page 1© crown copyright 2005 progress with high resolution modelling with the unified model peter...

© Crown copyright 2005

Progress with high resolution modellingwith the Unified Model

Peter ClarkGroup Leader Mesoscale Modelling

Met Office Joint Centre for Mesoscale MeteorologyUniversity of Reading


Talk Outline

1. Met Office operational models.

2. High Resolution model configurations.

3. Rainfall verification and model products.


Organisation

JCMM (Reading)

Mesoscale Modelling

Peter Clark + 6

Mesoscale Data Assimilation

Sue Ballard+3

General ParametrizationData Assimilation Satellite ApplicationsDynamics ResearchEvaluation and DiagsUM Systems

Met Office (Exeter)


Met Office Regional NWP Strategy

North Atlantic and European (NAE) limited area model Europe/Storm tracks, T+48…. 12 km 4D Var Data Assimilation main forecast 24 km ensemble system embedded in global ensemble

New UK Model quasi-operational April 2005 T+36 4 km, UK weather especially surface impacts ‘Spin-up’ from NAE analysis through summer 2005. Full 3DVAR/MOPS assimilation cycle from end 2005 (tomorrow!). Vertical resolution enhancement 2006

Experimenting with 1 km since 2002.

‘On-demand’ small area 1.5 km model by 2007.

Expect UK model to move to 1.5 km in 2009.


Future UM Operational Configurations

Global 40 km

North Atlantic & European 12 km

Old UK 12 kmRetiring

New UK 4 km

Levels:3850 Deep Strat70+


Other UM high resolution areas

S Africa and Ethiopia 4 km

New Zealand and Alps (MAP) 60km, 40km, 20km, 12km, 4km, 2km and 1km – studies of stress convergence (Stuart Webster)

TOTAL

RESOLVED

SSO

OR

2km 60km


Motivation for high resolution forecasting

Severe convective storms can lead to flash flooding.

strong winds associated with storms.

Boscastle (SW England), 16th August 2004 ~£500,000,000 damage Fortunately, no one

killed Even 2 hours warning

useful

•Forecasting of convective precipitation is primary public safety need


Emphasis for UK modelling

Main emphasis on ~1 km very short range model.

4 km very useful for temperature/visibility/wind forecasting. Uncomfortable about precipitation. Nevertheless, more useful than expected.

Much depends on intelligent upscaling in post-processing.


Unified Model at 4 km and 1 km resolution

Non-hydrostatic, compressible, deep atmosphere, semi-Lagrangian, semi-implicit dynamics.

Arwakawa C horizontal rotated lat/long, Charney Philips vertical flexible terrain following height based.

Philosophy has been to start with existing UM physics and enhance only where evidence shows need.

Main physics developments are microphysics and turbulence Still taking conservative approach

Additional developments: Enhanced urban scheme Surface slope in radiation


New UK 4 km Model

Broad Leaf Trees Needle Leaf Trees C3 Grass

C4 Grass Shrubs Urban

Lakes Bare Soil Land Ice

Post-processing products area


Microphysics

Operational UM Wilson and Ballard microphysics– prognostic total ice+snow, diagnostic ice/snow split, diagnosic rain.

Since UM6.0 there is a prognostic representation of:

3D SL advection initially Separate 3D advection by wind and 1D SL transport relative to

air Useful for single column Cheaper and no significant impact on solution (we think!)

Developed from standard UM Wilson and Ballard microphysics New microphysics fully flexible Working towards (optional) convergence with Met Office CRM. Working on improvements to numerics.

Cloud liquidwater

Water vapour

GraupelIce

crystalsSnow

aggregatesRain


10th July 2004 1 km UM 0700 Z


10th July 2004 1 km UM 0800 Z

Convergence Line


10th July 2004 1 km UM 0900 Z


10th July 2004 1 km UM 1000 Z


10th July 2004 1 km UM 0900 Z

Convergence Line


Impact of reduced snow fallspeed and enhanced sublimation

Standard Run Reduced ice fallspeedDouble evaporation rate

Convergence Line


Quantifying Systematic and Local Impacts

Graupel → very small systematic increase in rainfall, small local impact.

Increasing rain evap. rate → small systematic decrease in rain, larger local impact.

Water loading → small systematic decrease in rainfall, significant local impact

Decreasing the snow fall speed → large systematic impact, significant local impact.


Two alternative turbulence treatments

Standard UM 1D boundary layer (Lock et al, 2000)Non local eddy diffusivityMoistMultiple regimeNot using shallow convection (future work)Implicit solution

Fixed Horizontal hyper-diffusion (del-4). Arbitrary chosen to give most reasonable power spectra.Explicit solution

Smagorinsky-Lilly 3D with stability dependent length scale.

Stability functions same as local part of standard UM scheme.

Basic length scale proportional to horizontal grid length.

Same numerical solution method as standard scheme.


Impact of turbulence scheme on convective forecast (4th July 2005)

UM BL 3D Smagorinsky.

