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The Impact of Data Assimilation on a Mesoscale Model of the New Zealand Region (NZLAM-VAR)

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.

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The Impact of Data Assimilation on a Mesoscale Model of the New Zealand Region (NZLAM-VAR)

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  1. 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

  2. Outline • The NZ mesoscale weather prediction system (NZLAM-VAR): • Mesoscale & Global components • Data • Initial results: • Global • NZLAM (nodata assimilation) • NZLAM-VAR • Compared with AMSU-B • Forecast error covariance • Summary, issues & future directions

  3. 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)

  4. 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)

  5. Example NZLAM-VAR Increments • We want to use data at high spatial resolution, but • High resolution (probably)  “noisey” analyses…

  6. NZLAM UM Global NZLAM-VAR AMSU 23.8GHz AMSU 150GHz NZLAM-VAR AMSU 89GHz Total Water Forecast (725 hPa, 36 h Prediction) • GMSIR,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

  7. 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

  8. Global UM Verifying Analysis NZLAM-VAR MSLP Forecasts (12 hour) – Significant Weather • mslp • NZLAM-VAR verification better

  9.  Unrotated RH Unrotated Rotated Rotated 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?

  10. 230 hPa 300 hPa 970 hPa 900 hPa 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…

  11. 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 UMglobal 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?

  12. 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

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