scholarly journals Enhanced turbulent heat fluxes improve Meiyu‐Baiu simulation in high‐resolution atmospheric models

2021 ◽  
Author(s):  
Tian Ding ◽  
Tianjun Zhou ◽  
Xiaolong Chen ◽  
Liwei Zou ◽  
Puxi Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Atmospheric Models ◽  
Turbulent Heat Fluxes

scholarly journals Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system

2017 ◽  
Vol 38 (8-10) ◽  
pp. 3003-3026 ◽  
Author(s):  
Claire Brenner ◽  
Christina Elisabeth Thiem ◽  
Hans-Dieter Wizemann ◽  
Matthias Bernhardt ◽  
Karsten Schulz
Keyword(s):  
High Resolution ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Thermal Imagery ◽  
Turbulent Heat Fluxes ◽  
Spatially Distributed

scholarly journals Reference crop evapotranspiration estimate using high-resolution meteorological network's data

2009 ◽  
Vol 3 (1) ◽  
pp. 113-118
Author(s):  
C. Lussana ◽  
F. Uboldi
Keyword(s):  
High Resolution ◽  
Similarity Theory ◽  
Heat Fluxes ◽  
Optimal Interpolation ◽  
Turbulent Heat ◽  
Crop Evapotranspiration ◽  
Reference Crop Evapotranspiration ◽  
Turbulent Heat Fluxes ◽  
Evapotranspiration Rate ◽  
Reference Crop

Abstract. Water management authorities need detailed information about each component of the hydrological balance. This document presents a method to estimate the evapotranspiration rate, initialized in order to obtain the reference crop evapotranspiration rate (ET0). By using an Optimal Interpolation (OI) scheme, the hourly observations of several meteorological variables, measured by a high-resolution local meteorological network, are interpolated over a regular grid. The analysed meteorological fields, containing detailed meteorological information, enter a model for turbulent heat fluxes estimation based on Monin-Obukhov surface layer similarity theory. The obtained ET0 fields are then post-processed and disseminated to the users.

Spatial Variation in Turbulent Heat Fluxes in Drake Passage

2012 ◽  
Vol 25 (5) ◽  
pp. 1470-1488 ◽  
Author(s):  
ChuanLi Jiang ◽  
Sarah T. Gille ◽  
Janet Sprintall ◽  
Kei Yoshimura ◽  
Masao Kanamitsu
Keyword(s):  
Heat Flux ◽  
High Resolution ◽  
Air Temperature ◽  
Drake Passage ◽  
Polar Front ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Length Scales ◽  
Sea Temperature ◽  
Turbulent Heat Fluxes

High-resolution underway shipboard atmospheric and oceanic observations collected in Drake Passage from 2000 to 2009 are used to examine the spatial scales of turbulent heat fluxes and flux-related state variables. The magnitude of the seasonal cycle of sea surface temperature (SST) south of the Polar Front is found to be twice that north of the front, but the seasonal cycles of the turbulent heat fluxes show no differences on either side of the Polar Front. Frequency spectra of the turbulent heat fluxes and related variables are red, with no identifiable spectral peaks. SST and air temperature are coherent over a range of frequencies corresponding to periods between ~10 h and 2 days, with SST leading air temperature. The spatial decorrelation length scales of the sensible and latent heat fluxes calculated from two-day transects are 65 ± 6 km and 80 ± 6 km, respectively. The scale of the sensible heat flux is consistent with the decorrelation scale for air–sea temperature differences (70 ± 6 km) rather than either SST (153 ± 2 km) or air temperature (138 ± 4 km) alone. These scales are dominated by the Polar Front. When the Polar Front region is excluded, the decorrelation scales are 10–20 km, consistent with the first baroclinic Rossby radius. These eddy scales are often unrepresented in the available gridded heat flux products. The Drake Passage ship measurements are compared with four recently available gridded turbulent heat flux products: the European Centre for Medium-Range Weather Forecasts high-resolution operational product in support of the Year of Coordinated Observing Modeling and Forcasting Tropical Convection (ECMWF-YOTC), ECMWF interim reanalysis (ERA-Interim), the Drake Passage reanalysis downscaling (DPRD10) regional product, and the objectively analyzed air–sea fluxes (OAFlux). The decorrelation length scales of the air–sea temperature difference, wind speed, and turbulent heat fluxes from these four products are significantly larger than those determined from shipboard measurements.

Estimation of turbulent heat fluxes and exchange coefficients for heat at Dumont d'Urville, East Antarctica

1999 ◽  
Vol 11 (1) ◽  
pp. 93-99 ◽  
Author(s):  
S. Argentini ◽  
G. Mastrantonio ◽  
A. Viola
Keyword(s):  
Boundary Layer ◽  
Heat Fluxes ◽  
East Antarctica ◽  
Turbulent Heat ◽  
Convective Boundary ◽  
Doppler Sodar ◽  
Turbulent Heat Fluxes ◽  
Unstable Boundary Layer ◽  
Sodar Measurements ◽  
The Antarctic

Simultaneous acoustic Doppler sodar and tethersonde measurements were used to study some of the characteristics of the unstable boundary layer at Dumont d'Urville, Adélie Land, East Antarctica during the summer 1993–94. A description of the convective boundary layer and its behaviour in connection with the wind regime is given along with the frequency distribution of free convection episodes. The surface heat flux has been evaluated using the vertical velocity variance derived from sodar measurements. The turbulent exchange coefficients, estimated by coupling sodar and tethered balloon measurements, are in strong agreement with those present in literature for the Antarctic regions.

