NRL RESEARCH FROM TPARC/TCS-08 C. Reynolds, J. Doyle, R. Langland, J. Goerss, J. McLay and E. Serra Marine Meteorology Division, Naval Research Laboratory,

NRL RESEARCH FROM TPARC/TCS-08

C. Reynolds, J. Doyle, R. Langland, J. Goerss, J. McLay and E. SerraMarine Meteorology Division, Naval Research Laboratory, Monterey, CA

A. Snyder and Z. Pu, University of Utah, Salt Lake City, UtahC. Velden and H. Berger, CIMSS, University of Wisconsin, Madison, WI.

Thanks Very Much to JMA and OPRF

OUTLINE:

Real-time Products

Nuri Data Denial Experiments

NOGAPS Ensembles Experiments

COAMPS Adjoint Experiments

NRL REAL-TIME TARGETED OBSERVING PRODUCTS

T-PARC/TCS-08: Observe TCs and their environment from genesis to extratropical transition. Aug-Oct 2008; 9 nations; 4 aircraft (lidar, Eldora radar, dropsondes), driftsondes, rapid-scan satellite obs, off-time radiosondes, buoys.

Targeted Observing Objective: Take additional observations in regions where they are most likely to improve forecasts

Ensemble-based and adjoint-based guidance provided from operational, research, and academic centers around the world

NRL real-time products:

- Navy Operational Global Atmospheric Prediction System (NOGAPS) Singular Vectors and Ensembles

- Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS®) Forecasts and Adjoint sensitivity.

Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

COAMPS® is a registered trademark of NRL

NOGAPS SVS FOR TARGETING

SVs (shading): Fastest growing (linear) perturbation to a given forecast

500-hPa streamlines (blue) help relate sensitivity to steering dynamics

SVs related to TC dynamics.

Associated with weakness in the ridge on the north side of storm and peripheral high to southeast of storm.


Jangmi 2008092800

• Strongest sensitivity often to low- and mid-level and qv.• C130 often sampled key portions of the sensitivity.• More details on COAMPS sensitivity later in talk.

24-h adjoint sensitivity 36-h lead time

Valid at 12Z 10 Sep 2008

2-km vorticity sensitivity Total energy sensitivity

C130 Flight Track

C130 Flight Track

DropsondesDropsondes

Best Track

COAMPS Real-Time AdjointTyphoon Sinlaku


DROPSONDE and DRIFTSONDE OBS: September 2008

TPARC/TCS08 Observations

Source: Fleet Numerical Meteorology and Oceanography Center.

NOAA Hurricane Observations

DOTSTAR

Falcon

C130, P3

Driftsondes


Evaluate impact of additional observations, including Atmospheric Motion Vectors (AMVs), and Dropsondes.

Recently (Sept. 2009) the operational global DA system has been upgraded from 3DVAR (NAVDAS) to 4DVAR (NAVDAS-AR).

Performing data denial experiments with and without atmospheric motion vectors (AMVS) and dropsondes with NOGAPS and NAVDAS-AR


NURI DATA DENIAL EXPERIMENTS

Atmospheric motion vectors (AMVs) improve general forecast skill measures, but no systematic improvement on TC track.

No systematic improvement from Dropsondes.

Ensemble results (shown later) also indicate erroneous recurvature is a robust feature in the forecast.

All dataNo AMV

No AMV No Drop

2008081900

All data

No AMVNo AMV No Drop

2008081912

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


00 hr: NURI south of subtropical high.

Black contours: Forecast TCBlue Contours:

SV sensitivity

Red contours: Analyzed TC (850-vort).

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


24h: Anticylone weaker in forecast than in analysis. Initial sensitivity highlights region between TC and anticyclone

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


48h: Anticylone weaker in forecast than in analysis. Forecast storm moves westward too slowly. Evolved sensitivity shows east-west dipole.

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


72h: Anticylone weaker in forecast than in analysis. Forecast storm moves westward too slowly. Evolved sensitivity shows east-west dipole.

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


96h: Anticylone weaker in forecast than in analysis. Forecast storm starting to move northward.

Verifying

Analysis

With Drops

No AMVs

With Drops

With AMVs

No Drops

No AMVs


120: Forecast storm moves northward. Trough deeper in forecasts.

