Heat Flux Measurements in Homogeneous Curved Shear Flow

1999 ◽  
Vol 121 (1) ◽  
pp. 190-194 ◽  
Author(s):  
A. G. L. Holloway ◽  
S. A. Ebrahimi-Sabet
Keyword(s):  
Heat Flux ◽  
Shear Flow ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Turbulent Shear ◽  
Turbulent Heat ◽  
Curvature Effects ◽  
Turbulent Shear Stress ◽  
Uniform Shear ◽  
Flow Curvature

Turbulent heat fluxes were measured far downstream of a fine heating wire stretched spanwise across a curved, uniform shear flow. The turbulence was approximately homogeneous and the overheat small enough to be passive. Strong destabilizing and stabilizing curvature effects were produced by directing the shear toward the center of curvature and away from the center of curvature, respectively. The dimensionless turbulent shear stress was strongly affected by the flow curvature, but the dimensionless components of the turbulent heat flux were found to be relatively insensitive.

Turbulent Transfer of Momentum and Heat in a Separated and Reattached Flow over a Blunt Flat Plate

1980 ◽  
Vol 102 (4) ◽  
pp. 749-754 ◽  
Author(s):  
Terukazu Ota ◽  
Nobuhiko Kon
Keyword(s):  
Heat Flux ◽  
Shear Stress ◽  
Flat Plate ◽  
Finite Thickness ◽  
Leading Edge ◽  
Turbulent Heat Flux ◽  
Turbulent Shear ◽  
Turbulent Heat ◽  
Turbulent Shear Stress ◽  
Separated And Reattached Flow

Turbulent shear stress and heat flux were measured with a hot-wire anemometer in the separated, reattached, and redeveloped regions of a two-dimensional incompressible air flow over a flat plate of finite thickness having blunt leading edge. The characteristic features of the turbulent heat flux are found to be nearly equal to those of the turbulent shear stress in the separated and reattached flow regions. However, in the turbulent boundary layer downstream from the reattachment point, the development of turbulent heat flux appears to be much quicker than that of turbulent shear stress. Eddy diffusivities of momentum and heat are evaluated and then the turbulent Prandtl number is estimated in the thermal layer downstream of reattachment. These results are compared with the available previous data.

scholarly journals Air–Sea Turbulent Heat Fluxes in Climate Models and Observational Analyses: What Drives Their Variability?

2019 ◽  
Vol 32 (8) ◽  
pp. 2397-2421 ◽  
Author(s):  
R. Justin Small ◽  
Frank O. Bryan ◽  
Stuart P. Bishop ◽  
Robert A. Tomas
Keyword(s):  
Heat Flux ◽  
Climate Models ◽  
Climate Model ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Small Scale ◽  
Turbulent Heat ◽  
Realistic Representation ◽  
Frontal Zones ◽  
The Tropics

Abstract A traditional view is that the ocean outside of the tropics responds passively to atmosphere forcing, which implies that air–sea heat fluxes are mainly driven by atmosphere variability. This paper tests this viewpoint using state-of-the-art air–sea turbulent heat flux observational analyses and a climate model run at different resolutions. It is found that in midlatitude ocean frontal zones the variability of air–sea heat fluxes is not predominantly driven by the atmosphere variations but instead is forced by sea surface temperature (SST) variations arising from intrinsic oceanic variability. Meanwhile in most of the tropics and subtropics wind is the dominant driver of heat flux variability, and atmosphere humidity is mainly important in higher latitudes. The predominance of ocean forcing of heat fluxes found in frontal regions occurs on scales of around 700 km or less. Spatially smoothing the data to larger scales results in the traditional atmosphere-driving case, while filtering to retain only small scales of 5° or less leads to ocean forcing of heat fluxes over most of the globe. All observational analyses examined (1° OAFlux; 0.25° J-OFURO3; 0.25° SeaFlux) show this general behavior. A standard resolution (1°) climate model fails to reproduce the midlatitude, small-scale ocean forcing of heat flux: refining the ocean grid to resolve eddies (0.1°) gives a more realistic representation of ocean forcing but the variability of both SST and of heat flux is too high compared to observational analyses.

