myoung-hwan ahn, jae-cheol nam meteorological research institute

METRIMETRI

Remote Sensing Research Lab.

Myoung-Hwan Ahn, Jae-Cheol NamMyoung-Hwan Ahn, Jae-Cheol NamMeteorological Research InstituteMeteorological Research Institute

Korea Meteorological AdministrationKorea Meteorological Administration

B.J. SohnB.J. SohnSeoul National UniversitySeoul National University

27 August 2003, APAN 1627 August 2003, APAN 16thth Meeting Busan Meeting Busan

Myoung-Hwan Ahn, Jae-Cheol NamMyoung-Hwan Ahn, Jae-Cheol NamMeteorological Research InstituteMeteorological Research Institute

Korea Meteorological AdministrationKorea Meteorological Administration

B.J. SohnB.J. SohnSeoul National UniversitySeoul National University

27 August 2003, APAN 1627 August 2003, APAN 16thth Meeting Busan Meeting Busan

KMA Plans for the GPMKMA Plans for the GPM

METRIMETRI


Background

Objectives

Future Plans

Concluding remarks

Contents

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Backgrounds

Flash flood causes the most damaging natural disaster in Korea

0

20

40

60

80

100

120

140

160

인명

피해

Heavy rain Tropical cyclone Severe storm Hailstorm

Causes of meteorological disasters (average of 1983 to 1992)

142

6758

3 Damages caused by heavy rain fall and flash flood, due to the typhoon RUSA in 2002

Hum

an

deat

h

Severe droughtHeavy snowfall

Human loss: 246Disaster relief expenditure about 6 billion USD

METRIMETRI


Backgrounds

The monitoring and prediction of disaster inducing phenomena including tropical cyclone and severe storms are critically important for the KMA’s mission

Tropical cyclone monitoring is mainly done by IR and Radar observation. There is clear advantages of MW data compared to IR.

Assimilation of precipitation data shows a promising improvement in the performance of numerical weather prediction model.

Use of highly accurate rainfall information in the global circulation model could increase the accuracy of seasonal outlooks of floods and drought conditions.

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Backgrounds

Core Satellite• TRMM-Like S/C, NASA• H2-A Launch, NASDA• Non-Sun Synchronous Orbit ~ 65° Inclination ~450 km Altitude• Dual Frequency Radar, NASDA Ku & Ka Bands ~ 4 km Horizontal Resolution ~250 m Vertical Resolution• Multifrequency Radiometer, NASA 10.7, 19, 22, 37, 85, 150 GHz V&H

OBJECTIVES∑ Understand Horizontal &

Vertical Structure of Rainfall, its Microphysical Nature, & Associated Latent Heating

∑ Train & Calibrate Algorithms for Constellation Radiometers

OBJECTIVES∑ Provide Sufficient Global

Sampling to Reduce Uncertainty in Short-Term Rainfall Accumulations

∑ Extend Scientific and Societal Applications

Global Precipitation Processing Center

• Produces Global Precipitation Data Product Streams Defined by GPM Partners

Precipitation Validation Sites • Selected & Globally Distributed Ground- Based

Supersites (Multiparameter radar, up looking radiometer/radar/profiler, raingages, & disdrometers)

• Dense Regional Raingage Networks NASA/GSFC

METRIMETRI


Backgrounds:GPM Program

Extend the spatial (mid to high latitudes) and temporal (about 3 hours) coverage, and data record (more than 10 years) of high quality rainfall measurement.

Improve accuracy and reduce uncertainty in rainfall measurements from better radar microphysics capability.

Observe broader spectrum of precipitation (e.g., light/warm rain, & snow).

Expand applications to climate change simulations, weather forecasts, and so on.

Well matched with KMA’s future improvement direction.

METRIMETRI


The KMA objectives through the GPM program in Korea could be categorized into four folds;

The Calibration and Validation with ground observation data(AWS, Radar, Microwave radiometer…). The Assimilation of GPM data into the Numerical Weather

Prediction(NWP) Models. Understanding of the Severe Weather System(Rain structure, energy cycle,…..) as well as the climate system. Monitoring of tropical cyclone and severe storms with higher

spatial and temporal resolution in real time.

KMA’s Objectives

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Cal/Val.: Potential Validation Site

Supersite Regional Raingage Site Supersite & Regional Raingage Site

Japan

South Korea

IndiaFrance (Niger & Benin)

Italy

Germany

Brazil

England

Spain

NASA KSC

NASA Land

Canada

Taiwan

ARM/UOK

NASA

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Ca./Val.:Ground Observation Network

Surface

Upper-air

Aeronautical

74

3

9

8 - 24

2-4

24 - 48

No. of stations

No. of daily observations

Surface temp., wind, preci., etc.

