GNSS satellite or Quasar 10m [5] (n 1) 10 6 = K 1 ( P d T ) + K 2( P v T ) + K 3( P v T 2 ) (2) O 1 S G Earth Atmosphere [4] (ray bending) 1 S

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あきひろみのしま
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1 - - ( ) A Software Package Development for Estimating Atmospheric Path Delay based on JMA Numerical Weather Prediction Model Ryuichi ICHIKAWA (KASHIMA SPACE RESEARCH CENTER, NICT) Key words: GNSS, VLBI, atmospheric path delay, ray tracing, numerical weather prediction model, mapping function Abstract Space geodetic positioning systems based on microwave signals, such as the Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI), need to carefully model or cancel the atmospheric path delays in order that these propagation effects do not undermine positioning accuracy. Recently, the development of atmospheric model based on data from numerical weather prediction models have progressed to remove the delays. To apply such atmospheric model for the real correction around Asia monsoon region we have to take account for the highly variable mesoscale disturbances. We have started the development of the software package for estimating the path delays by ray tracing through the output fields of a numerical weather prediction model by Japan Meteorological Agency (JMA). In this paper we describe the preliminary results using the package. 1. GPS GLONASS Galileo (GNSS: Global Satellite Navigation System) 1000km VLBI (Atmospheric Path Delay) GNSS VLBI [1] 1 GNSS VLBI [2], [3] EU 1 numerical weather prediction

2 GNSS satellite or Quasar 10m [5] (n 1) 10 6 = K 1 ( P d T ) + K 2( P v T ) + K 3( P v T 2 ) (2) O 1 S G Earth Atmosphere [4] (ray bending) 1 S L L = [n(s) 1]ds + [S G] (1) L G GPS n(s) L s S L ds L c atm c 0 1cm P d P v T (hpa) (hpa) (K) K 1 K 2 K 3 [6] [5] (2) (2) (n 1) 10 6 = K 1 ( P T ) + K 2( P v T ) 2 (3) K 2 = (K 2 mk 1 )T + K 3 (4) (4) P (hpa) (4) m (4) T (4) mean temperature T m T m = [ ( P v T )dz]/[ ( P v )dz] (5) T 2 T [7] (5) T m [8] Davis [7] [5], [6] (2) K 1 K 2 K 3 N = (n 1) 10 6 = 77.6 ( P T ) ( P v T 2 ) (6) (4) (hydrostatic term) (hydrostatic delay) (4) (wet term) (wet delay) (zenith delay) (zenith hydrostatic

wet delay 5~40cm hydrostatic delay 210~230cm zenith direction 2 Earth mapping function GNSS satellite or Quasar Atmosphere delay/zhd) (zenith wet delay/zwd) (zenith total delay/ztd) 2.

3 wet delay 5~40cm hydrostatic delay 210~230cm zenith direction 2 Earth mapping function GNSS satellite or Quasar Atmosphere delay/zhd) (zenith wet delay/zwd) (zenith total delay/ztd) 2. 2 GNSS VLBI (mapping function) θ L m(θ) = 1 (8) a sin θ + b sin θ(or tan θ) + c sin θ + sin θ +... θ a b c GNSS VLBI GNSS VLBI 1300 GPS (GEONET: GPS Earth Observation Network System) [10] [11] L = L z hm h (θ) + L z wm w (θ), (7) L z h L z w ZHD ZWD M h (θ) M w (θ) 2 sin(θ) [9] 3 []

4 4 [] 3 ( ) (analysis) (analysis data) ( 4 ) (NWM:numerical weather model) 3. 2 GNSS VLBI [2], [3], [12] km ( 10km ) [4] GNSS VLBI [13], [14] 5km 2 2km 00km

5 10 35N km E 140E 1mm gradient vector cm Zenith Wet Delay UTC 10km ( [11] ) 1 [13] 2 ( ) / (3 ) 10hPa ( 10km) (00UTC 21UTC) / 10hPa ( km) + (00UTC, 12UTC) / (6 ) 0.4hPa ( ) (00UTC 18UTC) [16] [17] ( )

2 [13] ( ) 721 577 ( 5km) 50 8 / 325 257 ( km) 40 2 2 / 640 3 ( 60km) 40 9 4 / 271 271 ( 24km) 25 3.

6 2 [13] ( ) ( 5km) 50 8 / ( km) / ( 60km) / ( 24km) / ( 110km) / ( 110km) / UT 10/18/04 40 Kashima km Zenith Wet Delay(cm) UTC ZWD ZW D 30cm [13] ZWD 3 (6) ZWD 10km ( ) ( )

7 [i = 251, j = 149] Slant Delay Residual (mm) UTC GPS GPS ( L pd ) ( L sym) ( L P D L sym) ( ) ZWD VLBI GPS GLONASS Galileo GNSS 5km 3 ( ) ( ) 10km [17] [1] Macmillan, D. S. and C. Ma, Evaluation of very long baseline interferometry atmospheric modeling improvements, J. Geophys. Res., 99, , [2] Boehm, J. and H. Schuh, Vienna Mapping Functions in VLBI analyses, Geophys. Res. Lett., 31, L01603, doi: /03gl018984, 04. [3] Boehm, J., B. Werl and H. Schuh, Troposphere mapping functions for GPS and very long baseline interferometry from European Centre for Medium-Range Weather Forecasts operational analysis data, J. Geophys. Res., 111, B02406, doi: /05jb003629, 06. [4] Ichikawa, R., M. Kasahara, N. Mannoji, and I. Naito, Estimations of atmospheric excess path delay based on three-dimensional, numerical prediction model Data, J. Geod. Soc. Japan, 41, , [5] Thayer, G. D., An improved equation for the radio re-

8 fractive index of air, Radio Sci., 9, , [6] Smith, E. K. and S. Weintraub, The constants in the equation for atmospheric refractive index at radio frequencies, Proc. IEEE, 41, , [7] Davis, J. L., T. A. Herring, I. I. Shapiro, A. E. E. Rogers, and G. Elgered, Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length, Radio Sci.,, , 1985, [8] Bevis, M., S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, GPS Meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System, J. Geophys. Res., 97, , [9] Niell, A. E., Global mapping functions for the atmosphere delay at radio wavelengths. J. Geophys. Res., 101, , 1996 [10] MacMillan, D.S. Atmospheric gradients from very long baseline interferometry observations, Geophys. Res. Lett., 22, , [11] Chen, G. and T. A. Herring, Effects of atmospheric azimuthal asymmetry on the analysis of space geodetic data, J. Geophys. Res., 102, , [12] Niell, A. E., A. J. Coster, F. S. Solheim, V. B. Mendes, P. C. Toor, R. B. Langley, and C. A. Upham, Comparison of measurements of atmospheric wet delay by radionsonde, water vapor radiometer, GPS, and VLBI, J. Atmos. Oceanic Technol., 18, , 01. [13] 17, ( ), 38, [14] 18, ( ), 39, [] WEB, [16] GPV/JMA Archive( ), gpvjma/index.html [17],

* Meso- -scale Features of the Tokai Heavy Rainfall in September 2000 Shin-ichi SUZUKI Disaster Prevention Research Group, National R

38 2002 7 2000 9 * Meso- -scale Features of the Tokai Heavy Rainfall in September 2000 Shin-ichi SUZUKI Disaster Prevention Research Group, National Research Institute for Earth Science and Disaster Prevention,