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UM 4D-Var Regional Reanalysis Progress. Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey, Dale Barker, Tom Green, DingMin Li. Contents. Technical highlights First full reanalysis 2008/9 Validation Developments for 2013/14.

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Um 4d var regional reanalysis progress

UM 4D-Var Regional Reanalysis Progress

Richard Renshaw, Stephen Oxley, Adam Maycock, Peter Jermey,

Dale Barker, Tom Green, DingMin Li


Contents
Contents

Technical highlights

First full reanalysis 2008/9

Validation

Developments for 2013/14


Wp2 1 building capacity for advanced regional data assimilation
WP2.1 Building capacity for advanced regional data assimilation

orography

12km grid

480 x 384

reanalysis period 2008/2009


Technical highlights
Technical Highlights assimilation

Capability to generate ODBs


Odb obs monitoring database
ODB – obs monitoring database assimilation

ODB stores observations + qc + O-B + O-A + ...

Established ECMWF database + utilities

Array of tools available “for free”

Metview macros (quick look)

Obstat (detailed stats / graphics)


Technical highlights1
Technical Highlights assimilation

Capability to generate ODBs

Able to archive reanalysis fields in ECMWF mars


First run 2008 9
First run 2008/9 assimilation


4 parallel streams
4 Parallel Streams assimilation

2009

2008

A

B

C

D

with 1 month overlap for spin-up


How long to spin up
How long to spin up ? assimilation

rms screen temperature


How long to spin up1
How long to spin up ? assimilation

rms surface pressure


Var resolution
Var Resolution assimilation

UM 12km

Var 24km


Var resolution1
Var Resolution assimilation

4DVar run time:

36km 1 node hour

24km 3 node hours

12km 20 node hours

UM T+24 1.5 node hours



Observations
Observations assimilation

Surface (SYNOP, buoy, etc) incl visibility

Upper air (sonde, pilot, wind profiler)

Aircraft

AMV (‘satwinds’)

GPS-RO

Scatterometer winds

ATOVS

AIRS

IASI

GPSRO

MSG clear sky radiances


Bias correction of assimilationsatellite radiances

Initial reanalysis:

Radiances processed, not assimilated

monthly bias statistics

Final reanalysis:

Radiances assimilated


Validation peter jermey
Validation assimilationPeter Jermey


Verification results
Verification - Results assimilation

  • Verifying at T+6 – a good analysis should produce a good short range forecast

Jul/Aug 08

Jan 08

+6.6 wtd skill diff

+2.7 wtd skill diff

Sept/Oct 09

+3.2 wtd skill diff


Statistics and

Statistics and assimilation

EXTREME Statistics!


First run 2008-2009 assimilation


Statistics
Statistics assimilation

Extreme Statistics are defined as ‘core indices’ of climate change by The joint CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices.

Standard Statistics

Extreme Statistics

  • We calculate these statistics for ERA and EURO4M and compare with observations statistics from European Climate Assessment & Dataset ECA&D http://eca.knmi.nl

  • Mean

  • Max of Daily Max

  • Std Dev

  • Max of Daily Min

  • Range

  • Min of Daily Max

  • Min of Daily Min

Summer Days Count of days for which daily max T>25 degrees

  • Icing days, Frost days, Summer days, Tropical Nights

Wet Days A day on which precip is greater than 1mm

  • Maximum count of consecutive dry/wet days

Percentiles For base period (ERA) 1961-1990

  • Count of wetdays, count of days with precip above 10mm, 20mm.

  • Average precip on wetdays, max precip on 5 consecutive days, maximum precip on a single day, total precip, precip above 95th and 99th percentiles.

  • Percentage of days where max temp > 90th percentile etc…


Validation of climate indices assimilationPeter Jermey


Validation of climate indices assimilationPeter Jermey


July 2008

July 2008 assimilation


July 2008 floods
July 2008 Floods assimilation

Max of 5 Daily Precip/mm

RMS

Mean

-37

73

ERA

ERA

69

-36

EURO4M

  • EURO4M more detail

  • EURO4M closer to Obs

  • Both models not as wet as Obs

EURO4M

mm


Developments for 2013
Developments for 2013 assimilation


Cloud assimilation
Cloud assimilation assimilation

NAE assimilates 3D cloud fields from nowcasting system

(combines satellite imagery + surface reports)

EURO4M will have to rely on using surface reports directly


Cloud from SYNOP reports assimilationPeter Francis

Wattisham, 00Z 2012/03/13AAXX 13004 03590 11238 83504 10064 20060 30240 40352 53002 60001 71022 886// 92350 333 55/// 20411 84703 86706 88708


Cloud from SYNOP report assimilation

Wattisham, 00Z 2012/03/13AAXX 13004 03590 11238 83504 10064 20060 30240 40352 53002 60001 71022 886// 92350 333 55/// 20411 84703 86706 88708

84703 4 oktas Stratus, height 90m


Cloud from SYNOP report assimilation

Wattisham, 00Z 2012/03/13AAXX 13004 03590 11238 83504 10064 20060 30240 40352 53002 60001 71022 886// 92350 333 55/// 20411 84703 86706 88708

