The Impact of Data Assimilation on a Mesoscale Model of the

New Zealand Region

(NZLAM-VAR)

P. Andrews, H. Oliver, M. Uddstrom, A. Korpela X. Zheng and V. Sherlock

National Institute of Water and Atmospheric Research (NIWA) Wellington, New Zealand

Outline• The NZ mesoscale weather prediction

system (NZLAM-VAR):– Mesoscale & Global components

– Data

• Initial results:– Global

– NZLAM (no data assimilation)

– NZLAM-VAR • Compared with AMSU-B

– Forecast error covariance

• Summary, issues & future directions

Mesoscale Prediction System: NZLAM-VAR

• Using Met Office Unified Model– NIWA implementation– Met Office Data (initially)

• Mesoscale Component – UM: 324 324 38 ( 12 km)– 3DVar and IAU– High resolution data (direct

readout)– Cycling: 3 hourly – 2 48 h forecasts / day– Verification (VER)• Global Component– Lateral Boundary Conditions– UM: 432 325 30 ( 60

km)

Data Types: Dec 1999 – Feb 2000

• Conventional (from NZMetS)– Rawinsondes – Ships – Buoys – SYNOPS – AMDAR

• Satellite (NIWA)– Winds

• SSM/I • Hourly CMV (GMS)

– SST (14 day mean)– HIRS (NOAA14 & 15)– AMSU-A(NOAA15)

Example NZLAM-VAR Increments

• We want to use data at high spatial resolution, but• High resolution (probably) “noisey” analyses…

• GMS IR, 1800 Z, 17 Dec 99• QT Validity time: 1800 Z

– UM Global Model– NZLAM, no DA– NZLAM-VAR

• AMSU: 2010 Z– Ch 1 23.8 GHz– Ch 16 89 Ghz– Ch 17 150 GHz

UM GlobalNZLAMNZLAM-VAR

Total Water Forecast (725 hPa, 36 h Prediction)

AMSU 23.8GHzAMSU 89GHzAMSU 150GHzNZLAM-VAR

• NZLAM-VAR appears to “verify” well…

• Model and Data contain high spatial structure

• Rain signal:-– Absorption at 89 Ghz– Scattering at 150 GHz

Microphysics: Cloud Predictions

NZLAM-VAR 12 hour forecast: low, low + mid, low + mid + high

“Verifying” GMS 11m image for 16 Dec 1999, 1640 UTC

Global UM

MSLP Forecasts (12 hour) – Significant Weather

• mslp• NZLAM-VAR verification better

NZLAM-VARVerifying Analysis

Forecast Errors: Vertical Modes• NMC Method

– 112 forecast pairs (6 & 12 h)– 1 month (Feb 2000)– EOF decomposition of vertical

errors• Analysis variables

– Stream function ()– Velocity potential ()– Unballanced pressure (Ap)– Relative humidity ()

• Varimax rotation of EOFs– Simpler vertical structure– Useful physical interpretation?

Rotated

Unrotated

Rotated

RH Unrotated

Forecast Errors: Horizontal Scales• Correlation length scales to

r = 0.29• Stream function ():

– SOAR best fit– Similar length scales in the

troposphere 290 340 km

• RH:– Not Gaussian or SOAR?– 85% of variance above 850

hPa– Length scales 50 80– High density AMSU-B

should help… 900 hPa

300 hPa

230 hPa

970 hPa

Summary• Thanks to the Met Office• Utilising the UM – a complete mesoscale prediction system “test

bed” has been implemented:-– Large (synoptic scale) maritime domain– High resolution model (spatial & boundary layer)– 3DVar (including HIRS & AMSU-A)– 3 hour assimilation cycle ( 2 48h forecasts / day)– LBCs from compatible UM global model– Objective verification– High resolution local data sources– Current emphasis: OSIS

• Initial results verify quite well (subjectively)• Forecast error covariance statistics re-evaluated

– Need rotated EOF characterisation– For RH analysis need AMSU-B data at high density

• High analysis resolution = noisey increment fields?

Issues & Future Research• The “verification problem”

– How, and what?– Conventional methods as well as QPF:-

• Global• NZLAM (no DA)• NZLAM-VAR

– Hydrological model• The “data density” problem (i.e. contaminant detection at high spatial

resolution)– AMSU A/B rain, ice & beam filling: NACA data fusion– HIRS cloud: AVHRR (SRTex cloud mask, SST, AMSU) data fusion

• Forcast error covariance characterisation• RTM error characterisation (bias correction)• OSIS

– Conventional, SST, HIRS, AMSU, … AIRS