Post on 02-Jan-2016
description
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
Remote Sensing Research Lab.
Background
Objectives
Future Plans
Concluding remarks
Contents
METRIMETRI
Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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.
METRIMETRI
Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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
METRIMETRI
Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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
METRIMETRI
Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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)
Cal./Val.: Example-1
METRIMETRI
Remote Sensing Research Lab.
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.
Cal./Val.: Example-1
METRIMETRI
Remote Sensing Research Lab.
Cal./Val.: Example-2
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)
METRIMETRI
Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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
METRIMETRI
Remote Sensing Research Lab.
Assimilation
Horizontal & Vertical Winds in Tropical Cyclone Bonnie
J.-F. MahfoufECMWF
METRIMETRI
Remote Sensing Research Lab.
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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Remote Sensing Research Lab.
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
METRIMETRI
Remote Sensing Research Lab.
Monitoring
Ahn et al.(2002)
Comparison between IR and MW Imagary for the decaying stage of the tropical cyclone
GMS-5/IRTMI/85GHz
METRIMETRI
Remote Sensing Research Lab.
Composition of N-16/AMSU, F-13,14,and 15/SSMI, and TRMM/TMI data
METRIMETRI
Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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
Remote Sensing Research Lab.
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.