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temperature, with the warm equatorial Kelvin wave penetrating into the equatorial western Pacific and anchoring the anomalous anticyclone ( Xie et al. 2016 ). Figure 7b shows an anticyclonic anomaly in the northeastern part of the eastern Indian Ocean heat source, which is shown to be conducive to suppressed convection, less cloud cover, and hinders the northward propagation of intraseasonal oscillation, leading to less occurrences of individual intraseasonal oscillation phase with the active convection
temperature, with the warm equatorial Kelvin wave penetrating into the equatorial western Pacific and anchoring the anomalous anticyclone ( Xie et al. 2016 ). Figure 7b shows an anticyclonic anomaly in the northeastern part of the eastern Indian Ocean heat source, which is shown to be conducive to suppressed convection, less cloud cover, and hinders the northward propagation of intraseasonal oscillation, leading to less occurrences of individual intraseasonal oscillation phase with the active convection
1. Introduction Tropical easterly waves (TEWs) are quasi-periodic wave disturbances embedded in the easterly trade winds during boreal summer and autumn ( Nitta and Takayabu 1985 ; Lau and Lau 1992 ; Roundy and Frank 2004 ; Serra et al. 2010 ). They influence the circulation dynamics of tropical America at synoptic scales ( Nitta and Takayabu 1985 ; Thorncroft and Hodges 2001 ; Cárdenas et al. 2017 ; Cornforth et al. 2017 ; Dominguez et al. 2020 ). TEWs have been related to
1. Introduction Tropical easterly waves (TEWs) are quasi-periodic wave disturbances embedded in the easterly trade winds during boreal summer and autumn ( Nitta and Takayabu 1985 ; Lau and Lau 1992 ; Roundy and Frank 2004 ; Serra et al. 2010 ). They influence the circulation dynamics of tropical America at synoptic scales ( Nitta and Takayabu 1985 ; Thorncroft and Hodges 2001 ; Cárdenas et al. 2017 ; Cornforth et al. 2017 ; Dominguez et al. 2020 ). TEWs have been related to
decadal variability which may have a delayed effect on mei-yu precipitation over East Asia by a stable Rossby wave train that in summer moves from West Eurasia to East Asia. Mei-yu precipitation accounts for a large proportion of the total NTCR in Southeast China. Therefore, compared with the influence of SST over the North Atlantic Ocean on TCR, NTCR impacted by the variability of SST over this region should be paid more attention to. For the decreasing TCR ( Fig. 9b ) and NTCR ( Fig. 9d ), SST over
decadal variability which may have a delayed effect on mei-yu precipitation over East Asia by a stable Rossby wave train that in summer moves from West Eurasia to East Asia. Mei-yu precipitation accounts for a large proportion of the total NTCR in Southeast China. Therefore, compared with the influence of SST over the North Atlantic Ocean on TCR, NTCR impacted by the variability of SST over this region should be paid more attention to. For the decreasing TCR ( Fig. 9b ) and NTCR ( Fig. 9d ), SST over
over the Indian region ( Mahto and Mishra 2020 ). The mechanisms underlying these droughts are multifaceted, encompassing internally driven processes linked to intraseasonal oscillations and the dynamics of monsoon–midlatitude interactions facilitated by the intrusion of Rossby waves ( Krishnamurti et al. 2010 ; Krishnan et al. 2000 , 2009 ; Goswami 2005 ). Monsoon droughts are influenced by external factors like El Niño–Southern Oscillation (ENSO) and the Indian Ocean ( Niranjan Kumar et
over the Indian region ( Mahto and Mishra 2020 ). The mechanisms underlying these droughts are multifaceted, encompassing internally driven processes linked to intraseasonal oscillations and the dynamics of monsoon–midlatitude interactions facilitated by the intrusion of Rossby waves ( Krishnamurti et al. 2010 ; Krishnan et al. 2000 , 2009 ; Goswami 2005 ). Monsoon droughts are influenced by external factors like El Niño–Southern Oscillation (ENSO) and the Indian Ocean ( Niranjan Kumar et
, radiosonde, dropsonde, and GPS radio occultation profiles ( Hersbach et al. 2020 ). The model includes coupling between the atmosphere and the land/ocean and handles convective parameterization using a bulk mass flux scheme ( ECMWF 2020 ). d. Methods At each 0.1°, we compile the diurnal variability by averaging the unconditional (i.e., including zeros) precipitation rates at each time step for the entire record. To conduct a comparison of the precipitation diurnal cycle from the three datasets, we
, radiosonde, dropsonde, and GPS radio occultation profiles ( Hersbach et al. 2020 ). The model includes coupling between the atmosphere and the land/ocean and handles convective parameterization using a bulk mass flux scheme ( ECMWF 2020 ). d. Methods At each 0.1°, we compile the diurnal variability by averaging the unconditional (i.e., including zeros) precipitation rates at each time step for the entire record. To conduct a comparison of the precipitation diurnal cycle from the three datasets, we
