scholarly journals Modelling longwave radiation to snow beneath forest canopies using hemispherical photography or linear regression

2008 ◽  
Vol 22 (15) ◽  
pp. 2788-2800 ◽  
Author(s):  
Richard Essery ◽  
John Pomeroy ◽  
Chad Ellis ◽  
Tim Link
Keyword(s):  
Linear Regression ◽  
Longwave Radiation ◽  
Hemispherical Photography ◽  
Forest Canopies

Observations and modeling of incoming longwave radiation to snow beneath forest canopies in the west Tianshan Mountains, China

2014 ◽  
Vol 11 (5) ◽  
pp. 1138-1153 ◽  
Author(s):  
Heng Lu ◽  
Wen-shou Wei ◽  
Ming-zhe Liu ◽  
Xi Han ◽  
Wen Hong
Keyword(s):  
Longwave Radiation ◽  
Tianshan Mountains ◽  
The West ◽  
Forest Canopies

Analyses of Forest Foliage II: Measurement of Carbon Fraction and Nitrogen Content by End-Member Analysis

1994 ◽  
Vol 2 (1) ◽  
pp. 15-23 ◽  
Author(s):  
John D. Aber ◽  
Katherine L. Bolster ◽  
Stephen D. Newman ◽  
Margaret Soulia ◽  
Mary E. Martin
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Chemical Constituents ◽  
High Spectral Resolution ◽  
Statistical Techniques ◽  
Mixing Models ◽  
Difference Spectra ◽  
Forest Canopies

Chemical constituents of forest canopies are accepted as key indicators of ecosystem state and function. Remote sensing of these parameters offers the potential for rapid and accurate assessment of rates of key biogeochemical processes over large regions. NASA's Accelerated Canopy Chemistry Program was established to test the accuracy and generality of high-spectral resolution remote sensing in the measurement of lignin, cellulose and nitrogen concentrations in forest canopies. One of the most straightforward methods for extracting constituent concentration information from spectra is through simple linear mixing models of pre-determined end-member or pure compound spectra. While there are ample reasons to expect that linear mixing models will not work in foliar and whole-canopy samples, end-member analysis might still provide important information in support of standard and emerging statistical techniques, such as linear regression and partial least squares regression on first and second difference spectra, by indicating which parts of the spectrum should contain information on the concentrations of important constituents. The purpose of this paper is to present the results of two approaches to determining the value of end-member spectra for estimating constituent concentrations in foliage of temperate zone forest species. The first examines the spectral changes accompanying each step in the chemical proximate analysis method used to determine concentrations in the laboratory, and from them to infer the spectra of the fractions removed at each step. The second is the combination of known materials which approximate these same fractions in foliage into well-mixed samples to determine whether a simple linear mixing model can be used to predict the spectrum of the resulting mixture. Results confirm that the combination of linear mixing models and end-member analysis is not an appropriate technique for obtaining quantitative estimates of constituent concentrations for the major components of foliage of native woody plants, nor do we expect that more detailed analyses of plant ultrastructure or foliar spectra will correct the deficiencies identified. However, these results do suggest that the wet chemical procedures used to extract different carbon fractions produce consistent results with regard to the location of spectral features associated with the compounds removed at each step, and that the spectra of the cellulose and lignin isolated by this technique are very similar to those from pure materials. This in turn suggests that statistical techniques such as linear regression on first and second difference spectra and partial least squares methods which allow or correct for non-linear mixing, should be successful.

scholarly journals Simulated single-layer forest canopies delay Northern Hemisphere snowmelt

2019 ◽  
Author(s):  
Markus Todt ◽  
Nick Rutter ◽  
Christopher G. Fletcher ◽  
Leanne M. Wake
Keyword(s):  
Snow Cover ◽  
Boreal Forests ◽  
Single Layer ◽  
Longwave Radiation ◽  
Length Change ◽  
Day Length ◽  
Forest Canopies ◽  
Diurnal Cycles ◽  
Community Land Model ◽  
The Impact

Abstract. Single-layer vegetation schemes have been found to overestimate diurnal cycles in longwave radiation beneath forest canopies. This study derives a correction from forest stand-scale simulations, which reduces diurnal cycles of sub-canopy longwave radiation. Correction factors are subsequently implemented in land-only simulations of the Community Land Model version 4.5 (CLM4.5) in order to assess the impact on snow cover. Nighttime underestimations of sub-canopy longwave radiation outweigh daytime overestimations, which leads to underestimated averages over the snow cover season. As a result, snow temperatures are underestimated and snowmelt is delayed in CLM4.5 across evergreen boreal forests. Increasing insolation and day length change the impact of overestimated diurnal cycles on daily average sub-canopy longwave radiation throughout the snowmelt season. Consequently, delay in snowmelt is more substantial where winters are warm and snowmelt occurs early, which results in a shortened snowmelt duration across boreal forests.

scholarly journals A Comparative Study of Bulk Parameterization Schemes for Estimating Cloudy-Sky Surface Downward Longwave Radiation

2019 ◽  
Vol 11 (5) ◽  
pp. 528 ◽  
Author(s):  
Jie Cheng ◽  
Feng Yang ◽  
Yamin Guo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Acceptable Accuracy ◽  
Parameterization Schemes ◽  
Bulk Formula ◽  
Downward Longwave Radiation

