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A Study on the Effect of Ground-based GPS Data Assimilation into Very-short-range Prediction Model
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  • Journal title : Atmosphere
  • Volume 25, Issue 4,  2015, pp.623-637
  • Publisher : Korean Meteorological Society
  • DOI : 10.14191/Atmos.2015.25.4.623
 Title & Authors
A Study on the Effect of Ground-based GPS Data Assimilation into Very-short-range Prediction Model
Kim, Eun-Hee; Ahn, Kwang-Deuk; Lee, Hee-Choon; Ha, Jong-Chul; Lim, Eunha;
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 Abstract
The accurate analysis of water vapor in initial of numerical weather prediction (NWP) model is required as one of the necessary conditions for the improvement of heavy rainfall prediction and reduction of spin-up time on a very-short-range forecast. To study this effect, the impact of a ground-based Global Positioning System (GPS)-Precipitable Water Vapor (PWV) on very-short-range forecast are examined. Data assimilation experiments of GPS-PWV data from 19 sites over the Korean Peninsula were conducted with Advanced Storm-scale Analysis and Prediction System (ASAPS) based on the Korea Meteorological Administration`s Korea Local Analysis and Prediction System (KLAPS) included "Hot Start" as very-short-range forecast system. The GPS total water vapor was used as constraint for integrated water vapor in a variational humidity analysis in KLAPS. Two simulations of heavy rainfall events show that the precipitation forecast have improved in terms of ETS score compared to the simulation without GPS-PWV data. In the first case, the ETS for 0.5 mm of rainfall accumulated during 3 hrs over the Seoul-Gyeonggi area shows an improvement of 0.059 for initial forecast time. In other cases, the ETS improved 0.082 for late forecast time. According to a qualitative analysis, the assimilation of GPS-PWV improved on the intensity of precipitation in the strong rain band, and reduced overestimated small amounts of precipitation on the out of rain band. In the case of heavy rainfall during the rainy season in Gyeonggi province, 8 mm accompanied by the typhoon in the case was shown to increase to 15 mm of precipitation in the southern metropolitan area. The GPS-PWV assimilation was extremely beneficial to improving the initial moisture analysis and heavy rainfall forecast within 3 hrs. The GPS-PWV data on variational data assimilation have provided more useful information to improve the predictability of precipitation for very short range forecasts.
 Keywords
Ground-based GPS PWV;very-short-range forecast;data assimilation;
 Language
Korean
 Cited by
1.
Sensitivity Study on High-Resolution WRF Precipitation Forecast for a Heavy Rainfall Event, Atmosphere, 2017, 8, 6, 96  crossref(new windwow)
 References
1.
Ahn, M.-H., H. Y. Won, D. Han, Y.-H. Kim, and J.-C. Ha, 2015: Characterization of downwelling radiance measured from the ground-based microwave radiometer using the theoretical reference data. Atmos. Meas. Tech. Discuss., 8, 4347-4377.

2.
Albers, S. C., J. A. McGinley, D. L. Birkenheuer, and J. R. Smart, 1996: The local analysis and prediction system: analysis of clouds, precipitation and temperature. Wea. Forecasting, 11, 273-287. crossref(new window)

3.
Benjamin, S. G., D. Devenyi, S. S. Weygandt, K. J. Brundage, J. M. Brown, G. A. Grell, D. Kim, B. E. Schwartz, T. G. Smirnova, and T. L. Smith, 2004: An hourly assimilation forecast cycles: The RUC. Mon. Wea. Rev., 132, 495-518. crossref(new window)

4.
Bennitt, G. V. and A. Jupp, 2012: Operational assimilation of GPS zenith total delay observations into the Met Office numerical weather prediction models. Mon. Wea. Rev., 140, 2706-2719. crossref(new window)

5.
Bevis, M., S. Businger, T. A. Herring, C. Rocken, R. A. Anthes, and R. H. Ware, 1992: GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System. J. Geophys. Res., 97, 15787-15801. crossref(new window)

6.
Birkenheuer, D., 2006: The initial formulation of a technique to employ gradient information in a simple variational minimization scheme. NOAA Technical Memorandum OAR-GSD-32. 26 pp.