; Vigaud et al. 2017 ). Ocean models with increasing resolutions are better able to simulate subsurface heat fluxes and have increased precipitation predictability along with improved MJO simulations stressing the importance of air–ocean feedbacks ( Seo et al. 2014 ). The next GEFSv12-fcst update is scheduled to include a fully coupled atmosphere–ocean–wave–ice model, and improvements in RZSM and FD forecast skill can be assessed when this becomes available. b. Errors influencing RZSM forecast
; Vigaud et al. 2017 ). Ocean models with increasing resolutions are better able to simulate subsurface heat fluxes and have increased precipitation predictability along with improved MJO simulations stressing the importance of air–ocean feedbacks ( Seo et al. 2014 ). The next GEFSv12-fcst update is scheduled to include a fully coupled atmosphere–ocean–wave–ice model, and improvements in RZSM and FD forecast skill can be assessed when this becomes available. b. Errors influencing RZSM forecast
appear to be a source for thermal forcing of the wave. However, the upward latent and sensible heat fluxes decrease in this same region, and the turbulent fluxes tend to increase where the precipitation is reduced. This implies that the surface ocean is in fact responding to the atmosphere, with, for example, southerly flow enhancing moist advection inducing precipitation, suppressing surface turbulent heat fluxes, and warming SSTs. That is, the SSTs appear to be responding to, not forcing, the wave
appear to be a source for thermal forcing of the wave. However, the upward latent and sensible heat fluxes decrease in this same region, and the turbulent fluxes tend to increase where the precipitation is reduced. This implies that the surface ocean is in fact responding to the atmosphere, with, for example, southerly flow enhancing moist advection inducing precipitation, suppressing surface turbulent heat fluxes, and warming SSTs. That is, the SSTs appear to be responding to, not forcing, the wave
tracers embedded in WRF-WVT to “tag” moisture originating from terrestrial ET processes in d02 (evaporation from the ocean area within d02 is not included) as “tracer moisture” and track its movement through modeled dynamical and physical processes. Tracer moisture from d02 can be transported across boundaries between d01 and d02 through lateral advection; therefore, it can be also tracked outside the convection-permitting domain. The only sink for the tracer moisture is through precipitation. Like
tracers embedded in WRF-WVT to “tag” moisture originating from terrestrial ET processes in d02 (evaporation from the ocean area within d02 is not included) as “tracer moisture” and track its movement through modeled dynamical and physical processes. Tracer moisture from d02 can be transported across boundaries between d01 and d02 through lateral advection; therefore, it can be also tracked outside the convection-permitting domain. The only sink for the tracer moisture is through precipitation. Like
a La Niña ( Demaria et al. 2019 ). SST variability in the adjacent Pacific Ocean (Gulf of California) or the Gulf of Mexico may also induce regional wind shifts and changes in humidity necessary for monsoonal moisture to reach the southwestern United States. Alternatively, the Madden–Julian oscillation (MJO) may impact the NAM by amplifying easterly waves in the eastern North Pacific and promoting tropical cyclone genesis, which fosters moisture surges toward northern Mexico and the southwestern
a La Niña ( Demaria et al. 2019 ). SST variability in the adjacent Pacific Ocean (Gulf of California) or the Gulf of Mexico may also induce regional wind shifts and changes in humidity necessary for monsoonal moisture to reach the southwestern United States. Alternatively, the Madden–Julian oscillation (MJO) may impact the NAM by amplifying easterly waves in the eastern North Pacific and promoting tropical cyclone genesis, which fosters moisture surges toward northern Mexico and the southwestern
1. Introduction Unexpected flash flooding is one of the most devastating natural hazards, causing economic losses and fatalities (e.g., Glancy and Harmsen 1975 ; Randerson 1976 ; Ogden et al. 2000 ; Ashley and Ashley 2008 ). The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) recorded 1075 flash-flood-related fatalities across the United States from 1996 to 2014 ( Gourley et al. 2013 ). Urbanized watersheds are more prone to flash flooding
1. Introduction Unexpected flash flooding is one of the most devastating natural hazards, causing economic losses and fatalities (e.g., Glancy and Harmsen 1975 ; Randerson 1976 ; Ogden et al. 2000 ; Ashley and Ashley 2008 ). The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) recorded 1075 flash-flood-related fatalities across the United States from 1996 to 2014 ( Gourley et al. 2013 ). Urbanized watersheds are more prone to flash flooding
