Hans Huang: WRFDA 2013 overview 1 DA WRFDA 2013 Overview Hans Huang NCAR Acknowledgements: WRFDA team and many visitors, NCAR, CWB, AirDat, NRL, BMB, NSF-OPP,

Hans Huang: WRFDA 2013 overview

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WRFDA 2013 Overview

Hans Huang

NCAR

Acknowledgements:

WRFDA team and many visitors,

NCAR, CWB, AirDat, NRL, BMB, NSF-OPP,

AFWA, NSF-AGS, USWRP, NASA, PSU, CAA, KMA, EUMETSAT


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WRFDA• Goal: Community WRF DA system for

• regional/global• research/operations• deterministic/probabilistic applications

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

• Model: WRF (ARW, NMM, Global)• Observations:

• Conv.+Sat.+Radar• (+Bogus)

• Supported by NCAR/ESSL/MMM/DAS


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


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WRFDA tutorials

WRFDA online tutorial and user guide

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

21-22 July 2008. NCAR.02-04 Feb 2009. NCAR.20-22 July 2009. NCAR.03-05 Aug 2010. NCAR.20-21 July 2011. NCAR.23-25 July 2012. NCAR.

18 April 2009. South Korea. 15-31 Oct 2009. Nanjing, China.10 April 2010. Seoul, South Korea.16 April 2011. Busan, South Korea.10-20 Oct 2011. Bangkok, Thailand.21 April 2012. Seoul, South Korea.

The next: 22-24 July 2013


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WRFDA v3.5

• Release date: April 2013• New features:

– Support for data from additional satellite instruments:• METOP: Infrared Atmospheric Sounding Interferometer (IASI)• Suomi NPP: Advanced Technology Microwave Sounder (ATMS)• FY3: Microwave Temperature Sounder (MWTS) and Microwave

Humidity Sounder (MWHS)

– Direct wind speed/direction assimilation – ETKF code updated and documentation added– Support for new ECMWF cloud detection scheme– Allowing variable number of inner-loop cost function

minimizations for each outer-loop


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

• Remotely sensed retrievals:- Atmospheric Motion Vectors (geo/polar)- GPS refractivity (e.g. COSMIC)- Ground-based GPS Total Precipitable Water (PW)/Zenith Total Delay (ZTD)- SATEM thickness- Scatterometer oceanic surface winds- SSM/I oceanic surface wind speed and TPW- Radar radial velocities and reflectivities- Satellite temperature/humidity/thickness profiles- Stage IV precipitation/rain rate data (4D-Var)- Wind profiler wind profiles

Satellite radiances (using RTTOV or CRTM):– HIRS NOAA-16, NOAA-17, NOAA-18, NOAA-19, METOP-A– AMSU-A NOAA-15, NOAA-16, NOAA-18, NOAA-19, EOS-Aqua, METOP-A– AMSU-B NOAA-15, NOAA-16, NOAA-17– MHS NOAA-18, NOAA-19, METOP-A– AIRS EOS-Aqua– SSMIS DMSP-16, DMSP-17, DMSP-18 – IASI METOP-A– ATMS Suomi-NPP– MWTS FY-3– MWHS FY-3

•Bogus: – TC bogus– Global bogus

WR

FD

A O

bser

vati

ons

New, v3.5


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Planned and ongoing work (I) (after WRFDA v3.5)

• WRFPLUS and 4DVAR optimization• Revision of the energy norm used by FSO• Background error covariance improvements

– Smoothing options at various stages of GEN_BE– Use of u, v, T, ps and RH as control variables for convective scale DA– GEN_BE 2.0 development and documumentation

• More satellite platform/instruments – CrIS– GOES sounder and GOES imager– MSG SEVIRI – GCOM-W1 AMSR2– Update RTTOV and CRTM interfaces for their latest release versions– GPS RO DA

• More options for vertical data thinning• Nonlocal GPS RO parallelization


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Planned and ongoing work (II)(after WRFDA v3.5)

• Radar data assimilation development– An indirect radar reflectivity DA scheme– Radar DA with WRFDA 4DVAR

• Dual-resolution hybrid DA• WRFDA online thinning for conventional data

• Blending backgrounds or analyses using a spatial filter• Displacement analysis• Multivariate cloud analysis• Ensemble Variational Integrated Lanczos (EVIL)


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Assimilation of Wind Speed and Direction Observations

Xiang-Yu Huang1,

Feng Gao1,2, Neil A. Jacobs3, Hongli Wang1

1National Center for Atmospheric Research, Boulder, Colorado, USA2Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China

3AirDat LLC, Morrisville, North Carolina, USA


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• All current DA systems turn sp and dir to u and v before assimilating them, assuming observation errors in u and v are known and not correlated.

• Many observing systems observe wind speed (sp) and direction (dir); should be more straightforward to derive obs sp and dir errors and assume them uncorrelated.

• Wind direction observation errors vary significantly in space and time (e.g. small at the jet level, large close to surface) and should influence the analysis results.

• WRFDA is the only DA system which has an option to assimilate wind sp and dir observations taking the sp and dir observation errors into account.

Introduction


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A simple illustration (probably simplified too much)

(Over Simplified) Assumptions:

• Wind component (sp,dir) or (u,v) can be analyzed univariately;

• The background error and observational error are the same.

Assimilate u and v (asm_uv):

Assimilate sp and dir (asm_sd):


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ou

ov

bu

bv

BGOBS ANAuv

ANAsd

u

v

From the same background and observations, asm_uv and asm_sd produce very different analyses!


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From the same background and observations, asm_uv produces weaker winds than asm_sd does!


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Single observation experiments using WRFDA and observation errors estimated from a previous data study

(without the over simplified assumptions)


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Again, now using WRFDA, asm_uv produces weaker winds than asm_sd does!

From WRFDA single ob experiments From the highly simplified solutions


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WRFDA analysis increments

asm_uv

asm_sd

U V

U V

Vector

Vector


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Observing system simulation experiments

Nature run: 6 km grid space with 57 vertical levels

Observations – simulated wind observations

at every 30x30th grid

across 12 standard pressure levels

0000 UTC 15 Dec. – 0000 UTC 20 Dec. 2011

Cycle run: 18 km grid space with 43 vertical levels

6 h full-cycle from 0000 UTC 16 Dec. 2011

48 h forecasts are generated every 6 h

Boundary condition:

• FNL for nature run

• GFS forecast for cycle experiments

Background Errors – cv5 by NMC

Verification: against nature run


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Verification scores

sp

vu

dir


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Concluding remarks for the wind DA study

• A new formulation for assimilating wind speed and direction

observations is presented and compared with the traditional formulation

for assimilating u and v.

• Simple illustrations are used to show the differences and single pair

observation experiments with WRFDA qualitatively confirm the

differences.

• OSSEs show potential benefits of the new formulation.

• Real observation experiments are ongoing.

Huang, X.-Y., Gao, F., Jacobs, N. and Wang, H. 2013. Assimilation of wind speed and direction observations: a new formulation and results from idealized experiments. Tellus A, 65, 19936, http://dx.doi.org/10.3402/tellusa.v65i0.19936.

Documents

Hans Huang: WRFDA 2013 overview 1 DA WRFDA 2013 Overview Hans Huang NCAR Acknowledgements: WRFDA team and many visitors, NCAR, CWB, AirDat, NRL, BMB, NSF-OPP,