page 1© crown copyright 2005 numerical space weather prediction: can meteorologists forecast the...

© Crown copyright 2005

Numerical space weather prediction: can meteorologists forecast the way ahead?

Dr Mike Keil,

Dr Richard Swinbank and Dr Andrew Bushell

ESWW, November 2005


Introduction

•What National Met Services (NMS) do and how does this fit in with Space Weather?

•How did they get there?

•What can be learned for Numerical Space Weather Prediction?

•What does the future hold?


National Met Services

What do they do?


How’s it done? Numerical Weather Prediction

ModelObservations

Analysis

data assimilation

Forecast

T+1T+2T+3T+4T+5T+6T+12T+24T+48T+…


Development of NWP:

Vilhelm Bjerknes (1862-1951) had a vision!

L.F. Richardson’s first forecast sometime between 1916 and 1918.

1950 Charney ran the first forecast on a computer

It took longer to subjectively quantify the ICs than run the forecasts!

So far, no mention of Data Assimilation…

Clearly a need for an objective way of specifying the initial conditions and analysis


Development of DA:

•1949 Panofski had been creating objective analysis using interpolation techniques

•1954 Gilchrist and Cressman had two ideas:•numerical forecasts as a source of background info •automatic quality control of data

•1955 Bergothorsson and Doos – analyse observation increments

•1961 Thompson – use DA to propagate info into data voids


NWP in the present day:

State

Time

Corrected forecast

Initial forecast

T+0 T+6

Observations

•Development of NWP models and increased computer performance has led to more sophisticated assimilation schemes


The virtuous cycle

observations

assimilation

modelling

science


1953 Storm


The virtuous cycle

observations

assimilation

modelling

science


Lessons from Numerical Weather Prediction

Data Assimilation combines information from observations with a background state.

The background state could come from a number of sources:

subjective analysis, climatological averages, empirical models

To exploit the full potential of data assimilation, the background state should be produced using a physically-based numerical model.

This should be the approach to follow for SW assimilation


Lessons from Numerical Weather Prediction

A physically-based numerical model is not just required for data assimilation.

A physically-based model is an essential part in fully establishing the virtuous cycle.

Empirical models can serve a useful purpose; however their potential for development is restricted.

Physically-based models provide a route for long-term space weather scientific growth


Lessons – data issues

Satellite data is the most obvious crossover area

Co-ordination is handled by WMO Global Observing System info / education / transition

Most NMS assimilate data from around 25 operational satellites What about experimental satellites?WMO set the “rules”

GTS infrastructure

NMS have experience in handling and processing vast amounts of data


Lessons – common data sources

GPS RO observations is a good example

Mid-90s humidity and temperature profiles from GPS

Realistic assimilation first carried out at the Met Office

Operational use next year

Techniques can be applied to assimilate TEC

COSMIC: Constellation Observing System for Meteorology, Ionosphere and Climate

6 space craft – provide TEC, allow operational monitoring

Data available in near real time for scientific research


Lessons – other questions along the way

There are issues relevant to SW that have already been tackled by the met community:

Bias correction of data

Assimilation of derived products or raw values?

Pain before the gain – increasing complexity Potential for development

Timeliness of data

Ensembles


The future: operational met models

Most operational met models are pushing beyond the stratosphere

Why?

Met Office global model will have a lid at 63km

Research model with a 86km lid

Other centres go higher – eg CMAM 210km

Sensible to have a joined-up approach to common issues


The future: scientific collaboration

The Met Office are interested in Space Weather science!Potential areas of research:

Coupling between weather and space weather models Lower boundary forcings? Upwards/downwards control? Fully coupled models (whole atmosphere approach)?

Applying data assimilation expertise to space weather assimilation Radio occultation assimilation experience

Funding


The future: numerical space weather prediction

Within a decade (?) there will be a requirement for operational numerical space weather prediction

Why? Primarily military with commercial applications

How? Following the framework used in operational NWP Learning from met experience in key areas Utilising the facilities of NMS eg supercomputers,

observation supply, 24/7 capabilities, down-stream dissemination to end users

This way of working already exists in operational oceanography at the Met Office


Conclusions

The development over many years of NWP presents a framework for Numerical Space Weather Prediction

Fully establish the “virtuous cycle” for SW

Some pain can be avoided by learning from the met community!

Science can be pushed forward through collaboration

Operational Space Weather within a decade? National Met Services offer crucial facilities Successful partnerships of this kind already exist

Thanks for listening!


Questions


The framework of modern DA:

Analysis

Model Observations

data assimilatio

n

ForecastT+1T+2T+3T+4T+5T+6

Bjerknes / Richardson / Charney

Panofski Gilchrist and Cressman

Thompson

Bergothorsson and Doos


DA: hierarchy

Most assimilation schemes operate sequentially.

As long as the evolution of errors is close to linear, an extended Kalman filter is the optimum statistical assimilation method.

Hierarchy of different approximations to the Kalman filter:

Direct insertion Nudging Statistical interpolation 3D-variational (old Met Office system) 4D-variational (current Met Office global system) Ensemble Kalman Filter

Choose appropriate level of complexity / cost.


DA: cost function

Analysis is found by minimising a cost function quantifying misfit between model fields x and both obs yo and background xb

Where y=H(x) is a prediction of yo

In 3D-VAR, the analysis is calculated using observations at one particular time

In 4D-VAR, the analysis uses observations at their correct validity time

Met Office system written in incremental form

1 11 1( )

2 2

T Tb b o oJ x x - x B x - x y y R y y


DA: 4D Var

4D-VAR uses observations over a given time window.

Allows use of observations at correct time, and exploit information in a time sequence.

Requires use of a (simplified, linear) model and its adjoint. (Not clear what level of simplification is appropriate for ionosphere).


DA: summary

3DVAR (with a 6h assimilation cycle for global model) currently used for the stratospheric version of the UM. 4D VAR in the global model was implemented during 2004.

Main advantage of 4DVAR is use of observations at correct time / use of time sequence of obs. Requires adjoint of forecast model. (3DVAR only requires adjoint of observation operator.)

Kalman Filter updates error covariance as well as state – much more expensive; requires drastic simplification.

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