# Presentation: 073093

## GV for the Evaluation of High Resolution Precipitation Products using WPMM in Korea

**J.C. Nam*****1*****, K.Y. Nam*****1*****, G.H. Ryu*****2*****, and B.J. Sohn*****2***
***1 ******Korea Meteorological Administration******(KMA)***
***2 ******Seoul National University****** ******(SNU)***

**The 2****nd**** GPM-GV Workshop, 27-30, September 2005, Taiwan**

## Ground Observation Networks of KMA
    - Automatic Weather Station(AWS) Network 
    - Radar Network
    - Haenam Supersite
Window Probability Matching Method (WPMM) 
Evaluation Precipitation Products  using WPMM
Comparison precipitation products in space and time
Concluding Remarks

**    ****- Automatic Weather Station(AWS) Network **
**    ****- Radar Network**
**    ****- Haenam Supersite**
**Window Probability Matching Method (WPMM) **
**Evaluation Precipitation Products  using WPMM**
**Comparison precipitation products in space and time**
**Concluding Remarks**

**Contents**

## Schematic Diagram of Korea GPM(K-GPM)

**GPM Product**

**II. KEOP Supersites**
**     ****(Hae**** ****Nam)**
**   ********  ****Micro rain radar**
**   ********  ****Autosonde**
**   ********  ****Wind profiler **
**   ********  ****Optical rainguage**
**   ********  ****Meteorological tower(30m)**

**I. ****Basic Rainfall Validation**
**       ******** **** ****Raingauges(536 AWS)**
**       ******** **** ****Radar(10)**
**       ******** **** ****Radiometer(2)**

**Rainfall**
**Retrieval**

**GPM**
**Satellite**
**Data**

**Potential Applications**

| Weather prediction |
| --- |
| Water management |
| Severe weather monitoring |
| Flood prediction |
| Cloud microphysics
Cloud-radiation modeling |
| Climate research |
| Understanding weather   
  phenomena |
| Agriculture   |

**III. Data Assimilation**

**GV**

**Basic Science **
**Modeling**
**Physics, etc.**

**Calibration**

**Calibration**

**Improve Retrieval **
**Algorithms**

**Schematic Diagram of Korea GPM(K-GPM)**

## Automatic Weather Station

**Observation Network**

**Conventional Station **

**Automatic Weather Station**

|           No.  
     Obs. | No. of station | No. of daily observation |
| --- | --- | --- |
| Surface | 74 | 8-24 |
| Upper-air | 3 | 2-4 |
| Aeronautical | 9 | 24-48 |

|      No.
Obs. | No. of station | No. of daily observation | Measured elements |
| --- | --- | --- | --- |
| AWS | 536 | Every min.
(Continuous) | Temp., Wind, Preci., etc |
| Ocean Buoy | 5 | 24 | Temp., Wind, Wave, SST, etc. |

**Ground Observation Network**

## Ground Observation Network

-   **- ****ASOS(Automated Surface Observing System)****	****:  42 sites**
**  ****- Manned AWS(Automatic Weather System)****	****:  35 sites**
**  ****- Unmanned AWS(Automatic Weather System)****	****: 459 sites**

**ASOS**

**AWS**

**Mountain**** ****AWS**

**Ground Observation Network**

## Ground Observation Network

** ****Spatial resolution**
**  ****ASOS + AWS network    : 13 km**
**  ****Unmanned AWS network : 14 km**
** ****Temporal resolution : 1 min.  **
**Data Collection**
**   ****- DSU Modem leased line(9,600 bps)**
**   ****- DSU Modem + Microwave comm.**
**   ****- ORBCOMM Satellite comm.**

**Ground Observation Network**

## Real Time Data Collection Network

**Real Time Data Collection Network**

- Telecommunication Network in KMA

## Radar Network of KMA

|  | No. of station | Time resolution | Observation eliments |
| --- | --- | --- | --- |
| Operational Weather Radar | 10(1) | Every 10 min. | Reflectivity, Radial Velocity, Spectral Width |

**Operational radar**** **
** ****5 C-band radars**
**Baekryungdo**
**Kunsan**
**Donghae**
**Cheju**
**Chungsong**
** ****4 S-band radars**
**Gwangduksan**
**Jindo**
**Gwannaksan**
**Pusan**
** ****1 C-band(Airport)**
**Incheon**
**Research radar **
**  ****1 X-band radar**
**Muan**

## Ground Observation Supersite

- C-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

**Ground Observation Supersite**

## Ground Observation Supersite

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

**Intensive**
**Observation**

**Micro Rain Radar**
- Producing vertical profiles
- of rain rate, LWC and
- drop size distribution

**Flux Tower**
- Producing sensible,
- latent, and radiative
- fluses over land surface

**Optical Rain Gauge**
- Continuous accurate
-  rain rate observation.

**Autosonde**
- Continuous upper air
- observation

**Boundary Layer Radar**
- Producing one-minute profile
- of vertical and horizontal winds

- Produce high resolution temporal
- and spatial data for the monitoring,
- analysis and prediction of severe
- weather phenomena(typhoon, fronts...)

