Influence of Assimilating Satellite-Derived High-resolution data on

Analyses and Forecasts of Tropical Cyclone Track and Structure: A case study of Sinlaku (2008)

Ting-Chi Wu1, Hui Liu2, Sharanya J. Majumdar1, Christopher S. Velden3, Jun Li3 and Jeffrey Anderson2

University of Miami, RSMAS1

National Center for Atmospheric Research2

University of Wisconsin, CIMSS3IR image of Sinlaku

• Forecast error covariance of TC and its environment is highly flow-dependent and multivariate.

• Use multiple and integrated satellite data sets at their highest resolution to build up an advanced analysis/forecast system for tropical cyclones and their environments.

• Seek an optimal assimilation strategy for integrated satellite data, under WRF-DART EnKF framework.– Dynamic variables: Atmospheric Motion Vectors (AMVs),

ASCAT surface wind– Thermodynamic variables: Temperature and moisture

sounding, Total Precipitable Water (TPW)

Challenges and overall goals

The satellite observation types in TC caseSatellite-derived observation types

TPWASCAT winds

Work of Cloudy sky IR soundings is underway.

IR Temperature and moisture sounding (clear-sky so far)

AMV

H

• WRF (v3.1.1) - DART (EAKF) with 84 members• 9km moving nest grid with feedback to 27km grid when TC is present in forecast.• Assimilation cycle started 1 September 2008. (one week before genesis)• Analysis variables: U, V, W, PH, T, MU, T2, Q2, TH2, Psfc, U10, V10, Qvapor, Qcloud,

Qrain, Qice, Qsnow,

WRF-DART Cycles

CIMSS: Cooperative Institute for Meteorological Satellite Studies

Case Conventionaldata

Satellitedata

Cycling interval

CTL Radiosondes > 200km (U,V,T,Q), aircraft (U,V,T),

surface pressure from NCEP/GFS and

JTWC advisory TC positions, 6-hourly

analyses

NCEP bufr AMVs (origin: JMA) 6h

AIRS-TQ AIRS clear-sky Single FOV TQ-profile 6h

TPW AMSR-E Total Precipitable Water 6h

CIMSS(h) CIMSS Hourly AMVs 3h

CIMSS(h+RS) CIMSS Hourly + Rapid-Scan AMVs(Rapid-Scan is available after 12UTC September 10, 2008)

3h

ASCAT ASCAT surface wind 3h

ALL All above satellite data 3h

Satellite data: Dynamic

Contour every 200km

701-999mb

401-700mb

251-400mb

100-251mb

Wind vectors from ASCAT are only at sea surface

Satellite data: Thermodynamic

• The coverage of AIRS T/Q soundings are quite remote from Sinlaku at most of the times.

• AMSR-E derived TPW has better data coverage through the lifetime of Sinlaku.

Analysis track and intensity

Against Independent Obs (I)QuikSCAT-UCF CTL CIMSS(h) CIMSS(h+RS) ASCAT

CIMSS(h+RS) is available after

2008/09/10:12Z

CIMSS(h+RS) is available after

2008/09/10:12Z

• CIMSS-bt: Modified JTWC best track from CIMSS-Wisconsin.

• As seen in previous slide, assimilating AMVs helps to resolve reasonable TC sizes especially in the early stage of Sinlaku.

Against Independent Obs (II)Size

Analysis Structure: Azimuthal mean (I)

Shading: VrContour: VtGrey line: RMW

Shading: WContour: Divergence

CTL ASCATCIMSS(h) 2008/09/09:12Z

Analysis Structure: Azimuthal mean (II)CTL TPWAIRS-TQ

Shading: Relative Vorticity; Contour: Potential temperature

Analysis Increment (Vwind)

EW

CTL CIMSS(h)

Cyclonic increments!

EW

2008/09/09:12Z

• It is until 00Z 10 Sep, CTL starts to show cyclonic increment, half day later than CIMSS(h).

• For ASCAT, it is around 18Z 10 Sep, 6 hours later than CIMSS(h).

ASCAT

EW

Analysis Structure Spread

CTL CIMSS(h) CIMSS(h+RS)

• Analysis mean and spread of RMW as function of height in 12 hourly intervals.

• 72h forecast from 16 members• Initialized at 00Z September 11, 2008• ALL combines all satellite-derived

observations mentioned above.• ALL is dominated by CIMSS(h+RS).• CTL and TPW erroneously made early

landfall in central/south part of Taiwan.

Ensemble Forecast from Analyses

24H 48H 72H

Shading: 500 mb geopotential height difference CIMSS(h+RS) – CTLContour: CTL, CIMSS(h+RS)

Forecast Track differences (I)

• CIMSS(h+RS) has the best ensemble mean track forecast.• Trough to the NE of and ridge to the E of Sinlaku.

Summary• Assimilation of the various satellite data types, particularly

AMVs and TPW, using ensemble DA shows potential to improve analyses and forecasts of the hurricane track and intensity.

• Assimilating CIMSS hourly AMV exhibits best track and intensity analysis in early stage of Sinlaku. However, the inclusion of Rapid-Scan AMVs has problem with large uncertainty in the later time of Sinlaku that needs further investigation.

• The ALL cycles seem to be dominated by CIMSS(h+RS). However, this is preliminary results. More investigation of impacts from individual satellite data type is needed before conducting the ALL cycles.

Current and future work• Investigate the impacts of each satellite-

derived data experiment in more depth.• Data denial experiments on AMVs to clarify

which layer/region of AMVs has the most impacts on intensity/track. (or ensemble based observation impact first)

• Look for advanced diagnostic metrics?• Investigate the linkage between model

covariance structure and storm evolution.

Data Denial ExperimentsBy height

By distance