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intensified mixing in the SCS. Various processes are known to provide energy for mixing in the SCS from shallow shelf to deep water. Convection caused by solar radiation is mainly responsible for diurnal variability of mixing in the mixed layer (e.g., Yang et al. 2014b ). There are many internal solitary waves in the northern SCS (e.g., Zhao et al. 2004 ; Klymak et al. 2006 ; Lien et al. 2014 ), which contribute to mixing in the shallow continental shelf of the SCS. In the deep water, internal
intensified mixing in the SCS. Various processes are known to provide energy for mixing in the SCS from shallow shelf to deep water. Convection caused by solar radiation is mainly responsible for diurnal variability of mixing in the mixed layer (e.g., Yang et al. 2014b ). There are many internal solitary waves in the northern SCS (e.g., Zhao et al. 2004 ; Klymak et al. 2006 ; Lien et al. 2014 ), which contribute to mixing in the shallow continental shelf of the SCS. In the deep water, internal
Comparison of Turbulence Intensity from CTD-Attached and Free-Fall Microstructure Profilers1. Introduction Vertical turbulent mixing is a key process in the global ocean circulations, affecting the diapycnal transport of heat and salt as well as biogeochemical substances, such as nutrients, carbon, and trace metals. Turbulent mixing is estimated from measurements of the microstructure in velocity or temperature, with spatial scales of a few centimeters. As the measurements are susceptible to contamination by the vibration of an instrument, free-fall or free-rise profilers have been
1. Introduction Vertical turbulent mixing is a key process in the global ocean circulations, affecting the diapycnal transport of heat and salt as well as biogeochemical substances, such as nutrients, carbon, and trace metals. Turbulent mixing is estimated from measurements of the microstructure in velocity or temperature, with spatial scales of a few centimeters. As the measurements are susceptible to contamination by the vibration of an instrument, free-fall or free-rise profilers have been
EU–U.S. project designed to study winds in nominally parallel double-ridge topography, with the aim of eliciting dynamical and thermodynamical processes of microscale flow in complex terrain while contributing to the development of microscale models for wind-resource prospecting and mapping. One of the ridges has a microscale depression (gap), and therefore a subexperiment was conducted by deploying a dedicated set of instruments within the overall Perdigão campaign. To our knowledge, this is the
EU–U.S. project designed to study winds in nominally parallel double-ridge topography, with the aim of eliciting dynamical and thermodynamical processes of microscale flow in complex terrain while contributing to the development of microscale models for wind-resource prospecting and mapping. One of the ridges has a microscale depression (gap), and therefore a subexperiment was conducted by deploying a dedicated set of instruments within the overall Perdigão campaign. To our knowledge, this is the
nugget and the slope . The slope parameter indicates how fast Z ( x ) is varying with the distance lag. The nugget, similarly to c 0 for spatial correlation, quantifies the variability at very short distance lags (with respect to the minimum distance lag of the network). The nugget effect is explained by the possible variability of the considered process at interdistances smaller than the minimum interdistance in the network (all microscale variability; below 80 m in our case) and/or by
nugget and the slope . The slope parameter indicates how fast Z ( x ) is varying with the distance lag. The nugget, similarly to c 0 for spatial correlation, quantifies the variability at very short distance lags (with respect to the minimum distance lag of the network). The nugget effect is explained by the possible variability of the considered process at interdistances smaller than the minimum interdistance in the network (all microscale variability; below 80 m in our case) and/or by
1. Introduction Microscale turbulent mixing has always been a research focus, since it furnishes the downward energy cascade by viscously dissipating a part of turbulent kinetic energy (TKE) into irreversible heat (e.g., St. Laurent 2008 ) and converting the rest of the TKE into background potential energy (e.g., Fernando 1991 ), which further modulates the large-scale overturning circulation ( Wunsch and Ferrari 2004 ). For a stationary turbulent flow in a finite region, the temporal
1. Introduction Microscale turbulent mixing has always been a research focus, since it furnishes the downward energy cascade by viscously dissipating a part of turbulent kinetic energy (TKE) into irreversible heat (e.g., St. Laurent 2008 ) and converting the rest of the TKE into background potential energy (e.g., Fernando 1991 ), which further modulates the large-scale overturning circulation ( Wunsch and Ferrari 2004 ). For a stationary turbulent flow in a finite region, the temporal
