terrain-influenced local wind forecasting for sasebo typhoon haven

TERRAIN-INFLUENCED LOCAL WIND FORECASTING FOR SASEBO

TYPHOON HAVEN: IMPROVED EMPIRICAL TECHNIQUES

Joel W. Feldmeier, Wendell A. Nuss, Russell L. Elsberry

Naval Postgraduate School

Monterey, California USA

DEFINITIONS

Typhoon haven: Harbor for which it will be safe for a ship to remain in

port in the western North Pacific as a typhoon approaches

Terrain-blocking: In addition to the slowing due to the land friction

effect, significant topography around the harbor will decrease the local

winds from that direction

Terrain-channeling (gap winds): Openings between topographic

features that enhance the local winds due to funnel effect and

downslope acceleration

International Workshop Tropical Cyclone Landfall Processes III

8-10 December 2014 1

TOPOGRAPHIC FEATURES SURROUNDING SASEBO HARBOR

• Harbor opening is to the south with adjacent hills

• North-south oriented gap between MT. Yumihari and Mt. Eboshi

• Lower terrain to the east of the harbor

International Workshop Tropical Cyclone Landfall Processes III 8-10 December 2014

2

VIEW FROM MT YUMIHARI LOOKING TO SOUTHEAST

• Mt Yumihari is ~ 1100 ft (364 m) high

• Mt. Eboshi to east is ~ 1700 ft (568 m) high

• Sasebo harbor is on right side


3

Mt. Eboshi

Downtown Sasebo

North

SASEBO HARBOR IS NOT ALWAYS A TYPHOON HAVEN

• Several incidents of wind-related damage to U. S. Navy

ships during past 30 years

• Ships dragging anchors or almost breaking mooring lines

even with two tugs holding ship against the wind

• When Typhoon Tokage (2004) was passing well to east of

Sasebo (and thus northerly winds at Sasebo) sustained (10

min average) winds exceeded 40 kt, with the strongest wind

gust of 96 kt (49.3 m s-1) during the past 50 years

• Fundamental question: In what situations with a typhoon

passing within 200 n mi of Sasebo should a ship leave the

typhoon haven to avoid damaging winds?


4

LOCAL WIND FORECASTING INCLUDING TERRAIN INFLUENCE

• Intensity (or the wind structure such as radius of 34 kt

winds) of a typhoon is a maximum surface wind speed over

the ocean

• Define a landfall as a typhoon passing close enough to the

site to potentially cause damaging winds over the land

• Question: How should the over-water wind structure values

be modified to estimate those potential damaging winds

over land?

• In the presence of complex terrain such as Sasebo harbor,

how do you account for terrain-blocking or terrain-

channeling effects?


5

EMPIRICAL TECHNIQUE BASED ON JTWC TRACK

AND WIND STRUCTURE

• Range and bearing of Sasebo from a TC as defined in Knaff

et al. (2007)

• JTWC provided maximum wind (Vm) as radius (rm)

• JTWC provided the forecast track relative to Sasebo


6

PARAMETRIC UNADJUSTED WINDS (PUW)

FROM MODIFIED RANKINE VORTEX

Knaff et al. (2007) parametric model for wind structure outside rm


7

Note: Outer wind structure defined by single size parameter (no use of outer

wind radii information)

Two basic equations for the modified Rankine Vortex:

V(r,q) = (vm – a)(rm/r)x + a cos(q - q0) for r > rm

V(r,q) = (vm – a)(r/rm) + a cos(q - q0) for r < rm

with

r = range from TC center to a point of interest (Sasebo)

= the included angle measured counterclockwise from

the bearing 90∘ to the right of the storm

motion vector to the point of interest

vm = TC maximum winds

x = a size parameter

a = the magnitude of wavenumber-1 azimuthal asymmetry

q0 = the degree of rotation of vm measured in the same manner as q

rm = the radius of TC maximum winds

winds calculated from these with no additional adjustment termed:

Parametric Unadjusted Wind (PUW)

Observed/analyzed

(or forecast)

Calculated

parametrically

PARAMETRIC ADJUSTED WINDS (PAW) INCLUDING WIND RADII


8

• JTWC best-tracks or forecasts

provide:

• - Wind radii

• - Radius of maximum

winds

- For a given wind radii, have V at r -> solve for x

- Between 2 wind radii, interpolate to a power function

- Use analyzed rm vice parametrically calculated

Adjustments

lead to

Parametric

Adjusted Wind

(PAW)

COMPARISON OF UNADJUSTED (PUW) VS ADJUSTED (PAW)

AT MINAMITORISHIMA

Minamitorishima is a tiny island (area 1.2 km2) with highest elevation of 9 m.