The importance of including sea surface current when estimating air-sea turbulent heat fluxes and wind stress in the Gulf Stream region

2020 ◽  
Author(s):  
Xiangzhou Song
Keyword(s):  
Heat Flux ◽  
Wind Stress ◽  
Gulf Stream ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Surface Currents ◽  
Mean Flow ◽  
Geostrophic Currents ◽  
Turbulent Heat Fluxes

AbstractUsing buoy observations from 2004 to 2010 and newly released atmospheric reanalysis and satellite altimetry-derived geostrophic currents from 1993 to 2017, the quantitative contribution of daily mean surface currents to air-sea turbulent heat flux and wind stress uncertainties in the Gulf Stream (GS) region is investigated based on bulk formulas. At four buoy stations, the daily mean latent (sensible) heat flux difference between the estimates with and without surface currents ranges from -18 (-4) to 20 (4) Wm-2, while the daily mean wind stress difference ranges from -0.04 to 0.02 Nm-2. The positive values indicate higher estimates with opposite directions between surface currents and absolute winds. The transition between positive and negative differences is significantly associated with synoptic-scale weather variations. The uncertainties based on buoy observations are approximately 7% and 3% for wind stress and turbulent heat fluxes, respectively. The new reanalysis and satellite geostrophic currents confirm the uncertainties identified by buoy observations with acceptable discrepancies and provide a spatial view of the uncertainty fields. The mean geostrophic currents are aligned with the surface wind along the GS; therefore, the turbulent heat fluxes and wind stress will be ‘underestimated’ with surface currents included. However, on both sides of the GS, the surface flow can be upwind due to possible mechanisms of eddy-mean flow interactions and recirculations, resulting in higher turbulent heat flux estimations. The wind stress and turbulent heat flux uncertainties experience significant seasonal variations and show long-term trends.

Effects of spatial resolution and temporal offset of air-sea boundary-layer variables on turbulent heat flux estimates

2021 ◽  
Author(s):  
Tong Lee ◽  
Chelle Centemann ◽  
Carol Anne Clayson ◽  
Mark Bourassa ◽  
Shannon Brown ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
High Resolution ◽  
Surface Wind ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Turbulent Heat ◽  
Input Variables ◽  
Temporal Offset

<p>Air-sea turbulent heat fluxes and their spatial gradients are important to the ocean, climate, weather, and their interactions. Satellite-based estimation of air-sea latent and sensible fluxes, providing broad coverage, require measurements of sea surface temperature, ocean-surface wind speed, and air temperature and humidity above sea surface. Because no single satellite has been able to provide simultaneous measurements of these input variables, they typically come from various satellites with different spatial resolutions and sampling times that can be offset by hours. These factors introduce errors in the estimated heat fluxes and their gradients that are not well documented. As a model-based assessment of these errors, we performed a simulation using a Weather Research and Forecasting (WRF) model forced by high-resolution blended satellite SST for the Gulf Stream extension region with a 3-km resolution and with 30-minute output. Latent and sensible heat fluxes were first computed from input variables with the original model resolutions and at coincident times. We then computed the heat fluxes by (1) decimating the input variables to various resolutions from 12.5 to 50 km, and (2) offsetting the “sampling” times of some input variables from others by 3 hours. The resultant estimations of heat fluxes and their gradients from (1) and (2) were compared with the counterparts without reducing resolution and without temporal offset of the input variables. The results show that reducing input-variable resolutions from 12.5 to 50 km weakened the magnitudes of the time-mean and instantaneous heat fluxes and their gradients substantially, for example, by a factor of two for the time-mean gradients. The temporal offset of input variables substantially impacted the instantaneous fluxes and their gradients, although not their time-mean values. The implications of these effects on scientific and operational applications of heat flux products will be discussed. Finally, we highlight a mission concept for providing simultaneous, high-resolution measurements of boundary-layer variables from a single satellite to improve air-sea turbulent heat flux estimation.</p>

Augmentations to the Noah Model Physics for Application to the Yellow River Source Area. Part II: Turbulent Heat Fluxes and Soil Heat Transport

