Tropical Cyclone Applications of Satellite Data Andrea Schumacher

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

Application: Track Forecasting • 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 2

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 3

Storms with eyes are easiest… but still can have errors due to parallax 4

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 of satellite data types 5

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 Quik. SCAT Ocean Surface Winds 6

Satellite data used in NCEP’s operational data assimilation systems • MODIS IR and water vapor winds • • GMS, Meteosat, and GOES cloud drift • IR and visible winds NOAA-15, NOAA-16, NOAA-18 and AQUA AMSU-A 1 b radiance • GOES water vapor cloud top winds • • SSM/I wind speeds NOAA-15, -16, and -17 AMSU-B 1 b radiances • SSM/I precipitation estimates • GOES-12 5 x 5 cloud cleared radiances • TRMM TMI precipitation estimates • NOAA-16 and -17 SBUV ozone profiles • NOAA-17 HIRS 1 b radiances AQUA AIRS 1 b radiances Satellite data also used to help estimate initial storm position, motion, intensity and wind structure for “TC Vitals” file used to initialize regional models 7

Classification of Satellite Input • 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 sea surface properties – Boundary conditions and atmosphere-ocean interface variables • Satellite altimetry – Sub-surface ocean structure 8

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 9

Impact of Removing Satellite Data on NCEP GFS Track Forecasts 10

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 11

Application: Intensity Forecasting • 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 12

Intensity Forecasting (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 13

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. 14

Empirical relationship between T number and wind speed 15

Patterns of Visible Dvorak Technique 1. Curved Band 3. CDO 4. Eye 2. Shear Pattern 4 a. Banded Eye 16

Patterns and associated T Numbers 17

Infrared (IR) Technique • Can be used during night as well as during day • At times more objective than visible technique 18

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 19

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AMSU-A Temperature/Gradient Wind Retrievals (Demuth et al 2006, JAM) d e t c re r o c n U Ps(x, y) V(r, z) Co rre ct ed T(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: 10 Hurricane 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

8 -Frame Water Vapor Imagery Loop 9/20/98 23: 45 to 9/21/98 23: 45 Hurricane/Trough Interaction 28

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 29

Ocean Heat Content Retrievals from Satellite Altimetry 30

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 31

Input from GOES Imagery and OHC Analysis Hurricane Floyd 14 Sept 1999 OHC 26 Sept 2002 32

SHIPS Model Improvements with Satellite Input (2002 -2003 Experimental Forecasts) 34

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 36

Ocean Heat Content for Hurricane Ivan 37

Application: Wind Structure • 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 Quik. SCAT AMSU-A wind nonlinear balance retrievals Empirical relationships with IR data • Experimental, K. Mueller MS Thesis 38

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 39

x Cloud drift and water vapor winds Hurricane Ivan 12 Sept 2004 From CIMSS 40

Quik. SCAT and AMSU Nonlinear Balance Winds for Hurricane Ivan QSCAT ( Plot provided by Remote Sensing Systems available at www. remss. com ) 41

SSM/I Wind Speeds for Hurricane Ivan (Plot provided by Remote Sensing Systems available at www. remss. com ) 42

Hurricane FLOYD – 1515 UTC 14 Sep 99 Hurricane IRIS – 0015 UTC 9 Oct 01 MSLP 932 mb MSLP 954 mb MAX Sustained Winds 125 kt MAX Sustained Winds 120 kt NE SE SW NW 64 kt 110 75 60 90 64 kt 15 15 10 15 50 kt 180 140 105 150 50 kt 25 25 15 25 34 kt 250 190 150 190 34 kt 125 50 40 60 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

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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 + [ ( r. Uij 2 + r. Vij 2) + ( Uij 2 + Vij 2] • • • 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 and Wm 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 46

Example: Hurricane Ivan 0912 18 Z R 34 175 180 125 185 R 50 120 115 80 125 R 64 80 65 60 60 From satellite analysis R 34 150 120 100 150 R 50 100 90 70 90 From NHC R 64 80 60 45 55 18 Z advisory 47 http: //rammb. cira. colostate. edu/products/tc_realtime/

