Search Results
–Julian oscillation (MJO) is a factor that generates the extreme events. The MJO influence was also identified by Tangang et al. (2008) in peninsular Malaysia during flood events in December 2006 to January 2007, where an active phase of the Indian Ocean dipole was also viewed as another factor responsible for the extremes. However, because of a lack of observations, the general mechanism for the extreme precipitation dynamics over the MC is still not fully understood. Substantial amounts of precipitation data
–Julian oscillation (MJO) is a factor that generates the extreme events. The MJO influence was also identified by Tangang et al. (2008) in peninsular Malaysia during flood events in December 2006 to January 2007, where an active phase of the Indian Ocean dipole was also viewed as another factor responsible for the extremes. However, because of a lack of observations, the general mechanism for the extreme precipitation dynamics over the MC is still not fully understood. Substantial amounts of precipitation data
formulations (i.e., physical parameterizations, dynamical core). For example, simulated precipitation usually peaks too early in the day compared to observations, especially at lower resolutions, and regional models tend to produce too much precipitation over land and too little over the ocean ( Gianotti et al. 2012 ; Kwan et al. 2013 ; Birch et al. 2016 ; Hassim et al. 2016 ; Vincent and Lane 2017 ; Im and Elthair 2018 ). Previous studies ( Love et al. 2011 ; Birch et al. 2015 ; Bhatt et al. 2016
formulations (i.e., physical parameterizations, dynamical core). For example, simulated precipitation usually peaks too early in the day compared to observations, especially at lower resolutions, and regional models tend to produce too much precipitation over land and too little over the ocean ( Gianotti et al. 2012 ; Kwan et al. 2013 ; Birch et al. 2016 ; Hassim et al. 2016 ; Vincent and Lane 2017 ; Im and Elthair 2018 ). Previous studies ( Love et al. 2011 ; Birch et al. 2015 ; Bhatt et al. 2016
degree of air–sea coupling. For question (ii), we investigate physical processes contributing to SST evolutions in observations and two CFSv2 simulations with contrasting levels of fidelity in simulating the MJO eastward propagation, which results from different convection schemes. The paper is organized as follows: The model, the experimental design, and the datasets are described in the section 2 . The results are presented in section 3 , where factors influencing MJO propagation simulations are
degree of air–sea coupling. For question (ii), we investigate physical processes contributing to SST evolutions in observations and two CFSv2 simulations with contrasting levels of fidelity in simulating the MJO eastward propagation, which results from different convection schemes. The paper is organized as follows: The model, the experimental design, and the datasets are described in the section 2 . The results are presented in section 3 , where factors influencing MJO propagation simulations are
the complex topography and the absence of in situ measurements over the sea. Satellite precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM) 3B42 V7 ( Huffman et al. 2007 ; Goddard Space Flight Center 1998 ) and the Climate Prediction Center morphing technique (CMORPH; Joyce et al. 2004 ; Climate Prediction Center 2011 ) were used in the study. TRMM estimates are derived from passive microwave sensor observations from polar-orbiting satellites, together with brightness
the complex topography and the absence of in situ measurements over the sea. Satellite precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM) 3B42 V7 ( Huffman et al. 2007 ; Goddard Space Flight Center 1998 ) and the Climate Prediction Center morphing technique (CMORPH; Joyce et al. 2004 ; Climate Prediction Center 2011 ) were used in the study. TRMM estimates are derived from passive microwave sensor observations from polar-orbiting satellites, together with brightness
from that over the open water of the Indian and Pacific Oceans. In observations, when the MJO propagates over the MC, it often weakens, its propagation speed becomes uneven, and it may completely break down and fail to reemerge on the Pacific side ( Rui and Wang 1990 ; Hendon and Salby 1994 ; Hsu and Lee 2005 ; Kim et al. 2014 ). The weakening and blocking of the MJO by the MC is known as a “barrier effect” on MJO propagation. This barrier effect of the MC in nature is often exaggerated in
from that over the open water of the Indian and Pacific Oceans. In observations, when the MJO propagates over the MC, it often weakens, its propagation speed becomes uneven, and it may completely break down and fail to reemerge on the Pacific side ( Rui and Wang 1990 ; Hendon and Salby 1994 ; Hsu and Lee 2005 ; Kim et al. 2014 ). The weakening and blocking of the MJO by the MC is known as a “barrier effect” on MJO propagation. This barrier effect of the MC in nature is often exaggerated in
2015. We apply the Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates (version 1.0 CRT; Joyce et al. 2004 ; Xie et al. 2017 ) as rainfall data. It is derived by combining satellite infrared and microwave sounders, with calibration against surface gauge observations. The temporal and spatial resolution of CMORPH data used in the present study is 3 hourly and 0.25° × 0.25° in the latitude–longitude grid. The period for computing rainfall climatology is
2015. We apply the Climate Prediction Center (CPC) morphing technique (CMORPH) satellite precipitation estimates (version 1.0 CRT; Joyce et al. 2004 ; Xie et al. 2017 ) as rainfall data. It is derived by combining satellite infrared and microwave sounders, with calibration against surface gauge observations. The temporal and spatial resolution of CMORPH data used in the present study is 3 hourly and 0.25° × 0.25° in the latitude–longitude grid. The period for computing rainfall climatology is
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
