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

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

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

10.7  m 3.9  m Tropical Storm Lee 2005 Example of the Utility of 3.9  m (GOES Channel 2) data Storms without an eye more difficult Helps to use combination of of satellite data types 5

Multi-Spectral Views of Hurricane Katrina (from Visible GOES IR (10.7  m) GOES Microwave: DMSP SSMI 85 Ghz/H QuikSCAT 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 GOES water vapor cloud top winds SSM/I wind speeds SSM/I precipitation estimates TRMM TMI precipitation estimates NOAA-17 HIRS 1b radiances AQUA AIRS 1b radiances NOAA-15, NOAA-16, NOAA-18 and AQUA AMSU-A 1b radiance NOAA-15, -16, and -17 AMSU-B 1b radiances GOES-12 5x5 cloud cleared radiances NOAA-16 and -17 SBUV ozone profiles 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 and 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

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

Empirical relationship between T number and wind speed 15

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

20

21

AMSU-A Temperature/Gradient Wind Retrievals (Demuth et al 2006, JAM) Uncorrected Corrected T(r,z)P s (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

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

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

GOES Versus OHC Predictors GOES Predictors –Small adjustments for most cases (~0-4 kt) Ocean Heat Content Predictor (Atlantic only) –Little to no impact for OHC < 50 kJ/cm 2 (~ 1kt) –Moderate impact for OHC > 50 kJ/cm 2 (~7-8 kt) OHC primarily effects west Atlantic cases –Examine satellite impact for recent category 5 hurricanes 33

SHIPS Model Improvements with Satellite Input ( Experimental Forecasts) 34

SHIPS Model Improvements with Satellite Input (2004 Operational Forecasts) 35

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 QuikSCAT –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

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

QSCAT ( Plot provided by Remote Sensing Systems available at ) QuikSCAT and AMSU Nonlinear Balance Winds for Hurricane Ivan 41

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

Hurricane FLOYD – 1515 UTC 14 Sep 99 Hurricane IRIS – 0015 UTC 9 Oct 01 MSLP932mb MAX Sustained Winds125 kt NESESWNW 64 kt kt kt MSLP954 mb MAX Sustained Winds120 kt NESESWNW 64 kt kt kt

Inner Core TC Winds from IR Imagery (Mueller et al 2007, WF) Model wind field by sum of storm motion and symmetric Assume V m is know from Dvorak or other methods Estimate x and R m from IR imagery, V m and latitude V m =100 kts, R m =55 km, x=0.5 44

45

Experimental RAMMB Product Satellite-Only Wind analysis Combine all available satellite inputs in variation analysis Find U ij V ij to minimize cost function C: C =  w k [(u k -U k ) 2 + (v k -V k ) 2 ] +  w m (s m -S m ) 2 +  [  (  r U ij 2 +  r V ij 2 ) +  (   U ij 2 +   V ij 2 ] U ij V ij are gridded radial and tangential wind u k, v k = obs, U k V k = model counterpart of u k v k s m,S m are observed wind speeds and model counterpart W k and W m 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

R R R From satellite analysis R R R From NHC 18Z advisory Example: Hurricane Ivan Z 47

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

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

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

Application: Formation (Genesis) arm/gparm_glob_test/ Total Precipitable Water RAMMB TC Formation Probability Product 52

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 53

4 km GOES-8 IR 1 km MODIS IR ABI coverage in 5 minGOES coverage in 5 min 16-Channel Imager ( 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 GOES-R Advanced Baseline Imager (ABI) 54

55 Ground-Based Measurements to Study TC Intensity Change 55

1)VIIRS (Visible/Infrared Imager/Radiometer Suite) 2)CrIS (Cross-track Infrared Sounder, Hyperspectral) 3)ATMS (Advanced Technology Microwave Sounder) NPP and JPSS Isabel Eye Sounding from AIRS (proxy for NPP CrIS/ATMS) Eye Sounding Environment Sounding Eye – Environment Temp Integrate Hydrostatic Equation Downward from 100 hPa to Surface Environment Sounding: P s = 1012 hPa Eye Sounding: P s = 936 hPa Aircraft Recon: P s = 933 hPa 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 –QuikSCAT, ASCAT, AMSU, SSM/I and IR –Rainfall GFDL model has some rainfall forecast skill IR and microwave data for QPE Extrapolation (TRaP) and rainfall CLIPER for QPF 58

Tropical Satellite Data Resources Tropical RAMSDIS – NRL TC Webpage – CIRA TC Real-Time Webpage –TC-centered satellite imagery and derived products –Global TCs, archived online through 2006 – CIMMS TC Webpage – Tropical Cyclone Formation Probability Product –Current and climatological TC formation probabilities and input parameters –NESDIS Operational Product (N. Atlantic, NE Pacific and NW Pacific): –Experimental Product (Global): 59