84703 4 oktas Stratus, height 90m

86706 6 oktas Stratus, height 180m

88708 8 oktas Stratus, height 240m


Precipitation assimilation
Precipitation assimilation assimilation

  • Operational UK models assimilate radar rainrate (latent heat nudging)

  • For EURO4M, aim to assimilate raingauge accumulations


Precipitation assimilation1
Precipitation assimilation assimilation

Plan

Use E-Obs gridded daily precipitations

Keith Ngan, Andrew Lorenc, Richard


Precipitation assimilation2
Precipitation assimilation assimilation

Plan

Use E-Obs gridded daily precipitations

System to disaggregate 24hr accumulations to 6hrs

Var outer loop with spin-up problems minimised

(analysis increments trigger rain in model).

Keith Ngan, Andrew Lorenc, Richard


Variational Bias Correction assimilation

Airmass-dependent bias correction of satellite radiances (based on Harris and Kelly, 2001)

Currently coeffs c are calculated off-line monthly

VarBC will give smooth and automatic updating

Code is in place – hope to trial in 2013

(DingMin Li, Andrew Lorenc , Dale Barker)


Collaboration cross validation
Collaboration – Cross-Validation assimilation

Compare our reanalysis against:

SMHI

ERA

Obs climatologies

Peter


Summary
Summary assimilation

  • Initial reanalysis is run (2008-9)

    • new validation tools

  • Production reanalysis will be better:

    • satellite radiances

    • surface cloud

    • ODBs and mars archive

  • A final reanalysis aims to include later developments

    • precipitation assimilation

    • variational bias correction


Questions
Questions ? assimilation


Extra slides
Extra slides... assimilation


model orography assimilation

ERA-Interim

Model T255 (80km)

Var T159 (125km)

Met Office

Model 12km

Var 24km


Era interim vs euro4m
ERA-Interim assimilationvs EURO4M

  • T255 (80km), 60 levels

  • T159 (125km) 4D-Var

  • 12-hour analysis window

  • assimilate:

    • conventional obs

    • satellite radiances

  • 12km, 70 levels

  • 24km 4D-Var

  • 6-hour analysis window

  • assimilate:

    • conventional obs incl vis

    • satellite radiances

    • GPS (ground & RO)

    • Cloud

    • Precipitation

  • Initial state and boundary conditions from ERA-Interim analyses


Observation processing
Observation processing assimilation

Corrections to radiosonde temperature, surface pressure,

- use same as UKMO Global

Rejection lists

- use old UKMO Global and NAE lists


Observations from ecmwf
Observations from ECMWF assimilation

Surface (SYNOP, buoy, etc) incl visibility

Upper air (sonde, pilot, wind profiler)

Aircraft

AMV (‘satwinds’)

ATOVS

AIRS

IASI

GPSRO

MSG clear sky radiances


Observations from metdb
Observations from MetDB assimilation

Ground-based GPS

Scatterometer winds


Verification

(Per(Anal)) assimilation2 – Fc2

Skill =

(Per(Anal))2

Verification

  • Description of Work:

Temperature,

Water Vapour,

Rel. Hum.

Surf. P.,

Surf. Radiation Budget,

Wind,

Earth Radiation Budget,

Cloud Properties,

Tot/Base

Snow Cover,

SST,

Precip,

and Visibility.

  • Skill Scores (as in NWP Index):

  • ETS as (in UK Index):

Total Cloud

3, 5, 7 octs.

Cloud Base

100m, 500m, 1500m

Precip

0.5mm, 1mm, 4mm

Vis (1.5m)

200m, 1000m, 4000m


February 2009

February 2009 assimilation


February 2009 snow
February 2009 Snow assimilation

Min of Daily Min Temp/degrees

Max of Daily Precip/mm

ERA

EURO4M


February 2009 snow1
February 2009 Snow assimilation

Min of Daily Min Temp/degrees

RMS

Mean

0.3

2.8

ERA

ERA

2.3

0.9

EURO4M

  • EURO4M closer to Obs

  • EURO4M larger bias

EURO4M

  • EURO4M and ERA warmer than obs


February 2009 snow2
February 2009 Snow assimilation

Max of Daily Precip/mm

RMS

Mean

-5.6

7.5

ERA

ERA

6.2

-0.45

EURO4M

  • EURO4M closer to Obs

  • EURO4M smaller bias

EURO4M

  • EURO4M and ERA dryer than obs






Covariances
Covariances assimilation

NAE

Old covariances from Global model

Horizontal length scales are ‘guesses’

EURO4M

Covariances calculated (NMC method) in CVT

Horizontal length scales also from CVT

Marek, Gordon, Jean-Francois


Cvt covariances psi horizontal length scales
CVT covariances: assimilation psi horizontal length scales

350km

100km

vertical mode (1 to 70)


Cvt covariances psi horizontal length scales1
CVT covariances: assimilation psi horizontal length scales

350km

NAE 180km

100km

vertical mode (1 to 70)


Cov cvt vs nae
Cov: CVT vs NAE assimilation


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