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extreme rainfall and gives insight into their connection with equatorial waves, and section 5 provides the summary and conclusions. 2. Data and methods a. Satellite data Gridded daily outgoing longwave radiation (OLR) ( Liebmann and Smith 1996 ) on a regular 2.5° × 2.5° latitude–longitude grid for the 1 January 1998–30 June 2019 period was used. Daily precipitation estimates (combined microwave-IR) based on the 3-hourly Tropical Rainfall Measuring Mission (TRMM) ( Huffman et al. 2010 ) gridded
extreme rainfall and gives insight into their connection with equatorial waves, and section 5 provides the summary and conclusions. 2. Data and methods a. Satellite data Gridded daily outgoing longwave radiation (OLR) ( Liebmann and Smith 1996 ) on a regular 2.5° × 2.5° latitude–longitude grid for the 1 January 1998–30 June 2019 period was used. Daily precipitation estimates (combined microwave-IR) based on the 3-hourly Tropical Rainfall Measuring Mission (TRMM) ( Huffman et al. 2010 ) gridded
offshore behavior of the Maritime Continent’s diurnal wind cycles using satellite scatterometer observations. So far, detailed studies of the Maritime Continent’s diurnal wind cycles have relied on mesoscale models like WRF, as widespread in situ observations are generally lacking over the region’s seas. However, the growing record of satellite scatterometer data provides a new observational dataset that can be used for this purpose. This study presents a new method of utilizing these datasets by
offshore behavior of the Maritime Continent’s diurnal wind cycles using satellite scatterometer observations. So far, detailed studies of the Maritime Continent’s diurnal wind cycles have relied on mesoscale models like WRF, as widespread in situ observations are generally lacking over the region’s seas. However, the growing record of satellite scatterometer data provides a new observational dataset that can be used for this purpose. This study presents a new method of utilizing these datasets by
surface temperature, which are of importance in the context of decision making. Empirical forecast tools have been developed that exploit this link and utilize MJO information for predictions ( Zhou et al. 2012 ; Riddle et al. 2013 ; Johnson et al. 2014 ). In the last decade, advances have been made in the prediction of MJO using dynamical models (e.g., Vitart 2017 ). These are due to improvements in the observations and data assimilation systems, improvements in the physical parameterization
surface temperature, which are of importance in the context of decision making. Empirical forecast tools have been developed that exploit this link and utilize MJO information for predictions ( Zhou et al. 2012 ; Riddle et al. 2013 ; Johnson et al. 2014 ). In the last decade, advances have been made in the prediction of MJO using dynamical models (e.g., Vitart 2017 ). These are due to improvements in the observations and data assimilation systems, improvements in the physical parameterization
al. 2001 ) and the radiosonde and radar data observed aboard the Research Vessel Mirai . The reader is referred to Yoneyama and Zhang (2020) for information regarding the availability of the radiosonde and radar data. The time interval of the T b data was 30 min. In addition, the radiosonde observations were conducted on a 3-hourly basis, and the shipborne polarimetric radar performed volume scans every 6 min. The reader is referred to Geng and Katsumata (2020) for the main specifications
al. 2001 ) and the radiosonde and radar data observed aboard the Research Vessel Mirai . The reader is referred to Yoneyama and Zhang (2020) for information regarding the availability of the radiosonde and radar data. The time interval of the T b data was 30 min. In addition, the radiosonde observations were conducted on a 3-hourly basis, and the shipborne polarimetric radar performed volume scans every 6 min. The reader is referred to Geng and Katsumata (2020) for the main specifications
