Report to 20 th Data Exchange Meeting by Met Office

© Crown copyright 2006 Page 1

Report to 20th Data Exchange Meeting by

Met Office

Roger Saunderswith the help of Steve English

Bill Bell, Mary Forsythe,

Brett Candy, Mike Rennie,

Adrian Jupp, Sam Pullen,

Jon Taylor


Met Office Operational Models : 2007

Global ~40km ~50Level

North Atlantic/Europe 12km ~30level

UK 4km(1km) ~30Level

Re-locatable Defence and Civilian

Ensemble (global & regional) at half horizontal resolution

Data assimilation 4DVar 6 hour window for global and regional models


SuperComputer Changes

Model Now 2009 2010 2011 2012 2013

Global 40km 40km 25km 25km 18km 18km

50 levels*(*Dec

20070

70 levels 70 levels 70 levels 100 levels 100 levels

N At Eur 12km 8km 8km 5km 5km 5km

50 levels 70 levels 70 levels 70 levels 100 levels 100 levels

UK 4km 1.5km 1.5km ~0.8km ~0.8km ~0.8km

50 levels 70 levels 70 levels 70 levels 100 levels 100 levels

Specialist 0.25km 0.25km 0.25km 0.25km 0.25km

100 levels 100 levels 100 levels 100 levels 100 levels


Operational data usage (Apr 2007)

Observation group

ObservationSub-group

Items used Daily extracted % used inassimilation

Ground-based vertical profiles

TEMPPILOTPROFILER

T, V, RH processed to model layer averageAs TEMP, but V onlyAs TEMP, but V only

13008006500

86,92,479045

Satellite-based vertical profiles

METOP-ANOAA-15/16/18AIRS, HIRS, AMSU-A/B, MHS, DMSP-SSMISRadio-occultationCOSMIC, Champ, Grace

Radiances directlyassimilated with channel selection dependent onsurface instrument and cloudiness.Profiles of refractive index

ATOVS:4,000,000AIRS:324,000COSMIC: 1600

4430

Aircraft Manual AIREPSAutomated AMDARS

T, V as reported with duplicate checking and blacklist

24000220,000

17, 1527, 26

Satellite atmospheric motion vectors

GOES 11,12 BUFRMeteosat 7, 9 BUFRMTSAT BUFRMODIS AMVs

High resolution IR windsIR, VIS and WV windsIR, VIS and WV winds

3,000,000 1

Satellite-basedsurface winds

DMSP-SSM/I-13SeawindsERS-2 scatt

In-house 1DVAR wind-speed retrieval NESDIS retrieval of ambiguous winds. Ambiguity removal in 4DVAR.

3,000,0001,800,000ERS-2 not incuded

0.51.5

Ground-basedsurface

Land SYNOPSHIP, Fixed BuoyDrifting BUOYGPS IWV

Pressure only (processed to model surface)Pressure and windPressureTotal column water

29500800012000

7391, 9076

Cloud/Rain observations

METEOSAT-9 SEVIRI and UK rain radar network

Nimrod – MOPS cloud in NAE.

15000 rain12000 cloud

100100


Satellite data delays May 2007

Main run data cutoff

0 60 120 180 240 300 360 420 480 540

ATOVS METOP-A

ATOVS Aqua

ATOVS N15

ATOVS N16

ATOVS N17

ATOVS N18

IASI

AIRS

QuikScat

ASCAT

Delay (minutes)

Max Delay

Average Delay


Mean Delay 8th-13th May 2007

050

100150200250300350400450500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

Del

ay (m

inut

es)

NOAA18 NOAA16 NOAA15 NOAA17 METOP-A

ATOVS Data


Recent changes to use of satellite data

GPS-RO Cosmic, Champ/Grace refractivity pofiles assimilated briefly in Dec 06 and again from May 2007.

DMSP-F16 SSMIS 50GHz channels assimilated from Sept 2006

METOP AMSU-A, MHS added in Jan 07NOAA-17 HIRS added in Jan 07Groundbased GPS IWV assimilated in NAE from April

07.AMVs

Meteosat-8 replaced by Meteosat-9 Meteosat-5 replaced by Meteosat-7 MTSAT SATOB replaced by MTSAT BUFR MODIS direct broadcast used to increase coverage


Global Forecast Improvements since mid-2005

-2

0

2

4

6

8

10

12

6 7 8 9 10 11 12 13 14 15

Cu

mu

lati

ve I

mp

act

of

NW

P

ind

ex

Total Impact Satellite Impact

NOAA-18 50 Levels AIRS WF, ERS-2, Met-8

GPS RO, SSMIS

GOES-BUFR, Scat bias correction

ATOVS 3hr thinning + incr. use EARS

METOP-A,

NOAA-17 reintro.