SVs indicate larger potential growth for Sinlaku and Jangmi than for Nuri. Sinlaku and Jangmi data denial experiments underway.

Taiwan Region SV Energy Amplification

0

10

20

30

40

50

60

Date

Am

plif

ica

tion

SV1SV2SV3

Kamuri

Vongfong

Nuri

Sinlaku

Hagupit

Jangmi

Higos


SV AMPLIFICATION FACTORS FOR TAIWAN REGION

Forecasts• NOGAPS global ensemble forecasts during August-September

2008 in West Pacific Basin • 32 ensemble members plus control at 00Z, T119L30• CTL: Ensemble Transform (ET, McLay et al 2008) initial

perturbations• STO: Stochastic convection perturbations (Teixeira and

Reynolds 2008, Reynolds et al 2008) and ET perturbations• Forecast tracks compared to the Joint Typhoon Warning

Center (JTWC) warning data

Objectives• Qualitative assessment of ensemble’s ability to capture TC genesis • Assess spread-skill relationship for ensemble mean TC track errors


NOGAPS ENSEMBLE FORECASTS

TRACKING METHOD AND GENESIS CRITERIA

• Each ensemble forecast was tracked manually on a plan-view map, both before and after genesis.

• Three 850-hPa variables used for tracking: geopotential height, vorticity, and wind vectors. Using all three variables best represents the features of the storm and helps identify weaker systems.

•Genesis Criteria (examined analysis when system was declared TD)-Multiple closed height lines (at 10 m interval) within 5o of center -Closed circulation in the wind field-Vorticity greater than 1 x 10-4 s-1

• If only one or two of the above criteria were met, the system was labeled “vortex-like”


EXAMPLE: JANGMI 66-h BEFORE TROPICAL DEPRESSION

“X” marks spot where ensemble disturbance becomes TD.

Purple: Ensemble tracks of feature starting 66-h before TD.

Black: Observed track after TD. For pre-genesis cases, black and purple lines will not start at same point.

Light Blue: Member 0 – No Initial Perturbation

Dashed Red: Ensemble mean


14 Aug. 00UTC (72h before TD)

Reasonable tracks, but no TD forecasts

Reasonable tracks, more spread, some TDs.

19 Aug. 00UTC(48h after TD)Small spread, all

recurve.More spread, but all still recurve.


CTL (LEFT) and STOC (RIGHT) for NURI

Similar to the data denial experiments, all ensembles recurve for Nuri.

21 Sept. 00UTC(66h before TD)

Reasonable tracks, but no TD forecasts.

Reasonable tracks, more spread, TDs.

26 Sept. 00UTC(54h after TD)

Small spread, few recurve.

More spread, more members recurve.


CTL (LEFT) and STOC (RIGHT) for JANGMI

In contrast to Nuri, NOGAPS error for Jangmi is to NOT recurve storm. In ensembles, some members do recurve.

PREDICTION OF GENSIS (%)

Lead Time

Genesis Genesis + vortex

Non dev

-3 d 14 24 76-2 d 14 46 54-1 d 31 59 41

Lead Time


Non dev

-3 d 26 34 66-2 d 33 48 52-1 d 50 67 33

• % of forecasts predicting genesis increases as lead time decreases• % of forecasts predicting genesis is higher when stochastic convection included• % of forecasts predicting genesis is smaller for the non-developing cases than for the TC cases• Number of cases too small for probabilistic verification. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

5 TC Cases

2 Non-developing cases (TCS03 and TCS017, 4-day periods)Genesis Genesis

+ vortexNon dev

1 20 80


Non dev

9 25 75

ENSEMBLE MEAN VS SPREAD (1-5 DAY AVERAGE)

R2=0.443 R2=0.545

R2=0.525 R2=0.796

Mean Error (km)

Sta

nd

ard

Devi

ati

on

(k

m)


Inclusion of Stochastic Convection improves spread-skill relationship

NOGAPS ENSEMBLE MEAN TRACK ERROR: 2008 NH SEASON

0

50

100

150

200

250

300

350

24 48 72 96 120

G119-9G119-17G159-9G159-17

331 258 200 Number of Forecasts149 105

T159 9-member ensembles have lower mean track error than T119 17-member ensembles. T239 experiments and experiments with model uncertainty underway.


