Latent heat and sensible heat flux simulation in maize using artificial neural networks

2018 ◽  
Vol 154 ◽  
pp. 155-164 ◽  
Author(s):  
Babak Safa ◽  
Timothy J. Arkebauer ◽  
Qiuming Zhu ◽  
Andy Suyker ◽  
Suat Irmak
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Latent Heat ◽  
Sensible Heat Flux ◽  
Sensible Heat ◽  
Artificial Neural

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes using solar-induced fluorescence

2016 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Julia K. Green ◽  
Jana Kolassa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Sensible Heat Flux ◽  
Turbulent Fluxes ◽  
Input Dataset ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn from the training datasets: a target input dataset is generated using three independent data sources and a triple collocation (TC) algorithm to define a prior distribution. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides surface turbulent fluxes from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are validated using FLUXNET tower measurements across various climates and conditions. WECANN performs well in most cases and is strongly constrained by SIF information. The impact of SIF on WECANN retrievals is evaluated by removing it from the input dataset of the ANN, and it shows that SIF has significant influence, especially in regions of high vegetation cover and in humid conditions. When compared to in situ eddy covariance observations, WECANN typically outperforms other estimates, particularly for sensible and latent heat fluxes.

scholarly journals Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

2017 ◽  
Vol 14 (18) ◽  
pp. 4101-4124 ◽  
Author(s):  
Seyed Hamed Alemohammad ◽  
Bin Fang ◽  
Alexandra G. Konings ◽  
Filipe Aires ◽  
Julia K. Green ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Eddy Covariance ◽  
Latent Heat ◽  
Gross Primary Production ◽  
Turbulent Fluxes ◽  
Uncertainty Estimates ◽  
Artificial Neural ◽  
The Impact

Abstract. A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1°  ×  1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

scholarly journals Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3531
Author(s):  
Tomasz Tietze ◽  
Piotr Szulc ◽  
Daniel Smykowski ◽  
Andrzej Sitka ◽  
Romuald Redzicki
Keyword(s):  
Neural Networks ◽  
Heat Flux ◽  
Artificial Neural Networks ◽  
Phase Change ◽  
Mass Flow Rate ◽  
Phase Change Material ◽  
Flow Rate ◽  
Smoothing Effect ◽  
Artificial Neural ◽  
Change Material

The paper presents an innovative method for smoothing fluctuations of heat flux, using the thermal energy storage unit (TES Unit) with phase change material and Artificial Neural Networks (ANN) control. The research was carried out on a pilot large-scale installation, of which the main component was the TES Unit with a heat capacity of 500 MJ. The main challenge was to smooth the heat flux fluctuations, resulting from variable heat source operation. For this purpose, a molten salt phase change material was used, for which melting occurs at nearly constant temperature. To enhance the smoothing effect, a classical control system based on PID controllers was supported by ANN. The TES Unit was supplied with steam at a constant temperature and variable mass flow rate, while a discharging side was cooled with water at constant mass flow rate. It was indicated that the operation of the TES Unit in the phase change temperature range allows to smooth the heat flux fluctuations by 56%. The tests have also shown that the application of artificial neural networks increases the smoothing effect by 84%.

scholarly journals Observation of sensible and latent heat flux profiles with lidar

2020 ◽  
Vol 13 (6) ◽  
pp. 3221-3233 ◽  
Author(s):  
Andreas Behrendt ◽  
Volker Wulfmeyer ◽  
Christoph Senff ◽  
Shravan Kumar Muppa ◽  
Florian Späth ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Sensible Heat Flux ◽  
Ground Level ◽  
Sensible Heat ◽  
Flux Divergence ◽  
Convective Boundary ◽  
The Mean

