convective-scale data assimilation applications - lmu

© Crown Copyright 2013. Source: Met Office

Dale Barker

International Symposium on DA, LMU, Munich, 25 February 2014

Acknowledge: B. Macpherson, S. Ballard, C. Johnson, G. Dow, R. Marriott,

C. Piccolo.

Convective-Scale Data Assimilation Applications

© Crown Copyright 2013 Source: Met Office


UK Convective-Scale Weather Impacts

Boscastle: 16/08/2004

CNN

Luxair crash

06/11/2002 – 18 dead

BBC

Accident on M4

near Cardiff 10/1

2/2003

BBC

BBC

Fog, low cloud

Birmingham Tornado

13/07/2005

Damaging winds Flash floods

Sue Ballard


UK Convective-Scale Weather Impacts

– Not All About Convection!

Somerset, January 2014


Outline

1. The Era of Convective-Scale Probabilistic NWP

2. Observations For Convective-Scale NWP

3. Observation Impact Via Data Denial Experiments

4. Convective-Scale DA

a. Motivation/Challenges

b. Covariances

c. 4D-Var

d. Role Of Ensembles

5. The Way Forward


1. The Era Of Convective-Scale NWP


UKV and MOGREPS-UK 1.5km 70L (40km model top)

3DVAR (3 hourly) (->hourly 4DVAR)

36hr forecast

8 times per day

12-member EPS - 2.2km 4x/day 36h

Global and MOGREPS-G 17km 70L (80km model top) (17km from PS34)

Hybrid 4DVAR – 60km

66hr forecast twice/day

144hr forecast twice/day

12-member EPS - 33km 4x/day 72hr

Met Office Main NWP Models (2014-

15)

• Before 2011, cost of global NWP

>> convective-scale NWP.

• In 2013, costs are similar.

• Next HPC (2015-16) – cost of

high-res NWP > global NWP.

• Similar situation for people costs.


Convective-Scale NWP (UKV 1.5km)

The ‘Morpeth Flood’, 6 Sept 2008 •Prototype UK-V : 1.5 km L70 •No Data Assimilation

•Driven by 12 UTC 05 Sept 12 km Regional Model •Starting from T+3 15 UTC Regional Model

0600 UTC 6 Sept Model Simulated Imagery

18UTC 5 Sept => 15UTC 6 Sept

Model Precipitation Rate

18UTC 5 Sept => 15UTC 6 Sept


The ‘Morpeth Flood’, 6th Sept 2008

Forecast Validation

12 km L50

1.5 km L70

Observed Totals

Morpeth


2. Observations For Convective-Scale

DA


Observations Assimilated Into UKV

Model (September 2013)

* *

* *

* Subset of data assimilated only in UK model


UKV Observations (Day)

SEVIRI SONDES SURFACE

• Observations are ingested via data assimilation 8 times a day, 24/7.


UKV Observations (Night)

• Need alternative sources of data at night: UAVs, night sonde launches, WV lidar?

SEVIRI SONDES SURFACE


Novel Obs for Convective-Scale DA

TAMDAR: T, u/v, RH, icing, turbulence:

Lidar Water Vapour:

SEVERI Cloud Top Height:

Aerosol/visibility:

Radar (reflectivity, refractivit

y)

PV Cell (radiation):

http://wow.metoffice.gov.uk



Current Observation Impact Via Data

Denial Experiments

Gareth Dow


Convective-Scale NWP – Why Bother?

* UK Index = Forecast skill for surface weather: surface u/T, cloud fraction/amount, precip, visibility

• Global NWP improvements included in baseline above (~1-2%/yr).

• So 10% benefit of UKV represents > 5-10yrs lead over global model.

Percentage

benefit wrt

UK Index*


UKV Benefit Over Global Model:

Broken Down by Weather Variable


UK Data Assimilation Impact

Studies (3hourly 3DVAR)

No. of Forec

asts

Dates Period

4x21=84 Mar 10th → Mar 31st Spring

4x38=152 Jan 3rd → Feb 10th Winter

4x44=176 Nov 1st → Dec 14th Autumn

4x40=160 Jul 1st → Aug 10th Summer

Forecasts to T+24 at 00Z, 06Z, 12Z & 18Z

Period picked at Random

Period picked due to specific (SCu) event


Verification by Element

Colours indicate best setup for element/period

Full DA(9) Partial DA(11) DownScaler(4)

Mar 2012

Jan 2012

Nov 2011

Jul 2011

Overall Wind Temp Cloud B

ase Heig

ht

Cloud A

mount

Precip Vis Period

Note: Some boxes are more significant than others Partial DA = Assimilate only those obs assimilated in global model as well


Impact of Global/CS-Scale DA

DA = Cycling Convective-Scale DA, DS = Downscaler (Global DA)

NoDA = No DA (forcing through LBCs)

• Most benefit through CS-scale DA (DA vs DS).