9

• AMEDAS sustained winds (m s-1) when tropical cyclones were within another 200 n mi of Minatorishima

•Linear fit equation with zero intercept for PAW in panel (b) has a larger coefficient of fit, which demonstrates

improved local wind speed accuracy if information on outer wind structure is included

SASEBO LAND INFLUENCE FACTOR WITH ADJUSTED (PAW)

WINDS

AMEDAS sustained wind (m s-1) observations (422) when TCs during 2003–2010 seasons were within 200 n mi of Sasebo versus (a) PAW winds and (b) 0.54 PAW as first-order land-frictional effect that is tested with an independent sample of TCs during 2002 and 2011-2012.


10

• Linear fit regression coefficient of 0.5437 for the land-influence factor in panel (a) for Sasebo versus

0.8415 for Minamotorishima indicates a much larger impact of land friction and other terrain influences at

Sasebo.

• While linear fit line for adjusted PAW in the independent sample in panel (b) gets the overall trend, the

large spread about that trend line represents terrain-blocking and terrain-channeling.

y = 0.5437x R² = 0.3151

0

5

10

15

20

25

30

35

40

0 10 20 30 40

AM

EDA

S W

ind

Sp

ee

d (

m/s

)

PAW (m/s)

y = 0.9021x R² = 0.4302

0

5

10

15

20

0 5 10 15 20

AM

EDA

S W

ind

(m

/s)

0.54 Times PAW (m/s)

(a) (b)

SASEBO LOCAL WIND DIRECTION VERSUS

PAW PARAMETRIC WIND DIRECTIONS


11

• PAW parametric wind (see PPT 6) direction assumes a tangential wind direction of a

symmetric vortex

• Local wind direction at Sasebo is turned inward by 1-2 cardinal wind (15 deg.) directions

relative to symmetric vortex

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Re

lati

ve F

req

ue

ncy

of

Occ

urr

en

ce

Number of Cardinal Point Shifts from Parametric to AMEDAS Wind Directions

SASEBO WIND SPEED VARIABILITY VERSUS

PAW SPEED VARIABILITY

• Sasebo wind speed variability (blue lines) for an independent sample of individual TCs when

only the simple Sasebo land influence factor of 0.54 PAW (red boxes) is applied.


12

• YEAR/MONTH/DATE/HOUR of first time the TC was within 200 n mi of Sasebo is indicated along

abscissa.

• Simple land-influence factor PAW speeds under-estimate the Sasebo maximum winds (channeling

effect) and over-estimate the minimum winds (blocking effect); that is, range of variability is too small.

0

2

4

6

8

10

12

14

16

18

20

Win

d S

pe

ed

(m

/s)

Date Time Group (UTC)

Range of Variability

PARAMETRIC-BASED ACCELERATION FACTORS

• Calculated PAW for 22 TCs that passed within 200 n m of

Sasebo during 2003-2010 dependent sample

• 422 hourly sustained (10-minute) wind observations are divided

by the corresponding PAW wind speed to define an acceleration

factor

• Group by these acceleration factors according to cardinal wind

direction of observed winds at Sasebo

• Calculate mean and standard deviation for each cardinal wind

direction


13

Sasebo

3. Use derived acceleration

factors to adjust speed for

forecast

DEVELOPMENT OF PAW WIND DIRECTION-BASED

ACCELERATION FACTORS • Direction-dependent acceleration factors for terrain-channeling or deceleration for

terrain-blocking effects are calculated for PAW


14

2. Wind ‘forcing’ to Sasebo system after taking locally or frictionally caused direction

shifts

Typhoon – a source of

synoptic/mesoscale forcing

Wind radii

1. Wind ‘forcing’ to Sasebo system from typhoon scale

forecast and/or a simple parametric model

TC cyclonic wind flow (Northern Hemisphere)

SUMMARY OF SASEBO ACCELERATION FACTORS

BASED ON TC PASSAGES

• Acceleration factors calculated from

ratio of AMEDAS speed to PAW

speed for dependent sample of TCs

passing within 200 n mi of Sasebo

• Any mean acceleration factor >

0.54 (simple land-influence factor) is

an enhancement, but the

predominance of factors < 0.54 for

both the mean and the mean minus

std. dev. columns indicates Sasebo

harbor is highly likely to be a safe

typhoon haven.