**Ground Observation Supersite**

## Ground Observation Network

**Ground Observation Network**

| Facility | Agency | Equipment | Status | Additional Info |
| --- | --- | --- | --- | --- |
| National | KMA | 536 Rain Gauges, 13 x 13 km 1 minute readout | Operational | .1 and .5 mm tipping bucket |
|  |  | 10 Radars (C-, S-band) | Operational |  |
|  |  | 10 Wind Profilers | Planned
2004 (2),
2005-’08 (8) |  |
| Haenam
KEOP
Supersite | METRI
(KMA) | X-band Doppler Radar | Operational | Operating as CEOP Supersite |
|  |  | Autosonde | Operational |  |
|  |  | Boundary Layer Wind Profiler | Operational |  |
|  |  | Micro Rain Radar | Operational |  |
|  |  | Flux Tower (10 m) | Operational |  |
|  |  | Optical Rain Gauge | Operational |  |
|  |  | Radiometer | Operational |  |

## Window Probability Matching Method (WPMM)

**       ****- Special resolution**** ****: 1 km (Reflectivity, using Radar Software Library)**
**       ****- Temporal resolution: 1 min.**** ****(Rain rate, using TRMM/GSP algorithm)**

** ****Z-R Relationship**** **
**      ****- Set the minimum radar reflectivity corresponding with rain gauge (10 dBZ)**
**      ****- Estimation of Z-R relationship from Z-R pairs in real-time**

**Window Probability Matching Method (WPMM)**

## Data Procedure for WPMM

**NCAR/SPRINT**
**NCAR/CEDRIC**
**2-D Reflectivity data**

**Raingauge Data**
**Raw data check**
**TRMM/GSP**
**Rain rate****(****mm/h) Data**

**Calculated the ****Z-R relationship**** using WPMM each radar**

**Convert ****Rain Intensities**** using the real-time Z-R relationship**

**Composite of all of ****Radar Intensity**** **
**(Overlapping Maximum value select****)**

**Data Procedure for WPMM**

## Radar Scan strategy and characteristics

- Bright Band

- Ground and sea clutter

**Jindo, Gwangduksan, Kwanaksan, Pusan (4 S-band Radar)**
**Baekyeongdo, Donghae, Kunsan, Cheju, Myeonbongsan, Youngjongdo ****(6 ****C-band)**
**0.0 – 7.0****° (C-band: 8 elevations ****, ****interval 10 minutes****)**
**0.0 – 19.0 ° ( S-band: 10 elevations, interval 10 minutes)**
**Melting layer level is about ****3.5 – 5.5 ****km from June to August**

## Beam blocking area
Range attenuation error
Bright-band contamination
C-band(5), S-band(4)

- Range attenuation error
- Bright-band contamination
- C-band(5), S-band(4)

**NCAR/SPRIINT**** **
**-Resolution**** : ****1x 1 x 0.5 km(Cartesian****)**
**-****Height 1.5 – 4.0 km (interval:**** 0.5 ****km)**

**NCAR/CEDRIC**
**Standard deviation check**
**Ground clutter check**
**Beam filling**

**2-****Dimensional Reflectivity Data**
**Effective reflectivity height eliminated **
** ****the ground and bright band**

**Rdar Data**

**Radar Data Processing**

## Raingauge Data Processing

**Half tip ****(****C****)****  : if the time interval between tips is within ****20-30 ****minutes,****  ****insert the half tip in the middle**

**Calculate the 1-minute rain rate(mm/h) using ****Cubic Spline Interpolation**** ****each rain event**
**Calculate the bias of  measured rainfall and interpolated rainfall**
**Bias = rainfall(measured) / rainfall(interpolationed)**

**Rain-rate greater than ****1000 mm/hr**** **** **

**single tip or isolated tips (****A****) -> ****Gaussian interpolation**** is applied****( ****R=R****0****exp(-x****2****/100) )**

**Calculated rain-rate = bias * {rain(1****st**** sec. of min.) – rain(2****nd**** sec. of min.} *60**

- TRMM/GSP

** ****TRMM/GSP algorithm**

**Raingauge Data Processing**

## Raingauge Data Processing

-  Cubic Spline Interpolation
-      - tip interval within 30 minutes,  effective data > 3 point
-      - the slope of accumulated precipitation convert to rain-rate

-  Gaussian Interpolation(Not TRMM/GSP algorithm)
-      - tip interval greater than 30 minutes => Single tip
-      - single tip consider as small convective precipitation

**Raingauge Data Processing**

## Single tip does not accord to the rain-rate of ORG (Optical Raingauge)
Rain event accords to the rain-rate of ORG 
One tip of AWS raingauge is 0.5 mm (rain-rate=30 mm/h)

