nwp 4: probabilistic and ensemble forecasting at short and medium-range 13/09/2013
DESCRIPTION
“ High resolution ensemble analysis: linking correlations and spread to physical processes ” S . Dey , R . Plant , N . Roberts and S . Migliorini. NWP 4: Probabilistic and ensemble forecasting at short and medium-range 13/09/2013. Overview. - PowerPoint PPT PresentationTRANSCRIPT
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“High resolution ensemble analysis: linking correlations and spread to physical processes ”
S. Dey, R. Plant, N. Roberts and S. Migliorini
NWP 4: Probabilistic and ensemble forecasting at short and medium-range
13/09/2013
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Overview
• Linking ensemble evolution with physical processes
• Understanding of convective events• Evaluating on believable scales
Objective: Investigate methods of evaluating high resolution ensembles
Background Case study Results
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Background 1: spatial predictabilityPredictability limits“certain turbulent systems, possibly including the earth’s atmosphere, possess for practical purposes a finite range of predictability”
(Lorentz 1969)
Scale dependence – Faster error growth at smaller scales
(Hohenegger and Schär 2007, BAMS)– Need ensembles at convective scale
Upscale error growth: A forecast can be unpredictable at grid scale but predictable at larger scales.
– Should be evaluating on scales that are believable
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Background 2: correlations
Bannister 2008, QJRMS
Auto-correlations
Autocross- correlations
(x…
,y…
,z…
)
(x…,y…,z…)
Data Assimilation: Background error covariance matrix (B)
• Sampling uncertainties• Localization
• Present method of analysing the ensemble using correlations. • Present one case study to show utility of techniques: future
work to test on more cases
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Method 1: case study
• MOGREPS-UK domain, UK Met Office UM 7.7 • 11 members + control• 8th July 2011• 2.2km grid spacing
>2mm
>10mm
13:00- 14:00
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Method 2: Analysis
2σ
1. Vertical auto- and autocross-correlations
2. Neighbourhood approach
Gaussian weighting of perturbationsWidth set by FSS scale
• Believable scale• Variable dependant• Spatially varying
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Results 1: Gaussian width
Rain rate spatial scales Horizontal divergence spatial scales
0 4 8 12 16 Grid points
15:00 on 8th July 2013
0 4 8 12 16 Grid points
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Results 2: rain rate correlations
Convective layer
09:00 12:00 15:00
18:00 Single point sampling
error
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Results 3: auto-correlations• 12:00 on 8th July 2013• Horizontal divergence
Single column
Spatially augmented ensemble
Heig
ht [k
m]
Heig
ht [k
m]
Height [km]
Height [km]
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Results 4: autocross-correlations
Convergence
Divergence
-ve correlatio
n
+ve correlatio
n
Single columnHe
ight
[km
]
Height [km]
Spatially augmented ensemble
Heig
ht [k
m]
Height [km]
Clou
d Fr
actio
n
Horizontal divergence
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Conclusions
1. Extra information from convective scale ensemble using correlations.
2. Neighbourhood sampling for analysis on meaningful scales.
3. Reduce sampling error and increase confidence.
4. Application to one case: future work to look at multiple cases.
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Thanks for listening. Questions?
Bannister, R. N., 2008: A review of forecast error covariance statistics in atmospheric variational data assimilation. i: Characteristics and measurements of forecast error covariances. Quart. J. Roy. Meteor. Soc., 134, 1951–1970
Hohenegger, C. and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88 (7), 1783–1793.
Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21 (3), 289–307.
Roberts, N., 2008: Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model. Meteorol. Appl., 15 (1), 163–169.
Roberts, N. M. and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136 (1), 78–97.