1 wmo swfdp macau 8 april 2013 anders persson introduction to ensemble forecasting

1 WMO SWFDP Macau 8 April

2013 Anders Persson

Introduction to ensemble forecasting


2013 Anders Persson23-04-21

Computer models

Forecaster

Customer/Public

Atmosphere

Scientists

The meteorological science in the service of MankindThe meteorological science in the service of Mankind

…is investigated and explored byScientists

…who summarize their finding into mathematicalComputer models

…which are used as an important tools by Forecasters

…whose final work is used as a basis for decision making by Customers/Public

But are they still needed?

In 1966 I was told that “in 5-10 years time there will be no need for human weather forecasters”


2013 Anders Persson

The arrival of the computer meant

increasing forecast skill and efficiency

but also new educational needs.

Irony: In agriculture nobody said:“ -With the

introduction of the tractor in 5-10 years there will be

no need of farmers”

The progress of weather forecasting

The human weather forecaster before the scientific age: simple rules and no complicated machinery

The arrival of the scientific method meant increasing forecast skill and efficiency but also an increased burden with thousands of observations, complex rules and more stressful work


2013 Anders Persson21/04/2321/04/23 4

On the contrary:There are perhaps more weather forecasters today than ever, even in – or particularly in – the commercial sector

Training Course at Meteo Group, Wageningen, NL

But what are they doing?


2013 Anders Persson

How can human weather forecasters compete with the super computers?

• Humans should not try to compete with them

• Instead they should play an entirely other “game”!

• The key word is not “skilful”, but “useful”

– How to best serve the people!



Computer models

Forecaster

Customer/Public

Atmosphere

Scientists

The forecaster misled me!

The NWP misled me!

Erroneous observations misled the NWP!

The atmosphere is ”chaotic”!

Some don’t and engage in the Blame GameSome don’t and engage in the Blame Game



Computer models

Forecaster

Customer/Public

Atmosphere

Scientists

Now I make better decisions!

I will take the uncertainty into

account!

Erroneous observations may mislead the NWP!

The atmosphere is chaotic!

Most meteorologists surely do this!Most meteorologists surely do this!


2013 Anders Persson

The main reason why we need ensemble forecasting: We want to estimate the uncertainties, in

particular the risks of extreme or high-impact weather

-But I do not want any risks, or probabilities or uncertainties – I

want to KNOW!

OK, let’s take your words seriously


2013 Anders Persson

Come with me to nice friendly Scandinavia

You venture out in the forest. . and who might turn up there?

Although the risk of meeting a wolf is small you would have liked to be warned


2013 Anders Persson

-12 0 12 24 36 48 60 72 84 h

ψ

Computer based “accurate-looking” forecast

Dangerous threshold

No risk? No problems? Should we go ahead?

Computer made

weather forecast

(NWP)


2013 Anders Persson

-12 0 12 24 36 48 60 72 84 h

ψ

Computer based “accurate-looking” forecasts are far from perfect

Dangerous threshold Computer made

weather forecast

(NWP)●

●●

● ●

●●●

●

●

●●

obs ●● ●

1. Forecast doesn’t verify “now”

2. Good timing but systematically too low

3. Forecast out of phase


2013 Anders Persson

1st problem: -Is the forecast correct

“now”?


2013 Anders Persson21/04/23 13

0 12 24 36 48 60 72 84 96 h

ψ

obs● ●●

The forecast does not verify “now”

It did not even verify at initial time (t=0)

Most NWP models do not analyse the weather!

The forecasters nudge the forecast towards the observation

The problem with very short computer forecasts


2013 Anders Persson

Most state-of-the-art NWP models do not assimilate weather observations, only:

1. Upper air temperature, wind, relative humidity and winds from radio sondes

2. Radiances from satellites to be converted to temperature and humidity

3. Upper air winds from drifting clouds

4. Surface winds from satellites, ocean based ships and buoys

5. Surface or MSL pressure from land and sea platforms

They do NOT assimilate 10 m winds, 2 m temperatures or dew points, clouds and weather

These are (pretty well) calculated from the other parameters!


2013 Anders Persson

2nd problem: -Are the NWP systematically wrong?


2013 Anders Persson

ψ

obs - Ψ= corr

●●

●

●

●●

●

●

● ●

●

●●

●●corr = AΨ + B

Statistical interpretation (archived data)



0 12 24 36 48 60 72 84 96 h

●●

Statistical correction or “calibration”

●●

●

●● ●

ψ

From experience (verification or statistical interpretation) we know that the NWP model underestimates high forecast values, which can be corrected for

The solution to the problem of systematically misleading computer forecasts


2013 Anders Persson

3rd problem: -Is the forecast “jumpy”?


2013 Anders Persson

-12 0 12 24 36 48 60 72 84 h

ψ

Computer based “accurate” forecast can not only be wrong but also “jumpy”

Dangerous threshold

Today’s forecast

yesterday’s forecast

Tomorrow’s forecast


2013 Anders Persson

L

HL

LH

L

HL

HL

HL

LH HH

LL

Downstream development of influence

Day 2

Day 0

Day 4

Energy propagation


2013 Anders Persson

L

But the influence can also be in the opposite direction

Persson-Petersen WMO workshop 1996

Extra-tropical influence → Tropics


2013 Anders Persson

At the same time as we try to improve the initial analysis by

1. Increasing the number of observations2. Improving their quality3. Improving our analysis methods

…. we also do the opposite:

We “tickle” the analysis by imposing perturbations (possible errors) to fins out how it affects the NWP


2013 Anders Persson

Where and how are the atmospheric analyses perturbed?


