Moisture observation by a dense GPS receiver network and
its assimilation to JMA Meso‑Scale Model
Koichi Yoshimoto1, Yoshihiro Ishikawa1, Yoshinori Shoji2, Takuya Kawabata2 and Ko Koizumi1
1 Numerical Prediction Division2 Meteorological Research Institute
Japan Meteorological Agency
GPS signal is delayed by moisture en route
Water vapor amount can be retrieved from the “movement” of a fixed GPS receiver
Moisture observation by fixed GPS receivers
GPS receiver
GPS satellites
Inhomogeneous moisture distribution
GPS observation network in JapanGEONET (GPS Earth Observation NETwork) is a GPS network operated by the Geographical Survey Institute, the Ministry of Land, infrastructure and transportation, Japan. Approximately 1,200 GPS receivers are located throughout Japan with a separation of 20 km in order to monitor crustal deformation of the earth.
GEONET JMA-AWS(AMeDAS )
GPS PW accuracy of real time processing
GPS (2007 7 1 8 31可降水量とゾンデ可降水量の比較 年 月 日~ 月 日)
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ゾンデ可降水量
GPS
可降
水量
Bias -0.10mmRMS 2.69mmCor 0.96
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Real Time (IGS Ultra Rapid)
The near-real time processing provides enough accuracy of GPS PW
Jul – Aug 2007
PW
(GP
S)
PW(Sonde)
Moisture monitoring with GPS PW
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②
Time seriesBlue line: hourly GPS PWGreen line: GPS PW of 15-day running meanRed line: typical PW estimated from temperatureOrange dashed line: Temperature
Monitoring web page is updated hourly
Analyzed precipitation
amount
GPS PW and surface wind
Flux divergence
Departure from 15-day running average
PW deviation from typical value
6-hour trend
SSI
Column Humidity = GPS PW / saturate PW
Moisture monitoring with GPS PW
地上風地上風地上風地上風
After several hoursAfter several hours
GPS PW FLUX GPS PW FLUX = div= div (( u×PW u×PW ,, v×PWv×PW ))
= = {∂{∂ (u×PW) /∂x + ∂(v×PW) /∂y (u×PW) /∂x + ∂(v×PW) /∂y } }
( ( unitunit :: m/s×kg/m2×1/mm/s×kg/m2×1/m == (kg/m2) / (kg/m2) / ss ))
surface wind : surface wind : the AWS network AMeDASthe AWS network AMeDAS
GPS PW FLUX
Surface windSurface wind
Water vapor convergence can be a precursor of local (heavy-) rainfall phenomena. GPS PW FLUX calculated from surface wind vector and GPS PW is an indicator of the water vapor convergence.
14:00 JST
Aug 8 , 2008
Some parameters can be used as precursor of heavy rain
14:30 JST15:00 JST15:30 JST16:00 JST
Meso-scale model
• Resolution– Horizontal : 5km– Vertical : 50 layers up to 21800 m
• Forecast frequency : eight times a day– 15-hour forecast from 00, 06, 12 and 18 UTC initials– 33-hour forecast from 03, 09, 15 and 21 UTC initial
• Purpose– Severe weather warning– Input to Very Short Range Forecast for precipitation amount– Aviation use (TAF etc.)
Non-hydrostatic meso-scale 4D-Var (JNoVA)Non-hydrostatic meso-scale 4D-Var (JNoVA)
Method Non-Hydrostatic 4DVAR
Outer esolution 5km・ L50
Inner Resolution 15km ・ L40
Assimilation window
3hour
Iteration About 30
Region size 3600km× 2880km
Impacts on MesoScale ModelHeavy rain during 20 July 21UTC to 21 July 00UTC 2009
GPS data not used
Obsevation MSM forecast (init: 21UTC July 20)
GPS data assimilated
Forecast time (hour) Forecast time (hour)
Red line: with GPSGreen line: w/o GPS
Equitable Threat Score for precipitation forecast(17-25 July 2006)
(1mm/3hr) (10mm/3hr)
Currently used – Zenith Total Delay
Signal delay is measured for each satellite
Mapping
Zenith Total DelayHomogeneity assumption
∆L=න [𝑛(𝑠) −1]𝑑𝑠+ [S−G]𝐿
Next challenge – slant delay
ሺ𝑛−1ሻ× 106 = 𝐾1൬𝑃𝑑𝑇൰+ 𝐾2൬𝑃𝑣𝑇൰+ 𝐾3൬𝑃𝑣𝑇2൰
G : distance between receiver and satelliteS : signal path lengthAssuming S=G
K1,K2,K3 : const. Pd:pressure of dry air Pv:vapor pressure T: temperature
(a) Observation operator of refractivity
(b) Integration along signal path
Dry Wet
Homogeneity assumption becomes unnecessaryInhomogeneous water-vapor distribution can be retrieved
Observed delays on grid-points
Preliminary impact test(2km model)
SLT Radar observation
PWV
1136 JST1201 JST1231 JST
50km
Summary
• GEONET GPS receiver network works also as a moisture observation system
• Moisture nowcasting can provide precursor information for heavy rain
• GPS PW data have a positive impacts on MSM precipitation forecast
• Preliminary test of slant delay data shows some potential for further improvement with high-resolution NWP