Understanding in and above canopy-atmosphere interactions by combining large-eddy simulations with a comprehensive observational set

2021 ◽  
Author(s):  
Xabier Pedruzo-Bagazgoitia ◽  
Arnold F. Moene ◽  
Huug Ouwersloot ◽  
Tobias Gerken ◽  
Luiz A.T. Machado ◽  
...  
Keyword(s):  
Roughness Length ◽  
Surface Fluxes ◽  
State Variables ◽  
Atmospheric Models ◽  
Carbon Cycles ◽  
Shallow Cumulus ◽  
Large Eddy ◽  
Biophysical Processes ◽  
Leaf Level ◽  
Canopy Model

<p>The vegetated canopy plays a key role in regulating the surface fluxes and, therefore, the global energy, water and carbon cycles. In particular, vulnerable ecosystems like the Amazonia basin can be very sensitive to changes in vegetation that exert subsequent shifts in the partition of the energy, water and carbon in and above the canopy. Despite this relevance, most 3D atmospheric models represent the vegetated canopy as a flat 2D layer with, at most, a rough imitation of its effect in the atmospheric boundary layer through a modified roughness length. Thus, the representations often describe quite crudely the surface fluxes. In this work, particular emphasis is placed in the biophysical processes that take place within the canopy and its impact above. Our approach is to represent the coupling of the flow between the canopy and the atmosphere including the following processes: radiative transfer, photosynthesis, soil evaporation and CO2 respiration, combined with the mostly explicit atmospheric turbulence within and above the canopy. To this end, we implemented in LES a detailed multi-layer canopy model that solves the leaf energy balance for sunlit and shaded leaves independently, regulating the exchange of heat, moisture and carbon between the leaves and the air around. This allows us to connect the mechanistically represented processes occurring at the leaf level and strongly regulated by the transfer of diffuse and direct radiation within the canopy to the turbulent mixing explicitly resolved at the meter scale.</p><p>We test and validate this combined photosynthesis-turbulence-canopy model by simulating a representative clear day transitioning to shallow cumulus. We based our evaluation on observations by the GoAmazon2014/5 campaign in Brazil in 2014. More specifically, we systematically validate the in-canopy radiation profiles; sources, sinks and turbulent fluxes of moisture, heat and CO2, and main state variables within the canopy, and also study the effects of these in the air above. Preliminary results show an encouraging satisfactory match to the observed evolution of the profiles. As a first exploration and demonstration of the capabilities of the model, we test the effects of a coarser in-canopy resolution, a different radiation scheme and the use of a more simple 2D canopy representation.</p>

Combined state-parameter estimation with the LETKF for convective-scale weather forecasting

2020 ◽  
Author(s):  
Yvonne Ruckstuhl ◽  
Tijana Janjic
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Roughness Length ◽  
Surface Wind ◽  
Model Error ◽  
Surface Fluxes ◽  
Model Parameters ◽  
State Variables ◽  
Augmented State ◽  
The Impact

<p>We investigate the feasibility of addressing model error by perturbing and  estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are updated by observations via their correlation with observed state variables. This online approach offers a flexible, yet consistent way to better fit model variables affected by the chosen parameters to observations, while ensuring feasible model states. We show in a nearly-operational convection-permitting configuration that the prediction of clouds and precipitation with the COSMO-DE model is improved if the two dimensional roughness length parameter is estimated with the augmented state approach. Here, the targeted model error is the roughness length itself and the surface fluxes, which influence the initiation of convection. At analysis time, Gaussian noise with a specified correlation matrix is added to the roughness length to regulate the parameter spread. In the northern part of the COSMO-DE domain, where the terrain is mostly flat and assimilated surface wind measurements are dense, estimating the roughness length led to improved forecasts of up to six hours of clouds and precipitation. In the southern part of the domain, the parameter estimation was detrimental unless the correlation length scale of the Gaussian noise that is added to the roughness length is increased. The impact of the parameter estimation was found to be larger when synoptic forcing is weak and the model output is more sensitive to the roughness length.</p>

scholarly journals Combined State-Parameter Estimation with the LETKF for Convective-Scale Weather Forecasting