2015 ◽  
Vol 16 (6) ◽  
pp. 2677-2694 ◽  
Author(s):  
Donghai Zheng ◽  
Rogier van der Velde ◽  
Zhongbo Su ◽  
Xin Wang ◽  
Jun Wen ◽  
...  
Keyword(s):  
Heat Flux ◽  
Organic Matter ◽  
Surface Temperature ◽  
Heat Transport ◽  
Yellow River ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
The Yellow River ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

Abstract This is the second part of a study on the assessment of the Noah land surface model (LSM) in simulating surface water and energy budgets in the high-elevation source region of the Yellow River. Here, there is a focus on turbulent heat fluxes and heat transport through the soil column during the monsoon season, whereas the first part of this study deals with the soil water flow. Four augmentations are studied for mitigating the overestimation of turbulent heat flux and underestimation of soil temperature measurements: 1) the muting effect of vegetation on the thermal heat conductivity is removed from the transport of heat from the first to the second soil layer, 2) the exponential decay factor imposed on is calculated using the ratio of the leaf area index (LAI) over the green vegetation fraction (GVF), 3) Zilitinkevich’s empirical coefficient for turbulent heat transport is computed as a function of the momentum roughness length , and 4) the impact of organic matter is considered in the parameterization of the thermal heat properties. Although usage of organic matter for calculating improves the correspondence between the estimates and laboratory measurements of heat conductivities, it is shown to have a relatively small impact on the Noah LSM performance even for large organic matter contents. In contrast, the removal of the muting effect of vegetation on and the parameterization of greatly enhances the soil temperature profile simulations, whereas turbulent heat flux and surface temperature computations mostly benefit from the modified formulation. Further, the nighttime surface temperature overestimation is resolved from a coupled land–atmosphere perspective.

Experimental and Numerical Investigation of Turbulent Mixing in Film Cooling Applications

2017 ◽  
Author(s):  
Michael Straußwald ◽  
Karin Schmid ◽  
Hagen Müller ◽  
Michael Pfitzner
Keyword(s):  
Heat Flux ◽  
Film Cooling ◽  
Density Ratio ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Flow Dynamics ◽  
Frame Rate ◽  
Experimental Investigations ◽  
Turbulent Heat ◽  
Future Design

Fundamental knowledge on the flow dynamics and in particular the turbulent heat flux in film cooling flows is essential for the future design process of efficient cooling geometries. Thermographic PIV has been used to measure temperature and velocity fields in flows emanating from cylindrical effusion holes simultaneously. The measurements were carried out in a closed-loop, heated wind tunnel facility at a repetition rate of 6 kHz. Due to the high frame rate of the measurements, the unsteady flow dynamics could be resolved. For a density ratio of DR = 1.6 and a momentum ratio of I = 8, the jet ejected from the cylindrical effusion hole lifts off the surface. From the instantaneous measurements it could be observed that pockets of hot air are entrained into the coolant forcing the relatively fast cooling air to dodge the slow main flow air. These shear layer fluctuations result in turbulent heat fluxes that do not follow the gradient diffusion hypothesis which is often used in RANS models. In addition to these experimental investigations, numerical results from RANS simulations with the k-ω-SST turbulence model are presented that were carried out as basis for future investigations on turbulent heat flux modeling.

scholarly journals Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

2018 ◽  
Vol 10 (12) ◽  
pp. 1994 ◽  
Author(s):  
Xinlei He ◽  
Tongren Xu ◽  
Sayed Bateni ◽  
Christopher Neale ◽  
Thomas Auligne ◽  
...  
Keyword(s):  
Heat Flux ◽  
Error Term ◽  
Structural Model ◽  
Model Error ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Model Errors ◽  
Turbulent Heat Fluxes ◽  
Study Sites

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.

scholarly journals Characteristics of Satellite-Based Ocean Turbulent Heat Flux around the Korean Peninsula and Relationship with Changes in Typhoon Intensity

2020 ◽  
Vol 13 (1) ◽  
pp. 42
Author(s):  
Jaemin Kim ◽  
Yun Gon Lee
Keyword(s):  
Heat Flux ◽  
Korean Peninsula ◽  
Energy Exchange ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Specific Humidity ◽  
Turbulent Heat ◽  
Ocean Atmosphere ◽  
Buoy Data