Application: Hurricane Rainfall Forecasting • Majority of recent U. S. loss of life from tropical cyclones from inland flooding – Except for Katrina (2005) • Forecast accuracy not well documented – Evaluation in progress • Forecast methods – – Rule of thumb: STR=100/c Numerical models: NAM, GFS, GFDL Rainfall rate estimates from IR/microwave (QPE) Satellite rainfall forecasts (QPF) • Extrapolation of QPE • Rainfall CLIPER model 48

Climatological Rain Rates 49

Rainfall Estimation From GOES Hydro-Estimator> <Multi-Spectral 50

QPE/QPF Forecast Examples: TC 02 W and TC 26 S, 9 April 2003 SSM/I Rainfall Rate AMSU Rainfall Rate 26 S 02 W Extrapolated SSM/I Rainfall Extrapolated AMSU Rainfall 02 W 26 S 51

Application: Formation (Genesis) Total Precipitable Water http: //rammb. cira. colostate. edu/ramsdis/online/tropical. asp RAMMB TC Formation Probability Product http: //rammb. cira. colostate. edu/projects/gp 52 arm/gparm_glob_test/

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 53 advanced instruments

GOES-R Advanced Baseline Imager (ABI) ABI coverage in 5 min GOES coverage in 5 min • 16 -Channel Imager (0. 47 -13. 3 micrometer) • 0. 5 km res. visible channel • 1 -km res. w/ 3 other daytime channels • 2 -km res. w/ all other channels • Improved rapid-scanning capability 4 km GOES-8 IR 1 km MODIS IR 54

Ground-Based Measurements to Study TC Intensity Change 55 55

NPP and JPSS 1) VIIRS (Visible/Infrared Imager/Radiometer Suite) 2) Cr. IS (Cross-track Infrared Sounder, Hyperspectral) 3) ATMS (Advanced Technology Microwave Sounder) Isabel Eye Sounding from AIRS (proxy for NPP Cr. IS/ATMS) Eye – Environment Temp Eye Sounding Environment Sounding Integrate Hydrostatic Equation Downward from 100 h. Pa to Surface Environment Sounding: Ps = 1012 h. Pa Eye Sounding: Ps = 936 h. Pa Aircraft Recon: Ps = 933 h. Pa 56

Track Forecasting Summary – Forecasts primarily based upon numerical models – Satellite radiances/winds improve model analysis – Imagery useful for identifying storm properties • Location, Intensity, Size – Imagery useful for evaluation of model analyses, identification of synoptic features affecting track 57

Intensity/Structure/Rainfall Summary – Intensity Forecasting • Large and small scales fundamental – More difficult forecast problem • • • Satellite radiances/winds improve model analysis Satellite data improve statistical intensity models Dvorak used world-wide to estimate storm intensity SST, altimetry data used for ocean heat content Satellite data helps identify large-scale shear, and storm response to shear • WV Imagery helpful for identification of trough interaction – Wind structure • Multi-platform analysis needed – Quik. SCAT, AMSU, SSM/I and IR – Rainfall • GFDL model has some rainfall forecast skill • IR and microwave data for QPE • Extrapolation (TRa. P) and rainfall CLIPER for QPF 58

Tropical Satellite Data Resources • Tropical RAMSDIS – http: //rammb. cira. colostate. edu/ramsdis/online/tropical. asp • NRL TC Webpage – http: //www. nrlmry. navy. mil/tc_pages/tc_home. html • CIRA TC Real-Time Webpage – TC-centered satellite imagery and derived products – Global TCs, archived online through 2006 – http: //rammb. cira. colostate. edu/products/tc_realtime/ • CIMMS TC Webpage – http: //tropic. ssec. wisc. edu/ • Tropical Cyclone Formation Probability Product – Current and climatological TC formation probabilities and input parameters – NESDIS Operational Product (N. Atlantic, NE Pacific and NW Pacific): http: //www. ssd. noaa. gov/PS/TROP/TCFP/index. html – Experimental Product (Global): http: //rammb. cira. colostate. edu/projects/gparm_glob_test/ 59