Tropical (Cyclone) Applications of Satellite Data

Andrea SchumacherCooperative Institute for Research in the Atmosphere (CIRA)

Fort Collins, Colorado

Mark DeMariaNESDIS Center for Satellite Applications and Research (StAR)

Regional and Mesoscale Meteorology BranchFort Collins, Colorado

COMET Faculty CourseAugust 10, 2010

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

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

Storms with eyes are easiest…

Storms with eyes are easiest…

but still can have errors due to parallax

10.7 m

Tropical Storm Lee 2005

Storms without an eye more difficult

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 satellite data types

Multi-Spectral Views of Hurricane Katrina(from www.nrlmry.navy.mil/tc_pages/tc_home.html)

VisibleGOES

IR (10.7 m)GOES

Microwave:DMSP SSMI

85 Ghz/H

QuikSCATOcean Surface

Winds

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

Impact of Removing Satellite Dataon NCEP GFS Track Forecasts

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”

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

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

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

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.

Empirical relationship between T number and wind speed

Patterns of Visible Dvorak Technique

1. Curved Band 2. Shear Pattern

3. CDO 4. Eye 4a. Banded Eye

Patterns and associated T Numbers

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

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

AMSU-A Temperature/Gradient Wind Retrievals(Demuth et al 2006, JAM)

Uncorrecte

d

Correcte

d

T(r,z) Ps(x,y) V(r,z)

R2 = 0.72

0

20

40

60

80

100

120

140

0 20 40 60 80 100 120 140

Predicted Max Wind (kt)

Ob

se

rve

d M

ax

Win

d (

kt)

AMSU Predicted vs. Observed Maximum Winds(Statistical relationships between AMSU retrievals and Intensity)

Single or multiple channel methods also developed by Brueske et al 2003And Spencer and Braswell 2001

Presentation

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

Intensity Trends

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

Eyewall Cycle of Hurricane Floyd Seen in SSM/I Data

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

• 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

Ocean Heat Content Retrievals from Satellite Altimetry

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

Input from GOES Imagery and OHC Analysis

Hurricane Floyd 14 Sept 1999 OHC 26 Sept 2002

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

-8

-4

0

4

8

12

16

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Fo

reca

st Im

pro

vem

ent

(%) Atlantic W of 50 W

East Pacific

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

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

12 24 36 48 60 72 84 96 108 120

Forecast Interval (hr)

Pe

rce

nt

Imp

rov

em

en

t

All Cases

Isabel 2003

Ivan 2004

Emily 2005

Katrina 2005

Rita 2005

Wilma 2005

Ocean Heat Content for Hurricane Ivan

Convective Response of Ivan to OHC

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

Cloud drift and water vapor windsHurricane Ivan 12 Sept 2004From CIMSS

x

QSCAT

( Plot provided by Remote Sensing Systems available at www.remss.com )

QuikSCAT and AMSU Nonlinear Balance Winds for Hurricane Ivan

(Plot provided byRemote Sensing Systems

available at www.remss.com )

SSM/I Wind Speeds for Hurricane Ivan

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

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?

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 CoreWinds

(especially for smaller TCs)

Hurricane FLOYD – 1515 UTC 14 Sep 99

Hurricane IRIS – 0015 UTC 9 Oct 01

MSLP 932mb

MAX Sustained Winds 125 kt

NE SE SW NW

64 kt 110 75 60 90

50 kt 180 140 105 150

34 kt 250 190 150 190

MSLP 954 mb

MAX Sustained Winds 120 kt

NE SE SW NW

64 kt 15 15 10 15

50 kt 25 25 15 25

34 kt 125 50 40 60

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

mxm

m

mm

m

Rrr

RVrV

RrR

rVrV

)()(

)()(

Vm=100 kts, Rm=55 km, x=0.5

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 +rVij

2) + (Uij2 + 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

R34 175 180 125 185R50 120 115 80 125R64 80 65 60 60

From satelliteanalysis

R34 150 120 100 150R50 100 90 70 90R64 80 60 45 55

From NHC 18Z advisory

Example: Hurricane Ivan 0912 18Z

http://rammb.cira.colostate.edu/products/tc_realtime/

Formation (Genesis) Applications

http://rammb.cira.colostate.edu/projects/gparm/gparm_glob_test/http://rammb.cira.colostate.edu/ramsdis/online/tropical.asp

Total Precipitable Water RAMMB TC Formation Probability Product

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

4 km GOES-8 IR 1 km MODIS IR

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

GOES-R Advanced Baseline Imager (ABI)

53

Ground-Based Measurements to Study TC Intensity Change

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)

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14 16 18

Temperature Anomaly (C)

Pre

ss

ure

(h

Pa

)

Eye Sounding

EnvironmentSounding

Eye – Environment Temp

Integrate Hydrostatic Equation Downward from 100 hPa to SurfaceEnvironment Sounding: Ps = 1012 hPaEye Sounding: Ps = 936 hPaAircraft Recon: Ps = 933 hPa

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

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

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/gparm_glob_test/

Tropical Satellite Training• SHYMET Tropical Page

– http://rammb.cira.colostate.edu/training/shymet/tropical_topics.asp