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Variational data assimilation experiments at NCEP using ocean surface winds and sea surface temperatures data PowerPoint Presentation
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Variational data assimilation experiments at NCEP using ocean surface winds and sea surface temperatures data

Variational data assimilation experiments at NCEP using ocean surface winds and sea surface temperatures data

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Variational data assimilation experiments at NCEP using ocean surface winds and sea surface temperatures data

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  1. Variational data assimilation experiments at NCEP using ocean surface winds and sea surface temperatures data Tsann-wang Yu Environmental Modeling Center National Centers for Environmental Prediction National Weather Service, NOAA Washington, D. C., 20233

  2. Outline • Current status of NWP operations at NCEP • Data assimilation and development strategies for ocean and atmospheric • Use of QuikSCat winds and GOES and AVHRR data assimilation experiments • Current developments in data assimilation at NCEP / JCSDA

  3. Evolution of forecast skill for the northern and southern hemispheres

  4. Satellite data currently used in NCEP’s operational global and regional model data assimilation • NOAA –14 TOVS radiance data (HIRS-2, MSU) • NOAA-15, NOAA-16, NOAA-17 ATOVS radiance data (HIRS-3, AMSU-A, AMSU-B, SBUV etc. ) • SSMI ocean surface wind speed, QuikSCAT /SeaWind ocean surface wind vectors in GDAS • SSMI / TRIM total precipitable water, and rain rate • NEXRAD radar radial velocity in ETA model • GOES radiance data in GDAS and EDAS

  5. Data Assimilation Observation pdf Initial Condition: Analysis Mean Analysis pdf For the Grid t3 Forecast pdf For the Grid t1 t2

  6. 3D-Variational data assimilation at NCEP Distance to forecast Distance to observations • x is a model state vector, with 106-8d.o.f. xaminimizes J • yo is the set of observations, with 105-9 d.o.f. • In 3D-Var B is assumed to be constant: it does not include “errors of the day” • The methods that allow B to evolve are veryexpensive: 4D-Var and Kalman Filtering, and require the linear tangent and adjoint models.

  7. Specifying background error covariances: general remarks • There is not enough information (and never will be) to determine all the elements of (typically > O(1010)). • must be approximated by a statistical model (e.g., prescribed covariance functions) with a limited number of tunable parameters. • In 3D-Var/4D-Var, is implemented as an operator (a matrix-vector product). • For the preconditioning transformation we require access to a square-root operator (and its adjoint ). • Constructing an effective operator requires substantial development and tuning. • It is preferable to have a flexible covariance model first before spending considerable effort tuning statistical parameters.

  8. Specifying background error covariances: specific remarks for the ocean • Ocean observations are relatively sparse so it is difficult to estimate background error statistics from innovations. Considerable spatial and temporal averaging is required (e.g., Martin et al. 2002). • With few observations the role of is critical for exploiting available data-sets effectively (e.g., surface altimeter data). • Added complexity due to the presence of continental boundaries (boundary conditions, scales, spectra, balance). • Rich variety of scales: mesoscale (Gulf Stream, Kuroshio regions) ~O(10km) and synoptic scale (tropics) ~ O(100km).

  9. Ocean surface winds and sea surface temperatures are two of the most important fields responsible for • Physical coupling of ocean and atmosphere - air sea interaction. • Directly driving ocean waves and current circulations – ocean general circulations. • Affecting accuracy of numerical weather and climate forecasts.

  10. Observing System Experiments (OSE) to test Quikscat winds and infra-red sea surface temperatures data from AVHRR and GOES Data assimilation experiments to test QuikSCAT winds and AVHRR and GOES infra-red radiance SST data SCAT Winds, or SST+ Conventional data Assimilation Conventional data Assimilation CNTL TEST Impact of SCAT winds or SST = (TEST –CNTL) Effect of SCAT Winds(TEST – CNTL)

  11. Radar backscattering Specular scattering from a smooth surface - most energy is reflected away. Defuse Scattering from a rough surface - energy is reflected in all directions.

  12. Bragg scattering

  13. Bragg Scattering and scatterometer wind retrieval geophysical model function • Ocean surface waves with a wavelength that satisfies the Bragg resonance condition will contribute the most to radar cross section, 0 • 0 = function (S, , , P), where S is wind speed,  , the incidence angle of radar beam to the ocean surface,  is the relative angle of surface wind direction with respect to the radar beam, and P is polarization of radar beam.