COSMIC intro

Parallel Suite


METOP Payload

Denotes used

Denotes

In test


METOP AMSU-A/MHS Antenna Corrections

Met OfficeEUMETSAT (on EUMETCAST)No correction

IASI, NAST-I, SHISMean spectra

BT

(K

)D

iff (

K)

IASI minus NAST-I, IASI minus SHIS(using double obs-calc method)

0.12 K0.20 K

NAST-I: S-HIS:

0.04 K0.03 K

-0.19 K-0.08 K

IASI Midwave Validation

Wavenumber (cm-1)

* Correlated IASI differences with NAST-I and SHIS, shown in the plot below, can result from errors in the simulation of the radiance contributions to IASI from the atmosphere above the aircraft (~ 18 km).


IASI Implementation

IASI poses huge challenges because of the volume of data

8461 channels, 120 observations per scan

We will reduce this data volume by using only 300 channels and one in four observations

Channels selected on the basis of information content

Reduces data volume of one IASI to about the level of three ATOVS

Initially, we will use the data in a very similar way to AIRS

Sea only

Clear only (via 1D-Var cloud detection)


Met Office SSMIS assimilation experiments

UKMO-2SAT-3DN216:Low resolution 3D-Var test of SSMISon top of 2 satellite system (NOAA16 & 18)

UKMO-OPNS-3DN216:Low resolution 3D-Var test of SSMISon top of 3 satellite system(NOAA15, 16 & 18)

UKMO-OPNS-4DN320:High resolution 4D-Var test of SSMISon top of 3 satellite system

Implemented operationally 26th September 2006


SSMIS developments

Efforts are ongoing to unify SSMIS pre-processing (combining NRL & Met Office approaches) . Expect new data stream by October 2007 (F-16 SSMIS).

Fast RT model for Zeeman split Upper Atmospheric Sounding channels available (Y. Han, NESDIS) and used to evaluate ECMWF fields to 0.01 hPa (S. Swadley, NRL & W. Bell).

Cal/Val for F-17 ongoing (NRL) expect data stream Spring 2008.


Meteosat-7Mar 07

AMV Satellite Status Update

equator

60N

60S

NESDIS MODIS polar winds (TERRA and AQUA)

GOES-10 GOES-12 Meteosat-8 Meteosat-5 MTSAT-1R

SATOB

GOES-1118 Jul 06

Other changes in 2007

1. Goes hourly data

2. Increased coverage from MSG (out to ~65N)

3. Storage and monitoring of FY-2C winds

4. GOES-10 over S. America (?)

5. AVHRR polar winds (?)

MTSAT-1R BUFRApr 07

NESDIS MODIS polar winds (TERRA and AQUA)

NESDIS and direct broadcast MODIS polar winds (Terra and Aqua)

NESDIS and direct broadcast MODIS polar winds (Terra and Aqua)

Monitoring 22 datasets from 9 geos and 2 polar satellites

Meteosat-7Mar 07

Meteosat-9 Apr 07


MODIS direct broadcast winds

MODIS direct broadcast winds from McMurdo Station and Tromsø assimilated operationally in the Met Office model since 13th December 2006.

Provide

• Partial polar coverage.

• Similar quality to conventional MODIS winds.

• Shorter time lag (averages 180 min rather than 280 min).

• Translates into more polar AMVs in our main forecast and update runs.

Conventional Direct Broadcast

Main forecast ~18% ~45%

Update ~70% ~90%


Comparison to model best-fit pressure

Vector Differencei = √((ObU – BgUi)2 + (ObV – BgVi)2)

100

200

300

400

500

600

700

800

900

1000

-20 0 20 40 60m/s

Pre

ssu

re (

hP

a)

u component

v component

Diff

Model best-fit at minimum in vector difference profile.

AMV pressure and model best-fit pressure agree well in this case.


Model best-fit pressure comparisons

Low level GOES winds show a significant high height bias compared to model best-fit pressure (worse over sea than land). The RMS also increases as the pressure decreases from 1000 hPa to 600 hPa.

Mean observed - best-fit pressure

600

650

700

750

800

850

900

950

1000

-200 0 200

Mean difference (hPa)

Pre

ssu

re (

hP

a)

VIS Mean

IR Mean

RMS difference600

650

700

750

800

850

900

950

1000

0 200RMS difference (hPa)

Pre

ssu

re (

hP

a)

VIS RMS

IR RMS

EUMETSAT winds are less affected (particularly since March 07 change), probably partly due to use of an Inversion Correction method.

