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Data Assimilation of Side-looking Radio Occultation by Observing System Simulation Experiment

Data Assimilation of Side-looking Radio Occultation by Observing System Simulation Experiment. Hiromu Seko (MRI) , Toshitaka Tsuda and Naoto Yoshida (Kyoto Univ.) and Y.-H. Kuo (UCAR). Concept of the side-looking observation. Tangent point. Low Earth Orbit Satellite. T 3.

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Data Assimilation of Side-looking Radio Occultation by Observing System Simulation Experiment

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  1. Data Assimilation of Side-looking Radio Occultation by Observing System Simulation Experiment Hiromu Seko (MRI) , Toshitaka Tsuda and Naoto Yoshida (Kyoto Univ.) and Y.-H. Kuo (UCAR)

  2. Concept of the side-looking observation Tangent point Low Earth Orbit Satellite T3 Forward/backward-looking T2 GPS Satellite T1 P3 P2 P2 P1 P3 • Low earth orbit satellite (LEO) observes the signal of GPS satellite from the moving direction of the LEO satellite, because the short shift of the tangent point is required for the precise estimation of the tangent point profiles.

  3. Concept of the side-looking observation Tangent point Low Earth Orbit Satellite T3 Forward/backward-looking T2 GPS Satellite T1 P3 P2 P2 P1 P3 • Low earth orbit satellite (LEO) observes the signal of GPS satellite from the moving direction of the LEO satellite, because the short shift of the tangent point is required for the precise estimation of the tangent point profiles. • If slant path data is used in the assimilation, it is not necessary to use the data which have the small angle from the moving direction of LEO satellite.

  4. Concept of the side-looking observation Low Earth Orbit Satellite Forward/backward-looking GPS Satellite Side-looking • In general, impact of RO data is weak because the slant path data extends several hundred kilometers. For this reason, the total amount of assimilated data should be increased by using ‘side-looking’ data.

  5. Concept of the side-looking observation Low Earth Orbit Satellite Forward/backward-looking GPS Satellite Side-looking • In general, impact of RO data is weak because the slant path data extends several hundred kilometers. For this reason, the total amount of assimilated data should be increased by using ‘side-looking’ data. • In this study, the assimilation experiment of side-looking data is performed by ‘Observing System Simulation Experiment’.

  6. Intense rainfall occurred in the city of Kobe 03-06UTC 28 July 2008 Kobe • We have focused on the intense rainfall that occurred in the city of Kobe. • Intense rainfall raised the water level of Toga river, and five people drowned in the riverside park.

  7. Flow chart of Assimilation of conventional data Initial: Operational Meso-analysis 18UTC 27 July MSM Boundary: GSM output 00 UTC28 July 21UTC 18UTC 27 July Analysis => initial condition Meso-4DVar system GSM Boundary: GSM output Conventional data Conventional data JMA-NHM(x=10km)

  8. Assimilation results using conventional data Observation NoRO (Conventional data) FT=0-3hour FT=0-3hour FT=3-6hour FT=3-6hour Conventional data is not enough to reproduce the intense rainfall.

  9. Flow chart of Observation System Simulation Experiment Initial: Operational Meso-analysis 18UTC 26 July MSM Boundary: GSM output 00 UTC28 July 21UTC 18UTC 27 July Analysis => initial condition Meso-4DVar system GSM Boundary: GSM output Conventional data Conventional data + Simulated occultation data (slant path data) JMA-NHM(x=10km) True data First guess

  10. Number of OC All received data(28 July 2008) Angle (degree) Number of OC occ-type=44(28 July 2008) Angle (degree) We checked the occultation data. -Signal of data of which the angles from the moving direction are less than 60 degrees are received. -However data was not received properly in half of data where the angles were larger than 50 degrees.

  11. Number of OC occ-type=44(28 July 2008) Angle (degree) 73% 14% Number of OC obtained from satellite positions(28 July 2008) 11% Angle (degree) Number of occultation where the angle from moving direction is larger than 60 degrees is about 11% when the same day’s data is used. Because half of the data where the angles of 50-60 degrees are not received properly, the data that is expected to be increased byside-looking observation becomes about 18%.

  12. Assimilation of ground GPS ( Shoji et al. 2009) YSSK CHAN bjfs osn2 SUWN DAEJ SHAO tnml twtf • Assimilation period: 30hour (06UTC 27- 00UTC 28) • Assimilated data: Conventional data , Analyzed rainfall, PWV obtained by satellites, Wind profiler etc. + GPS (GEONET(blue triangles) + Korean and Chinese GPS data(red dot)

  13. Assimilation of ground GPS ( Shoji et al. 2009) NoGPS (only Convection data) Observation 03UTC 28 06UTC 28 FT=0-3h FT=3-6h GPS+Convection data • Intense rainfall reproduced when GPS data were assimilated.  • Analyzed fields were used as true data FT=0-3h FT=3-6h

  14. Positions of Occultations during 12-15 UTC 13 September 2006 There are several occultations of which the angle from moving direction were larger than 60 degrees.

  15. Positions of Occultations during 12-15 UTC 13 September 2006 There are several occultations of which the angle from moving direction were larger than 60 degrees. These occultation data were shifted to Japan.

  16. 10km 10km True data - NoROwater vapor at lowest layer Enlarged map 10km 0km Sat_dir 65 10km 0km Sat_dir 65 65deg. Because the region where the occultations were shifted to are positive, the rainfall is expected to be higher when this data is assimilated.

  17. 10km 10km True data - NoROwater vapor at lowest layer Vertical profiles of D-value (Simulated - First guess) Height (km) 10km 1.3km 10km 0.9km D-value (N) Below the height of 3km, path-averaged refractivity of true data is larger than that of first guess. The enhancement of the rainfalls is expected, when this data is assimilated.

  18. Conventional data + Occultation data NoRO (Conventional data ) FT=0-3hour FT=0-3hour FT=3-6hour FT=3-6hour As it is expected, the rainfall became more intense when the forecast was performed from the analyzed fields, which were obtained by assimilation of conventional data and occultation data.

  19. Conventional data + Occultation data Observation FT=0-3hour FT=0-3hour FT=3-6hour FT=3-6hour Intensity and area of the heavy rainfall was similar to the observed one, although the position shifted westward at FT=0-3hour.

  20. FT=0-3hour +Occultation datalowest data: 0km Conventional data (NoRO) 1km 2km 3km FT=3-6hour +Occultation datalowest data: about 0 km Conventional data (NoRO) 1km 2km 3km In this case, the improvement of forecast is expected, if the lowest data reached the height of 2km.

  21. FT=0-3hour +Occultation datalowest height: 0km Conventional data (NoRO) 1km 2km 3km +Occultation datalowest height: 0 km 1km 2km 3km Increment of lowest water vapor The improvement of rainfall was confirmed by the increment of water vapor at the lowest layer of the model.

  22. Summary of preliminary results • When the side-looking data, of which angles from the moving direction of LEO are large, are included in assimilation data, analyzed fields are further improved. • Assimilation of side-looking occultation data are expected as the useful method because it improves the numerical forecast. • However, the problems including hardware are not considered.So, more experiments are needed under the more actual condition. • Thank you for your kind attention.

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