FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309682006> ?p ?o ?g. }

• W4309682006 endingPage "371" @default.
• W4309682006 startingPage "337" @default.
• W4309682006 abstract "Abstract Measurements of three flux towers operated during the land atmosphere feedback experiment (LAFE) are used to investigate relationships between surface fluxes and variables of the land–atmosphere system. We study these relations by means of two machine learning (ML) techniques: multilayer perceptrons (MLP) and extreme gradient boosting (XGB). We compare their flux derivation performance with Monin–Obukhov similarity theory (MOST) and a similarity relationship using the bulk Richardson number (BRN). The ML approaches outperform MOST and BRN. Best agreement with the observations is achieved for the friction velocity. For the sensible heat flux and even more so for the latent heat flux, MOST and BRN deviate from the observations while MLP and XGB yield more accurate predictions. Using MOST and BRN for latent heat flux, the root mean square errors (RMSE) are 107 Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> and 121 Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> , respectively, as well as the intercepts of the regression lines are $$approx 110$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>110</mml:mn> </mml:mrow> </mml:math> Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> . For the ML methods, the RMSEs reduce to 31 Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> for MLP and 33 Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> for XGB as well as the intercepts to just 4 Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> for MLP and $$-1$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> Wm $$^{-2}$$ <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> for XGB with slopes of the regression lines close to 1, respectively. These results indicate significant deficiencies of MOST and BRN, particularly for the derivation of the latent heat flux. In fact, in contrast to the established theories, feature importance weighting demonstrates that the ML methods base their improved derivations on net radiation, the incoming and outgoing shortwave radiations, the air temperature gradient, and the available water contents, but not on the water vapor gradient. The results imply that further studies of surface fluxes and other turbulent variables with ML techniques provide great promise for deriving advanced flux parameterizations and their implementation in land–atmosphere system models." @default.
• W4309682006 created "2022-11-29" @default.
• W4309682006 creator A5020163492 @default.
• W4309682006 creator A5030096952 @default.
• W4309682006 creator A5032267791 @default.
• W4309682006 creator A5046269095 @default.
• W4309682006 creator A5075919503 @default.
• W4309682006 creator A5088924165 @default.
• W4309682006 creator A5090890684 @default.
• W4309682006 date "2022-11-22" @default.
• W4309682006 modified "2023-09-26" @default.
• W4309682006 title "Estimation of the Surface Fluxes for Heat and Momentum in Unstable Conditions with Machine Learning and Similarity Approaches for the LAFE Data Set" @default.
• W4309682006 cites W1633562794 @default.
• W4309682006 cites W1638359206 @default.
• W4309682006 cites W1838347895 @default.
• W4309682006 cites W1984457886 @default.
• W4309682006 cites W2002538376 @default.
• W4309682006 cites W2011927829 @default.
• W4309682006 cites W2018182103 @default.
• W4309682006 cites W2022294230 @default.
• W4309682006 cites W2024750834 @default.
• W4309682006 cites W2028122638 @default.
• W4309682006 cites W2050689426 @default.
• W4309682006 cites W2054641514 @default.
• W4309682006 cites W2077292938 @default.
• W4309682006 cites W2093203661 @default.
• W4309682006 cites W2097744787 @default.
• W4309682006 cites W2105187327 @default.
• W4309682006 cites W2105284846 @default.
• W4309682006 cites W2133513151 @default.
• W4309682006 cites W2154534159 @default.
• W4309682006 cites W2161467829 @default.
• W4309682006 cites W2337404279 @default.
• W4309682006 cites W2566442743 @default.
• W4309682006 cites W2571533408 @default.
• W4309682006 cites W2605575389 @default.
• W4309682006 cites W2773050709 @default.
• W4309682006 cites W2776094521 @default.
• W4309682006 cites W2786756369 @default.
• W4309682006 cites W2883412419 @default.
• W4309682006 cites W2887176132 @default.
• W4309682006 cites W2901415630 @default.
• W4309682006 cites W2911964244 @default.
• W4309682006 cites W2918335093 @default.
• W4309682006 cites W2968780500 @default.
• W4309682006 cites W2978954487 @default.
• W4309682006 cites W3016289855 @default.
• W4309682006 cites W3080945685 @default.
• W4309682006 cites W3102476541 @default.
• W4309682006 cites W3135383589 @default.
• W4309682006 cites W3137394100 @default.
• W4309682006 cites W3138739389 @default.
• W4309682006 cites W3160826234 @default.
• W4309682006 cites W3185822169 @default.
• W4309682006 cites W4211072747 @default.
• W4309682006 cites W4220978700 @default.
• W4309682006 cites W2889849854 @default.
• W4309682006 doi "https://doi.org/10.1007/s10546-022-00761-2" @default.
• W4309682006 hasPublicationYear "2022" @default.
• W4309682006 type Work @default.
• W4309682006 citedByCount "1" @default.
• W4309682006 countsByYear W43096820062023 @default.
• W4309682006 crossrefType "journal-article" @default.
• W4309682006 hasAuthorship W4309682006A5020163492 @default.
• W4309682006 hasAuthorship W4309682006A5030096952 @default.
• W4309682006 hasAuthorship W4309682006A5032267791 @default.
• W4309682006 hasAuthorship W4309682006A5046269095 @default.
• W4309682006 hasAuthorship W4309682006A5075919503 @default.
• W4309682006 hasAuthorship W4309682006A5088924165 @default.
• W4309682006 hasAuthorship W4309682006A5090890684 @default.
• W4309682006 hasBestOaLocation W43096820061 @default.
• W4309682006 hasConcept C103278499 @default.
• W4309682006 hasConcept C105795698 @default.
• W4309682006 hasConcept C11413529 @default.
• W4309682006 hasConcept C115961682 @default.
• W4309682006 hasConcept C119857082 @default.
• W4309682006 hasConcept C139945424 @default.
• W4309682006 hasConcept C154945302 @default.
• W4309682006 hasConcept C33923547 @default.
• W4309682006 hasConcept C41008148 @default.
• W4309682006 hasConceptScore W4309682006C103278499 @default.
• W4309682006 hasConceptScore W4309682006C105795698 @default.
• W4309682006 hasConceptScore W4309682006C11413529 @default.
• W4309682006 hasConceptScore W4309682006C115961682 @default.
• W4309682006 hasConceptScore W4309682006C119857082 @default.
• W4309682006 hasConceptScore W4309682006C139945424 @default.
• W4309682006 hasConceptScore W4309682006C154945302 @default.
• W4309682006 hasConceptScore W4309682006C33923547 @default.
• W4309682006 hasConceptScore W4309682006C41008148 @default.
• W4309682006 hasFunder F4320306084 @default.
• W4309682006 hasFunder F4320317350 @default.
• W4309682006 hasFunder F4320320879 @default.
• W4309682006 hasFunder F4320332377 @default.
• W4309682006 hasIssue "2" @default.
• W4309682006 hasLocation W43096820061 @default.
• W4309682006 hasLocation W43096820062 @default.
• W4309682006 hasLocation W43096820063 @default.
• W4309682006 hasOpenAccess W4309682006 @default.

• next