**Rain event accords to the rain-rate of ORG **
**One tip of AWS raingauge is 0.5 mm (rain-rate=30 mm/h)**

**TRMM/GSP & ORG**

## Rain Rate Product

-  **Computation of Z-R pair every 10 minutes during 1 hour**  :
-     (1) Rain rates of raingauges (- 9 ~ 0 minutes)
-     (2) Reflectivites of radar grids(3×3) around raingauge (horizontal res.: 1 km)
-     (3) Threshold : 10 ~ 60 dBZ,  0.5 mm/h
-     (4) Rain-rate calculates using  M – P relationship (if Reiteration Num. = 1)

- Computation of rain rates applied **Z-R relationship** every 10 min.
-  (No precipitation under threshold dBZ)

- Z-R Fitting :
-    - Median Fitting
-     ( Under and Over the 30 dBZ )

- Z-R pairs > Threshold Num.

- Z-R Fitting :
-    - Z-R relationship
-         of former time

- Yes

- No

-   **Data reading** :
-      (1) 1-minute rain rate of each raingauge using TRMM-GSP
-      (2) 10-minute reflectivity of each radar using RSL

- Produce **Rain Rate** from each radar-raingauges rain rate

- WPMM (Window Probability Matching Method)

- Reiteration Num. > 1

- Yes

- No

**Rain Rate Product**

## Y=AX +B Linear equation
Intersection of  Y-axis : 10 loga
Slope : 10 b
Fitting Method : Median Fit

**Y**

**X**

- Y=AX +B Linear equation
- Intersection of  Y-axis : 10 loga
- Slope : 10 b
- Fitting Method : Median Fit

- Fitting example

- [Gwangduksan] 1720 LST July 7, 2004.

**Z-R fitting**

## Sensitivity test for Z-R relationship

**7 July 2004**

** ****18 August 2004**

- S-band Radar
- Operation stop
- (reinstalling)

**Sensitivity**** test for Z-R relationship**

- S-Band

- C-Band

## Precipitation Product Comparison 

**20 ****Jun.**

**19 ****Jun.**

**20 ****Jun.**

**Nowon(407)**
**Corr. = 0.94,  RMSE=3.04 mm/h**

**Dongdaemun (408)**
**Corr. = 0.95,  RMSE=3.05 mm/h**

**Jungrang(409)**
**Corr. = 0.90,  RMSE=3.12 mm/h**

**Dongjak(410)**
**Corr. = 0.97,  RMSE=4.4 mm/h**

**Precipitation Product Comparison **

- 19-20 June 2004

- TRMM/GSP
- WPMM

## Comparison Total Rainfall

**19 Jun.**

**20 Jun.**

**Comparison Total Rainfall**

## Comparison BAIS

**Comparison BAIS**

## Precipitation Product Comparison 

**18 August 2004**

**[ Time: 1200 – 1400 KST ]**

**Precipitation Product Comparison **

- TRMM/GSP
- WPMM

## Verification in Space

**Radar – AWS  difference**

**Radar rain intensity object analysis**** **
**(****AWS grid point****)**

**AWS rain intensity object analysis **
**(****AWS grid poing****)**

**Verification in Space**

## Verification in Time

**Verification Area**
**Manned AWS site**
**Seoul-Gyungki**
**Gwangwon**
**Chungcheong**
**Jeonla**
**Gyungsang**
**Cheju**

**Verification in Time**

## Real time Web Service

**Products**
**Rain intensity Composite**
**Each radar rain intensity**
**Each radar reflectivity**
**Verification in space**
**Verification in time series**

*http://*wpmm.metri.re.kr

**Real time Web Service**

##  KMA’s operational Automatic Weather Station Network (13km*13km, one minute) and Weather radar(10 stations) can be used for GPM calibration and validation. 
High resolution(1km x 1km) precipitation intensity were estimated from radar reflectivity with the various Z-R relationship obtained by WPMM using raingauges data of AWS operated by Korea Meteorological Administration (KMA).
 Rain intensity produced by WPMM has a good agreement with ground rainfall data measured by raingauge and Optical Rain Gauge (ORG). 
Rain intensities of S-band and C-band radars obtained by WPMM were more accurate than Z-R relationship (Z=200R1.6) and S-band radars were more accurate than C-band radars.

**High resolution(1km x 1km) precipitation intensity were estimated from radar reflectivity with the various Z-R relationship obtained by WPMM using raingauges data of ****AWS operated by Korea Meteorological Administration (KMA).**
** ****Rain intensity produced by WPMM has a good agreement with ground rainfall data measured by raingauge and Optical Rain Gauge (ORG). **
**Rain intensities of S-band and C-band radars obtained by WPMM were more accurate than Z-R relationship (Z=200R****1.6****) and S-band radars were more accurate than C-band radars.**

**Concluding Remarks**

## Thank you for your attention !