2013 Anders Persson

Stochasticphysics

everywhere

Singular vectors

Singular vectors

Tropical singular vectors (when

a cyclonic feature is formed)

EDA Singular vectors

EDA Singular vectors


2013 Anders Persson

EDA in action – typhoon Aere over northern Philippines

The first guess is fairly reliable to the SW of the typhoon, but not to the NE of the typhoon


2013 Anders Persson

00 UTC 03 UTC 06 UTC 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time

Surface pressure

10 (from June this year 25) EDA short range forecasts are constantly running in parallel randomly perturbed by stochastic physics and varying SST

Now we want to make a new analysis for the 12 UTC forecast


2013 Anders Persson

00 UTC 03 UTC 06 UTC 09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time

Surface pressure

To arrive at the best possible analysis for 12 UTC we consider all the forecasts 09-21 UTC as 12-hour first guesses in anew assimilation cycle

Assimilation window

To launch a 10 day forecast from here


2013 Anders Persson

09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window

●●

●

10 forecasts (first guesses)

Observations perturbed within their error estimates

Surface pressure

These 10 forecasts, 12-hour first guesses, are confronted with observation, perturbed to account for observation errors and representativeness


2013 Anders Persson


●●

●

Su

rfac

e p

ress

ure

4 DVAR trajectories

Influenced by these observations the 10 first guesses are modified


2013 Anders Persson


Su

rfac

e p

ress

ure

4 DVAR trajectories

Influenced by these observations the 10 first guesses are modified

Odd member 3 is perturbed by SV 6 times to

produce members 3, 4, 23, 24, 43 and 44

Even member 8 is perturbed by SV 4 times to produce members 17,

18, 37 and 38


2013 Anders Persson

1 1 2 21 22 41 422 11 12 31 323 3 4 23 24 43 444 13 14 33 345 5 6 25 26 45 466 15 16 35 367 7 8 27 28 47 488 17 18 37 389 9 10 29 30 49 5010 19 20 39 40

EDA memberCorresponding EPS members 1-50


2013 Anders Persson

09 UTC 12 UTC 15 UTC 18 UTC 21 UTC Time Assimilation window Forecast

Su

rfac

e p

ress

ure

Ensemble forecast 50 members perturbed by singular vectors and stochastic physics

EDA forecast 10 members perturbed by stochastic physics, varying SST and perturbed observations

Formally starting from 12 UTC


2013 Anders Persson

-12 0 +12 +24 +36 +48 +60 +72 +84 h

ψ

Exchanging the “accurate” forecast with a more “honest” one

Dangerous threshold

Today’s NWP forecast

Today’s EPS forecast



ψ

Correction for systematic errors

-12 0 +12 +24 +36 +48 +60 +72 +84 h



ψ

●●

●

● ●

●●●

●

●

●●

obs ●● ●

The final ensemble forecast – with verification

70% 50%

-12 0 +12 +24 +36 +48 +60 +72 +84 h


2013 Anders Persson

Prob(> 15 m/s) 20 March 2013 12 UTC + 156h



Probability maps of the 10 m wind exceeding 15 m/s

+12 h forecast (verification) →


2013 Anders Persson

Probability maps of more than 20 mm rain in 24r h

Prob(> 20 mm/d) 24 March 2013 12 UTC + 60h





2013 Anders Persson

Storm

Tropical storms genesis map 2 March 12 UTC VT 3-5 March


2013 Anders Persson

Tropical cyclones genesis map 2 March 12 UTC VT 3-5 March


2013 Anders Persson

Tropical cyclones genesis map 3 March 00 UTC VT 4-6 March


2013 Anders Persson

The TC was born on the 6 March!

6 March 00 UTC ensemble plume



2013 Anders Persson




2013 Anders Persson

Summary:

Ensemble forecasts help us

1. To judge the (un)certainty of the weather situation

2. To acquire probability estimates of anomalous events (extreme or high impact)

3. To get the most accurate and least “jumpy” deterministic forecast value


2013 Anders Persson

Other advantages with ensemble forecasting


2013 Anders Persson

Southern Sweden

Central Sweden

The jumpiness is decreased by 50%-75%

24 hour ”jumpiness” of 2 m temperature forecasts

ECMWF

Ens Mean

Ens Mean

ECMWF


2013 Anders Persson

Error decreased after lagging

Jumpiness decreased

even more

Lagging reduced the MA error by 20% but the jumpiness by 70%


2013 Anders Persson

1.0

a

f

g

h ●

●●

Averaging will decrease error by 13%

87.05.01 2

…and jumpiness

by 50%

Why does an ensemble technique affect the jumpiness more than the error?? Look at a small ensemble of consecutive forecasts

f

gh

gh

a

0.50

error = f-a g-a

h-a

Mean of g and herror



The perturbations on average make the analysis

worse

On average 35% of the perturbed analyses are better



The perturbed forecasts are individually on average 1-1½ day worse than the unperturbed forecast


2013 Anders Persson

L

HL

LH

L

HL

HL

HL

LH HH

LL

Downstream development of influence

Day 2

Day 0

Day 4

Analysis perturbed

Response

Response


2013 Anders Persson

L

HL

LH

L

HL

HL

HL

LH HH

LL

Downstream development influence

Day 2

Day 0

Day 4

Response

Response

Analysis perturbed

1 wmo swfdp macau 8 april 2013 anders persson introduction to ensemble forecasting

Documents

wmo swfdp macau

anders perssoncome

anders perssonhow

anders perssonthe arrival

human weather forecasters

weather forecast nwpobs1

anders perssonthe main

forecast doesnt