1km UM 6 hour forecastsurface rainfall rate.


Impact of turbulence scheme on convective forecast (4th July 2005)

Number of cellsReference With Turbulence

Histogram of cell sizes

Average cell size

Time →

Time →


Summary – Turbulent Mixing

3D sub-grid turbulent mixing parametrization introduced into the UM (based on Smagorinsky-Lilly).

Tested in idealised and real case studies and can have a very significant impact on convective initiation and evolution.

Reduces over-prediction of small convective cells at 1km. Reduces excessive rain rates in larger storms.

BUT not appropriate for all situations (e.g. very stable).

Work is ongoing into most appropriate formulation for different resolutions, and enhancing the scheme (e.g. stochastic backscatter).


Convection at 4 km

We don’t know how to parametrize convection at 4 km.

We don’t all agree what a correct solution would look like!

We have decided not to try to develop a ‘4 km’ convection scheme.

Gregory-Rowntree ‘hands over’ to explicit smoothly but not correctly

Depends on parameter choice Some modes of behaviour not necessarily physical

Behaviour different for boundary forced domain compared with periodic, homogeneous (CRM)

Nested has additional sink of small scale energy Domain and problem dependent

Pragmatism (fudges!) necessary CRM equilibrium behaviour used for guidance

30

Convection scheme closure

CAPE (J/Kg)

Mas

s flu

x

Mass flux CAPE /

CA

PE

clo

sure

tim

esca

le

3/9/02 Nigel Roberts, JCMM

CAPE (J/Kg)


Severe Organised Convection 3rd August 2004

NIMRODRadar Rainrate5/2/1 kmComposite



Operational00Z 03/08/200412 km MesoscaleTotal Rainfall rate(Part of domain)

Every timestep



As Operational00Z 03/08/20044 km UKTotal Rainfall rate(Part of domain)

Every timestep



Radar 12 km 4 km

1600 UTC T+16 Forecasts


Why (and when) 4 km can be useful for precipitation

Convergence lines that trigger new convection are quite well resolved by 4 km model.

This can give good spatial indication of rain.

Especially true with cold pool dynamics – (our) parametrized convection poor to useless. (Probably true of any quasi-equilibrium scheme).


CSIP IOP 18 – 25th August 2005

Network radar – 1/2/4 km Composite

09 UTC




10 UTC




11 UTC 19C

11C




12 UTC




13 UTC


CSIP IOP 18 – 25th August 2005 – 12 UTC

4 km model

10 m wind and convergence Rainfall rate


CSIP IOP 18 – 25th August 2005 – 12 UTC

4 km 12 km

Screen Temperature


Orographic features important to radiation

• Oliphant et. al. 2003, ‘Spatial variability of surface radiation fluxes in mountainous terrain’

• Characteristics in order of importance: slope aspect, slope angle, elevation, albedo, shading, sky view factor, leaf area index

• The most important factor is the area presented by each grid-box to the incoming direct SW radiation


Grid-box mean slope aspect and angle

Slope aspect: Slope angle:


4km Mesoscale Unified Model: 8 hr forecast

Extra direct SW surface flux: Temperature difference at 1.5 metres:


4km Mesoscale Unified Model: 16 hr forecast

Extra direct SW surface flux: Temperature difference at 1.5 metres:


Summary – Orography and Radiation

Included slope aspect and angle into the incoming direct short-wave radiation scheme.

Tested in UM with grid resolutions ranging from 60km (global) to 1km (over the southern UK).

At high resolution, small (0.5K) surface temperature changes resulting from the scheme can lead to differences in convective initiation and evolution.


Variable Resolution

• An alternative approach to 1-way nesting.

• Grid varies from coarse resolution at the outer boundaries smoothly to a uniform fine resolution in the interior of the domain

• Benefits close to hires domain boundary, e.g. reduces spin-up of convection at inflow boundaries

UniformHigh Res

zoneVar-Res 2Var-Res 1

UniformCoarse Res 1

UniformCoarse Res 2

Typically, there are 3 regions, and inflation ratio R1 = R2 = 5~10%

R2R1


May 3 2002 Case - Variable Resolution Model

Rainfall at 14 UTC. The three regions of the variable resolution domain are shown


Summary – Variable Resolution

Variable resolution grid capability implemented in the UM.

Tested in idealised and real case studies with a nesting ratio of 1 : 4 and results look promising.

Currently working on the model parametrizations to make them depend more appropriately on the local grid-length in different parts of the domain (e.g. grid-length dependent convection scheme). (More fudges!)


Known UM problems

4 km convective cells much too large, too few. (Expected and forecasters have adapted).

Latest physics produces acceptable area average precipitation but..

Non-fatal grid-point storms at 4 km. Solutions have run into problems. (No problems at 1 km).

Valley cooling problem at all scales. Caused by vertical non-interpolating SL advection. Fixed (we hope!) by selective fully interpolating mod.


