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Tropical (Cyclone) Applications of Satellite Data

Tropical (Cyclone) Applications of Satellite Data. Andrea Schumacher Cooperative Institute for Research in the Atmosphere (CIRA) Fort Collins, Colorado Mark DeMaria NESDIS Center for Satellite Applications and Research (StAR) Regional and Mesoscale Meteorology Branch Fort Collins, Colorado

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Tropical (Cyclone) Applications of Satellite Data

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  1. Tropical (Cyclone) Applications of Satellite Data Andrea Schumacher Cooperative Institute for Research in the Atmosphere (CIRA) Fort Collins, Colorado Mark DeMaria NESDIS Center for Satellite Applications and Research (StAR) Regional and Mesoscale Meteorology Branch Fort Collins, Colorado COMET Faculty Course August 10, 2010

  2. Track Forecasting Applications • Initial position/storm structure analysis • Improvement of numerical models • Assimilation of satellite data • “Evaluation” of numerical models • Synoptic feature identification for qualitative prediction • Model analysis/satellite loop overlays provide assessment of t=0 hr accuracy

  3. Center Fixing • Accurate positions necessary for estimation of storm motion • Animation of imagery • Visible/IR during day • GOES IR window and shortwave IR at night • Microwave imagery (SSM/I and AMSU-B) • Formal center fixes began in 2003

  4. Storms with eyes are easiest…

  5. Storms with eyes are easiest… but still can have errors due to parallax

  6. Storms without an eye more difficult Tropical Storm Lee 2005 10.7 m

  7. Storms without an eye more difficult Tropical Storm Lee 2005 Example of the Utility of 3.9 m (GOES Channel 2) data 10.7 m 3.9 m Helps to use combination of satellite data types

  8. Multi-Spectral Views of Hurricane Katrina(from www.nrlmry.navy.mil/tc_pages/tc_home.html) Visible GOES Microwave: DMSP SSMI 85 Ghz/H IR (10.7 m) GOES QuikSCAT Ocean Surface Winds

  9. Satellite Data Assimilation • Satellite Radiances (IR and microwave sounders) • T , water vapor and trace gas (e.g. ozone) profiles • Indirect impact on wind through assimilation • Satellite winds • Feature track winds • Scatterometer surface winds (ASCAT and Windsat) • Satellite precipitation and TPW estimates • Model moisture condensate variables • Land and sea surface properties • Boundary conditions and atmosphere-ocean interface variables • Satellite altimetry • Sub-surface ocean structure

  10. Impact of Removing Satellite Dataon NCEP GFS Track Forecasts

  11. Model “Evaluation” Overlay of NCEP Global Model Analysis and Water Vapor Imagery - Check for Consistency of Synoptic Features- 300 mb GFS Winds and WV Imagery 8 Nov 2008, Hurricane Paloma

  12. Satellite Wind Measurements:Feature Tracking Methods • Track features in imagery • Measures total wind component • Height assignment is necessary • Winds are layer averages • Views sometimes blocked by clouds • Higher resolution with GOES RSO

  13. Intensity Forecasting Applications • Less skillful than track forecasts • Intensity change sensitive to wide range of physical processes • eyewall and other convection • boundary layer and air-sea interaction • microphysical processes • synoptic scale interaction • ocean interaction • Numerical forecasts often inaccurate • Greater reliance on extrapolation, empirical and statistical forecast methods

  14. Intensity Forecasting Applications (cont…) • Intensity monitoring • Dvorak method • AMSU method • Detection of intensity trends • Storm relative, time average IR loops • Microwave data to identify concentric eye structure • Qualitative analysis of storm environment • Improved SST analysis • Ocean altimetry data (heat content) • Quantitative use in statistical models • Wind structure analysis

  15. Overview of the Dvorak Technique • Visible and Infrared Techniques • Uses patterns and measurements as seen on satellite imagery to assign a number (T number) representative of the cyclone’s strength. • The T number scale runs from 0 to 8 in increments of 0.5.