Searching for new physics: Using explainable AI to understand deep learned parameterizations of turbulent heat fluxes

2021 ◽  
Author(s):  
Andrew Bennett ◽  
Bart Nijssen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Coupled System ◽  
Hydrologic Model ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Phase Lag ◽  
Geophysical Research ◽  
Turbulent Heat Fluxes

<p>Machine learning (ML), and particularly deep learning (DL), for geophysical research has shown dramatic successes in recent years. However, these models are primarily geared towards better predictive capabilities, and are generally treated as black box models, limiting researchers’ ability to interpret and understand how these predictions are made. As these models are incorporated into larger models and pushed to be used in more areas it will be important to build methods that allow us to reason about how these models operate. This will have implications for scientific discovery that will ensure that these models are robust and reliable for their respective applications. Recent work in explainable artificial intelligence (XAI) has been used to interpret and explain the behavior of machine learned models.</p><p>Here, we apply new tools from the field of XAI to provide physical interpretations of a system that couples a deep-learning based parameterization for turbulent heat fluxes to a process based hydrologic model. To develop this coupling we have trained a neural network to predict turbulent heat fluxes using FluxNet data from a large number of hydroclimatically diverse sites. This neural network is coupled to the SUMMA hydrologic model, taking imodel derived states as additional inputs to improve predictions. We have shown that this coupled system provides highly accurate simulations of turbulent heat fluxes at 30 minute timesteps, accurately predicts the long-term observed water balance, and reproduces other signatures such as the phase lag with shortwave radiation. Because of these features, it seems this coupled system is learning physically accurate relationships between inputs and outputs. </p><p>We probe the relative importance of which input features are used to make predictions during wet and dry conditions to better understand what the neural network has learned. Further, we conduct controlled experiments to understand how the neural networks are able to learn to regionalize between different hydroclimates. By understanding how these neural networks make their predictions as well as how they learn to make predictions we can gain scientific insights and use them to further improve our models of the Earth system.</p>

The Effect of Soil on the Summertime Surface Energy Budget of a Humid Subarctic Tundra in Northern Quebec, Canada

2021 ◽  
Vol 22 (10) ◽  
pp. 2547-2564
Author(s):  
Georg Lackner ◽  
Daniel F. Nadeau ◽  
Florent Domine ◽  
Annie-Claude Parent ◽  
Gonzalo Leonardini ◽  
...  
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Arctic Region ◽  
Heat Fluxes ◽  
Surface Energy Budget ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

AbstractRising temperatures in the southern Arctic region are leading to shrub expansion and permafrost degradation. The objective of this study is to analyze the surface energy budget (SEB) of a subarctic shrub tundra site that is subject to these changes, on the east coast of Hudson Bay in eastern Canada. We focus on the turbulent heat fluxes, as they have been poorly quantified in this region. This study is based on data collected by a flux tower using the eddy covariance approach and focused on snow-free periods. Furthermore, we compare our results with those from six Fluxnet sites in the Arctic region and analyze the performance of two land surface models, SVS and ISBA, in simulating soil moisture and turbulent heat fluxes. We found that 23% of the net radiation was converted into latent heat flux at our site, 35% was used for sensible heat flux, and about 15% for ground heat flux. These results were surprising considering our site was by far the wettest site among those studied, and most of the net radiation at the other Arctic sites was consumed by the latent heat flux. We attribute this behavior to the high hydraulic conductivity of the soil (littoral and intertidal sediments), typical of what is found in the coastal regions of the eastern Canadian Arctic. Land surface models overestimated the surface water content of those soils but were able to accurately simulate the turbulent heat flux, particularly the sensible heat flux and, to a lesser extent, the latent heat flux.

The signature of mesoscale eddies on the air‐sea turbulent heat fluxes in the South Atlantic Ocean

2015 ◽  
Vol 42 (6) ◽  
pp. 1856-1862 ◽  
Author(s):  
A. B. Villas Bôas ◽  
O. T. Sato ◽  
A. Chaigneau ◽  
G. P. Castelão
Keyword(s):  
Atlantic Ocean ◽  
Mesoscale Eddies ◽  
South Atlantic ◽  
Heat Fluxes ◽  
South Atlantic Ocean ◽  
Turbulent Heat ◽  
The South ◽  
Turbulent Heat Fluxes

scholarly journals Evaluation of the heat balance components over the Baltic Sea using four gridded meteorological databases and direct observations

2005 ◽  
Vol 36 (4-5) ◽  
pp. 381-396 ◽  
Author(s):  
A. Rutgersson ◽  
A. Omstedt ◽  
Y. Chen
Keyword(s):  
Baltic Sea ◽  
Short Wave ◽  
Heat Fluxes ◽  
Wave Radiation ◽  
Turbulent Heat ◽  
The Baltic Sea ◽  
Short Wave Radiation ◽  
Turbulent Heat Fluxes ◽  
The Baltic ◽  
The Heat Balance

In this paper, which reports on part of the BALTEX project, various components of the heat balance over the Baltic Sea are calculated using a number of gridded meteorological databases. It is the heat exchange between the Baltic Sea surface and the atmosphere that is of interest. The databases have different origins, comprising synoptic data, data re-analysed with a 3D assimilation system, an ocean model forced with gridded synoptic data, ship data and satellite data. We compared the databases and found that the greatest variation between them is in the long- and short-wave radiation values. However, considerable upward long-wave radiation is followed by considerable downward short-wave radiation, so the total radiation component is partly compensated for in the total budget. The variation in the total heat transport in the databases therefore appears smaller (1.5±3 W m−2) as the average and one standard deviation. The turbulent heat fluxes estimated from satellite data have very low values; this can largely be explained by the method of calculating air temperature, which also produces an unrealistic stratification over the Baltic Sea. The ERA40 data was compared with measured values: there, we found a certain land influence even in the centre of the Baltic proper. The indicated turbulent heat fluxes were too large, mainly in the fall and winter, and the sensible heat flux was too large in a downward direction in spring and summer.

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