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Satellite Data Assimilation for meso-scale models

Hans Huang National Center for Atmospheric Research

(NCAR is sponsored by the National Science Foundation)

Acknowledge: NCAR/NESL/MMM/DAS, NCAR/RAL/JNT/DAT, DTCAFWA, USWRP, NSF-OPP, NASA, AirDat, PSU,KMA, CWB, CAA, BMB, EUMETSAT

Slides are collected from: Zhiquan Liu, Thomas Auligne, Xin Zhang, Hui Shao,

Chunhua Zhou, Syed Rizvi,Yaodeng Chen, Craig Schwartz, Thomas

Nehrkorn, Bill Skamarock, …

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Outline

1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?

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WRFDAhttp://www.mmm.ucar.edu/wrf/users/wrfda

Goal: Community WRF DA system for • regional/global, • research/operations, and • deterministic/probabilistic applications.

Techniques: • 3D-Var• 4D-Var (regional)• Ensemble DA, • Hybrid Variational/Ensemble DA.

Model: WRF (ARW, NMM, Global)

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WRFDA Observations In-Situ:

- Surface (SYNOP, METAR, SHIP, BUOY).- Upper air (TEMP, PIBAL, AIREP, ACARS, TAMDAR).

• Remotely sensed retrievals:- Atmospheric Motion Vectors (geo/polar).- SATEM thickness.- Ground-based GPS Total Precipitable Water/Zenith Total Delay.- SSM/I oceanic surface wind speed and TPW.- Scatterometer oceanic surface winds.- Wind Profiler.- Radar radial velocities and reflectivities.- Satellite temperature/humidity/thickness profiles.- GPS refractivity (e.g. COSMIC).

Radiative Transfer (RTTOV or CRTM):– HIRS from NOAA-16, NOAA-17, NOAA-18, NOAA-19, METOP-2– AMSU-A from NOAA-15, NOAA-16, NOAA-18, NOAA-19, EOS-Aqua, METOP-2– AMSU-B from NOAA-15, NOAA-16, NOAA-17– MHS from NOAA-18, NOAA-19, METOP-2– AIRS from EOS-Aqua– SSMIS from DMSP-16

•Bogus: –TC bogus.–Global bogus.

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WRFDA Radiance Assimilation(Liu and Auligne, MMM)

• BUFR 1b radiance ingest.• RTM interface: RTTOV (v9.3) or CRTM (v2.0.2)• NESDIS microwave surface emissivity model• Range of monitoring diagnostics.• Quality Control for HIRS, AMSU, AIRS, SSMI/S.• Bias Correction: Adaptive or Variational• Variational observation error tuning• Parallel: MPI• Flexible design to easily add new satellite sensors

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NCAR/RAL/JNT/DAT: Atlantic Testbed (AFWA T8)

Land Use Category

• 361*325*57L, 15km• Model top: 10mb• Full cycling exp. for 6 days

• 15 ~ 20 August 2007• GTS: assimilate NCAR conventional obs

• Select similar data type used by AFWA• GTS+AMSU+MHS (use NCEP BUFR rad.)

• NOAA-15/16/18, AMSU-A, ch. 5~10• NOAA-15/16/17, AMSU-B, ch. 3~5• NOAA-18, MHS (similar to AMSU-B)• Radiance used only over water • thinned to 120km• +-2h time window• Bias Correction (H&K, 2001)

• 48h forecast twice each day• 00Z, 12Z

(Liu, MMM and Shao, RAL)

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48h forecast error vs. sound

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92/22/08 9

102/22/08 10

4DVAR vs. 3DVAR

45km resolution

(4DVAR is still very slow)

model top = 10mb

Only assimilate radiance data

(AMSU/MHS), 6h time window

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(Adjoint based) Observation Impact: Conventional Data

(Auligne, MMM)

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Observation Impact: Satellite radiances

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Outline

1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?

Radiance Data Assimilation with DARTZhiquan Liu, Craig Schwartz, Xiang-Yu Huang (NCAR/MMM)

Yongsheng Chen (York University)

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Practical Implementation• Make use of observation operators built in the WRFDA-3DVAR.

– Obs. prior is calculated/QCed/output from WRFDA-3DVAR– For both conventional observations and radiances

• Convert 3DVAR output files into the modified DART obs_seq files.