• Mean plus std. dev. factors do

indicate enhanced gap winds from

north-northeast and northeast and

from east-southeast and southeast;

note the irregularity for adjacent

wind directions and some small

sample sizes.

International Workshop Tropical Cyclone Landfall

Processes III 8-10 December 2014 15

Cardinal Direction

Mean minus S.D.

Mean

Mean plus S.D.

Number of AMEDAS to PAW Comparisons

N 0.4 0.6 0.8 91

NNE 0.4 0.7 1.0 55

NE 0.4 0.7 1.0 13

ENE 0.4 0.5 0.7 8

E 0.3 0.4 0.5 11

ESE 0.1 0.6 1.3 21

SE 0.2 0.6 1.1 38

SSE 0.4 0.7 0.9 24

S 0.4 0.7 1.0 19

SSW 0.4 0.6 0.8 22

SW 0.2 0.4 0.6 11

WSW 0.2 0.3 0.4 8

W 0.2 0.5 0.7 13

WNW 0.3 0.4 0.6 28

NW 0.3 0.4 0.6 26

NNW 0.3 0.5 0.6 33

Acceleration Factor

SASEBO WIND SPEED VARIABILITY VERSUS PAW SPEED VARIABILITY

INCLUDING DIRECTION-DEPENDENT ACCELERATION FACTORS


16

• Sasebo wind speed variability (blue

lines) for an independent sample of

individual TCs when the mean (red boxes)

or mean plus std. dev. (green triangles)

direction-dependent acceleration factors

are applied to the PAW speeds

Note that mean plus std. dev. direction-

dependent acceleration factor applied to

the PAW winds provides more accurate

peak winds, but over-estimates the weaker

winds.

•YEAR/MONTH/DATE/HOUR of first time the TC was within 200 n mi of Sasebo is indicated along abscissa.

ALTERNATE SASEBO ACCELERATION FACTORS

FROM CFSR REANALYSIS

• Climate Forecast System Reanalysis (CSFR) zonal and

meridional winds at 10 m each 6 h are extracted at nearest

gridpoint (33°N, 130°E) to Sasebo for a large sample during 1979

to 2012 rather than just during TCs.

• Frequency distribution and derived probability distribution

functions were created for the 1000 maximum wind speeds from

each cardinal wind direction at Sasebo

• Acceleration factors for the “Top 1000” 10 m winds compared to

Sasebo winds range from 0.7 to 2.0 because the CFSR winds

already have some frictional reduction at the over-ocean nearest

gridpoint


17

SUMMARY OF SASEBO ACCELERATION FACTORS

BASED ON CFSR REANALYSES

• Mean and ± std. dev. of Sasebo wind

acceleration factors for Top 1000 CFSR

10 m wind speed.

• Except for N and the WSW and W

wind directions, the mean minus std.

dev. CFSR factors are all less than 1.0

(i.e., terrain blocking) and vary smoothly

from direction to direction.

• “Worse-case scenario” mean plus std.

dev. factors also vary smoothly with

direction except for WSW and W

directions. Note that these acceleration

factors range from 0.3 to 0.7 larger than

mean acceleration factors.

International Workshop Tropical Cyclone Landfall Processes III 8-10 December 2014 18

Cardinal

Direction

Mean minus S.D.

Mean

Mean

plus

S.D.

Number

of

AMEDAS

to

CFSR

Comparisons

N 1 1.4 1.8 1000

NNE 0.7 1.1 1.5 1000

NE 0.4 0.7 1 1000

ENE 0.4 0.8 1.2 1000

E 0.4 1 1.6 1000

ESE 0.6 1.2 1.8 1000

SE 0.6 1.1 1.6 1000

SSE 0.5 1 1.5 1000

S 0.8 1.1 1.4 1000

SSW 0.9 1.2 1.5 1000

SW 0.7 1.1 1.5 1000

WSW 1.3 2 2.7 1000

W 1 1.7 2.4 1000

WNW 0.8 1.1 1.4 1000

NW 0.9 1.1 1.3 1000

NNW 0.8 1.1 1.4 1000

Acceleration Factor

MOST FREQUENTLY OCCURRING AND MOST LIKELY CFSR

ACCELERATION FACTORS

• Three Sasebo wind directions have CSFR acceleration factors > 1.0 – Wind direction N, representing wind channelling between Mt. Yumihara and Mt. Eboshi –Wind directions ESE and SE from a region of lower terrain – Direction from WSW and W associated with a terrain gap between Mt. Yumihara and Mt. Akasaki to the south