Parameterization schemes (bulk formulae) are widely used to estimate all-sky surface downward longwave radiation (SDLR) due to the simple, readily available inputs and acceptable accuracy from local to regional scales. Seven widely used bulk formulae are evaluated using the ground measurements collected from 44 globally distributed flux measurement sites of five networks. The Bayesian model averaging (BMA) method is introduced to integrate multiple bulk formulae to obtain an estimate of cloudy-sky SDLR for the first time. The second multiple linear regression model of Carmona et al. (2014) performs the best, with BIAS, RMSE, and R2 of zero, 20.13 W·m−2 and 0.87, respectively. The BMA method can achieve balanced results that are close to the accuracy of the second multiple linear regression model of Carmona et al. (2014) and better than the average accuracy of seven bulk formulae, with BIAS, RMSE, and R2 of −1.08 W·m−2, 21.99 W·m−2 and 0.87, respectively. In addition, the bulk formula of Crawford and Duchon (1999) is preferred if there is insufficient data to calibrate the bulk formulae because it does not need local calibration and has an acceptable accuracy, with BIAS, RMSE, and R2 of 0.96 W·m−2, 26.58 W·m−2 and 0.82, respectively. The effects of climate type, land cover type, and surface elevation are also investigated to fully assess the applicability of each bulk formula and BMA. In general, there is no cloudy-sky bulk parametrization scheme that can be successfully applied everywhere.

scholarly journals Simulated single-layer forest canopies delay Northern Hemisphere snowmelt

2019 ◽  
Vol 13 (11) ◽  
pp. 3077-3091 ◽  
Author(s):  
Markus Todt ◽  
Nick Rutter ◽  
Christopher G. Fletcher ◽  
Leanne M. Wake
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Single Layer ◽  
Longwave Radiation ◽  
Length Change ◽  
Day Length ◽  
Forest Canopies ◽  
Diurnal Cycles ◽  
Community Land Model ◽  
The Impact

Abstract. Single-layer vegetation schemes in modern land surface models have been found to overestimate diurnal cycles in longwave radiation beneath forest canopies. This study introduces an empirical correction, based on forest-stand-scale simulations, which reduces diurnal cycles of sub-canopy longwave radiation. The correction is subsequently implemented in land-only simulations of the Community Land Model version 4.5 (CLM4.5) in order to assess the impact on snow cover. Nighttime underestimations of sub-canopy longwave radiation outweigh daytime overestimations, which leads to underestimated averages over the snow cover season. As a result, snow temperatures are underestimated and snowmelt is delayed in CLM4.5 across evergreen boreal forests. Comparison with global observations confirms this delay and its reduction by correction of sub-canopy longwave radiation. Increasing insolation and day length change the impact of overestimated diurnal cycles on daily average sub-canopy longwave radiation throughout the snowmelt season. Consequently, delay of snowmelt in land-only simulations is more substantial where snowmelt occurs early.

Applications of a Multiple Linear Regression Model to the Analysis of Relationships between Eastward- and Westward-Moving Intraseasonal Modes

2004 ◽  
Vol 61 (24) ◽  
pp. 3041-3048 ◽  
Author(s):  
Paul E. Roundy ◽  
William M. Frank
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Spatial Scales ◽  
Statistical Significance ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Linear Regression Models ◽  
Linear Predictor

Abstract Multiple linear regression models with nonlinear power terms may be applied to find relationships between interacting wave modes that may be characterized by different frequencies. Such regression techniques have been explored in other disciplines, but they have not been used in the analysis of atmospheric circulations. In this study, such a model is developed to predict anomalies of westward-moving intraseasonal precipitable water by utilizing the first through fourth powers of a time series of outgoing longwave radiation that is filtered for eastward propagation and for the temporal and spatial scales of the tropical intraseasonal oscillations. An independent and simpler compositing method is applied to show that the results of this multiple linear regression model provide a better description of the actual relationships between eastward- and westward-moving intraseasonal modes than a regression model that includes only the linear predictor. A statistical significance test is applied to the coefficients of the multiple linear regression model, and they are found to be significant over broad regions of the Tropics. Correlations between the predictors are shown to not significantly influence results for this case. Results show that this regression model reveals physical relationships between eastward- and westward-moving intraseasonal modes. The physical interpretation of these regression relationships is given in a companion paper.

scholarly journals Measurement of Incoming Radiation below Forest Canopies: A Comparison of Different Radiometer Configurations

2016 ◽  
Vol 17 (3) ◽  
pp. 853-864 ◽  
Author(s):  
Clare Webster ◽  
Nick Rutter ◽  
Franziska Zahner ◽  
Tobias Jonas
Keyword(s):  
Longwave Radiation ◽  
Swiss Alps ◽  
Shortwave Radiation ◽  
Spatial And Temporal Variability ◽  
Field Site ◽  
Forest Canopies ◽  
Stationary Configuration ◽  
Annual Range ◽  
Spatial Noise ◽  
Sky Conditions

Abstract Ground-based, subcanopy measurements of incoming shortwave and longwave radiation are frequently used to drive and validate energy balance and snowmelt models. These subcanopy measurements are frequently obtained using different configurations (linear or distributed; stationary or moving) of radiometer arrays that are installed to capture the spatial and temporal variability of longwave and shortwave radiation. Three different radiometer configurations (stationary distributed, stationary linear, and moving linear) were deployed in a spruce forest in the eastern Swiss Alps during a 9-month period, capturing the annual range of sun angles and sky conditions. Results showed a strong seasonal variation in differences between measurements of shortwave transmissivity between the three configurations, whereas differences in longwave enhancement appeared to be seasonally independent. Shortwave transmissivity showed a larger spatial variation in the subcanopy than longwave enhancement at this field site. The two linear configurations showed the greatest similarity in shortwave transmissivity measurements, and the measurements of longwave enhancement were largely similar between all three configurations. A reduction in the number of radiometers in each array reduced the similarities between each stationary configuration. The differences presented here are taken to reflect the natural threshold of spatial noise in subcanopy measurements that can be expected between the three configurations.

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