• Modify DART to directly use obs prior calculated from 3DVAR– DART built-in observation operators are only applied after analysis (step

for diagnosing obs. posterior)

• For radiances, also output Jacobian from CRTM in addition to obs prior.– For vertical localization

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Vertical Localization

(K/K)

Take the height of peak levels of

Jacobian as vertical coordinate

Use DART built-in vertical

localization

AMSU-A Jacobian w.r.t. T

∂Tb∂T

(K/K)∂Tb∂T

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Bias Correction and QC

Bias correction coefficients from the end of 3DVAR experiment.

Use Ensemble Mean as reference for BC and QC.

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Typhoon Morakot

08050806

0808

0809 0807

Red: Typhoon

Blue: Tropical storm or depression

Numbers refer to date at 0000 UTC:

(0806…06 Aug 2009)

Produced very heavy precip. over Taiwan at

landfall.

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Outline

1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?

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DTC (GSI) taskshttp://www.dtcenter.org/com-GSI/users

• Provide current operational GSI capability to the research community (O2R);

• Provide a framework for distributed development of new capabilities & advances in data assimilation.

• Provide a pathway for data assimilation research to operations process. (R2O).

• Provide rational basis to operational centers and research community for enhancement of data assimilation technique and systems and, eventually, numerical weather forecast systems.

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DTC GSI T&E: end-to-end testing system

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DTC GSI T&E – Radiance Assimilation (Chunhua Zhou and

Hui Shao)• GSI candidate code (Q1FY11) v1.2 coupled with WRF-ARW

v3.2• 15 August 2007 (12 UTC) – 22 August 2007 (12 UTC)• GDAS PrepBUFR and AMSU_A data• 57 vertical levels, 10 mb model top• 15 km horizontal resolution• Global Background Errors• Full 6-hr cycling• AFWA T8 domainTwo Experiments:

AFWA T8 Domain

AMSUA: assimilating PrepBUFR + AMSU_A,

updated air-mass satbias from previous cycle,

all channels included

CONV: assimilating PrepBUFR data only

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Bias Correction

OMB without BC

OMB with BC

OMA

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Outline

1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?

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Use one set of BC coefs for 00Z/12Z

(oscillation still exists after BC)Use separated BC coefs for 00Z/12Z

(Oscillation is removed after BC)

Bias with diurnal cycle.

Morning (12Z): -0.60K

Evening (00Z): -0.15K(Related to Descending/Ascending nodes)

Consider diurnal cycle or descending/ascending

orbit issue with VarBC for regional applications

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CV5

CV6

Increments from single T observation at

5th level , 15N

Still need to work on BE (Rizvi, Krysta, Chen, Huang)

CV3

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Displacement DA Approach Conceptual view of using displacements to characterize errors

Partition:

background error displacements of coherent

features

additive (residual) error=> +

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Towards Cloudy Radiance Assimilation

Simulated mismatch in resolution:

•Perfect observations (high resolution)

•Perfect Background (lower resolution)

New interpolation scheme:

1. Automatic detection of sharp gradients

2. New “proximity” for interpolation

Representativeness Error

Innovations

Background

New Innovations

32WRF Workshop June 2010

Beyond WRF: MPAS - Summary3D Solvers• Hydrostatic 3D SVCT solver (pressure coordinate).• Nonhydrostatic 3D SVCT solver (height coordinate).• Both solvers work on the sphere and on 2D and 3D Cartesian

domains.• Tests results confirm viability of Voronoi C-grid

discretization at large scales (global) and cloud-permitting scales for both solvers.

• Variable-resolution grid results are encouraging.

Future Development• Weather, regional climate and climate physics suites.• Further testing of variable resolution meshes, physics

development.• Further development and testing of higher-order transport

schemes.

Expectations• NWP testing by the end of this year.• Friendly-user release summer 2011.

(Bill Skamarock)

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Summary1. WRFDA – DA for WRF2. DART and WRFDA3. GSI and WRF4. Future Directions?

• Regional radiance DA, BC (Bias Correction)• Improving BE• 4D-Var Optimization; EnKF; Hybrid 4D-Var/EnKF• Beyond WRF - MPAS• ACPAS (AFWA Coupled Prediction and Assimilation System)