NOGAPS 2009 Western North Pacific (17W-23W)9/24 to 11/1: Homogeneous TC Forecast Error (nm)

0

50

100

150

200

250

300

350

24 48 72 96 120

AR3DVARCONWJTWC

97 83 71 Number of Forecasts55 47

4DVAR

3DVAR

Consensus

JTWC

4DVAR better than 3DVAR at 2-5 days


Tropical Cyclogenesis Theories• Multi-scale aspects (waves, monsoon dynamics, gyres, ET lows, troughs)• “Bottom up” and “Top down” development theories

Vortical hot towers & upscale growth (Hendricks et al. 2004; Montgomery et al. 2006…)

Mid-level MCVs, thermodynamics (Ritchie & Holland 1997; Bister and Emanuel 1997…)

Predictability of TC Genesis has Yet to be Quantified“No facet of the study of tropical cyclones have proven more vexing than understanding and predicting their genesis”. (Emanuel 2003)

COAMPS ADJOINT: TROPICAL CYCLONGENESIS

Goals• Quantify TC genesis forecast sensitivity characteristics using an adjoint system.

THORPEX Pacific Asian Regional Campaign (T-PARC) Tropical Cyclone Structure ‘08 (TCS08)• Provided real-time targeting guidance for genesis using moist COAMPS adjoint.


COAMPS® Moist Adjoint Model Setup• Dynamics: nonhydrostatic, nested• Physics: PBL, surface fluxes, microphysics (no ice), Kuo• Response Function, J: kinetic energy in a box (lowest 1 km) (tested w, , p, TE..)• Resolution: x=40 km, 18 h (18 h lead time)

x=40 km / 13 km, 9 h (27 h lead time)

COAMPS Adjoint Model

LeadTime

Adjoint Model

Tangent Linear Model

Nonlinear Forward Model

Adjoint ModelTest TL Approx. Using Optimal Perturbations~1 m s-1, ~1 K

correlations 0.5-0.9

Adjoint allows for the mathematically rigorous calculation of forecast

sensitivity of a response function to changes in initial state

Sensitivity of response function (J) at time tn to the state at time t0

0( ) ( )T

n

J J

t t

M

x x

COAMPS® is a registered trademark of NRL

Adjoint Optimal Perturbations

Basic State

ti tfNonlinear Forward Model

Adjoint allows for the mathematically rigorous calculation of forecast

sensitivity of a response function to changes in initial state


TCS??: Experts Predict “Non-Developer”

Which of These Will Develop?2 Events from T-PARC/TCS08

08Z 18 Aug 200808Z 18 Aug 2008

Typhoon Nuri ~75 kts

03Z 3 Sep 200803Z 3 Sep 2008

TCS??: Experts Predict “Developer” TCS025 ~30 kts


Nuri Sensitivity0600 UTC 17 Aug 2008 (18-h lead time), x=40 km

850 hPa J/qv and winds850 hPa J/u and streamlines

• U and V sensitivity large near the storm center (increase ) and along ridge axis and inverted trough.

• Largest sensitivity to low- and mid-level and qv near the storm.

850 hPa J/ and winds

W E

L 1009

ResponseFunction

(KE)


• Deep layer of water vapor sensitivity near the storm center. • Vorticity sensitivity is a maximum in the low-levels.

Nuri Sensitivity: Vertical Structure0600 UTC 17 Aug 2008 (18-h lead time), x=40 km

vorticity sensitivitywater vapor sensitivitytrajectory water vapor & sensitivity trajectory vorticity & sensitivity

W EW E

56x10-5 s-1+


Evolved Perturbations (Nuri)0000 UTC 18 Aug 2008, x=13.3 km

31 m s-1 -12 hPa

Evolved Perturbations (in TLM) (9 h)

10-m Wind Perturbations Pressure Perturbations (and SLP)

• Extreme perturbation growth of 30x over 9 h; coincides with cyclone.• Opposite sign perturbations kill Nuri.• Rapid perturbation growth highlights difficulty in genesis/intensity forecasting


SUMMARY

• Real-time global and mesoscale products produced in support of targeting objective.

