The Utilization of Satellite Data in
Analyzing the Formation of Tropical
Cyclones
Gin-Rong Liu
Dean, Research and Development Office
Professor, Center for Space and Remote Sensing
Research
Institute of Atmospheric Physics
Institute of Hydrological Sciences
National Central University
E-mail : grliu@csrsr.ncu.edu.tw
Gin-Rong Liu, Dean/Prof.
Research and Development Office
Center for Space and Remote Sensing Research
Institute of Atmospheric Physics
Institute of Hytrological Sciences
National Central University
E-mail : grliu@csrsr.ncu.edu.tw
Ph.D., University of Wisconsin-Madison, Meteorology (Atmospheric
and Oceanic Sciences)
M.S., University of Wisconsin-Madison, Meteorology (Atmospheric
and Oceanic Sciences)
B.S., National Taiwan University, Atmospheric Sciences
Current Appointments
Dean, Research and Development office, National Central University, August
2004 ~ present.
Professor, Department of Atmospheric Science, National Central University
Professor, Institute of Hydrological Science, National Central University
Panel Chair, Nature Science Division, National Science Council (NSC), January
2004 ~ present.
Adjunct Professor, Department of Atmospheric Science, National Taiwan
University
Adjunct Professor, Department of Atmospheric Science, Chinese Cultural
University
Chief Editor, Journal of Atmospheric Science, ROC(Taiwan) Meteorological
Society.
Principal Investigator, Taiwan Chung-Li NCU station, Aerosol Robotic
Network (AERONET).
Responser, Meteorological Satellite Laboratory,NCU
Satellite Receiving Systems of CSRSR NCU
13 m Antenna
6.1 m Antenna
3000km
CKS International Airport (SPOT)
¾ Meteorological Satellite Laborotary
Antenna-GMS
Antenna-NOAA
Sand Index from GMS-5,
March, 2002
NOAA-17 AVHRR, 2004/01/02
Outline
.Introduction
.Theoretical concepts and Methods
.Data Acquirement and Processing
.Analysis and Discussion
.Conclusion
.Introduction
¾ In the research of tropical cyclone formations, Charney and
Eliassen(1964) believed that the conditional instability of
the second kind (CISK), along with the addition of lowlevel perturbations and the convection of cumulus clouds
were considered important factors.
¾ Gray (1968) defined a tropical cyclone genesis parameter as
the product of thermodynamic and dynamical terms, where
SST and midlevel moisture effects were included in the
thermodynamic term, and vertical shear and lowlevel
vorticity effects were included in the dynamical term.
¾ Mark et al. employed 3 thermal conditions mentioned
previously in computing a “Tropical Cyclone Genesis
Parameter”. This parameter was used to analyze the
cyclone cases that occurred over the Atlantic Ocean. The
results showed that the geopotential parameter trend was
strongly associated with the seasonal variation of cyclone
formations.
¾ Liu et al.(2000) used SSM/I data to show that the air-sea
interaction plays an important role in MCSs’ formations
over the ocean. This interaction may also play a similar role
in the development of typhoons by providing the required
thermal energy.
OPI Application (Case study: 1997/06/11)
3
1
2
OPI Application (Case study: 1997/05/16)
¾ Hack and Schubert (1988) suggested that improvements in
the system heating efficiency is conducive for the formation
of TCs, especially in stronger vorticity areas. Similar results
were reached by Ryan et al.(2002), where they employed
QuickSCAT data in analyzing the vorticity variations.
¾ This study makes use of SSM/I and QuickScat satellite data
in analyzing how the thermal and dynamic conditions-namely the daily air-sea energy flux and near-sea surface
vorticity variations-- are related to the formation of TCs over
SCS during the TC seasons (May~Oct, 2000~2002).
II. Theoretical concepts and Methods
¾ Utilization of Liu and Liu’s method (2002) to derive relevant
air-sea parameters
– Ts Ta Qs Qa Ws SHF LHF
¾ Using satellite-derived air-sea parameters in deriving the
objective thermal and dynamic indices
¾ In estimating air-sea parameters was applied
Liu et. al. 2002 .