Temp., wind wave onseas, etc.

Observing elements

Observing elements

AWS

OceanBuoy

No. of stationsNo. of

stations

5

460 continuous

24

No. of dailyobservationsNo. of daily

observations

Observation NetworkConventional Station

Automatic station

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Composite radar imageComposite radar image LightningLightning Weather radar networkWeather radar network

Observing elements No. of station

Weather radar Cloud, preci., wind, etc.6(3) Every 10 min.

Lightning Position. movement, etc.10 Every 10 min.

No. of daily observation

Cal./Val.: Radar Network in Korea

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Cal./Val.: Intensive Observation SiteC-band radar(ROKAF)

S-band radar

Aerosonde(from Australia)

X-band radar

Haenam Special observation site• autosonde for continuous upper air obs.• boundary layer wind profiler• micro rain radar for vertical structure of rain• optical rain gauge for continuous accurate rain rate observation• conventional synoptic weather observation

METRIMETRI


Heanam Super sites

Understanding of the land-surface hydrological and cloud-precipitation processes in cloud physics and numerical model.

IntensiveObservation

Period

Micro Rain RadarProducing vertical profiles

of rain rate, LWC anddrop size distribution

Flux TowerProducing sensible, latent, and radiative

fluses over land surface

Optical Rain GaugeContinuous accurate rain rate observation.

AutosondeContinuous upper air

observation

Boundary Layer RadarProducing one-minute profile

of vertical and horizontal winds

Produce high resolution temporal and spatial data for the monitoring, analysis and prediction of severeweather phenomena(typhoon, fronts…)

Cal./Val.: Intensive Observation Site

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Comparison between TRMM/PR and ground based AWS rain fall data for two different rain cloud structure.

Cal./Val.: Example-1

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220km

Rain Rate

Heavy rainfall by MCC(31 July, 1998)

Heavy rainfall by MCC(31 July, 1998) Heavy rainfall by typhoon Yanni

(30 September, 1998)

Heavy rainfall by typhoon Yanni(30 September, 1998)


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124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.033.0

34.0

35.0

36.0

37.0

38.0 TRMM NSR(14:45:07 - 14:45:50 LST)

longitude

lati

tude

longitude124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.0

longitude

33.0

34.0

35.0

36.0

37.0

38.0

latit

ude

3.0 to 6.0

6.0 to 10.0

10.0 to 15.0

15.0 to 50.0

50.0 to 91.0

AWS 30min(14:40 - 14:50 LST))

RR_max

Corr.= 0.71

105.4 mm/hr 91.0 mm/hr

RR_max

124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.0

longitude

33.0

34.0

35.0

36.0

37.0

38.0

latit

ude

3.0 to 6.0

6.0 to 10.0

10.0 to 15.0

15.0 to 50.0

50.0 to 258.1

AWS 30min(22:10 - 22:20 LST)

longitude124.0 125.0 126.0 127.0 128.0 129.0 130.0 131.0

33.0

34.0

35.0

36.0

37.0

38.0

3.0 to 6.0

6.0 to 10.0

10.0 to 15.0

15.0 to 50.0

50.0 to 302.6

TRMM NSR(22:13:57 - 22: 14:14 LST)

longitude

lati

tude

302.6 mm/hr 258.1 mm/hr

Corr.= 0.87 Regardless of rain type, sp

ace based TRMM/PR and ground based AWS shows a good agreement in spatial distribution

Correlation between PR and AWS rain rate is usually better for strong convective system compared to the rain associated with other system such as typhoon or frontal system.


METRIMETRI



Mean Bias(AWS time window = + 10min )

Grid Size (deg)

0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Ave

ragi

ng

Per

iod

1hr

2hr

3hr

6hr

12hr

24hr

3day

5day

10day

15day

30day

-0.10

-0.15

-0.15-0.15

-0.20

-0.10

-0.10

-0.10

-0.20

-0.20

-0.25

-0.25

-0.30

-0.30

-0.35-0.40

-0.45

-0.50

Correlation Coefficient(AWS time window = + 10min )

Grid Size (deg)

0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Av

era

gin

g P

erio

d

1hr

2hr

3hr

6hr

12hr

24hr

3day

5day

10day

15day

30day

RMS Error(AWS time window = + 10min )

Grid Size (deg)

0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Av

era

gin

g

Per

iod

1hr

2hr

3hr

6hr

12hr

24hr

3day

5day

10day

15day

30day

0.5

0.51.0

1.0

1.5

2.0

2.5

3.53.0

4.0

5.55.0

4.5

6.56.0

Grid Size [Deg]

Averaging

Tim

e

0.7

• Comparison between rain rate derived from IR and measured by AWS.