GOES-12 (final product)

3rd July – 15th Aug 06


GPS Radio Occultation

RO Missions:

GPS/Met : 1995 – 2000

Ørsted : 1998 –

SunSat : 1999 –

SAC-C : 1999 –

CHAMP: 2000 –

GRACE-A/B : 2002 –

COSMIC : Apr 2006

GRAS : Oct 2006


Vertical profile of T wrt radiosondes

Vertical profile for temperature mean error (top) and temperature RMS (bottom) in SH at 24 hr fc range. CONTROL, COSMICx6

The assimilation of GPSRO, as expected, reduces the RMS error in the uppermost troposphere and corrects model biases.

Note similar patterns but smaller impact seen in NH and TR


Bias and RMS as function of forecast range

Temp, 250 hPa, SH Wind speed, 100 hPa, SH


Differences in mean 24hr forecast T fields

GPSRO causing cooling over Antartica at 250hPa. 1D-Var solution to right supports this (1st Dec, -64 deg lat). This was frequently seen in these plots

Removing model bias?


Summary

COSMIC reduces both bias and RMS error in the UTLS for T,GPH,RH and wind speed.

Dominated by improvements in SH

Information from GPSRO leads to benefits in many other fields indirectly related to T,P,Pw,GPH through model physics. Perhaps changes in humidity can cause large scale wind/circultation adjustments (cf radiance info, see McNally 1994)

Zonal means show important changes in vertical structure of temperature above south pole, perhaps correcting biases in model.

Large average changes in pressure, wind fields can be seen over high latitudes


Ground-based GPS

Uses standard GPS navigation signals and standard geodetic-quality receivers

Atmospheric zenith total delay (ZTD) included in position solution

Information on ‘dry’ (ZHD) and ‘wet’ (ZWD) components

IWV = (ZTD-ZHD)/k = ZWD/k (k ~6.5)European collaboration via EUMETNET E-GVAP (& previously EU COST-715 & TOUGH)

Semi-operational hourly data downloads from 500+ stations over Europe

Processed to Total Zenith Delay in <2 hours


NRT GPS network

•COST 716/ TOUGH projects developed NRT GPS network, E-GVAP continues

•Lots of different processing centres

•Some overlaps- i.e. more than 1 processor for a given station

•Met Office processes UK sites in- house


Monitoring: http://www-nwp/~frmj/Ground_GPS/GPS_Monitor

•Black- NAE model

•Purple- ‘operational’

•Orange- test machine

•Green- test machine

This is a sensible way to make a change!


NWP impacts

FC Range

Surface temp

RMS fit to obs

Surface winds

RMS fit to obs

T+6 3.1% 0.1%

T+12 4.1% 0.2%

T+18 4.3% 0.1%

T+24 4.2% 0.3%

Green = improvement in FC

Red = degradation in FC

Also small improvements in visibility, cloud and precipitation ETS.

Overall weighted score showed 1.85% improvement


Use of NESDIS IMS Snow

NESDIS Interactive Multisensor Snow and Ice Mapping System (IMS)

GEO (GOES, Meteosat, MTSAT) LEO (AVHRR, MODIS, SSM/I, AMSU) Derived products (e.g. USAF Now and Ice Analysis Product) In situ data Analyst

Daily, 4km resolution, NH Polar stereographic 6144 X 6144 array GRIB from NCEP – snow cover (0 or 100), ice (0 or 1) Received on Radsat, to MetDB (snow only) in GRIB format Data extracted and processed within SURF system


An example

18/12/06 00Z Modifications to model snow amount field


NWP Satellite Application Facility

In first year of operational phase up to 2012Major deliverables are:

AAPP (ATOVS/IASI/AVHRR direct readout software)RTTOV (Fast radiative transfer model)SSMIS preprocessing package1DVAR retrieval packages (METO/ECMWF versions)Satellite data monitoring (Radiance, AMVs, O3)Scatterometer processor Advanced sounder preprocessing softwareReports on many aspects of satellite data

Also involved in GRAS (GPS RO) SAF


Work in progress…..

Add HIRS, IASI, ASCAT and GRAS from METOPInvestigating AMV height assignments and observation errors

Assimilation of MSG cloud informationExtend use of SSMIS to window/wv channels

Longer term….WINDSATAMSR-E precipitationScatterometer soil moistureADM doppler lidar windsNPP

Documents

Report to 20 th Data Exchange Meeting by Met Office