Abstract. We present the first measurement of the sensible heat flux (H) profile in the convective boundary layer (CBL) derived from the covariance of collocated vertical-pointing temperature rotational Raman lidar and Doppler wind lidar measurements. The uncertainties of the H measurements due to instrumental noise and limited sampling are also derived and discussed. Simultaneous measurements of the latent heat flux profile (L) and other turbulent variables were obtained with the combination of water-vapor differential absorption lidar (WVDIAL) and Doppler lidar. The case study uses a measurement example from the HOPE (HD(CP)2 Observational Prototype Experiment) campaign, which took place in western Germany in 2013 and presents a cloud-free well-developed quasi-stationary CBL. The mean boundary layer height zi was at 1230 m above ground level. The results show – as expected – positive values of H in the middle of the CBL. A maximum of (182±32) W m−2, with the second number for the noise uncertainty, is found at 0.5 zi. At about 0.7 zi, H changes sign to negative values above. The entrainment flux was (-62±27) W m−2. The mean sensible heat flux divergence in the observed part of the CBL above 0.3 zi was −0.28 W m−3, which corresponds to a warming of 0.83 K h−1. The L profile shows a slight positive mean flux divergence of 0.12 W m−3 and an entrainment flux of (214±36) W m−2. The combination of H and L profiles in combination with variance and other turbulent parameters is very valuable for the evaluation of large-eddy simulation (LES) results and the further improvement and validation of turbulence parameterization schemes.

Characteristics of Long-term Surface Heat Source and Its Climate Influence Factors in Central Tibetan Plateau

2021 ◽  
Author(s):  
Zeyong Hu ◽  
Xiaoqiang Yan
Keyword(s):  
Heat Flux ◽  
Heat Source ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Sensible Heat Flux ◽  
Surface Heat ◽  
Sensible Heat ◽  
Net Radiation ◽  
Maximum Value

<p>Based on multi-level AWS data during 2001 to 2015 and eddy covariance data during 2011 to 2014 at Nagqu Station of Plateau Climate and Environment, the turbulent fluxes were calculated by a surface energy balance combination (CM) and eddy covariance ( EC) method. A long-term heat fluxes and surface heat source were obtained with comparison and correction of EC and CM fluxes. The surface energy closure ratio is close to 1 in spring, summer and autumn. But it reaches to 1.34 in winter due to low net radiation observation value on snow surface. The sensible heat flux shows a ascend trend while latent heat flux shows a descend trend during 2002 to 2015. The surface heat source shows a descend trend. The analysis of the surface heat source indicates that it has a significant relationship with net radiation flux, surface temperature, soil moisture and wind speed. Particularly, the surface heat source has a significant response to net radiation flux throughout the year. There are obvious influences of surface temperature and soil moisture on the surface heat source in spring, autumn and winter. And the influence of wind speeds on surface heat source is strong only in spring. The annual variation of sensible heat flux and latent heat flux are obvious. Sensible heat flux reaches the maximum value of the year in April and the minimum value in July. however, latent heat flux shows the maximum value in July and the minimum value in January. </p>

scholarly journals Comparison of turbulent structures and energy fluxes over exposed and debris-covered glacier ice

2020 ◽  
Vol 66 (258) ◽  
pp. 543-555 ◽  
Author(s):  
Lindsey Nicholson ◽  
Ivana Stiperski
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Similarity Theory ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Temperature Fields ◽  
Sensible Heat ◽  
Energy Fluxes ◽  
Glacier Surface ◽  
Debris Cover

AbstractWe present the first direct comparison of turbulence conditions measured simultaneously over exposed ice and a 0.08 m thick supraglacial debris cover on Suldenferner, a small glacier in the Italian Alps. Surface roughness, sensible heat fluxes (~20–50 W m−2), latent heat fluxes (~2–10 W m−2), topology and scale of turbulence are similar over both glacier surface types during katabatic and synoptically disturbed conditions. Exceptions are sunny days when buoyant convection becomes significant over debris-covered ice (sensible heat flux ~ −100 W m−2; latent heat flux ~ −30 W m−2) and prevailing katabatic conditions are rapidly broken down even over this thin debris cover. The similarity in turbulent properties implies that both surface types can be treated the same in terms of boundary layer similarity theory. The differences in turbulence between the two surface types on this glacier are dominated by the radiative and thermal contrasts, thus during sunny days debris cover alters both the local surface turbulent energy fluxes and the glacier component of valley circulation. These variations under different flow conditions should be accounted for when distributing temperature fields for modeling applications over partially debris-covered glaciers.

scholarly journals Diurnal Energy Balance in a Mango Orchard in the Northeast of Pará, Brazil