7.
Clark, A. J., W. A. Gallus JR., and T.-C. Chen, 2007: Comparison of the diurnal precipitation cycle in convection-resolving and non-convection-resolving mesoscale models. Mon. Wea. Rev., 135, 3456-3473. crossref(new window)

8.
Davis, J. L., T. A. Herring, I. L. Shaprio, A. E. Rogers, and G. Elgered, 1985: Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio Sci., 20, 1593-1607. crossref(new window)

9.
Dudhia, J., 1989: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. J. Atmos. Sci., 46, 3077-3107. crossref(new window)

10.
Ha, J.-C., Y.-H. Lee, H.-C. Lee, J.-E. Nam, and J.-S. Lee, 2011: The operational manual of Korea Local Analysis and Prediction System. NIMR-TN-2011-006. 58 pp (in Korean).

11.
Ha, J.-C., J.-S. Lee, Y.-H. Lee, H.-C. Lee, and D.-E. Chang, 2010: The production of high resolution reanalysis data of Korean Peninsula based on KLAPS. Proc. Spring Meeting KMS, 227-228 (in Korean).

12.
Ha, J.-H., and K.-D. Park, 2008: Comparison of local mean temperature equations for GPS-based precipitable water vapor determination. J. Astron. Space Sci., 25, 425-434 (in Korean with English abstract). crossref(new window)

13.
Ha, J.-H., K.-D. Park., K.-H. Chang, and H.-Y. Yang, 2007: Precision validation of GPS precipitable water vapor via comparison with MWR measurements. Atmosphere, 17, 291-298 (in Korean with English abstract).

14.
Ha, J.-H., K.-D. Park., K.-H. Kim, and Y.-H. Kim, 2010: Comparison of atmospheric water vapor profiles obtained by GPS, MWR, and radiosonde. Asia-Pac. J. Atmos. Sci., 46, 233-241. crossref(new window)

15.
Hollingsworth, A., and P. Lonnberg, 1986: The statistical structure of short range forecast errors as determined from radiosonde data. Part I: The wind errors. Tellus, 38A, 111-136. crossref(new window)

16.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341. crossref(new window)

17.
Hopfield, H. S., 1969: Two-quartic tropospheric refractivity profile for correcting satellite data. J. Geophys. Res., 74, 4487-4499. crossref(new window)

18.
Jian, G.-J., S.-L. Shieh, and J. A. McGinley, 2003: Precipitation simulation associated with Typhoon Sinlaku (2002) in Taiwan area using the LAPS diabatic initialization for MM5. Terr. Atmos. Oceanic Sci., 14, 261-288. crossref(new window)

19.
Kain, J. S., M. Xue, M. C. Coniglio, S. J. Weiss, F. Kong, T. L. Jensen, B. G. Brown, J. Gao, K. Brewster, K. W. Thomas, Y. Wang, C. S. Schwartz, and J. J. Levit, 2010: Assessing Advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting-research environment. Wea. Forecasting., 25, 1510-1521. crossref(new window)

20.
Kang, J.-H., M.-S. Suh, and C.-H. Kwak, 2010: Land cover classification east Asian region using recent MODIS NDVI data (2006-2008). Atmosphere, 20, 415-426 (in Korean with English abstract).

21.
Kasahara, A., A. P. Mizzi, and L. J. Donner, 1992: Impact of cumulus initialization on the spinup of precipitation forecasts in the tropics. Mon. Wea. Rev., 120, 1360-1380. crossref(new window)

22.
Kim, K.-H., J.-C. Ha, Y.-H. Kim, and D.-E. Chang, 2011: The calculation of estimated precipitable water vapor and development its monitoring system using ground GPS. NIMR-TN-2011-001. 41 pp (in Korean).

23.
Kim, H.-H., Y.-H. Kim, and K.-Y. Chung, 2012: Monitoring precipitable water vapor in near real-time using ground GPS. NIMR-TN-2012-016. 35 pp (in Korean).

24.
Kim, Y.-S., C.-H. Cho, and O.-R. Park, 2004: A three dimensional cloud analysis for diabatic initialization of mesoscale model and its impact study. J. Korean Meteor. Soc., 40, 497-509 (in Korean with English abstract).

