1. Moisture observation by a dense GPS receiver network and its assimilation to JMA Meso ‑ Scale Model Koichi Yoshimoto 1, Yoshihiro Ishikawa 1, Yoshinori Shoji 2, Takuya Kawabata 2 and Ko Koizumi 1 1 Numerical Prediction Division 2 Meteorological Research Institute Japan Meteorological Agency

2. 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

3. GPS observation network in Japan GEONET (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 )

4. GPS PW accuracy of real time processing Bias- 0.10mm RMS2.69mm Cor0.96 Real Time (IGS Ultra Rapid) The near-real time processing provides enough accuracy of GPS PW Jul – Aug 2007 PW(GPS) PW(Sonde)

5. Moisture monitoring with GPS PW ① ② Time series Blue line: hourly GPS PW Green line: GPS PW of 15-day running mean Red line: typical PW estimated from temperature Orange dashed line: Temperature Monitoring web page is updated hourly

6. 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

7. 地上風 地上風 After several hours GPS PW FLUX = div （ u×PW ， v×PW ） GPS PW FLUX = div （ u×PW ， v×PW ） = ｛ ∂(u×PW) /∂x + ∂(v×PW) /∂y ｝ = ｛ ∂(u×PW) /∂x + ∂(v×PW) /∂y ｝ （ unit ： m/s×kg/m2×1/m ＝ (kg/m2) / s ） （ unit ： m/s×kg/m2×1/m ＝ (kg/m2) / s ） surface wind : the AWS network AMeDAS surface wind : the AWS network AMeDAS GPS PW FLUX Surface 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.

8. 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

9. 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.)

10. Non-hydrostatic meso-scale 4D-Var (JNoVA) MethodNon-Hydrostatic 4DVAR Outer esolution 5km ・ L50 Inner Resolution 15km ・ L40 Assimilation window 3hour IterationAbout 30 Region size3600km × 2880km

11. Impacts on MesoScale Model Heavy rain during 20 July 21UTC to 21 July 00UTC 2009 GPS data not used ObsevationMSM forecast (init: 21UTC July 20) GPS data assimilated

12. Forecast time (hour) Red line: with GPS Green line: w/o GPS Equitable Threat Score for precipitation forecast (17-25 July 2006) (1mm/3hr) (10mm/3hr)

13. Currently used – Zenith Total Delay Signal delay is measured for each satellite Mapping Zenith Total Delay Homogeneity assumption

14. Next challenge – slant delay G : distance between receiver and satellite S : signal path length Assuming S=G K 1,K 2,K 3 ： const. P d :pressure of dry air P v :vapor pressure T: temperature (a) Observation operator of refractivity (b) Integration along signal path DryWet Homogeneity assumption becomes unnecessary Inhomogeneous water-vapor distribution can be retrieved

15. Observed delays on grid-points

16. Preliminary impact test (2km model) SLT Radar observation PWV 1136 JST1201 JST1231 JST 50km

17. 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