2020 ◽  
Vol 148 (4) ◽  
pp. 1607-1628 ◽  
Author(s):  
Y. Ruckstuhl ◽  
T. Janjić
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Roughness Length ◽  
Surface Wind ◽  
Model Error ◽  
Surface Fluxes ◽  
Model Parameters ◽  
State Variables ◽  
Augmented State ◽  
The Impact

Abstract We investigate the feasibility of addressing model error by perturbing and estimating uncertain static model parameters using the localized ensemble transform Kalman filter. In particular we use the augmented state approach, where parameters are updated by observations via their correlation with observed state variables. This online approach offers a flexible, yet consistent way to better fit model variables affected by the chosen parameters to observations, while ensuring feasible model states. We show in a nearly operational convection-permitting configuration that the prediction of clouds and precipitation with the COSMO-DE model is improved if the two-dimensional roughness length parameter is estimated with the augmented state approach. Here, the targeted model error is the roughness length itself and the surface fluxes, which influence the initiation of convection. At analysis time, Gaussian noise with a specified correlation matrix is added to the roughness length to regulate the parameter spread. In the northern part of the COSMO-DE domain, where the terrain is mostly flat and assimilated surface wind measurements are dense, estimating the roughness length led to improved forecasts of up to 6 h of clouds and precipitation. In the southern part of the domain, the parameter estimation was detrimental unless the correlation length scale of the Gaussian noise that is added to the roughness length is increased. The impact of the parameter estimation was found to be larger when synoptic forcing is weak and the model output is more sensitive to the roughness length.

scholarly journals Large-Eddy Simulation and Single-Column Modeling of Thermally Stratified Wind Turbine Arrays for Fully Developed, Stationary Atmospheric Conditions

2015 ◽  
Vol 32 (6) ◽  
pp. 1144-1162 ◽  
Author(s):  
Adrian Sescu ◽  
Charles Meneveau
Keyword(s):  
Thermal Stratification ◽  
Layer Structure ◽  
Potential Temperature ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Shear Stresses ◽  
Column Model ◽  
Large Eddy ◽  
Single Column ◽  
Single Column Model

AbstractEffects of atmospheric thermal stratification on the asymptotic behavior of very large wind farms are studied using large-eddy simulations (LES) and a single-column model for vertical distributions of horizontally averaged field variables. To facilitate comparisons between LES and column modeling based on Monin–Obukhov similarity theory, the LES are performed under idealized conditions of statistical stationarity in time and fully developed conditions in space. A suite of simulations are performed for different thermal stratification levels and the results are used to evaluate horizontally averaged vertical profiles of velocity, potential temperature, vertical turbulent momentum, and heat flux. Both LES and the model show that the stratification significantly affects the atmospheric boundary layer structure, its height, and the surface fluxes. However, the effects of the wind farm on surface heat fluxes are found to be relatively small in both LES and the single-column model. The surface fluxes are the result of two opposing trends: an increase of mixing in wakes and a decrease in mixing in the region below the turbines due to reduced momentum fluxes there for neutral and unstable cases, or relatively unchanged shear stresses below the turbines in the stable cases. For the considered cases, the balance of these trends yields a slight increase in surface flux magnitude for the stable and near-neutral unstable cases, and a very small decrease in flux magnitude for the strongly unstable cases. Moreover, thermal stratification is found to have a negligible effect on the roughness scale as deduced from the single-column model, consistent with the expectations of separation of scale.

scholarly journals Atmospheric CO<sub>2</sub> modeling at the regional scale: an intercomparison of 5 meso-scale atmospheric models

2007 ◽  
Vol 4 (3) ◽  
pp. 1923-1952 ◽  
Author(s):  
C. Sarrat ◽  
J. Noilhan ◽  
A. J. Dolman ◽  
C. Gerbig ◽  
R. Ahmadov ◽  
...  
Keyword(s):  
Regional Scale ◽  
Potential Temperature ◽  
Surface Fluxes ◽  
Systematic Evaluation ◽  
Atmospheric Models ◽  
Experimental Protocol ◽  
Intercomparison Exercise ◽  
Meteorological Models ◽  
Meso Scale

