scholarly journals Latent heat flux measurements over complex terrain by airborne water vapour and wind lidars

2011 ◽  
Vol 137 (S1) ◽  
pp. 190-203 ◽  
Author(s):  
Christoph Kiemle ◽  
Martin Wirth ◽  
Andreas Fix ◽  
Stephan Rahm ◽  
Ulrich Corsmeier ◽  
...  
Keyword(s):  
Heat Flux ◽  
Water Vapour ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Complex Terrain ◽  
Flux Measurements

An analog period method for gap‐filling of latent heat flux measurements

2021 ◽  
Author(s):  
Lucas Emilio B. Hoeltgebaum ◽  
Nelson Luís Dias ◽  
Marcelo Azevedo Costa
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Gap Filling ◽  
Flux Measurements

Carbon dioxide and latent heat flux measurements in a windbreak-sheltered orchard

1993 ◽  
Vol 66 (3-4) ◽  
pp. 193-210 ◽  
Author(s):  
M.J. Judd ◽  
P.T. Prendergast ◽  
K.J. McAneney
Keyword(s):  
Carbon Dioxide ◽  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Flux Measurements

scholarly journals Analysis of the PKT correction for direct CO<sub>2</sub> flux measurements over the ocean

2014 ◽  
Vol 14 (7) ◽  
pp. 3361-3372 ◽  
Author(s):  
S. Landwehr ◽  
S. D. Miller ◽  
M. J. Smith ◽  
E. S. Saltzman ◽  
B. Ward
Keyword(s):  
Heat Flux ◽  
Water Vapour ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Co2 Flux ◽  
Sensor Data ◽  
Closed Path ◽  
Co2 Fluxes ◽  
Open Path ◽  
Order Of Magnitude

Abstract. Eddy covariance measurements of air–sea CO2 fluxes can be affected by cross-sensitivities of the CO2 measurement to water vapour, resulting in order-of-magnitude biases. Well-established causes for these biases are (i) cross-sensitivity of the broadband non-dispersive infrared sensors due to band-broadening and spectral overlap (commercial sensors typically correct for this) and (ii) the effect of air density fluctuations (removed by determining the dry air CO2 mixing ratio). Another bias related to water vapour fluctuations has recently been observed with open-path sensors, attributed to sea salt build-up and water films on sensor optics. Two very different approaches have been used to deal with these water vapour-related biases. Miller et al. (2010) employed a membrane drier to physically eliminate 97% of the water vapour fluctuations in the sample air before it entered a closed-path gas analyser. Prytherch et al. (2010a) employed the empirical (Peter K. Taylor, PKT) post-processing correction to correct open-path sensor data. In this paper, we test these methods side by side using data from the Surface Ocean Aerosol Production (SOAP) experiment in the Southern Ocean. The air–sea CO2 flux was directly measured with four closed-path analysers, two of which were positioned down-stream of a membrane dryer. The CO2 fluxes from the two dried gas analysers matched each other and were in general agreement with common parameterisations. The flux estimates from the un-dried sensors agreed with the dried sensors only during periods with low latent heat flux (&amp;leq;7 W m−2). When latent heat flux was higher, CO2 flux estimates from the un-dried sensors exhibited large scatter and an order-of-magnitude bias. Applying the PKT correction to the flux data from the un-dried analysers did not remove the bias when compared to the data from the dried gas analyser. The results of this study demonstrate the validity of measuring CO2 fluxes using a pre-dried air stream and show that the PKT correction is not valid for the correction of CO2 fluxes.

An attenuated eddy covariance method for latent heat flux measurements

2007 ◽  
Vol 43 (4) ◽  
Author(s):  
Nelson L. Dias ◽  
Henrique F. Duarte ◽  
Selma R. Maggiotto ◽  
Leocádio Grodzki
Keyword(s):  
Heat Flux ◽  
Eddy Covariance ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Eddy Covariance Method ◽  
Flux Measurements

scholarly journals Diurnal cycle of short-term fluctuations of integrated water vapour above Switzerland

2019 ◽  
Vol 19 (19) ◽  
pp. 12083-12090
Author(s):  
Klemens Hocke ◽  
Leonie Bernet ◽  
Jonas Hagen ◽  
Axel Murk ◽  
Matthias Renker ◽  
...  
Keyword(s):  
Heat Flux ◽  
Standard Deviation ◽  
Water Vapour ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Diurnal Cycle ◽  
Short Term ◽  
Filtering Method ◽  
Integrated Water Vapour ◽  
Band Pass

Abstract. The TROpospheric WAter RAdiometer (TROWARA) continuously measures integrated water vapour (IWV) with a time resolution of 6 s at Bern in Switzerland. During summer, we often see that IWV has temporal fluctuations during daytime, while the nighttime data are without fluctuations. The data analysis is focused on the year 2010, where TROWARA has a good data quality without data gaps. We derive the spectrum of the IWV fluctuations in the period range from about 1 to 100 min. The FFT spectrum with a window size of 3 months leads to a serious underestimation of the spectral amplitudes of the fluctuations. Thus, we apply a band pass filtering method to derive the amplitudes as a function of period Tp. The amplitudes are proportional to Tp0.5. Another method is the calculation of the moving standard deviation with time window lengths from about 1 to 100 min. Here, we get similar results to those with the band pass filtering method. At all periods, the IWV fluctuations are strongest during summer, while they are smallest during winter. We derive the diurnal variation of the short-term IWV fluctuations by applying a moving standard deviation with a window length of 10 min. The daily cycle is strongest during the summer season, with standard deviations up to 0.22 mm at about 14:00 CET. The diurnal cycle disappears during wintertime. A similar seasonal behaviour is observed in the diurnal cycle of latent heat flux as provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2 reanalysis) at Bern. Further, the 3 d averages of the latent heat flux and the magnitude of the short-term IWV variability show a strong correlation at Bern in 2010 (r=0.82 with a 95 % confidence interval from 0.75 to 0.87). Thus, we suggest that the diurnal cycle of short-term IWV fluctuations at Bern is mainly caused by large convective heating during daytime in summer.

scholarly journals ESTIMATIVA DE FLUXO DE CALOR LATENTE EM RESERVATÓRIOS ATRAVÉS DE UMA REDE NEURAL ARTIFICIAL

2016 ◽  
Vol 38 ◽  
pp. 361
Author(s):  
Dornelles Vissotto Junior ◽  
Lucas Emílio Bernardelli Hoeltgebaum ◽  
Ricardo Carvalho de Almeida
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Heat Fluxes ◽  
Transfer Model ◽  
Ann Model ◽  
Gap Filling ◽  
Flux Measurements ◽  
Index Of Agreement ◽  
Artificial Neural Network Ann

Micrometeorology monitoring has been used in reservoirs for latent heat flux measurements by eddy covariance. It is hard to establish long and continuous measurement datasets due to the complexity involved in this monitoring. When fails occur there is necessary a gap filling procedure to keep the continuity of the series. This filling could be performed through statistical techniques and use of model results. In this work we assessed the performance of a backpropagation Artificial Neural Network (ANN) Model to estimatives of latent heat fluxes at Furnas Lake – MG to fill the gaps in 50 days measurement dataset. The ANN was applied using various sets of input parameters, layer structures and trainning time. The performance of ANN estimatives were compared of a classic mass transfer model. The index of agreement are used to evaluate the performance of the models. The ANN Model index of agreement equal to 0.93536 showing better results than transfer model with 0.89681. The results showed that the ANN could be used with great performance to estimate latent heat flux and gap filling.

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