AN ANALYSIS OF SEASONAL THUNDERSTORM CLOUD DISTRIBUTION AND ITS RELATION TO RAINFALL OCCURRENCE IN THAILAND USING REMOTELY SENSED DATA

被引:0
|
作者
Bumrungklang, Pornthip [1 ]
Dasananda, Songkot [1 ]
Sukawat, Dusadee [2 ]
机构
[1] Suranaree Univ Technol, Inst Sci, Sch Remote Sensing, Nakhon Ratchasima 30000, Thailand
[2] King Mongkuts Univ Technol Thonburi, Sci Fac, Math, Bangkok 10140, Thailand
来源
关键词
Satellite cloud classification; thunderstorm cloud classification; estimate rainfall; MTSAT-1R; split windows;
D O I
暂无
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The main objective of this study is to analyze the relationship between rainfall intensity and the associated cloud properties which are cloud top temperature (CTT) and cloud cover in Thailand based on some selected case studies during years 2006 and 2007. In addition, the classified cloud data were also applied to the investigation of seasonal cloud and rainfall distribution during those specified years. To assist the efficient derivation of cloud top temperature maps, the automatic cloud classification model for the thermal infrared (TIR) images of the MTSAT-1R satellite was developed and applied as main tool for CTT mapping in the study. And to reduce possible confusion between high clouds and rain clouds (cumulonimbus), the high clouds were filtered off first using the split-window technique under the given thresholds. The classified CTT maps include all clouds with CTT less than 10 degrees C and, as a consequence, most warm clouds and cold clouds are depicted on the obtained maps. The analysis of seasonal cloud and rainfall distribution indicates that patterns of their distribution in Thailand are the product of the combined effects among several main driving factors. In summer, these are the local convective system, the cold air mass, the monsoon trough, the westerly wind, and the low pressure area from the ocean. In the rainy season, these are the monsoon trough, the southwest monsoon, and the tropical cyclone and low pressure area from the ocean. And in winter, these are the cold air mass, the northeast monsoon (for the south), and local convection. The amount of total daily rainfall has a high correlation with the amount of cloud cover area seen each day, with r(2) > 0.8 in all cases especially heavy rainfall (e.g. > 80 mm) or on the hail days (with r(2) = 0.8915).
引用
收藏
页码:71 / 86
页数:16
相关论文
共 50 条
  • [31] Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing
    Pedro Henrique Jandreice Magnoni
    César de Oliveira Ferreira Silva
    Rodrigo Lilla Manzione
    Sustainable Water Resources Management, 2020, 6
  • [32] Groundwater recharge and water table levels modelling using remotely sensed data and cloud-computing
    Jandreice Magnoni, Pedro Henrique
    Ferreira Silva, Cesar de Oliveira
    Manzione, Rodrigo Lilla
    SUSTAINABLE WATER RESOURCES MANAGEMENT, 2020, 6 (06)
  • [33] Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula
    AghaKouchak, Amir
    Bardossy, Andras
    Habib, Emad
    ADVANCES IN WATER RESOURCES, 2010, 33 (06) : 624 - 634
  • [34] Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand
    Hutasavi, Sirikul
    Chen, Dongmei
    REMOTE SENSING, 2021, 13 (22)
  • [35] Erratum to: Historical analysis of interannual rainfall variability and trends in southeastern Brazil based on observational and remotely sensed data
    Isela L. Vásquez P.
    Lígia Maria Nascimento de Araujo
    Luiz Carlos Baldicero Molion
    Mariana de Araujo Abdalad
    Daniel Medeiros Moreira
    Arturo Sanchez
    Humberto Alves Barbosa
    Otto Corrêa Rotunno Filho
    Climate Dynamics, 2018, 50 : 2283 - 2283
  • [36] Mapping impervious surface distribution in China using multi-source remotely sensed data
    Li, Guiying
    Li, Longwei
    Lu, Dengsheng
    Guo, Wei
    Kuang, Wenhui
    GISCIENCE & REMOTE SENSING, 2020, 57 (04) : 543 - 552
  • [37] Estimation of areal distribution of evapotranspiration using remotely sensed data during vegetation period in Hungary
    Dunkel, Z
    Szenyán, IG
    REMOTE SENSING FOR LAND SURFACE CHARACTERISATION, 2000, 26 (07): : 1051 - 1054
  • [38] Inference of Precipitation in Warm Stratiform Clouds Using Remotely Sensed Observations of the Cloud Top Droplet Size Distribution
    Sinclair, Kenneth
    van Diedenhoven, Bastiaan
    Cairns, Brian
    Alexandrov, Mikhail
    Dzambo, Andrew M.
    L'Ecuyer, Tristan
    GEOPHYSICAL RESEARCH LETTERS, 2021, 48 (10)
  • [39] Dynamic change analysis of water spread region and its impact assessment using spectral indices of remotely sensed data
    Anand, B.
    Rekha, R. Shanmathi
    Remitha, K. R.
    Maniyammai, V.
    Ramaswamy, K.
    Gautam, Sneha
    ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY, 2024, 26 (07) : 17635 - 17652
  • [40] Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data
    Keuchel, J
    Naumann, S
    Heiler, M
    Siegmund, A
    REMOTE SENSING OF ENVIRONMENT, 2003, 86 (04) : 530 - 541