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location and amount of precipitation generated by the model disagreed with observations. Only by including both a typical monsoonal wind profile (westerlies in the low levels, easterlies aloft) and ocean surface fluxes did the model simulations fall in accord with observations. In other model simulations, Xie et al. (2006) found that even the relatively small and narrow mountain ranges of Myanmar cause enough impedance of the monsoon flow to create the convergence necessary to generate convection and
location and amount of precipitation generated by the model disagreed with observations. Only by including both a typical monsoonal wind profile (westerlies in the low levels, easterlies aloft) and ocean surface fluxes did the model simulations fall in accord with observations. In other model simulations, Xie et al. (2006) found that even the relatively small and narrow mountain ranges of Myanmar cause enough impedance of the monsoon flow to create the convergence necessary to generate convection and
this time is nearly 10 times larger than in the west Pacific. Temperature inversions develop through autumn and winter ( Thadathil et al. 2016 ) as the sea surface temperature cools, while penetration of shortwave radiation below the shallow mixed layer continues to warm the subsurface ocean. Satellite microwave SST measurements ( Wentz et al. 2000 ) have been widely used to study the spatial structure and time evolution of SST cooling due to vertical mixing induced by tropical cyclones (e.g., D
this time is nearly 10 times larger than in the west Pacific. Temperature inversions develop through autumn and winter ( Thadathil et al. 2016 ) as the sea surface temperature cools, while penetration of shortwave radiation below the shallow mixed layer continues to warm the subsurface ocean. Satellite microwave SST measurements ( Wentz et al. 2000 ) have been widely used to study the spatial structure and time evolution of SST cooling due to vertical mixing induced by tropical cyclones (e.g., D
; Huffman et al. 2010 ) from 1998 to 2015. GPCP precipitation is based on microwave, infrared, and rain gauge observations ( Huffman et al. 1997 ), with a horizontal resolution of 1° × 1°. TRMM 3B42 is the merged microwave, Precipitation Radar (PR), and infrared level-3 rainfall product at a horizontal resolution of 0.25° × 0.25°, with the calibration of rain gauge data on a monthly basis. We used two precipitation estimates here to provide the possible range of observational uncertainty. The model
; Huffman et al. 2010 ) from 1998 to 2015. GPCP precipitation is based on microwave, infrared, and rain gauge observations ( Huffman et al. 1997 ), with a horizontal resolution of 1° × 1°. TRMM 3B42 is the merged microwave, Precipitation Radar (PR), and infrared level-3 rainfall product at a horizontal resolution of 0.25° × 0.25°, with the calibration of rain gauge data on a monthly basis. We used two precipitation estimates here to provide the possible range of observational uncertainty. The model
) presented field observations that showed an extremely regular diurnal cycle over both land and ocean near Borneo. Across the MC region, differential daytime heating between land and water due to the difference in heat capacity leads to sea-breeze circulations that converge near the center of the islands, and combine with mountain–valley breezes to enhance convection over mountains ( Qian 2008 ). Cells begin to merge and organize, particularly over larger islands, leading to a late afternoon peak in
) presented field observations that showed an extremely regular diurnal cycle over both land and ocean near Borneo. Across the MC region, differential daytime heating between land and water due to the difference in heat capacity leads to sea-breeze circulations that converge near the center of the islands, and combine with mountain–valley breezes to enhance convection over mountains ( Qian 2008 ). Cells begin to merge and organize, particularly over larger islands, leading to a late afternoon peak in
for Global Precipitation Mission (GPM; IMERG), version 5B, rainfall estimates. Both TRMM 3B42 and IMERG rainfall estimates are based on multiple microwave and infrared satellite retrievals and rain gauge observations ( Huffman and Bolvin 2018 ; Huffman et al. 2007 , 2018 ). Though some observational inputs are the same among TRMM 3B42 and IMERG, each contain unique observation sources that are not in the other product (Table 1 of Liu 2016 ). TRMM 3B42 rainfall is 3 hourly on a 0.25° × 0
for Global Precipitation Mission (GPM; IMERG), version 5B, rainfall estimates. Both TRMM 3B42 and IMERG rainfall estimates are based on multiple microwave and infrared satellite retrievals and rain gauge observations ( Huffman and Bolvin 2018 ; Huffman et al. 2007 , 2018 ). Though some observational inputs are the same among TRMM 3B42 and IMERG, each contain unique observation sources that are not in the other product (Table 1 of Liu 2016 ). TRMM 3B42 rainfall is 3 hourly on a 0.25° × 0
merged infrared SST observations from the (A)TSR series of radiometers from ERS-1 , ERS-2 , and Envisat , AVHRR from NOAA-16 , -17 , -18 , and -19 and MetOp-A , and microwave data from TMI, AMSR-E, and WindSat, supplemented with in situ observations from buoys and ICOADS ship data ( Brasnett 2008 ; Martin et al. 2012 ; https://podaac.jpl.nasa.gov/dataset/CMC0.2deg-CMC-L4-GLOB-v2.0 ). The gridded horizontal resolution of this CMC SST dataset is 0.2° × 0.2°. Multisatellite observations
merged infrared SST observations from the (A)TSR series of radiometers from ERS-1 , ERS-2 , and Envisat , AVHRR from NOAA-16 , -17 , -18 , and -19 and MetOp-A , and microwave data from TMI, AMSR-E, and WindSat, supplemented with in situ observations from buoys and ICOADS ship data ( Brasnett 2008 ; Martin et al. 2012 ; https://podaac.jpl.nasa.gov/dataset/CMC0.2deg-CMC-L4-GLOB-v2.0 ). The gridded horizontal resolution of this CMC SST dataset is 0.2° × 0.2°. Multisatellite observations
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