2018 ◽  
Vol 33 (3) ◽  
pp. 537-546 ◽  
Author(s):  
Paulo Jorge de Oliveira Ponte de Souza ◽  
Juliana Chagas Rodrigues ◽  
Adriano Marlisom Leão de Sousa ◽  
Everaldo Barreiros de Souza
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Soil Cover ◽  
Sensible Heat Flux ◽  
Sensible Heat ◽  
Net Radiation ◽  
Growing Seasons ◽  
Available Energy

Abstract This study aimed to evaluate the diurnal energy balance during the reproductive stage of two growing seasons of a mango orchard in the northeast of Pará, Brazil. Therefore, a micrometeorological tower was installed and instrumented, in the center of the experimental area, to monitor meteorological variables, besides the phenological evaluation of the mango orchard, which was carried out during growing seasons of 2010-2011 (October 2010 to January 2011) and of 2011-2012 (September 2011 to January 2012). The energy balance was obtained by the bowen ration technique, and the available energy partitioned into heat flux to the ground, sensible heat and latent heat. The amount of rainfall was crucial to the partition of the net radiation in the energy balance components. It provided the variation in the consumption of available energy between 69% and 78% as latent heat flux, and between 23% and 32% as sensible heat flux. The heat flux to the ground was small, representing less than 1% of the net radiation, showing that the mango orchard exhibits good soil cover preventing large variations in soil heating.

scholarly journals Atmospheric Dynamics Triggered by an Oceanic SST Front in a Moist Quasigeostrophic Model

2012 ◽  
Vol 69 (5) ◽  
pp. 1617-1632 ◽  
Author(s):  
Bruno Deremble ◽  
Guillaume Lapeyre ◽  
Michael Ghil
Keyword(s):  
Heat Flux ◽  
Heat Release ◽  
Latent Heat ◽  
Sensible Heat Flux ◽  
Sensible Heat ◽  
Latent Heat Release ◽  
Sst Front ◽  
Moist Processes ◽  
Surface Sensible Heat Flux ◽  
Sst Fronts

Abstract To understand the atmospheric response to a midlatitude oceanic front, this paper uses a quasigeostrophic (QG) model with moist processes. A well-known, three-level QG model on the sphere has been modified to include such processes in an aquaplanet setting. Its response is analyzed in terms of the upper-level atmospheric jet for sea surface temperature (SST) fronts of different profiles and located at different latitudes. When the SST front is sufficiently strong, it tends to anchor the mean atmospheric jet, suggesting that the jet’s spatial location and pattern are mainly affected by the latitude of the SST front. Changes in the jet’s pattern are studied, focusing on surface sensible heat flux and on moisture effects through latent heat release. It is found that latent heat release due to moist processes is modified when the SST front is changed, and this is responsible for the meridional displacement of the jet. Moreover, both latent heat release and surface sensible heat flux contribute to the jet’s strengthening. These results highlight the role of SST fronts and moist processes in affecting the characteristics of the midlatitude jet stream and of its associated storm track, particularly their positions.

scholarly journals Evaluation of functional properties of various types of vegetation cover using remotely sensed data analysis

2010 ◽  
Vol 4 (Special Issue 2) ◽  
pp. S49-S58 ◽  
Author(s):  
J. Brom ◽  
J. Procházka ◽  
A. Rejšková
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Sensible Heat Flux ◽  
Sensible Heat ◽  
Remotely Sensed Data ◽  
Mean Values

The dissipation of solar energy and consequently the formation of the hydrological cycle are largely dependent on the structural and optical characteristics of the land surface. In our study, we selected seven units with different types of vegetation in the Mlýnský and Horský catchments (South-Eastern part of the Šumava Mountains, Czech Republic) for the assessment of the differences in their functioning expressed through the surface temperature, humidity, and energy dissipation. For our analyses, we used Landsat 5 TM satellite data from June 25<SUP>th</SUP>, 2008. The results showed that the microclimatic characteristics and energy fluxes varied in different units according to their vegetation characteristics. A cluster analysis of the mean values was used to divide the vegetation units into groups according to their functional characteristics. The mown meadows were characterised by the highest surface temperature and sensible heat flux and the lowest humidity and latent heat flux. On the contrary, the lowest surface temperature and sensible heat flux and the highest humidity and latent heat flux were found in the forest. Our results showed that the climatic and energetic features of the land surface are related to the type of vegetation. We state that the spatial distribution of different vegetation units and the amount of biomass are crucial variables influencing the functioning of the landscape.

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