• Data denial experiments for Nuri show little systematic impact on track skill. Ensembles and SVs hint at larger initial sensitivity for Sinlaku and Jangmi. Data denial experiments planned for Sinlaku and Jangmi.

• Ensembles show increased detection of genesis and better spread-skill with inclusion of model uncertainty.

• Preliminary results indicate improved TC track forecasts from 4DVAR over 3DVAR.

• COAMPS Adjoint highlights strong sensitivity to temperature and moisture fields, and rapid perturbation growth. Data denial experiments planned.


Questions?

Jangmi: 26 Sept. 00UTC(54h after TD)

Small spread, few recurve.

More spread, more members recurve.


CTL (LEFT) and STOC (RIGHT) for SINLAKU AND JANGMI

Sinlaku: 10 Sept. 00UTC(36h after TD)

Most tracks miss Taiwan landfall.

Taiwan landfall more probable.

NOGAPS SVs: 5 Fixed Regions, Twice Daily

• T79L30 adjoint/TLM resolution

• T239L30 (operational) trajectory

• Dry Total Energy norm

1

2

34 5

Details:• 48-h lead-time off 00Z run

(available 09 UTC, 39-h prior to target time)

• 60-h lead-time off 12Z run (available 21 UTC, 51-h prior to target time)

• 48-h opt times for all regions except 72-h opt time for North Pacific Region

During high-interest periods:• 24-h lead time and 36-h

lead time products • Flow-dependent

verification regions1) Centered on Guam2) Storms affecting Taiwan3) Storms affecting Japan4) ET Region5) Central North Pacific

NOGAPS SVs for Jangmi (2008092800)

Sensitivity dominated by wind field

WIND COMPONENT TEMPERATURE COMPONENT

Sensitivity to wind field max at 500-hPa

Sensitivity to temp field max in mid and upper troposphere.

NOGAPS SVs for Jangmi (2008092800)

Sensitivity dominated by wind field

500-hPa Vorticity 500-hPa Temperature

Elongated vorticity structures extend to southeast and northwest of storm

Temperature dipole about storm center

Final Time

Initial Time

NOGAPS SVS and COAMPS Sensitivity (shaded) with SLP (contour) for TC Fitow (Sep 6-8 ‘07) 48-h Forecast

NOGAPS COAMPS

•NOGAPS and COAMPS sensitivity similar for coarse-resolution, dry simulations•Complete (microphysics) adjoint of COAMPS provides unprecedented opportunities to study small spatial scales and short time scales

Reynolds, Doyle

NOGAPS/COAMPS COMPARISON

KE/qv 500 m

KE/qv KE/

Low-Level Moisture

Sensitivity

Mid-Level Sensitivity

e Perturbation

W E W E W E

Low-Level e Maximum

(Destabilization)

• Preferred regions of large sensitivity to low-level moisture and .• Low-level e optimal perturbations: destabilize & saturate core.

36-h KE sensitivity to 18-h state

COAMPS TC SensitivityCOAMPS TC Sensitivity

COAMPS Adjoint sensitivity of TC Fitow during early development stage highlights strong sensitivity to thermal and moisture fields

Difficulty in predicting intensity may be reflected in rapid perturbation growth.

Doyle

COAMPS TC SENSITIVITY

Sinlaku



Erratic tracks, little development

Tracks still erratic, more spread.



SUMMARY: Targeted Observing Products

•NOGAPS SVs for real-time targeted observing guidance:

•Five fixed region SVs provided twice daily

•Having many products available proved useful. Discussions led to targeting consensus.

•Often possible to relate position of sensitivity to general dynamic understanding of steering mechanisms

•For current configuration, sensitivity to wind field stronger than sensitivity to temperature field

•Data Denial Experiments Ongoing. Not much impact for Nuri.

