NASA Technical Reports Server (NTRS) 20150001405: A High-Resolution Merged Wind Dataset for DYNAMO: Progress and Future Plans

0.19 MB·English

by  NASA Technical Reports Server (NTRS)

A high-resolution merged wind dataset for DYNAMO: Progress and future plans
Timothy J. Lang, John Mecikalski, Xuanli Li, Themis Chronis, Tyler Castillo, Kacie Hoover, Alan Brewer, James Churnside, Brandi McCarty,
Paul Hein, Steve Rutledge, Brenda Dolan, Alyssa Matthews, Elizabeth Thompson
AMS 95th Annual Meeting (3MJOSYMP)
Poster 427
1. Introduction Osignnea olsf tohfe t hmeo Msta ddidsteinnc-Jtiuvleia n 3. HRDL/Scatterometer Comparison ASCAT/HRDL OSCAT/HRDL • fTaOctGoAr inra mdaarn syu pgogoers HtsR pDreLc/OipSitaCtiAoTn a
Oscillation (MJO) is the NOAA High-Resolution Doppler Lidar matches
(HRDL)
upscale development and • Example (11/27, ~1800 UTC):
• On Revelle for Cruises 1-3 (1
organization of convection in OSCAT = 13.2 m/s, HRDL = 8.7 m/s
September - 6 December 2011)
the Indian Ocean. DynamicsT. Aosfh craft
• HRDL scanning ability provided 20-min
the MJO (DYNAMO) campaign
averaged vertical profiles of wind speed
~1000 km occurred in late 2011 – early and direction from 12.5 m to ~2000 m
2012 to investigate this
~1000 km gneonne-ssaiste slltiateg ew.i nOdn dea otaf stheets b est • s1c2a.5tt-emro gmaetete urss e(Od StoC cAoTm apnadre A HSRCDATL) t o
• Examine relationship between winds
ever obtained over the ocean. and mean square slope as Richardson Preliminary Results
The Cyclone Global Navigation Satellite System (CYGNSS) number varies g(T −T )z •  Richardson number always negative for DYNAMO (unstable)
Ri = a w , •  ASCAT matches HRDL better than OSCAT
mission can exploit this dataset to better understand the TU2
w z
performance of the satellite constellation in regions of deep
convection, in particular for characterizing the MJO. 4. WRF Model Assimilation
WRF 3DVAR Progress Preliminary Radar Data Assimilation Results
Main Scientific Objectives
1. Produce a high-resolution surface wind dataset for WRF Model Setup • Finalizing WRF runs for 22001111-1111-2244 00880000 UUTTCC
multiple MJO onsets using WRF-assimilated winds and • Advanced Research WRF B/R matrices generation
other data from DYNAMO. • A: 9-km resolution Indian • Assimilation tests
Ocean domain underway
2. Use the DYNAMO datasets, along with available • B: 3-km DYNAMO
scatterometer observations, to study the causes and quadrilateral domain Assimilation Datasets
impacts of wind variability at spatial and temporal scales • C: 1-km high-resolution •  Radars - TOGA, Mirai, Control Radar DA
finer than those planned to be provided by CYGNSS, and domain focused on Revelle S-PolKa
3. toTtohobbhe-sseE eeisnmrredvvp aawSlittciiiinoomadnntui ssoml. an faotsopr r osD f( EwYth2NilelE AsbSMee) Opitnor.og Tpcerhesoistsdes udwec siieln lf topsorri m otChvueYidl aGCete NYadSGn C SNeY xScGSeN lEleSnnSdt - •••   4iNsBrnee0ae slp cososaktwilegvrgaedemrtro e 9amut r- 3noel3e-dps- v1a ooer nsusklscdpnm a hs(1 ml eres-urk ouenfmerc)se ac , r etlupuesnlrvusesefss ul s ll y •••••      SSoHAHbouSRRsurCDDfenaALLrdcvT eian/,Otg iSosSnh Csip A, Ta nd Buoy •   Prlfjaureorrssegmtul eiTlm t aOsimis nGsspauiAmargy cgdi ltaeast stiant g NNNNNNNNNNNNNNOOOOOOOOOOOOOOOOOOOOOOAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
core dataset for understanding how CYGNSS can for MJO events •  Improvements in
improve our understanding of convective inflow/outflow • Focus on late October, late precip system
structures, wind/precipitation feedbacks, and the initiation November, late December structure seen
and development of the MJO. 2011 MJO onsets
2. TOGA Radar Data Quality Control 5. Summary 6. Next Steps
n
DYNAMO Radars AZ VR VE ectio •  DYNAMO dataset prepped for assimilation •  Finalize assimilation plans tInhitiraote ugh more testing.
NASA TOGA (Revelle) orr into WRF. Terminate
C
Mirai C-band U •  Produce merged wind maps during each
WtNhCeo ArTkROe dGS w-AP itvohel KlCoaSc i(UtGie tason, d)a en-da laialsso Before IN •  bHgaoRnoDddL) o /bsvecetartaettlrel rtphoeamrnfeo OtremSr aCcnoAcmTe p,( aKwruiits-hbo Aann SsdCu)gA. gTe (sCts- DsmfitrYeastpNp, ssAt, h wMaeinnOlld -bw opeebr poswervoiirldlvd aeeud dctdo eM dmMJ foJOoOrr e oa cnd ofsaemeywtsm, daauatsn y ~inst0ye .p.e 5Wed-r1e ino dhdn. stiemt ea t
fix occasional azimuthal errors.
n •  WRF domains established and preparatory
Upeadscietkidna gPg eoy- ff AovRer lTboo cstiothief,t sww aaithrse nh eaendde d. Correctio 3finDaVliAzRed a. ssimilation matrices nearly •  Ippnorgoteecesntst iswaeli nsimd a pmta macptu solt ifpi nCletoY s GCcNaYlSGeSsN. SfoSr sEt2uEdySin. gS tMudJyO
NU •  Initial assimilation testing suggests realistic
CHounnttasvcitl lIen,f Ao:L T 8im05o1th2y; (L2a5n6g), 9N6A1S-7A8 6M1S, FtimC o(ZthPy1.j1.la),n [email protected] er I background runs receive large impacts from •  More detailed analysis of HRDL/scatterometer
Funding for this research has come from NASA (NNH13ZDA001N-WEATHER) Aft radar data. dataset. Examine wind speed-MSS relationship
Acknowledgements: DOE Py-ART Software Team, especially S. Collis and J.J. Helmus for positive Richardson number scenarios.
EUMETSAT and NASA JPL for the scatterometer data