25.
KMA, 2010: Development of technologies for intensive observation data processing and application: The development of hazard weather monitoring system and strategic study for Korean atmospheric refraction model. 116 pp (in Korean).

26.
Kwon, H.-T. and G.-H. Lim, 2008: Impact of GPS-PW assimilation on the rainfall forecast over the Korean Peninsula. Proceedings of the Autumn Meeting of KMS, 398-399.

27.
Kwon, H.-T., B.-M. Kim, and G.-H. Lim, 2001: The four-dimensional variational data assimilation experiments with GPS precipitable water vapor using OSSE. Atmosphere, 11, 229-231 (in Korean).

28.
Kwon, H.-T., E.-H. Jung, and G.-H. Lim, 2010: A comparison of GPS-and NWP-derived PW data over the Korean Peninsula. Adv. Atmos. Sci., 27, 871-882, doi: 10.1007/s00376-009-9069-4. crossref(new window)

29.
Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587-1612. crossref(new window)

30.
Lim, Y.-K., S.-O. Han, S.-P. Jung, and J.-H. Seong, 2013: The characteristic analysis of precipitable water vapor according to GPS observation baseline determination. Jour. Korean Earth Science Society, 34, 626-632 (in Korean with English abstract). crossref(new window)

31.
Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Jacono, and S. A. Clough, 1997: Radiative transfer for inhomogenous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663-16682. crossref(new window)

32.
NIMR, 2012: Development of the advanced storm-scale analysis and prediction system (I). 81 pp (in Korean with English abstract).

33.
Park, C.-G., J.-H. Baek, and J.-H. Cho, 2009: Comparison of precipitable water vapor observations by GPS, Radiosonde and NWP simulation. J. Astron. Space Sci., 26, 555-566 (in Korean with English abstract). crossref(new window)

34.
Saastamoinen, J., 1972: Introduction to practical computation of astronomical refraction. Bulletin Geodesique, 106, 383-397. crossref(new window)

35.
Schultz, P., and S. Albers, 2001: The use of three-dimensional analyses of cloud attributes for diabatic initialization of mesoscale model. Preprints, 14th Conf. Numerical Weahter Prediction, Fort Lauderdale, FL, Amer. Meteor. Soc., J122-J124.

36.
Shoji, Y., M. Kunii, and K. Saito, 2009: Assimilation of nationwide and global GPS PWV data for a heavy rain event on 28 July 2008 in Hokuriku and Kinki, Japan. SOLA, 5, 045-048, doi:10.2151/sola.2009-012. crossref(new window)

37.
Shoji, Y., M., 2009: A study of near-time water vapor analysis using a nationwide dense GPS network of Japan. J. Meteor. Soc. Japan, 87, 1-18. crossref(new window)

38.
Sun, J., M. Xue, J. W. Wilson, I. Zawadzki, S. P. Ballard, J. O. Hooimeyer, P. Joe, D. M. Barker, P.-W. Li, B. Golding, M. Xu, and J. Pinto, 2014: Use of nwp for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409-426. crossref(new window)

39.
Sun, J., and S. B. Trier, 2012: Sensitivity of 0-12-h warmseason precipitation forecasts over the central United Sates to model initialization. Wea. Forecasting., 27, 832-855. crossref(new window)

40.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN-475+STR. 96 pp.

41.
Smith, T. L., S. G. Benjamin, S. I. Gutman, and S. Sahm, 2007: Short-range forecast impact from assimilation of GPS-IPW observations into the rapid update cycle. Mon. Wea. Rev., 135, 2914-2930. crossref(new window)

42.
Vey, S., R. Dietrich, A. Rulke, and M. Fritsche, 2010: Validation of precipitable water vapor within the NCEP/DOE reanalysis using global GPS observations from one decade. J. Climate, 23, 1675-1695. crossref(new window)

43.
Zhang, M., Y. Ni, and F. Zhang, 2007: Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a Meso-${\beta}$ scale heavy precipitation event during the 2002 mei-yu season. Adv. Atmos. Sci., 24, 509-526. crossref(new window)