•COAMPS Adjoint sensitivity for Developing and Mature Storms

•24-h optimization time with variety of lead times

•Storm-centered Verification Regions

•Fine-scale products complementary to large-scale SVs

•Rapid perturbation growth associated with moist processes

SUMMARY: Ensemble Products

•NOGAPS ensemble products (time-longitude diagrams), useful for downstream impact problem

•Real-time 55-km 8-member Ensemble Tests ongoing

•Tests for 2008 season indicate

Impact of initial perturbation formulation relatively small

Impact of resolution significant

NOGAPS ET Ensemble 200-hPa V: black contours- control; shading – ens. spread, 35-60N

Squares shows longitude of TC Sinlaku

NOGAPS ET Ensembles with Stochastic Convection (T119L30, 32-member + control, 240 h, once daily)

NOGAPS Ensemble Products

Time-longitude diagrams for depicting energy propagation, forecast uncertainty

Large ensemble spread downstream from Sinlaku indicating uncertainty in ET

NOGAPS ET Ensemble 200-hPa V ensemble spread normalized by September average

Squares shows longitude of TC Sinlaku


Large anomalous ensemble spread downstream from Sinlaku indicating uncertainty in ET

NOGAPS ET Ensembles (T119L30, 32-member + control, 240 h, once daily)

NOGAPS ET Ensembles (T119L30, 32-member + control, 240 h, once daily)

NOGAPS ET Ensemble 500-hPa Z: black contours- control; shading – ens. spread, 35-60N

Arrow shows longitude of North Pacific SVs.


Time-longitude diagrams useful for depicting energy propagation, forecast uncertainty. Arrow indicates location of North Pacific SVs.

Initial SVs

After 24h

After 48h

Final SVs

Evolution of NOGAPS North Pacific 72-h SVs from 2008092600

Illustrates rapid downstream propagation

Useful for winter TPARC?

Try 96-h SVs?

DOWNSTREAM PROPAGATION OF SIGNAL

Real-time 8-member 55-km NOGAPS Ensemble tests for 2009

http://www.nhc.noaa.gov/refresh/graphics_ep5+shtml/?5-daynl?large

James D. DoyleClark Amerault, Carolyn Reynolds, Hao Jin, Jon Moskaitis1

Naval Research Laboratory, Monterey, CA1National Research Council, Monterey, CA

James D. DoyleClark Amerault, Carolyn Reynolds, Hao Jin, Jon Moskaitis1

Naval Research Laboratory, Monterey, CA1National Research Council, Monterey, CA

Acknowledgements: ONR, TCS08 TeamAcknowledgements: ONR, TCS08 Team

Aspects of Tropical Cyclogenesis Predictability during TCS08

Aspects of Tropical Cyclogenesis Predictability during TCS08

Typhoon Saomai (08W) and Tropical Storm Bopha (10W) 02Z 8 Aug 2006 (NASA MODIS)Typhoon Saomai (08W) and Tropical Storm Bopha (10W) 02Z 8 Aug 2006 (NASA MODIS)

Vertically-integrated total energy, C130 Track and

Drops

•COAMPS 24-h Adjoint sensitivity calculated for storms of interest•Complete (microphysics) adjoint of COAMPS provides unprecedented opportunities to study small spatial scales and short time scales

COAMPS ADJOINT SENSITIVITY FOR TARGETING

SINLAKU, 2008091012

800-hPa Vorticity Sensitivity with background wind and

height


Accuracy of Tangent Linear ApproximationU’ field [18-h integration (x=40 km)]

Adjoint AccuracyAdjoint Accuracy

Excellent agreement between perturbations evolved in the nonlinear and tangent linear models (dry & moist) for x= 40 km.

Dry

Nonlinear Tangent Linear

Moist

Accuracy of Tangent Linear ApproximationU’ field [9-h integration (x=13.3 km)]

Nonlinear Tangent Linear

Excellent agreement between perturbations evolved in the nonlinear and tangent linear models with moisture for x= 13 km.

Adjoint AccuracyAdjoint Accuracy

P3 LIDAR Winds at 500 m

Nuri Sensitivity1500 UTC 17 Aug 2008 (27-h lead time), x=13.3 km

500-m J/qv 500-m J/

• Strongest sensitivity on NE flank (strongest winds – observed and simulated).• Spiral bands of qv and sensitivities similar to stochastic optimals for swirling

flows (e.g., Nolan and Farrell 1999).• VHTs may have a bigger impact in these sensitive regions.

Nuri

D. Emmitt

500-m Winds (m s-1)

TCS025 Sensitivity0600 UTC 28 Aug 2008 (18-h lead time), x=40 km

• U and V sensitivity large near the storm center and upstream along 850-hPa trough (J/ large).