  16. Empirical relationship between T number and wind speed

  17. Patterns of Visible Dvorak Technique 1. Curved Band 2. Shear Pattern 3. CDO 4. Eye 4a. Banded Eye

  18. Patterns and associated T Numbers

  19. Infrared (IR) Technique • Can be used during night as well as during day • At times more objective than visible technique

  20. Example Digital IR: Hurricane Erika 1515 UTC 8 September 1997 • Warmest eye pixel 16 °C • Warmest pixel 30 nmi (55 km) from center -57 °C • Nomogram gives Eye no. =5.8 or close to 6

  21. Uncorrected Corrected AMSU-A Temperature/Gradient Wind Retrievals(Demuth et al 2006, JAM) T(r,z) Ps(x,y) V(r,z)

  22. AMSU Predicted vs. Observed Maximum Winds (Statistical relationships between AMSU retrievals and Intensity) Single or multiple channel methods also developed by Brueske et al 2003 And Spencer and Braswell 2001

  23. Intensity Trends Visible Imagery Loop 9/21/98 19:02 to 20:10Hurricane Georges During Rapid Intensification

  24. Animation of 6-hr Motion- Relative IR Average Images(Averaging helps separate short/long term intensity changes) • Loop 1 – just prior to onset of Mitch’s rapid intensification • Loop 2 – during Mitch’s rapid intensification

  25. Eyewall Cycle of Hurricane Floyd Seen in SSM/I Data

  26. Environmental Interactions • Vertical Shear of Horizontal Wind • Limits intensification • Prevents establishment of vertically aligned circulation • Increases ventilation of eyewall circulation • Trough Interaction • Sometimes leads to intensification • Positive momentum flux convergence in upper levels • Increases vertical depth of cyclonic flow • Possible trigger of eye-wall cycle

  27. Improved Sea Surface Temperature • SST has strong • influence on • intensity Change • Geo and Polar • data used in SST • products • Multi-sensor • approach • to correct for • aerosol effects

  28. Ocean Heat Content Retrievals from Satellite Altimetry

  29. Statistical Intensity Forecast Improvements Using Satellite Data(RAMM Branch Joint Hurricane Testbed Project) • Goal: To determine if satellite data (GOES and satellite altimetry) can improve the intensity forecasts from the statistical-dynamical SHIPS model • Method: Parallel version of SHIPS with satellite input was run in real-time for 2002-03 • Satellite SHIPS made operational in 2004 • Evaluation: Compare operational and parallel SHIPS forecasts for Atlantic and east Pacific

  30. Input from GOES Imagery and OHC Analysis Hurricane Floyd 14 Sept 1999 OHC 26 Sept 2002

  31. SHIPS Model Improvements with Satellite Input(2002-2003 Experimental Forecasts)

  32. Impact on SHIPS Forecasts for Category 5 Storms since OHC was added • Isabel (03), Ivan (04), Emily, Katrina, Rita, Wilma (05) • Verify only over-water part of forecast

  33. Ocean Heat Content for Hurricane Ivan

  34. Convective Response of Ivan to OHC

  35. Wind Structure Applications • Operational requirement for radii of 34, 50 and 64 kt surface winds • Satellite techniques • Feature tracked winds for outer circulation • SSM/I surface wind speeds • Scatterometer observations (ASCAT, former QuikSCAT • AMSU-A wind nonlinear balance retrievals • Empirical relationships with IR data • Experimental, K. Mueller MS Thesis

  36. x Cloud drift and water vapor winds Hurricane Ivan 12 Sept 2004 From CIMSS

  37. QuikSCAT and AMSU Nonlinear Balance Winds for Hurricane Ivan QSCAT ( Plot provided by Remote Sensing Systems available at www.remss.com )