by
Sea surface temperature (Ts)
Ts=118.186+3.458Tb(19V) -1.799Tb(19H)
-2.417Tb(37V)+1.119Tb(37H)+0.414Tb(85V)
-0.136Tb(85H)
------ K
Near sea surface air humidity (qa)
qa=-101.398+0.547Tb(19V)-0.677Tb(37V)
+0.078Tb(37H)+0.765Tb(85V)-0.254Tb(85H)
------ g/Kg
Near sea surface wind speed (Ws)
Ws=96.015+0.162Tb(19V)-0.228Tb(22V)
-0.701Tb(37V)+0.527Tb(37H)
------ m/s
Near sea surface air temperature (Ta)
⎧
⎡
⎪⎪
q (T ) ⎢
Ta = Ts − ⎨0.2 × (qs − qa ) ∗
qa ⎢
⎪
⎢
⎪⎩
⎣
⎫
⎤
⎪⎪
⎥
1
⎬
∂q* ⎥
⎪
⎥
∂T ⎦ T −Ta ⎪⎭
Ta: near-sea –surface air temperature
qa: near-sea-surface air humidity
qs the saturated specific humidity
Air sea latent heat flux and sensible heat flux
LHF = −lρCe (qs − qa )u
SHF = − ρC p Ch (Ts − Ta )u
Ch and Ce : bulk coefficients for sensible heat and latent heat flux
Subscript s : the field value of sea surface level
Subscript a :those of 10m above sea surface level
U : the wind speed of 10m above sea surface level.
T: temperature, q :humidity, ρ : air density, l : latent heat of evaporation,
Cp :specific heat of constant pressure.
¾ To derive the objective thermal and dynamic indices
Gaussian fitting of air-sea parameters
2000~2002 VT (Aug)
2000~2002 LHF+SHF (Aug)
0
SHF+LHF
0.5
0
LHA
0.5
Air sea latent heat flux and
sensible heat flux
LHF = −lρCe (qs − qa )u
SHF = − ρC p Ch (Ts − Ta )u
1
Latent heat release (LHR):In estimating latent heat
release was applied byAlliss et al.(1992) Rodgers et
al.(1994,1995)
LHR = Lρ ∫ Rda
A
L:condensation latent heat( 2.5 ×10 J /)Kg
ρ: density of air( 1.0 ×10 Kg / m) R:rainfall rate
da:the measure of area increment, A:the area of
integration.
This shows that the environmental
Not conducive for system development
atmosphere provides thermal energy
to TCs
Conducive for system development
6
3
1
3
•SSM/I
–Channels Tb19v, Tb19h, Tb22v,
Tb37v, Tb37h, Tb85v, Tb85h
–Time May-Step. (2000-2002)
–Satellites F10, F11, F13 and F14
–Test area 5°N~25°N 110°E~125°E
•QuikSCAT
–Channel L3
–Time Same as SSM/I
–Test area: Same as SSM/I
IV. Results and Discussions
¾Analysis of the objective Thermal and dynamic
indices of the initial typhoon development stages
over the South China Sea (SCS) during 2000-2002.
Type I Cimaron (2001)
Location of Typhoon
Formation
17.1N 119.1E
Date
2001.05.11-05.14
Air-sea parameters
analysis area
14.1N-20.1N
116.1E-122.1E
2001 05/12 00Z(-1day)
1st typhoon watch
announced by JTWC
¾ For Type I typhoons it shows that the objective thermal
index increases significantly 2 days before the it’s actual
formation
Type II Durian (2001)
Location of Typhoon
Formation
17.5N 114.6E
Date
2001.06.30-07.02
Air-sea parameters
analysis area
14.5N-20.5N
111.6E-117.6E
2001 06/30 00Z
The official time point
in the actual typhoon
formation announced by
JTWC
¾ For Type II typhoons, it also shows an evident increase in the thermal
objective index two days prior to the typhoon’s actual formation.
However, this phenomena can also be witnessed 4~7 days before the
typhoon’s genesis, which make it very difficult to distinguish the
specific time point for the typhoon’s formation.
Question:
Is it more precise if both
Thermal and dynamic
Parameters are considered?
For example, the add of
Vorticity information?
Type II Durian (2001)
¾The case study of Durian (2001) showed that the vorticity information
was Helpful to the determination of typhoon occurring time.
Note 1
¾ Based on the analysis of 15 typhoons cases over SCS during
2000-2002, the results showed that the formation time point for
10 typhoon cases could be determined in advance by the
objective thermal index. 4 typhoons cases could be determined
with the additional accompaniment of the objective dynamic
index. There was only 1 typhoon case--Yutu (2001)– that could
not be correctly determined.
