Systematic and Random Errors in Operational Forecasts by the UK Met Office Global Model

1

Systematic and Random Errors in Operational Forecasts by the UK

Met Office Global Model

Tim Hewson

Met Office

Exeter, England

Currently at SUNY, Albany (until Feb 2005)

Utility of different model forecasts

A multi-model (poor man’s) ensemble can provide the best forecast guidance

Operationally, can use be made of different models ?

Requires appropriate tools, and a detailed knowledge of typical model performance:

– Relative Errors, Seasonal and Regional differences [A]

– Individual Model Characteristics (systematic and random errors) [B]

A and B will be discussed here, focusing on the UK Met Office global model (~60km resolution, 38 levels)

A

Global Model Intercomparison:

Net, Seasonal and Regional differences

Northern Hemisphere RMS Mslp errors vs Lead Time

1 5 days 10

RANK Best - EC UK FR US JAP GER CAN… -Worst

Seasonal differences (NH mslp, RMS at T+72)

EC UK FR US JAP GER CAN

EC Best throughout; then UKMET, but NCEP consistently better in summer

Regional Performance – Europe, vs Lead Time Europe-based models perform better in forecasting for Europe

EC UK FR US JAP GER CAN

EC UK FR US JAP GER CAN

Regional Performance – N America, vs Lead Time Relative to performance over Europe: UKMET does worse over US/Canada, GFS better

B

UK Global Model Characteristics -

Systematic and Random Errors

Precipitation (net / orographic)

Low level Winds (Land / Ocean / Severe cyclonic storms)

Handling of Cyclones (Cyclone spectra / Regional / Random errors)

Global Precipitation

Precipitation ~ 30% overestimate globally

EnhancedResolution60km30L

3DVar & ATOVS

New DynamicsHadAM4 physics

c/o Sean MiltonMet Office, Exeter

Precipitation errors mainly oceanic – tropics and extra-tropical storm tracks

Largely ‘balanced’ by too much evaporation – boundary layer locally too dry

Soil moisture is one global weakness being addressed – led to under-prediction of daytime temperatures during 2003 European heatwave (UK bias -4C)

Orographic Precipitation

ODNDMTNS

A B C D E F G

New Model Old Model Orographic precipitation

Smoothed orography (in new model = “New Dynamics”) reduces upslope rainfall, and similarly reduces the rain shadow

Older model better (even if for the ‘wrong’ reason!)

Magnitude of impact is proportional to flow strength

Important for QPF

ODNDMTNS

A B C D E F G

B

A

C

DE F

B

A

C

DE F

GG

NE Region

Model orography peaks much lower than reality

Many key features missing – eg Hudson Valley

Expect similar ppn problems to those found in Europe – eg insufficient upslope rain in flow from SE quadrant (factor of 2?)

‘European’ higher resolution (20km) model may help

Convective Precipitation

Diurnal cycle in convection

A significant problem area (especially tropics, but also mid latitudes)

Decay can be too rapid towards dusk

Surface Winds over land

Example – Oct 2004

15kt winds in GFS model

(mslp v similar)

UK Global Model Effective Roughness Lengths

Account for roughness due to missing orography + …

Slows down low level winds considerably

10m winds especially poor in Albany: ~50% of reality

GFS model seems much better

Changes to be implemented in ~1 year

~50% reductionIn 10m winds

Surface Winds over Oceans

GFS model

Peak winds 55kts on S flank of deep, mature low

UKMET model

Peak 10m winds only 45kts

Gradients and low depth the same as GFS

Complex interface with ocean

GFS seemed to validate better in this case (and may well be better generally)

Surface winds in Extreme Storms

L

High resolution required (90 levels?) to model sting jet

Mslp may be OK but winds not

38 Levels(operational)

90 Levels

Greater strengthalong downwardtrajectory

Severe windstorms

c/o Pete ClarkJCMM, Reading

Cyclone Spectra

Cyclone Database - Snapshot

(a) standard frontal wave

(c) standard potential wave

(b) ‘barotropic’ low

(d) weak frontal wave

(e) weak potential wave

GM cyclone spectra for year 2000, categorised by ‘max wind speed within 300km radius of centre’

North Atlantic Domain

Geographical biases in cyclone forecasts, based on trends in total numbers T+0 to T+144

Under-predictionOver-prediction

Random Errors in Cyclogenesis

November 2003 Example

15Z

18Z

18Z

Intense cyclonic storm missed at short range – random error

Perhaps 3 similar poorly forecast events per year around UK

Expect similar problems elsewhere. High Impact.

Summary Met Office global model’s broadscale evolution is on average second only

to ECMWF (NH)– Performance over Europe better than over N America

– Performance in the 3 summer months lags behind GFS

Despite this a number of significant problem areas exist– Precipitation over-forecast globally by 30%

– Some significant errors around orography and in convection

– Low Level winds under-forecast over land with unresolved orography

– Some under-prediction of stronger winds over oceans?

– Wind maxima under-forecast in extreme storms (resolution limitation)

– No systematic drift with lead time in the number of intense storms

– Fewer modest cyclones predicted at longer lead times (main bias regions include Great lakes, Gulf stream wall)

– Significant random errors still occur occasionally, even at short leads

Many of the above noted through active forecaster-NWP liaison Most are now being addressed within NWP division at Met Office HQ