Ocean-atmosphere energy exchange is an important factor in the maintenance of oceanic and atmospheric circulation and the regulation of meteorological and climate systems. Oceanic sensible and latent heat fluxes around the Korean Peninsula were determined using satellite-based air-sea variables (wind speed, sea surface temperature, and atmospheric specific humidity and temperature) and the coupled ocean-atmosphere response experiment (COARE) 3.5 bulk algorithm for six years between 2014 and 2019. Seasonal characteristics of the marine heat flux and its short-term fluctuations during summer typhoons were also investigated. air-sea variables were produced through empirical relationships and verified with observational data from marine buoys around the Korean Peninsula. Satellite-derived wind speed, sea surface temperature, atmospheric specific humidity, and air temperature were strongly correlated with buoy data, with R2 values of 0.80, 0.97, 0.90, and 0.91, respectively. Satellite-based sensible and latent heat fluxes around the peninsula were also validated against fluxes calculated from marine buoy data, and displayed low values in summer and higher values in autumn and winter as the difference between air-sea temperature and specific humidity increased. Through analyses of spatio-temporal fluctuations in the oceanic turbulent heat flux and variations in intensities of typhoons, this study assessed the possibility of monitoring air-sea energy exchange using satellite-based ocean turbulent heat fluxes during high-impact weather.

scholarly journals Evaluation of OAFlux datasets based on in situ air-sea flux tower observations over the Yongxing Islands in 2016

2018 ◽  
Author(s):  
Fenghua Zhou ◽  
Rongwang Zhang ◽  
Rui Shi ◽  
Ju Chen ◽  
Yunkai He ◽  
...  
Keyword(s):  
Heat Flux ◽  
Sensible Heat Flux ◽  
Reanalysis Data ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Coefficient Of Determination ◽  
Dominant Factor ◽  
Turbulent Heat ◽  
Flux Tower

Abstract. The high-quality Yongxing air-sea flux tower (YXASFT), which was specially designed for air-sea boundary layer flux-related observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a one-year period from 2016/02/01 to 2017/01/31. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean-Atmosphere Response Experiment version 3.0 (COARE3.0) using those variables. This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) OAFlux reanalysis data products in the SCS. The study period was divided into the spring, summer_autumn and winter periods to conduct seasonal comparisons for each variable. First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF measurements with a coefficient of determination (R2) of 0.78. Second, to conduct seasonal comparisons, the overall reliabilities of the bulk OAFlux variables diminish in order from Ta, U, Qa to Ts based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates of both variables in the winter and spring than in the summer_autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 value is observed between the OAFlux-estimated and YXASFT-observed Ts, indicating that Ts is the least reliable product and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w/m2 (73 % of the observed mean) in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be poorly representative with enormous overestimations in the spring and winter, while its performance is much better during the summer_autumn period. Third, an analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer_autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer_autumn period.

scholarly journals Evaluation of OAFlux datasets based on in situ air–sea flux tower observations over Yongxing Island in 2016

2018 ◽  
Vol 11 (11) ◽  
pp. 6091-6106 ◽  
Author(s):  
Fenghua Zhou ◽  
Rongwang Zhang ◽  
Rui Shi ◽  
Ju Chen ◽  
Yunkai He ◽  
...  
Keyword(s):  
Heat Flux ◽  
Sensible Heat Flux ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
Coefficient Of Determination ◽  
Dominant Factor ◽  
Turbulent Heat ◽  
Autumn Period ◽  
Flux Tower

Abstract. The Yongxing air–sea flux tower (YXASFT), which was specially designed for air–sea boundary layer observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a 1-year period from 1 February 2016 to 31 January 2017. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE3.0). This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) Objectively Analyzed Air–Sea Fluxes (OAFlux) reanalysis data products. First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF with a coefficient of determination (R2) of 0.78. Second, the overall reliabilities of the bulk OAFlux variables were diminished in the order of Ta (air temperature), U(wind speed), Qa (air humidity) and Ts (sea surface temperature) based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates for winter and spring than in the summer–autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 is between the OAFlux-estimated and YXASFT-observed Ts, indicating that Ts is the least reliable dataset and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w m−2 in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be a poor representative, with enormous overestimations in the spring and winter, while its performance is much better during the summer–autumn period. Third, analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter, and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer–autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer–autumn period.

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