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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稚内札幌根室秋田仙台館野八丈島輪島潮岬米子福岡鹿児島名瀬那覇南大東石垣島y=xy=x-5y=x+5

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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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