Articles | Volume 19, issue 10
https://doi.org/10.5194/nhess-19-2241-2019
https://doi.org/10.5194/nhess-19-2241-2019
Research article
 | 
11 Oct 2019
Research article |  | 11 Oct 2019

Effects of high-resolution geostationary satellite imagery on the predictability of tropical thunderstorms over Southeast Asia

Kwonmin Lee, Hye-Sil Kim, and Yong-Sang Choi

Related authors

First results of cloud retrieval from the Geostationary Environmental Monitoring Spectrometer
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024,https://doi.org/10.5194/amt-17-453-2024, 2024
Short summary
First evaluation of the GEMS formaldehyde retrieval algorithm against TROPOMI and ground-based column measurements during the in-orbit test period
Gitaek T. Lee, Rokjin J. Park, Hyeong-Ahn Kwon, Eunjo S. Ha, Sieun D. Lee, Seunga Shin, Myoung-Hwan Ahn, Mina Kang, Yong-Sang Choi, Gyuyeon Kim, Dong-Won Lee, Deok-Rae Kim, Hyunkee Hong, Bavo Langerock, Corinne Vigouroux, Christophe Lerot, Francois Hendrick, Gaia Pinardi, Isabelle De Smedt, Michel Van Roozendael, Pucai Wang, Heesung Chong, Yeseul Cho, and Jhoon Kim
EGUsphere, https://doi.org/10.5194/egusphere-2023-1918,https://doi.org/10.5194/egusphere-2023-1918, 2023
Short summary
Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core
Ha-Rim Kim, Baek-Min Kim, Sang-Yoon Jun, and Yong-Sang Choi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-22,https://doi.org/10.5194/gmd-2020-22, 2020
Preprint withdrawn
Short summary
Examination of effects of aerosols on a pyroCb and their dependence on fire intensity and aerosol perturbation
Seoung Soo Lee, George Kablick III, Zhanqing Li, Chang Hoon Jung, Yong-Sang Choi, Junshik Um, and Won Jun Choi
Atmos. Chem. Phys., 20, 3357–3371, https://doi.org/10.5194/acp-20-3357-2020,https://doi.org/10.5194/acp-20-3357-2020, 2020
Short summary
Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products
Hye-Sil Kim, Bryan A. Baum, and Yong-Sang Choi
Atmos. Meas. Tech., 12, 5039–5054, https://doi.org/10.5194/amt-12-5039-2019,https://doi.org/10.5194/amt-12-5039-2019, 2019
Short summary

Related subject area

Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024,https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023,https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023,https://doi.org/10.5194/nhess-23-3285-2023, 2023
Short summary
A neural network model for automated prediction of avalanche danger level
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023,https://doi.org/10.5194/nhess-23-2523-2023, 2023
Short summary
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023,https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary

Cited articles

Avotniece, Z., Aniskevich, S., Briede, A., and Klavins, M.: Long term changes in the frequency and intensity of thunderstorms in Latvia, Boreal Environ. Res., 22, 415–430, https://doi.org/10.2166/nh.2008.033, 2017. 
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., and Okuyama, A.: An introduction to Himawari-8/9 – Japan's new generation geostationary meteorological satellites, J. Meteorol. Soc. Jpn. Ser. II, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. 
Choi, Y. S. and Ho, C. H.: Earth and environmental remote sensing community in South Korea: A review, Remote Sens. Appl.: Soc. Environ., 2, 66–76, https://doi.org/10.1016/j.rsase.2015.11.003, 2015. 
de Coning, E., Gijben, M., Maseko, B., and van Hemert, L.: Using satellite data to identify and track intense thunderstorms in South and southern Africa, S. Afr. J. Sci., 111, 1–5, https://doi.org/10.17159/sajs.2015/20140402, 2015. 
Escrig, H., Batlles, F. J., Alonso, J., Baena, F. M., Bosch, J. L., Salbidegoitia, I. B., and Burgaleta, J. I.: Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy, 55, 853–859, https://doi.org/10.1016/j.energy.2013.01.054, 2013. 
Download
Short summary
This study examined the advances in the predictability of thunderstorms using geostationary satellite imageries. Our present results show that by using the latest geostationary satellite data (with a resolution of 2 km and 10 min), thunderstorms can be predicted 90–180 min ahead of their mature state. These data can capture the rapidly growing cloud tops before the cloud moisture falls as precipitation and enable prompt preparation and the mitigation of hazards.
Altmetrics
Final-revised paper
Preprint