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
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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)
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–
–
–
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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…
Total Water Forecast (725 hPa, 36 h Prediction)
• 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
• NZLAM-VAR appears to
“verify” well…
• Model and Data contain high
spatial structure
• Rain signal:– Absorption at 89 Ghz
– Scattering at 150 GHz
UM
Global
NZLAM-VAR
NZLAM
NZLAM-VAR
AMSU
150GHz
AMSU 89GHz
23.8GHz
Microphysics: Cloud Predictions
NZLAM-VAR 12 hour forecast:
low, low + mid, low + mid + high
“Verifying” GMS 11m image for
16 Dec 1999, 1640 UTC
MSLP Forecasts (12 hour) – Significant Weather
Verifying Analysis
• mslp
• NZLAM-VAR verification better
NZLAM-VAR
Global UM
Forecast Errors: Vertical Modes
• NMC Method
– 112 forecast pairs (6 & 12 h)
– 1 month (Feb 2000)
– EOF decomposition of vertical
errors
• Analysis variables
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–
–
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Stream function ()
Velocity potential ()
Unballanced pressure (Ap)
Relative humidity ()
 Unrotated
RH
Unrotated
• Varimax rotation of EOFs
– Simpler vertical structure
– Useful physical interpretation?
Rotated
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
230 hPa
300 hPa
• RH:
– Not Gaussian or SOAR?
– 85% of variance above 850
hPa
– Length scales  50  80
– High density AMSU-B
should help…
970 hPa
900 hPa
Summary
• Thanks to the Met Office
• Utilising the UM – a complete mesoscale prediction system
“test bed” has been implemented:–
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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