. Quantifying rainfall variability at very small scales is particularly important for the “Maritime Continent,” where processes such as land–sea breezes and mountain–valley winds lead to complex spatial and temporal patterns in rainfall (e.g., Qian 2008 ). Several studies have investigated the spatial and temporal behavior of rainfall on the Maritime Continent, but a majority of them were carried out at regional scales using either satellite rainfall estimates or regional climate model simulations (e
. Quantifying rainfall variability at very small scales is particularly important for the “Maritime Continent,” where processes such as land–sea breezes and mountain–valley winds lead to complex spatial and temporal patterns in rainfall (e.g., Qian 2008 ). Several studies have investigated the spatial and temporal behavior of rainfall on the Maritime Continent, but a majority of them were carried out at regional scales using either satellite rainfall estimates or regional climate model simulations (e
, and appropriately representing turbulence at various scales. The goal of this study is to improve microscale predictions for transport and dispersion over complex urban topography by improving the downscaling and representation of turbulence within a multiscale simulation. We focus on quantifying model performance at predicting turbulence kinetic energy (TKE) through use of skill metrics and calculations of spectra. We also examine the role of modeling physical processes that are important to the
, and appropriately representing turbulence at various scales. The goal of this study is to improve microscale predictions for transport and dispersion over complex urban topography by improving the downscaling and representation of turbulence within a multiscale simulation. We focus on quantifying model performance at predicting turbulence kinetic energy (TKE) through use of skill metrics and calculations of spectra. We also examine the role of modeling physical processes that are important to the
capability that requires so little specification of the processes involved. A close examination of thermocline variability in depth and time is appropriate, with the objective of identifying the phenomena that establish the strain field. As representative of the diverse sites, observations from the open-ocean HOME Farfield Experiment and the California coastal AESOP experiment are presented. The HOME data were collected approximately 450 km southwest of the island of Oahu ( Table 1 ) over a 4-week period
capability that requires so little specification of the processes involved. A close examination of thermocline variability in depth and time is appropriate, with the objective of identifying the phenomena that establish the strain field. As representative of the diverse sites, observations from the open-ocean HOME Farfield Experiment and the California coastal AESOP experiment are presented. The HOME data were collected approximately 450 km southwest of the island of Oahu ( Table 1 ) over a 4-week period
studies, but the interstation distance typically exceeds 1 km. For example, Zemel and Lomas (1976) measured the air temperature in the Huleh Valley in Israel with a network of 70 stations. Kawashima and Ishida (1992) examined the temperature close to the surface in a 250 km × 300 km area in Japan with a network consisting of 130 stations, and Hubbard (1994) determined the spatial variability of daily measurements in the high plains in the United States. On the microscale up to 2 km ( Orlanski
studies, but the interstation distance typically exceeds 1 km. For example, Zemel and Lomas (1976) measured the air temperature in the Huleh Valley in Israel with a network of 70 stations. Kawashima and Ishida (1992) examined the temperature close to the surface in a 250 km × 300 km area in Japan with a network consisting of 130 stations, and Hubbard (1994) determined the spatial variability of daily measurements in the high plains in the United States. On the microscale up to 2 km ( Orlanski
developed turbulence. Herein, we first extend and then evaluate CP method to nonneutral ABLs. We focus on understanding the mechanisms that are responsible for turbulence transition and development in coupled mesoscale–microscale simulations under different stability conditions. The remainder of the paper is structured as follows. The numerical model, simulation setup, and cases of study are described in section 2 . A modified Richardson scaling is proposed and tested in five stable ABLs in section 3
developed turbulence. Herein, we first extend and then evaluate CP method to nonneutral ABLs. We focus on understanding the mechanisms that are responsible for turbulence transition and development in coupled mesoscale–microscale simulations under different stability conditions. The remainder of the paper is structured as follows. The numerical model, simulation setup, and cases of study are described in section 2 . A modified Richardson scaling is proposed and tested in five stable ABLs in section 3