• Mean bias and rms difference decrease with increasing grid size and averaging period

• Correlation coefficient of 0.7 can be achieved either by increasing spatial and/or temporal sampling.

• One min. data could be used for many val. Applications.Sohn et al. (2002)

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Heavy Rainfall at Mt. Jiri on 31 June 1998. Heavy Rainfall at Mt. Jiri on 31 June 1998. (Fail to forecast)(Fail to forecast)

AWS rainfall distributionModel Outputs Initial field (without Satellite)

AWS rainfall distributionModel Outputs Initial field (with Satellite)

Heavy Rainfall at Kyung-Gi Pro. on 31 June 1999. Heavy Rainfall at Kyung-Gi Pro. on 31 June 1999. (Success to forecast)(Success to forecast)

Assimilation

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Assimilation

red: best track (NOAA HRD) green: forecast from analysis without precip data blue: forecast from analysis with precip data

5-Day Storm Track Forecast from 08/20/98 @ 12:00 UTC

Surface Precipitation at Forecast Day 3

forecast-control forecast-precip

QPF Threat Scores at Forecast Day 3

verified against TRMM observations blue: forecast-control; red: forecast-precip

contours show verification rainrates derived from TMIHou et al., 2002: NASA/GSFC

GSFC-DAO

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Assimilation

Horizontal & Vertical Winds in Tropical Cyclone Bonnie

J.-F. MahfoufECMWF

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The portion of convective rain due to the cumulus parameterization scheme averaged for 1979-2001. Areas of precipitation intensity less than 100 mm/month are omitted.

Hong(2003)

Without a parameterized convection

With a parameterized convection

Weak convection

The Korean region is characterized by a smaller portion of convective rainfall.This is a reason why the parameterized convection plays a minor role in the simulation of heavy rainfall over Korea.

Precipitation structure

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Monitoring

TRMM 85h IR

TC 11S (ELINEA/LEON)

R.T. Edson(2002)

Comparison between IR and MW Imagary for the initial stage of the tropical cyclone

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Monitoring

Ahn et al.(2002)

Comparison between IR and MW Imagary for the decaying stage of the tropical cyclone

GMS-5/IRTMI/85GHz

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Composition of N-16/AMSU, F-13,14,and 15/SSMI, and TRMM/TMI data

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Future Plans/issues

Enhancement of current observation network. - Based on the KMA’s long-term plan number of AWS and instrumentation will be

expanded to have 13 km spatial resolution

- Two more doppler radar will be added by 2003 and one more by end of 2005 making total of 10 radar sites.

- Research level intensive observation site like the Haenam Observation Site will be added around the middle of Peninsula

Improvement of quality control procedure.- The accuracy of radar rainfall data will be improved by using of the collocated gr

ound observation and multi-radar composition.

- More comprehensive quality control procedure for the AWS data will be developed.

METRIMETRI


Development of cal./val. Procedure- From simple scatter diagram to complete physical validation procedure, there is

large areas of room to be improved.

- A regular ground based drop size distribution and vertical profiles of rain cloud will be implemented for this purpose.

Acquisition of DATA.- All ground based observation data will be stored at “National Data Center” and

can be provided to user community in near-real time.

- The means of GPM data exchange among the producer and to the user community seems not clear.

Possible responsibility of data processing and distribution.- There is possibility of Korea’s contribution to the constellation satellite.

Future Plans/issues

METRIMETRI


Concluding remarks

Automatic Weather Station Network(15km*15km, every minute) and weather radar(9 stations) in Korea could be used in calibration and validation of GPM data.

Improvement of the forecast skill of regional/global NWP model through data assimilation of GPM is expected.

Provide a significant contribution to the monitoring and understanding of flash flood producing severe storm such as tropical cyclone.

Plenty of data to be used further understanding of climate and weather related process including the rain cloud structure.

Successful utilization of the GPM data will be highly dependent on the reliable, fast, and efficient data communication among producers and users.

myoung-hwan ahn, jae-cheol nam meteorological research institute

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