Model Physics (at present)

12 km/L38 4 km/L38 1 km/L76

Timestep 300 s 100 s 30 s

Convection Scheme

Full Gregory-Rowntree

Gregory-Rowntree with restricted

mass flux

None

Microphysics Prognostic ice Prognostic ice and rain

Prognostic ice, rain. Two ice+graupel

under test.

Surface 9 Tile MOSES 9 Tile MOSES 9 Tile MOSES

Diffusion Del 4 theta + Targeted moisture

Del 4 theta +Targeted moisture

Del 4 To be replaced by

3D turbulence.

Boundary Layer / Turbulence

Standard 1D Standard 1D Standard 1D(3D Local likely)


Precipitation Verification Techniques

Philosophy based on assumption that small scales less skilful

than large.

Rather than doing point by point verification use fractions

(~probabilities) over a certain area surrounding each grid point.

Calculate various probability and categorical scores based on

accumulation thresholds.

Basis of products and investigation of skill as function of scale.


Radar 12 km forecast 1 km forecast

0.125 0.5 1 2 4 8 16 32 mm

The problem we face

0 100 km

Six hour accumulations 10 to 16 UTC 13th May 2003


Schematic example - different scales


1-km forecast Radar

0.125 0.5 1 2 4 8 16 32 mm

Six hour accumulations 10 to 16 UTC 13th May 2003


4 mm threshold, Fractions at grid scale (1 or 0)

Model Radar> 4 mm > 4 mm

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fraction


Model Radar> 4 mm > 4 mm

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fraction

4 mm threshold, Fractions within 35x35 km squares


Model Radar

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Fraction



Brier score for comparing fractions

Skill score for fractions/probabilities - Fractions Skill Score (FSS)

A score for comparing fractions with fractions


Graphical behaviour of the Fractions Skill Score


Summer 2004 Trial

• Seven cases from 2004 period (mostly convective)

• For each case run 4 forecasts at 3 hour intervals

• Run one suite with 4km, 1km assimilation and a second initialising 4km, 1km from 12km analyses.

• Forecasts out to T+7 for 1km model

• Aggregate statistics over forecasts and cases.


HRTM Domains

Note that operational UK 4km model uses larger (whole UK) domain


Area average rain rates over 2004 summer trial

Solid: AssimDotted: Spinup

Blue 12kmGreen 4kmRed 1kmBlack Radar


Scores for 6 hour accums 1, 4 and 16mm thresholds


Blue 12kmGreen 4kmRed 1km

1mm / 6hr threshold

4mm / 6hr threshold 16mm / 6hr threshold


Scores for 1 hour accums 1 and 4mm threshold


Blue 12kmGreen 4kmRed 1km


Intensity/Scale verification

Barbara Casati PhD in conjunction with Met Office.

Similar ideas to Nigel Roberts – Haar wavelet transform similar to successive ‘box averages’

Summary methods useful for comparison between models.

Radar Model forecast

from Casati (2004)

Radar > 1 mm Forecast > 1 mm Binary error image

X > u X < u

Y > u Hits a

False Alarms

b a+b

Y < u Misses c

Correct Rejections

dc+d

a+c b+d a+b+c+d=n

wavelet decomposition of the binary errorScale

L

lluu EE

1,

from Casati (2004)


MSE skill score

,

, , ,

1u u random uu

u best u random u random

MSE MSE MSESS L

MSE MSE MSE

1

0

-1

-2

-3

-4

luSS ,

threshold (mm/h)

spati

al sc

ale

(km

)

[from Casati (2004)]

Axes multiples of 2

12-18Z

12-18Z

12-18Z

4 km 00Z 6 hr rainfall

12 km 00Z 6 hr rainfall

4 km 00Z avg 12 km 6 hr rainfall

Err

or s

cale

(km

)

2x

16x

1 mm 64 mm

2x

16x

Max radar = 44 mm

68 mm 7 mm46 mm

Rainfall threshold (mm)


Distribution-free test as normality of errors can’t be assumed.

B = number of +ve skill scores for a given scale and intensity during a given time interval, e.g. 1 month.

Hypotheses:

H0 : SS >= 0 (implicit positive and skillful)

H1 : SS < 0 (less skill than a random forecast)

H0 is rejected if b <= bn,where B ~ bi(n, 0.5) for small samples

(n < 40), = 0.025

The value of (n – B) / n is shaded in intensity-phase space for each

scale and intensity where H0 is rejected.

Modified sign-test statistic


Added benefit: comparison of prevalent errors at the monthly time scale

(sub-)“grid” scale errors are more prevalent at trace rainfall totals for the 4 km model

prevalent errors at twice and four times the 12 km grid length for thresholds > 16 mm are less for the 4 km model (captures large totals better)

May 2005 12 km vs radar May 2005 4 km avg vs radar

X X X X X X X X X X X X X X

X X X

X X

X X

X48 km

32 mm


Questions?


18 UTC 09/12/2003 1km L76 Forecast

24 h loop

page 1© crown copyright 2005 progress with high resolution modelling with the unified model peter...

Documents

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network radar

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convection scheme closuremass