  38. SSM/I Wind Speeds for Hurricane Ivan (Plot provided by Remote Sensing Systems available at www.remss.com )

  39. Properties of Satellite Winds for TC Analysis • IR/WV/Vis cloud drift winds • High quality, but far from the center • Spatial coverage often limited • Height assignment errors • ASCAT (active)– surface wind vectors • Speeds good to ~50 kts • Rainfall effects winds • Directions sometimes unreliable • Windsat (passive) - surface wind vectors • Properties similar to ASCAT • SSM/I surface wind speeds • Rain free areas • Speed only • AMSU nonlinear balance winds • Temporal coverage limited • Not at the surface • Can not resolve inner core due to 50 km resolution

  40. Properties of Satellite Winds for TC Analysis • IR/WV/Vis cloud drift winds • High quality, but far from the center • Spatial coverage often limited • Height assignment errors • ASCAT (active)– surface wind vectors • Speeds good to ~50 kts • Rainfall effects winds • Directions sometimes unreliable • Windsat (passive) - surface wind vectors • Properties similar to ASCAT • SSM/I surface wind speeds • Rain free areas • Speed only • AMSU nonlinear balance winds • Temporal coverage limited • Not at the surface • Can not resolve inner core due to 50 km resolution What’s Missing?

  41. Properties of Satellite Winds for TC Analysis • IR/WV/Vis cloud drift winds • High quality, but far from the center • Spatial coverage often limited • Height assignment errors • ASCAT (active)– surface wind vectors • Speeds good to ~50 kts • Rainfall effects winds • Directions sometimes unreliable • Windsat (passive) - surface wind vectors • Properties similar to ASCAT • SSM/I surface wind speeds • Rain free areas • Speed only • AMSU nonlinear balance winds • Temporal coverage limited • Not at the surface • Can not resolve inner core due to 50 km resolution What’s Missing? TC Core Winds (especially for smaller TCs)

  42. Hurricane FLOYD – 1515 UTC 14 Sep 99 Hurricane IRIS – 0015 UTC 9 Oct 01

  43. Inner Core TC Winds from IR Imagery(Mueller et al 2007, WF) • Model wind field by sum of storm motion and symmetric • Assume Vmis know from Dvorak or other methods • Estimate x and Rm from IR imagery, Vm and latitude Vm=100 kts, Rm=55 km, x=0.5

  44. Putting Satellite Structure Data Together Experimental RAMMB Product Satellite-Only Wind analysis • Combine all available satellite inputs in variation analysis • Find Uij Vij to minimize cost function C: C = wk[(uk-Uk)2 + (vk-Vk)2] + wm(sm-Sm)2 + [(rUij2 +rVij2) + (Uij2 + Vij2] • Uij Vij are gridded radial and tangential wind • u k,vk = obs, Uk Vk= model counterpart of ukvk • sm,Sm are observed wind speeds and model counterpart • Wk andWm are data weights • , terms are smoothness constraints • For wind analysis, “model” is gridded function interpolated to observation point • azimuthal smoothing >> radial smoothing • Based on Thacker and Long (1990) • Could also add other constraints if necessary

  45. Example: Hurricane Ivan 0912 18Z R34 175 180 125 185 R50 120 115 80 125 R64 80 65 60 60 R34 150 120 100 150 R50 100 90 70 90 R64 80 60 45 55 From satellite analysis From NHC 18Z advisory http://rammb.cira.colostate.edu/products/tc_realtime/

  46. Formation (Genesis) Applications Total Precipitable Water RAMMB TC Formation Probability Product http://rammb.cira.colostate.edu/projects/gparm/gparm_glob_test/ http://rammb.cira.colostate.edu/ramsdis/online/tropical.asp

  47. Future Satellites: GOES-R / NPOESS Risk Reduction at RAMMB • Reduce the time needed to fully utilize GOES-R (Geostationary) and NPOESS (Polar) as soon as possible after launch • GOES-R (~2015) • Advanced Baseline Imager, 16 channels, higher temporal and spatial resolution • Lightning Detection • POES/DMSP > NPP (2011) > JPSS (~2014) • Improved IR/VIS/Microwave imager/sounders • Analyze case studies of tropical cyclones, lake effect snow events, and severe weather outbreaks • Use numerical simulations and existing in situ and satellite data to better understand the capabilities of these advanced instruments

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