• High-res DA benefit is ~half of total benefit of high-res NWP (~10%)

NWP Range (UK Index): T+6 to T+36


Impact of Global/CS-Scale DA

DA = Cycling Convective-Scale DA, DS = Downscaler (Global DA)

NoDA = No DA (forcing through LBCs)

• Increased benefit of DA for very-short range (LBC impact less)

Nowcasting Range: T+6 to T+12


UK4 Observation Network

denial experiments (Autumn period)

Surface +2.9%

Upper Air (excluding aircraft)

+2.1%

Aircraft +2.0%

Radar +2.0%

Satellite +1.7%

“Extra” (all obs networks not in

global model)

+0.5%

• Conclude: All ob types adding benefit, mainly from ‘standard obs’ in high-res DA.



a. Motivation/Challenges


• Flexibility/need to introduce observations

more frequently (e.g. sub-hourly).

• Improved fit to observations through

more accurate (higher resolution, less

biased) background forecast.

• Reduced obs representivity error.

• Additional, high-res ‘novel’ observations

available (e.g. radar reflectivity)

Why Convective-Scale Data

Assimilation?


• Limited predictability -> probabilistic NWP/DA.

• Fast processes (e.g. convection) require rapid

DA updates -> initialization/spin-up issues.

• Careful treatment of LBCs/large-scales.

• Highly nonlinear (non-Gaussian fcst errors).

• Need to add value to global DA/NWP.

• Many novel observation types, complex errors.

• Imperfect high-res NWP models – model error.

• Coupling with land, hydrological, ocean DA.

Challenges For Convective-Scale DA



b. Covariance Modelling


Convective-Scale Climatological B

Training data (J. F. Caron)

NMC method

• From a three week cycle of the hourly SUK 1.5km (16/03/10 to 06/04/10) in 3DVAR mod

e using the UKV Covstats

• 75 Lagged forecast differences (6h-3h) with forecast pairs using the same LBCs


• Variances (Eigen Values)

From a set of 75 6h-3h forecast using same LBCs, every 6h (valid at 00, 06, 12 and 18 UTC)

• First 5 vertical modes

Model

Lev

el

Model

Lev

el

Model

Lev

el

Model

Lev

el

Eigen Vector

Eigen Vector Eigen Vector

Eigen Vector




• SOAR horizontal length scales in vertical mode space


UKV = 130 km*

UKV = 130 km*

UKV = 90 km*

* For every mode

UKV = 150 km*

UKV = 180 km*




• Degree of mass - rotational wind coupling





Response to the assimilation of a pseudo

innovation = 2 K and observation error = 1 K

Single pseudo-obs experiments

NDP CVT CovStats UKV CovStats

temperature at level 20 (~850 hPa)

theta at level 20 (~850 hPa) theta at level 20 (~850 hPa)


Partition of Error Covariance Between

Different Regimes

Brousseau (2009)

No Rain

Rain


Adaptive Mesh Transform

• Motivation: lntroduce flow-dependence analysis

response near strong temperature inversions in

presence of stratocumulus clouds (no UKV

ensemble so can’t use hybrid method yet).

• Static adaptive mesh methods concentrate grid

points where there is a rapid variation of the

atmospheric field.

• Transformation from the physical grid to the

computational grid is guided by a monitor function:

• Grid transformation introduced within VAR control

variable transform:

Computational mesh

Nominal physical mesh

Analysis Increment for

single q ob above Sc band

x Uv UpUaUvUhv

22 )(1 zcM

(Piccolo & Cullen, 2011: Q. J. R. Met. Soc., 137, 631-640)


Iterative Calculation of Monitor Function

M (background-state - 3h forecast)

UK4 domain: 3 Jan 2011 00z

M (After 10 iteration 3D-Var) M (After 2nd converged 3D-Var)

Adaptive vertical grid provides a small positive impact to the UK index:

Period Vis Precip Cloud amount

Cloud base

Temp Wind Overall

23 Dec 2010 – 3 Jan 2011 -2.56% 5.48% -1.05% 3.03% 0.22% -0.04% +0.25%

10 Aug 2010 - 20 Aug 2010 12.20% 0.00% 0.00% 4.17% 0.23% 0.10% +0.55%



c. 4DVar


4D Variational Data Assimilation (4D-Var)

(old forecast)

(new)

(initial condition for NWP)

Outer-Loop: Re-run nonlinear model, update QC/obs/forcing, re-minimize.