¾ In other words, a 93% (14/15) ratio of typhoons showed a
significant raise in the objective thermal and dynamic indices 2
days prior to its actual formation.
¾ Monitoring of the variations in the objective
thermal and dynamic indices over SCS( 5-25N
110-125E) during the 2002-2003 typhoon
seasons
T.D.
Typhoon
¾Area: 5-25N, 110-125E, time: 2002
Data: SSM/I and QuickScat (resampling to a 0.5X0.5
Degree Grid) Method: (1)Computation of the objective
thermal and dynamic Indices (2)Shadowing the areas where the
Indices is larger than 0.5.
¾The results show that areas that had more shadow
areas, seemed to have a higher occurrence of typhoons.
T.D.
Typhoon
Independent
Case(2003)
¾ The result of 2003 showed the same trend like 2002.
OB:Objective Potential Index(OPI, Thermal Index)
Vt: Vorticity (Dynamic Index)
Verification of objective index
¾ Verification of the SCS
cases during 2000-2002
¾ Shadow areas indicate the
objective index values are
larger than 0.5
0.5X0.5
1.X1.
1.5X1.5
¾ Different grid resolutions were tested.
¾ It showed that 1X1 degree grid had a
better sensitivity, and was capable of
removing signal noises
Case study of objective index
¾ A 1x1 degree grid was used in the monitoring
of tropical cyclones
¾ Case I Nakri
Typhoon Lifetime 2002/07/08~13
Location of Typhoon Formation
[21.6 N 117.7 E]
¾ Case II Kammuri
Typhoon Lifetime 2002/08/03~06
Location of Typhoon Formation
[20.7 N 114.6 E]
Nakri
Nakri
Kammuri
Kammuri
Kammuri
Note 2
¾ Verification of Typhoon Formations in SCS during
2000-2002
2000 Æ 10 : 1(X) 9(O)
2001 Æ 20 : 6(X) 14(O)
2002 Æ 14 : 5(X) 9(O)
¾ Correction= 72.7% (32/44)
X: incorrect
O: correct
.Conclusions
¾ Based on the analysis of typhoons occurring over SCS during 2000-2002,
it revealed that the objective thermal index would raise significantly 2-3
days prior to the actual typhoon formation.
¾ From the analysis of the vorticity data from QuickScat satellite, it shows
that although the objective thermal indices for some cases increased
substantially, but due to weak dynamic conditions, they were unable to
evolve into typhoons. Thus, in addition to the supply of thermal energy, the
dynamic conditions must also be conducive for typhoons to development.
¾ From the typhoon cases over SCS during 2000~2002, areas where the
thermal and dynamic objective indices were larger than 0.5 were analyzed.
It reveals that the more areas where the indices are unstable, the higher
probability that typhoons will occur.
¾ Overall, a high accuracy ratio of 72.7% (32/44) was obtained
for the verification monitoring of tropical cyclones over SCS.
References
Liu, G.-R., H.-K. Lo and T.-H. Kuo, 1999: Applying AVHRR Data to Estimate the Vegetation Index of
Taiwan Area: (I)Geometrical Registration. Journal of Photogrammetry and Remote Sensing, Vol. 4, No. 1,
1-17.
•Liu, G.-R., S.-J. Huang, and T.-H. Kuo, 1999: The Atmospheric Effect Correction of the Ocean Color
Imager of ROCSAT-1—Simulations and Using SeaWiFS Data as the Example. TAO, Supplementary
Issue, 99-114. (SCI-Extended)
•Chen, W.-J., C.-Y. Tseng, G.-R. Liu and S.-J. Huang, 1999: The Effect of Atmosphere on the OCI
Channels: Rayleigh Scattering. TAO, Supplementary Issue, 115-126. (SCI-Extended)
•Liu, G.-R. T.-H. Lin, and A. J. Chen, 1999 Estimating Atmospheric Turbidity from SPOT and GMS-5
Data. Journal of Photogrammetry and Remote Sensing, Vol. 4, No. 3, 43-49.
•Huang, S.-J., G.-R. Liu, and T.-H. Kuo, 1999: The Atmospheric Correction of ROCSAT-1OCI Imagery—
Part I : OCITRAN-1. TAO, Vol. 10, No. 4, 855-864. (SCI-Extended)
•Liu, G.-R., S.-J. Huang, and T.-H. Kuo, 1999: The Atmospheric Correction of ROCSAT-1OCI Imagery—
Part II : OCITRAN-2. TAO, Vol. 10, No. 4, 865-872. (SCI-Extended)
•Liu, G.-R., A. J. Chen and L.-C. Chen, 2000: Introduction to CSRSR and General Situation of Remote
Sensing Activities in Taiwan. Asian Journal of Geoinformatics, Vol. 1, No.1, pp21-24.