  14. Ambiguous winds from QuikSCAT data

  15. North Atlantic Surface Analysis integrating ship & buoy observations with QuikSCAT winds Wind speed (Knots) 6550 35302520 QuikSCAT winds – introduced into Ocean Prediction Center operational workstations in the fall of 2001 – are now fully integrated into the warning & forecast decision process

  16. ---- QuikSCAT winds – a numerical model diagnostic NCEP GFS 40m Winds - 6 hr FCST 1800 UTC 17 Feb 03 QuikSCAT Winds 1800 UTC 17 Feb 03 Wind Speed (Knots): 6550 35302520

  17. Rain flagged data removed Potential Rain Contamination • Subtropical System are most affected • What is the warning category? • Is it an open wave or is it closed? • If closed, where is the center? Wind speed (Knots) 6550 35302520

  18. T.S. force wind radii Isabel (Cat 5) – In mature tropical cyclones, strong convection and rain in inner core prevents accurate wind speed retrievals. Even so, QuikSCAT is used to help determine the radial extent of tropical storm force winds MSW = 140 kt. MSLP = 932 mb

  19. Hurricane Force Extratropical Cyclone 48.5N 26.89W Hurricane Force Winds QuikSCAT pass from 06NOV 0630UTC

  20. Summary of Results of GDAS experiment Using 100 km resolution QuikSCAT winds at NCEP • NCEP Operational GDAS – T170, L42 • Assimilation Exp.- Oct. 2, 2001 to Nov. 10, 2001 (43 days) • Found positive impact on heights and winds at all levels for both N.H. and S.H., especially over the ocean surface • QuikSCAT winds became operational onJanuary 15, 2002 25

  21. Fig.1 Five Regions of Deep Ocean Buoys used in the evaluation North Sea WestCoast East Coast Gulf of Mexico TOGA

  22. 10-meter wind forecast errors (m/sec) with respect to mid-latitude deep-ocean buoys Courtesy of NCEP Environmental Modeling Center

  23. Mean sea level pressure forecast errors (mb) with respect to mid-latitude deep-ocean buoys Courtesy of NCEP Environmental Modeling Center

  24. Golbal data assimilation experiments at NCEP using high resolution QuikSCAT winds data ( Yu, 2003) • Scientific objective: High resolution (50 km) QuikSCAT winds should improve mesoscale features of analyses • Major findings: Most improvements are found in mesoscale winds forecasts over the tropics

  25. Pre-implementation QuikSCAT winds (~50km) GDAS Run ---- OPNL Run (~100 km) ---- Pre-implementation Run (~50km)

  26. Pre-implementation QuikSCAT winds (~50km) GDAS Run ---- OPNL (~100 km) ---- Pre-implementation Run (~50km)

  27. Mean anomaly correlations at 850 mb for tropical winds ( ---o--- Opnl Run; ---+---- Pre-implementation Run) U (waves 1-20) U (waves 10-20) V (waves 1-20) V (waves 10-20)

  28. Summary of GDAS experiment Using 50 km resolution QuikSCAT winds at NCEP • NCEP Operational GDAS – T254, L64 • Assimilation Exp. – January 8, 2003 to March 8, 2003 (60 days) • Found positive impact on heights and winds at all levels for both N.H. and S.H., especially for winds over the tropical oceans • QuikSCAT winds (50 km) – were implemented at NCEP GDAS on March 11, 2003 35

  29. Global data assimilation experiments using high resolution SST analyses at NCEP (Yu, 2004) • Motivation: ECMWF has already used NCEP high resolution SST analysis in NWP operation; high resolution SST are already used in NCEP EDAS operation • Purpose: To investigate the impact of high resolution SST on NCEP GDAS and NWP forecasts for possible implementation

  30. High resolution SST GDAS experiments

  31. High resolution SST GDAS experiment

  32. High resolution SST GDAS experiment

  33. High resolution SST GDAS experiment

  34. Use of AVHRR and GOES satellite infra-red temperature data at NOAA Coastal Ocean Forecast System (O’Connor, Lozano, andYu, 2004) • Scientific objective: High resolution (about 8km) infra-red sea surface temperatures data from AVHRR and GOES should improve mesoscale features of ocean analyses • Major findings: use of GOES’ SST in ocean data assimilation is found to lead to large improvements for depicting Gulf Stream feature

  35. Ocean Forecasting – Present (1-2 days) Coastal Ocean Forecast System (COFS) Princeton Ocean Model Domain: East Coast Vertical Coordinate: Sigma (19 levels) Horizontal Resolution: 10 km near coast to 20 km in deep ocean Lateral Boundary Condition: Monthly mean values for temperatures, salinity, and transport at the open ocean boundaries and monthly mean values for river run-off at the coastal boundaries Predictionof SST, Gulfstream, Hurricane-Ocean Coupling, Tides and Water Levels, Boundary Conditions for Bays and Estuaries, Search & Rescue Operations, Toxic Spill Containment, Ecosystem Management,.. Features:Primitive Equations, Forced by ETA Model Fluxes; Assimilation of SST, XBT, altimetry. SST Surface Currents

  36. Operational - With AVHRR data