Quasi-Static Variational Assimilation (QSVA) = Outer-Loop with increasing K


Example Multi-Outer Loop 4DVAR

Choi et al (2013): WRFDA applied in 6km Korean domain

black: KMA, blue: USAF, red: ROKAF

Conventional obs + Doppler

winds from 14 Korean radars:

4DVAR

OUTER


Example QSVA 4DVAR

Cost-function minimization:


0_0 0_10

0_20 0_30


Example QSVA 4DVAR

Relative error of 4DVAR linear model:


• Outer-loop/QSVA effectively extend the range of the validity of the 4DVAR linear

model


Example QSVA 4DVAR

Impact of QSVA on 4DVAR computational cost:


• QSVA also effectively reduces the cost of outer loop 4DVAR

• Possible extensions: Multi-resolution,

Note large cost

because only

running on 8PEs!


• 1.5 km NWP-based nowcasting system

• Southern UK only (May 2012 – April 2013)

• Hourly cycling 4DVAR (UKV=3hourly 3DVAR)

• Tested Improved (CVT) covariances

• No outer-loop/QSVA/reflectivity assimilation.

Nowcasting Demonstration Project (NDP) Sue Ballard


Sue Ballard

Verification of hourly precipitation forecasts against radar

Same validity time, Available at same time to forecasters

NDP better than older UKV forecast at all ranges

NDP better than STEPS extrapolation/merged nowcast from T+2

Fraction Skill Score (Roberts and Lean) for 1.0mm/h/40km square Against Forecast Range

NWP-Nowcasting: Precipitation Skill

July 2012 August 2012

Next stage: UK-wide implementation of hourly 4DVAR in 2015-2016.



d. Role Of Ensembles


• Are we able to predict convection?

• Crook (1996) found strong

sensitivity to surface conditions.

• Perturbations <= ob/model error

can make difference between

convection and blue skies.

• Solutions:

• Better/more observations.

• Improve models/DA.

• Probabilistic forecasting.

Predictability Of Convection

Integrated Rainwater Max. Vertical Vel.

Crook (1996)


MOGREPS-UK

• Initial implementation of

MOGREPS-UK:

downscaler of short-range

(T+3) MOGREPS-G

ensemble forecast.

• Initial & boundary

conditions from global

forecast.

• Model physics as 1.5km

UKV with no stochastic

physics

• 4 cycles per day, 12

members to T+36.

2.2 x 4 km

2.2 x 4 km

4 x 2.2 km 4 x 2.2 km

4 x 4 km 4 x 4 km

4 x 4 km 4 x 4 km

2.2 x

2.2 km

Transition

zone


Met Office Convective-Scale Ensemble (MOGREPS-UK: 12 members, 2.2km resolution)

Ensemble Mean Visibility Ensemble Probability (vis<1km)


Impact of Re-Centring MOGREPS-UK

On Convective-Scale Analysis

• Current downscaling of global ensemble:

xUK=R(xG)

• Test re-centring the MOGREPS-G perturbations

around the UKV

(1.5km) 3DVAR analysis:

xUK=xa+R(xG)-R(xG0)

Christine Johnson


Bias

mslp

T2

Perturbed

Downscaled Perturbed has:

• Improvement for mslp

• No change for temperature

Christine Johnson


mslp

T2

RMSE

and spread

Perturbed has:

• lower error and spread

• faster growth

Perturbed

Downscaled

wind

RMSE ens mean

RMSE control

Spread

RMSE ensemble mean

RMSE control

Spread

RMSE control

RMSE ensemble mean

Spread


Ensemble-Based KM-Scale DA:

Point Reflectivity Covariances

Shading : Full Fields Line Contours : Error Correlations

Tong and Xue, 2005


CS-Scale DA: The Way Forward

1. Short-term:

• Continue to implement upgrades to observation network/NWP model in

current, operational DA (3DVAR).

• Further develop VAR covariances (e.g. large-scale treatment).

• Active contribution to optimal design of UK observing network.

• Tropical, convective-scale NWP-Nowcasting in Singapore.

2. Medium-term (on next HPC from 2015, underway):

• Develop/implement convective-scale 4DVAR (~2015) (subject to

performance assessment!).

• Expand MOGREPS-UK ensemble ready for CS-scale EnDA.

• Possibly test hybrid 4D-Var?

3. Long-term (>3 years away, but starting now)

• Consider ensemble-based alternatives to 4DVAR (J Flowerdew).

• Software framework for Met Office next-generation ‘LFRic’ model.


Thanks.

Any Questions?

convective-scale data assimilation applications - lmu

Documents