•Liu C.-C. and G.-R. Liu, 2000: A Preliminary Study on the Genesis of Mesoscale Convection System
during Mei-Yu Period Using Satellite Data. Atmospheric Sciences, Vol. 28, No.4, 317-341.
•Liu, G.-R., T.-H. Lin and T.-H. Kuo, 2000: Ingestion and Application of Meteorological Satellite Data.
C.I.E.E. Magazine, Dec., 2000, 28-37.
•Liu, G.-R., C.-C. Liu and T.-H. Kuo, 2001: Rainfall Intensity Estimation by Ground-Based DualFrequency Microwave Radiometers. Journal of Applied Meteorology, Vol. 40, No. 6. Pages 1035-1041.
(SCI)
•Liu, G.-R., A. J. Chen, T.-H. Lin and T.-H. Kuo, 2001: Applying SPOT Data to Estimate the Aerosol
Optical Depth and Air Quality. Journal of Environmental Modeling and Software, Vol. 17, Issue 1, pp. 39. (EI)
•Liu, G.-R., C.-C. Liu and T.-H. Kuo, 2001: A Contrast and Comparison of Near-Sea Surface Air
Temperature/Humidity from GMS and SSM/I Data with an Improved Algorithm. IEEE
TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 39, No. 10, pp 2148-2157. (SCI)
•Liu, G.-R., P.-Y. Liu, T.-H. Kuo, and T.-H. Lin, 2001: An Estimation of Typhoon Intensity and the
Prediction of Its Track by Using MSU Data. TAO, Vol. 12, No. 4, pp. 615-634. (SCI-Extended)
•Liu, G.-R. , P.-J. Kao and T.-H. Kuo, 2001: An Improved Non-regression-Coefficients Method to Find
Sea Surface Temperature with AVHRR Data. Journal of Marine Science and Technology, Vol. 9, No. 2,
pp.145-152. (EI)
•Chen, T.-C., M.-C. Yen, G.-R. Liu and S.-Y. Wang, 2001: Summer Upper-level Vortex over the North
Pacific. Journal of Applied Meteorology, Vol. 82, No. 9. Pages 1991-2006. (SCI)
•Lin, T.-H., A. J. Chen, G.-R. Liu, and T.-H. Kuo, 2002: Monitoring the Atmospheric Aerosol Optical
Depth with SPOT Data in Complex Terrain. International Journal of Remote Sensing, Vol. 23, No. 4,
647-659. (SCI)
•Huang, S.-J., G.-R. Liu, T.-H. Lin, and T.-H. Kuo, 2002: The atmospheric correction algorithm of
ROCSAT-1/OCI data. COSPAR COLLOQUIA SERIES, Vol. 12, Space Weather Study using Multipoint
Techniques, pp. 295-300. Proceedings of the COPSPAR Colloquium in Taipei, Taiwan, 27-29 September
2000, edited by Ling-Hsiao Lyu, Pergamon, Elsevier Science, Oxford, UK. (SCI)
•Liu, G.-R., T.-H. Lin, T.-H. Kuo, and S.-J. Huang, 2002: Monitoring of Sandstorm with GMS S-VISSR
Data. Submitted to Asian Journal of Geoinformatics, Vol. 2, No. 4, pp. 3-8.
•Liu, G.-R., C.-C. Liu and T.-H. Kuo, 2002: A Satellite-derived Objective Potential Index for MCS
Development during the Mei-Yu Period. Journal of the Meteorological Society of Japan, Vol. 80, No. 3,
pp. 503-517. (SCI)
•Liu, G.-R., T.-H. Lin, and T.-H. Kuo, 2001: Estimation of Aerosol Optical Thickness by Applying the
Optimal Distance Number to NOAA AVHRR Data. Remote Sensing of Environment, 81/2-3, pp. 247-252.
(SCI)
•Sui, C.-H., C.-Y. Huang, Y.-B. Tsai, C.-S. Chen, P.-L. Lin, S.-L. Shieh, M.-H. Li, Y.-A. Liou, T.-C.