19

PROPOSED OPERATIONAL APPLICATION

OF ACCELERATION FACTORS

• Smooth direction-to-direction variations of the acceleration

factors based on CFSR reanalyses are based on much

larger samples than those based on PAW winds from

sample of TCs passing within 200 n mi of Sasebo.

• However, the parametric PAW winds including the JTWC

wind radii have a more realistic vortex structure than in the

CFSR reanalysis, and the CFSR vortex is not in the same

locations as the JTWC initial and forecast positions.

•Until a method of blending the CFSR-related and PAW-

related acceleration factors is developed, the PAW-related

acceleration factors based on the combined dependent and

independent samples of TCs are recommended.


20

OVERVIEW OF SASEBO TERRAIN-INFLUENCED

LOCAL WIND FORECASTING

• TC tracks within 200 n mi should be considered

• Use available wind radii to better define the outer wind structure

• Simple land-influence factor for complex terrain such as Sasebo will be much higher than for flat island (coastal location)

• Wind direction-dependent acceleration factors to account for terrain-blocking effect (additional wind reduction) or terrain-channeling effect (accelerated gap winds) may be derived from large sample of TC passages or from re-analyses

• Conclude that Sasebo harbor is a safe typhoon haven in nearly all scenarios, so a ship only needs to leave for a worse-case scenario


21

SITUATIONAL AWARENESS FOR TROPICAL CYCLONE EVENTS

(OR NO-TC GAP PERIODS)

• While original intent of Tsai and Elsberry (2013)

was for hydrological applications and disaster

management groups, Sasebo Harbormaster

and ship captains would benefit from a

continuously updated situational awareness of

the tropical cyclone threat

• Twice-weekly 15-30 day outlooks leading to

a low level of awareness

• Twice-daily 5-15 day forecasts of potential

threat that requires some planning,

especially if a TC is forecast to form and

have a track toward the Sasebo harbor

• Up to four-times daily 1-5 day forecasts of a

TC approach from a consensus of high-

resolution deterministic models and some

measure of confidence from spread of

ensemble tracks leading to heightened

awareness, initiation of disaster

preparedness plans, and ship deployment if

necessary


Tier 1: 30-15 day time scale Data: ECMWF 32-day ensemble on Mondays and

Thursdays

Focus: long-range outlook of a TC formation or no-TC gap

Action: ship in-bound and out-bound planning; ship repair

scheduling

Tier 2: 15-5 day time scale

Data: ECMWF 15-day ensemble every 12 hours or

ensemble of forecast models from other centers

Focus: progressive narrowing of TC track/spread;

forecast-to-forecast consistency check for potential

threat areas, or no-TC gap

Action: Identify potential ship deployments (or

diversions) as threat to Sasebo increases

Tier 3: 5-1 day time scale

Data: high-resolution deterministic forecast models

every 6 hours plus ensemble model

Focus: consensus of high-resolution TC forecast

tracks plus best estimate of track uncertainty to

identify specific threat areas

Action: schedule ship deployments from Sasebo

dependent on vulnerability – or cancel alerts

22

APPLICATION TO RECOMMENDING A SHIP LEAVE SASEBO

HARBOR

• Monitoring twice –weekly ECMWF 32-day ensemble, twice-daily ECMWF 15-day ensemble, and multi-center 5-day forecasts for worse-case track scenario within 200 n mi

• Monitoring the Weighted ANalog Intensity (WANI) forecasting for intensity and intensity uncertainty forecasts for all threatening ensemble storm track scenarios from ECMWF 15-day ensemble forecasts and then JTWC 5-day official track forecasts

• For worse-case track scenario and corresponding worse-case intensity, use climatological typhoon wind radii to calculate the over-water PAW winds at Sasebo location

• Apply mean plus std. dev. direction-dependent acceleration factors if one of the three local wind directions with gap winds might occur International Workshop Tropical Cyclone Landfall Processes III

8-10 December 2014 23

terrain-influenced local wind forecasting for sasebo typhoon haven

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