFLOP
30-min Forecast2GB
Repeat every 30 sec.
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A lot of challenges to make it happen…
0
Time (sec.)
10 20 30 40
Obs data
processing
DA
(2PFLOP)
30-sec.
Ensemble
forecasting
(2PFLOP)
30-min. forecasting (1.2PFLOP
30-sec.
Ensemble
forecasting
(2PFLOP)
30-min. forecasting (1.2PFLOP)
DA
(2PFLOP)
-10
Obs data
processing
200GB 200GB 200GB
2GB 2GB
~2GB
Computing requirement: 250TFLOPS (effective) Equiv. to 1/4 of the K computer
200GB DA
(2PFLOP
~2GB
Challenges
New DA algorithm for fast I/O
Fast QC and data processing at observing sites
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Future plans
• Explore a 30-sec. super-rapid DA cycle thorough
innovating the “Big Data Assimilation” technology.
(Funded by CREST, Japanese Science & Technology Agency)
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