Chen Wang, R.-S. Wu, G.-R. Liu, and Y.-H. Chu, 2002: Meteorology-Hydrology Study Targets Typhoon
Nari and Taipei Flood. EOS, Transactions, American Geophysical Union, Vol. 83, No. 24, pp. 265, 268270, June 11, 2002.
•Lin, T.-H., G.-R. Liu and A. J. Chen, 2002: Applying Satellite Data for Aerosol Optical Depth and
Atmospheric Environment Monitoring, Journal of Photogrammetry and Remote Sensing, Vol. 6 No. 4, pp.
1-26.
•Liu, G.-R., T.-H. Lin, and T.-H. Kuo, 2002: Estimation of Taiwan’s Forested Areas by Classified NDVI
Maps from NOAA AVHRR Data, Journal of Photogrammetry and Remote Sensing, Vol. 7, No. 4, pp. 6976.
•Kuo, T. H., G. R. Liu, C. G. Huang, T. H. Lin, and C. C. Liu, 2002: "Atmospheric Transmittance of
AMSU Channels: A Fast Computation Model", Journal of Marine Science and Technology, Vol. 10, No.
2, pp. 104 - 109. (EI)
•Huang, S.-J., G.-R. Liu, T.-H. Kuo, T.-H. Lin, and C.-K. Liang 2002: Monitoring of Sandstorms with
ROCSAT-1 OCI Data. Journal of Marine Science and Technology, Vol. 10, No. 2, pp. 92 - 97. (EI)
•Lin, I-I, L.- S. Wen, G.-R. Liu, and K. K. Liu, 2002: Retrieval of Suspended Pariculate Matter and
Chlorophyll-a Concentration in a High Turbid Tropical River by in situ Bio-Optical Data – A Case Study,
Journal of Photogrammetry and Remote Sensing, Vol. 7, No. 3, pp. 75-85.
•Lin, T.-H., G.-R. Liu, T.-H.Kuo, Huang, S.-J.,2002 Application Research of Satellite Data on
Sandstorm Monitoring, Bulletin of Environment Protection Society. Vol. 25, No.2, 213-229.
•Lin, T.-H., G.-R. Liu, A. J. Chen, and T.-H. Kuo, 2003: Applying Satellite Data for Shoreline Detection
in Tideland Areas, Journal of Photogrammetry and Remote Sensing, Vol. 8, No. 1, pp. 57-66.
•Liu, C.-C., G.-R. Liu, W.-J. Chen, and Hong-Yu Yang, 2003: Modified Bowen Ratio Method in NearSea-Surface Air Temperature Estimation by Using Satellite Data, IEEE TRANSACTIONS ON
GEOSCIENCE AND REMOTE SENSING, Vol. 41, No. 5, pp 1025-1033. (SCI)
Liu, G.-R., T.-H. Lin, S.-J. Huang, and T.-H. Kuo, 2003: Applying Airborne Multi-spectral Remotely
Sensed Data for Rice Yield Prediction. Journal of Photogrammetry and Remote Sensing, Vol. 8, No. 4, pp.
71-81.
Liu, G.-R., C.-K. Liang, T.-H. Kuo, T.-H. Lin, and S.-J. Huang, 2004: Comparison of the NDVI, ARVI
and AFRI vegetation index, along with their relations with the AOD using SPOT 4 Vegetation data, TAO,
Vol. 15, No. 1, pp. 1-17. (SCI)
•Liu, G.-R., and T.-H. Lin, 2004: Application of Geostationary Satellite Observations for Monitoring Asia
Dust Storms, TAO, Vol. 15, No. 5, pp. 825-837. (SCI)
•Lin, T.-H., G.-R. Liu, 2005: To Construct an Effective Coefficient of Aerosol Size Distribution for
Atmospheric Turbidity Retrieval, TAO, Vol. 16, No. 3. (SCI)
•Liu, G.-R., T.-H. Kuo, Y.-C. Chen, T.-H. Lin, and C.-K. Liang, 2005: Analyzing the rainfall and air-sea
parameter variations of Typhoon Toraji and Typhoon Nari via SSM/I satellite data, Submitted to Journal
of Marine Science and Technology. (EI)
•Liu, G.-R., T.-H. Kuo, Y.-C. Chen, T.-H. Lin, and C.-K. Liang, 2005: Drought forecasting with seaatmospheric parameters retrieved from satellite observations, To submit to TAO.(SCI)