PREDICTION OF CUMULONIMBUS (CB) CLOUD BASED ON INTEGRATED FORECAST SYSTEM (IFS) OF EUROPEAN MEDIUM-RANGE WEATHER FORECAST (ECMWF) IN THE FLIGHT INFORMATION REGION (FIR) OF JAKARTA AND UJUNG PANDANG

Main Article Content

Achmad Fahruddin Rais
Fani Setiawan
Rezky Yunita
Erika Meinovelia
Soenardi Soenardi
Muhammad Fadli
Bambang Wijayanto

Abstract

This study was focused on cumulonimbus (Cb) cloud prediction based on Integrated Forecast System (IFS) European Medium-Range Weather Forecast (ECMWF) model in the Flight Information Region (FIRs) Jakarta and Ujung Pandang. The Cb cloud prediction was calculated using convective cloud cover (CC) of the precipitation product. The model predictability was examined through categorical verification. The Cb cloud observation was based on brightness temperature (BT) IR1 and brightness temperature difference (BTD) IR1-IR2. The results showed that CC 50%' predictor was the best predictor to estimate the Cb cloud. The study in the period other than 2019 is suggested for the next research because Indian Ocean Dipole (IOD) is extreme that may affect the Cb cloud growth in the study area.

Article Details

Section
Articles

References

Bhawan, Mausam. (2013). Lecture Notes on

Aviation Meteorology. Central Aviation Meteorological Division India Meteorological Department.

Christian, H.J., Blakeslee, R.J., Boccippio, D.J., Boeck, W.L., Buechler, D.E., Driscoll, K.T., Goodman, S.J., Hall, J.M., Koshak, W.J., Mach, D.M., Stewart, M.F. (2003). Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. Journal of Geophysical Research, 108(D1), 4005. doi:10.1029/2002JD002347.

Eastman, R., Warren, S.G. (2014). Diurnal Cycles of Cumulus, Cumulonimbus, Stratus, Stratocumulus, and Fog from Surface Observations over Land and Ocean. Journal of Climate, 27(6), 2386–2404. doi: 10.1175/JCLI-D-13-00352.1

Evans, J.E. (1995). Safely reducing delays due to adverse terminal weather, In: Modelling and simulation in air traffic management, Bianco, L., Dell’Olmo, P., Odoni, A.R. (eds), Springer: Berlin Heidelberg, 85-202.

Geer, A., Ahlgrimm, M., Bechtold, P., Bonavita, M., Bormann, N., English, S., Fielding, M., Forbes, R., Hogan, R., Holm, E., Janiskov'a, M., Lonitz, K., Lopez, P., Matricardi, M., Sandu, I., Weston. P. (2017). Assimilating observations sensitive to cloud and precipitation. Technical Memorandum No. 815. doi: 10.21957/sz7cr1dym

Hamada, A.; Nishi, N.; Kida, H.; Shiotani, M.; Iwasaki, S.; Kamei, A.; Ohno, Y.; Kuroiwa, H.; Kumagai, H.; Okmoto, H. (2004). Cloud type classification by GMS-5 infrared split window measurements with millimeter-wave radar and TRMM observations in the tropics. Proceedings of the 2nd TRMM International Science Conference, Nara, Japan, 6–10 September 2004.

Inoue, T. (1987). A Cloud Type ClassificationWith NOAA 7 Split-Window Measurements. Journal of Geophysical Research, 92(D4), 3991-4000. doi: 10.1029/JD092iD04p03991

Inoue, T. (1989). Features of Clouds over the Tropical Pacific during Northern Hemispheric Winter Derived from Split Window Measurements. Journal of the Meteorological Society of Japan, 67(4), 621-637. doi: 10.2151/jmsj1965.67.4_621.

Klein, A., Kavoussi, S., Lee, R.S. (2009). Weather forecast accuracy: study of impact on airport capacity and estimation of avoidable costs. Proceeding of the Eighth USA/Europe Air Traffic Management Research and Development Seminar.

Maisey, P. (2012). Operational implementation of harmonised WAFS gridded products. Working paper at World Area Forecast System Operations Group (WAFSOPSG) Seventh Meeting.

Mazon, J., Rojas, J. I., Lozano, M., Pino, D., Prats, X., Miglietta, M.M. (2018). In?uence of meteorological phenomena on worldwide aircraft accidents, 1967–2010. Meteorological Application. doi: 10.1002/met.1686

Met Office, NOAA. (2016). Guidance on the Harmonized WAFS Grids for Cumulonimbus Cloud, Icing and Turbulence Forecasts Version 2.6.

Peck, L. (2015). The Impacts of Weather On Aviation Delays At O.R. Tambo

International Airport, South Africa. Master Thesis. University of South Africa.

Purbantoro, B., Aminuddin, J., Manago, N., Toyoshima, K., Lagrosas, N., Josaphat Sumantyo, T.S., Kuze, H. (2019). Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm. Remote Sens. 11, 2944. doi: 10.3390/rs11242944.

Sipayung, S. B., Risyanto. (2014). Frequency Distribution of Type Cb From Satellite Observation MTSAT in Indonesia. National Space and Atmospherical Science Seminar 2014.

Slingo, A., Slingo, J.M. (1991). Response of the National Center for Atmospheric Research Community Climate Model to Improvements in the Representation of Clouds. Journal of Geophysical Research, 96(D8). doi: 10.1029/91JD00930.

Suaydhi, F. Lesmono, A. Nafiisyanti. (2015). Variasi Musiman Berbagai Jenis Awan di Indonesia dalam Fisika, Kimia dan Dinamika Atmosfer di Indonesia. Bandung: Pusat Sains dan Teknologi Atmosfer, LAPAN.

Suseno, D.P.W., Yamada, T.J. (2012). Two-dimensional, threshold-based cloud type classification using MTSAT data. Remote Sensing Letters 3(8). doi: 10.1080/2150704X.2012.698320.

World Meteorological Organization (WMO). (2018). Aviation Hazards. AeM Series No. 3. Geneva: WMO.