© crown copyright met office um 4d-var regional reanalysis progress richard renshaw, stephen oxley,...

© Crown copyright Met Office

UM 4D-Var Regional Reanalysis ProgressRichard 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


WP2.1 Building capacity for advanced regional data assimilation

orography

12km grid

480 x 384

reanalysis period 2008/2009


Technical Highlights

• Capability to generate ODBs


ODB – obs monitoring database

• 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 Highlights

• Capability to generate ODBs

• Able to archive reanalysis fields in ECMWF mars


First run 2008/9


4 Parallel Streams

2008 2009

A

BC

D

with 1 month overlap for spin-up


How long to spin up ?

rms screen temperature


How long to spin up ?

rms surface pressure


Var Resolution

UM 12km

Var 24km


Var Resolution

4DVar run time:• 36km 1 node hour• 24km 3 node hours• 12km 20 node hours

UM T+241.5 node hours


Var Resolution: 24 vs 36 km


Observations• 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 ofsatellite radiances

Initial reanalysis:

Radiances processed, not assimilated

Final reanalysis:

Radiances assimilated

monthly bias statistics


Validation

Peter Jermey


Verification - Results

+2.7 wtd skill diff

+6.6 wtd skill diff

+3.2 wtd skill diff

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

Jan 08 Jul/Aug 08

Sept/Oct 09


Statistics and

EXTREME Statistics!


First run 2008-2009


Statistics

Standard Statistics• Mean• Std Dev• Range

Extreme Statistics• Max of Daily Max• Max of Daily Min• Min of Daily Max• Min of Daily Min• Icing days, Frost days, Summer days, Tropical NightsSummer Days

Count of days for which daily max T>25 degrees

• Maximum count of consecutive dry/wet days

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

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

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

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

Percentiles For base period (ERA) 1961-1990

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

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


Validation of climate indicesPeter Jermey


July 2008


July 2008 Floods

ERA

EURO4M

ERA

EURO4M

RMS

Mean

73

69 -36

-37

Max of 5 Daily Precip/mm

mm

•EURO4M closer to Obs•Both models not as wet as Obs

•EURO4M more detail


Developments for 2013


Cloud assimilation

NAE assimilates 3D cloud fields from nowcasting system

(combines satellite imagery + surface reports)

EURO4M will have to rely on using surface reports directly


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 reportsPeter 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

84703 4 oktas Stratus, height 90m


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





Precipitation assimilation

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

• For EURO4M, aim to assimilate raingauge accumulations



Plan

Use E-Obs gridded daily precipitations

Keith Ngan, Andrew Lorenc, Richard



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


n

ib

airi

scan xfccbias1

)(

Variational Bias Correction

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

Compare our reanalysis against:• SMHI• ERA• Obs climatologies

Peter


Summary

• 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 ?


Extra slides...


model orography

ERA-Interim

Model T255 (80km)

Var T159 (125km)

Met Office

Model 12km

Var 24km


ERA-Interim vs EURO4M

• 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

• T255 (80km), 60 levels• T159 (125km) 4D-Var• 12-hour analysis window• assimilate:

• conventional obs• satellite radiances


Observation processing

Corrections to radiosonde temperature, surface pressure,

- use same as UKMO Global

Rejection lists

- use old UKMO Global and NAE lists


Observations from ECMWF

• 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

• Ground-based GPS• Scatterometer winds


Verification

• Description of Work: Temperatur

e,Wind,

Water Vapour,

Surf. P., Surf. Radiation Budget,

Earth Radiation Budget,Cloud

Properties,SST, Precip,Snow Cover, and

Visibility.

Rel. Hum.

• Skill Scores (as in NWP Index):

Skill =(Per(Anal))2 – Fc2

(Per(Anal))2

• ETS as (in UK Index):

Tot/Base

Total Cloud

Cloud Base

3, 5, 7 octs.

100m, 500m, 1500m Precip 0.5mm, 1mm, 4mm

Vis (1.5m) 200m, 1000m, 4000m


February 2009


February 2009 Snow

Min of Daily Min Temp/degrees

Max of Daily Precip/mm

ERA

EURO4M


February 2009 Snow

Min of Daily Min Temp/degrees

ERA

EURO4M

ERA

EURO4M

RMS

Mean

2.8

2.3 0.9

0.3

•EURO4M closer to Obs•EURO4M larger bias•EURO4M and ERA warmer than obs


February 2009 Snow

ERA

EURO4M

ERA

EURO4M

RMS

Mean

7.5

6.2 -0.45

-5.6

Max of Daily Precip/mm

•EURO4M closer to Obs•EURO4M smaller bias•EURO4M and ERA dryer than obs


Reports of 24hr accum precip


12Z Reports of 6hr precip


Covariances

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

350km

100km

vertical mode (1 to 70)


CVT covariances: psi horizontal length scales

350km

100km

vertical mode (1 to 70)

NAE 180km


Cov: CVT vs NAE

© crown copyright met office um 4d-var regional reanalysis progress richard renshaw, stephen oxley,...

Documents

crown copyright met

crown copyright met

odbs slide

dingmin li slide

rms surface pressure

rms screen temperature

max precip

consecutive days