ISRO / UKSA Joint Working Group University of Reading29 March 2011

Use of observational information for convective-

scale predictions

Stefano MiglioriniUniversity of Reading

s.migliorini@reading.ac.uk

with contributions from: Ross Bannister, Mark Dixon, Stephen English, Graeme Kelly, Cristina Prates, Randhir

Singh and Robert Tubbs

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Contents

• Probabilistic forecasting at convective-scale

• Operational radiative transfer modelling for NWP

• Assimilation of cloudy radiances

• Advanced wind measurements from space

High-resolution numerical weather predictions

• Increase of computer power allows met agencies to run NWP forecasts at increasing resolution: deep-convection permitting models (~1 km res)

• Less need for parameterizations (e.g. convection) and better topography and surface fields

• Better use of high-res observation

• Aim is to improve forecasts of small-scale processes, storms, hazardous weather

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Predictability limits• Error growth timescales decrease with horizontal scale. It

is estimated that a 20-km wavelength (e.g. thunderstorm) has a predictability horizon of ~1-2h

• Errors are always present: initial and boundary conditions, model errors (e.g. approximations, resolution, parameterizations)

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Ensemble prediction at convective scale

• Error growth at convective scale stronger in the presence of convective instability: need for accurate representation of ongoing convection at initial time (e.g., from radar), particularly for nowcasting (0-6 hours).

• Large sensitivity to initial conditions and to model error motivates the need for probabilistic forecasts at convective scale.

• Initial-condition uncertainty for nowcasting should depend on available observational information content: ensemble filtering

• We also obtain flow-dependent (but low-rank) description of forecast uncertainty that can be used for high resolution data assimilation

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The Met Office EPS

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• Met Office Global and Regional Ensemble Prediction System (MOGREPS)

• BC from the global ensemble

• IC calculated using an ETKF

to generate 23 increments

Ensembles at convective scale

• 1.5 km resolution over 70 vertical levels over southern UK

• Ensemble of 23 forecast members + control

• Initial 1.5 km perts interpolated from NAE EPS fields at 24 km and 1.5 km operational analysis

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Case study: 26 July 2007• active front, line convection, strong dynamical forcing

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• T+1 forecast valid at 12 UTC, with LH nudging acting between 10.30 and 11.30.

• Substantial variability that “compensate” shortcomings in deterministic forecast (member 0): heavy rainfall over the north coast of South West England captured by members 2, 3, 4, 6 and 18; light precip in the East by several member (e.g., 11)

Case study: forecasts of precipitation

Sensing the Earth from space

• Line-by-line models calculates absorption coefficients for each spectral line for all relevant molecules over layers

• Radiative transfer equation solved; top-of-atmosphere monochromatic radiance convolved with instrument response; computationally expensive

• “fast” RT codes (e.g. RTTOV) determine transmittance from optical depth calculated as regression of set of predictors (T, WV)

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2

1

22 1 1 2

( , )( ) ( ) ( , ) ( ( ))

s

s

d s sL s L s s s B T s ds

ds

2

1

1 20

( , ) exp( ( ( )) ( ) )abs

s N

m mms

s s s s ds

• A fast radiative transfer model (RTM) to compute emitted infrared radiances for a very high resolution radiometer (VHRR), onboard the operational Indian geostationary satellite Kalpana has been developed and verified (Singh et al., 2009) . This work is a step towards the assimilation of Kalpana water vapor (WV) radiances into numerical weather prediction models.

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Operational radiative transfer codes

Singh, Rayer, Saunders, Migliorini, Brugge, O’Neill, 2009

Assimilation of cloudy radiances (1/2)

• Satellite observations regarding clouds can be assimilated as either raw radiances or as retrieved parameters.

• RTMs used in assimilation assume a simple cloud model: a single layer of geometric thin grey cloud

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Cld Cl Opc c cL ( ) (1 N )L ( ) N L ( ,p )

Cloud Parameters: Effective Cloud Fraction (ECF) and Cloud Top pressure (CTP)

Cloud-free radiance Radiance for 100% opaque cloud cover

• Statistics of 1D-Var retrieval obtained from a set of 200 pseudo IASI observations where a layer of cirrus cloud type is placed above a layer of cumulus cloud. The true cloud top is represented in orange and the retrieved in blue.

• New two-layer cloud model to increase the number of assimilated cloud-affected radiances

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cld clr op ope1 e2 e1 1 e2 e1 2L ( ) (1 N )(1 N )L ( ) N L (p , ) N (1 N )L (p , )

Assimilation of cloudy radiances (2/2)

Thanks to Cristina Prates

Atmospheric motion vectors for NWP

• Suitable features will be identified on forecasts at different lead times to determine a set of simulated AMVs.

• The relationship between estimated AMVs from simulated SEVIRI data and “true” atmospheric winds will be investigated, to improve the expression of the observation operator used for data assimilation.

• Experiments will be performed with model data at different horizontal resolutions, ranging from 1.5 km to 500 m.

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Court

esy

Met

Offi

ce

Conclusions

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• Accurate weather prediction relies on accurate observations and reliable models

• Uncertainty is inevitable (finite predictability) and needs to be quantified for meaningful predictions

• Satellite information is essential to guarantee global coverage

• Still a lot of research needs to be done to improve predictions at high-resolution (clouds, storms, high-impact weather)