• Largest sensitivity to low- and mid-level and qv near the storm, maxima along flanks of the trough.

850 hPa J/ u and SLP 850 hPa J/qv and winds

850 hPa J/ and winds

NW

SE

TCS025TCS025

850 hPa J/ u and streamlines

vorticity sensitivity

TCS025 Sensitivity: Vertical Structure0600 UTC 28 Aug 2008 (18-h lead time), x=40 km

• Water vapor sensitivity is largest in the low-levels. • Vorticity sensitivity is a maximum in the mid-levels

and to the SE of the vortex (less shear?).

water vapor sensitivitytrajectory water vapor & sensitivity trajectory vorticity & sensitivity

51x10-5 s-1+

NW SE NW SE

TCS025TCS025

TSC025 Verification0300 UTC 29 Aug 2008 (27-h lead time), x=13.3 km

500-m Winds (m s-1)GM6 (0030 UTC 29 Aug)

ASCAT (2300 UTC 28 Aug)

25 kts

TCS025TCS025

TCS025 Sensitivity0300 UTC 29 Aug 2008 (27-h lead time), x=13.3 km

500-m winds 500-m J/qv 500-m PV’

• Prominent asymmetry in the low-level winds (agrees w/ QuikSCAT).• Large sensitivity to water vapor and PV along low-level jet.

TCS025TCS025

Evolved Perturbations (TCS025)1200 UTC 29 Aug 2008 (27-h lead time), x=13.3 km

15 m s-1 -5 hPa

Evolved Perturbations (in TLM) (9 h)

10-m Wind Perturbations Pressure Perturbations (and SLP)

• Rapid growth (15x growth from initial perturbations of 1 K, 1 m s-1) in 9 h.• Perturbation growth is confined to small areas, SE of the broad low center.• Slower growth than Nuri.

L L

TCS025TCS025

Initial PerturbationDomain Average of Adjoint Optimal Perturbations

Evolved in TLM

Final Perturbation (18 h)

Upper-Level Max. Consistent With

Background

Deep Response Function Box is

Consistent

Perturbation Characteristics16 Cases (13 Storms) x=40 km

• Initial total energy maximum in low-levels.• Deep perturbation growth throughout troposphere.• Non-developers show weakest growth.

Hei

ght

(m)

Summary

• Moist adjoint was successfully applied (real time) during T-PARC/TCS08-Targeted observations and forecasting-Dropsondes from P3 & C130 often sampled sensitive regions

• Moist adjoint provides physically meaningful sensitivities. -Characteristics of both “bottom-up” and “top-down” development-Moisture and temperature show greater sensitivity than winds-Preferred locations of deep convection for intensification

• Significant differences in adjoint sensitivities for 2 genesis cases-Nuri: convectively dominated, vorticity sensitivity bands-TCS025: multi-scale aspects, baroclinic signatures

• Challenges for TC genesis predictability.-Convection introduces inherent uncertainty - motivates need for ensembles-Rapid growth rates: 50% cases show > 10x growth 18 h-1 for 500-m winds

ConclusionsSummary

Evolved PerturbationsTCS025: 0000 UTC 29 Aug 2008 (18-h lead time), x=40 kmNuri: 0000 UTC 18 Aug 2008 (18-h lead time), x=40 km

14 m s-1 9 m s-1

• Both cases exhibit fairly rapid adjoint perturbation growth.• Faster p’ growth for Nuri (-5 h Pa) vs. TCS025 (-3 h Pa)

leading to more organized development.

TCS025

Evolved Perturbation Pressure (in TLM) and SLP (18 h)

Nuri

Response Function Sensitivity0600 UTC 17 Aug 2008 (18-h lead time), x=40 km

KE in lowest 1 km in lowest 1 km

w in lowest 10 km• Response functions evaluated for KE,

TE, , w, u over various depths• The KE, TE, response function results

yield similar sensitivities.• Vertical velocity averaged over the

troposphere produces a similar pattern, but weaker sensitivity.

Sinlaku



Erratic tracks, little development

Tracks still erratic, more spread.



Documents

NRL RESEARCH FROM TPARC/TCS-08 C. Reynolds, J. Doyle, R. Langland, J. Goerss, J. McLay and E. Serra Marine Meteorology Division, Naval Research Laboratory,