Abstract. Atmospheric CO2 modeling in interaction with the surface fluxes, at the regional scale is developed within the frame of the European project CarboEurope-IP and its Regional Experiment component. In this context, five meso-scale meteorological models participate in an intercomparison exercise. Using a common experimental protocol that imposes a large number of rules, two days of the CarboEurope Regional Experiment Strategy (CERES) campaign are simulated. A systematic evaluation of the models is done in confrontation with the observations, using statistical tools and direct comparisons. Thus, temperature and relative humidity at 2 m, wind direction, surface energy and CO2 fluxes, vertical profiles of potential temperature as well as in-situ CO2 concentrations comparisons between observations and simulations are examined. This intercomparison exercise shows also the models ability to represent the meteorology and carbon cycling at the synoptic and regional scale in the boundary layer, but also points out some of the major shortcomings of the models.

scholarly journals Automated tracking of shallow cumulus clouds in large domain, long duration Large Eddy Simulations

2013 ◽  
Vol 6 (2) ◽  
pp. 2287-2323 ◽  
Author(s):  
T. Heus ◽  
A. Seifert
Keyword(s):  
Large Data ◽  
Current Method ◽  
Large Eddy Simulations ◽  
Large Data Sets ◽  
Data Sets ◽  
Cumulus Clouds ◽  
3 Dimensional ◽  
Shallow Cumulus ◽  
Long Duration ◽  
Large Eddy

Abstract. This paper presents a method for feature tracking of fields of shallow cumulus convection in Large Eddy Simulations (LES) by connecting the projected cloud cover in space and time, and by accounting for splitting and merging of cloud objects. Existing methods tend to be either imprecise or, when using the full 3 dimensional spatial field, prohibitively expensive for large data sets. Compared to those 3-D methods, the current method reduces the memory footprint by up to a factor 100, while retaining most of the precision by correcting for splitting and merging events between different clouds. The precision of the algorithm is further enhanced by taking the vertical extent of the cloud into account. Furthermore, rain and subcloud thermals are also tracked, and links between clouds, their rain, and their subcloud thermals are made. The method compares well with results from the literature. Resolution and domain dependencies are also discussed. For the current simulations, the cloud size distribution converges for clouds larger than an effective resolution of 6Δx, and smaller than about 20% of the horizontal domains size.

A study of shallow cumulus cloud droplet dispersion by large eddy simulations

2011 ◽  
Vol 25 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Xiaofeng Wang ◽  
Huiwen Xue ◽  
Wen Fang ◽  
Guoguang Zheng
Keyword(s):  
Cloud Droplet ◽  
Large Eddy Simulations ◽  
Cumulus Cloud ◽  
Droplet Dispersion ◽  
Shallow Cumulus ◽  
Large Eddy

scholarly journals Intraseasonal Variability in MERRA Energy Fluxes over the Tropical Oceans

2012 ◽  
Vol 25 (17) ◽  
pp. 5629-5647 ◽  
Author(s):  
Franklin R. Robertson ◽  
Jason B. Roberts
Keyword(s):  
Deep Convection ◽  
Intraseasonal Variability ◽  
Tropical Rainfall Measuring Mission ◽  
Precipitation Anomaly ◽  
Surface Fluxes ◽  
State Variables ◽  
Cloud Forcing ◽  
Tropical Oceans ◽  
Fractional Cloud ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract This paper investigates intraseasonal variability as represented by the recent NASA Global Modeling and Assimilation Office (GMAO) reanalysis, the Modern-Era Retrospective analysis for Research and Applications (MERRA). The authors examine the behavior of heat, moisture, and radiative fluxes emphasizing their contribution to intraseasonal variations in heat and moisture balance integrated over the tropical oceans. MERRA successfully captures intraseasonal signals in both state variables and fluxes, though it depends heavily on the analysis increment update terms that constrain the reanalysis to be near the observations. Precipitation anomaly patterns evolve in close agreement with those from the Tropical Rainfall Measuring Mission (TRMM) though locally MERRA may occasionally be smaller by up to 20%. As in the TRMM observations, tropical convection increases lead tropospheric warming by approximately 7 days. Radiative flux anomalies are dominated by cloud forcing and are found to replicate the top-of-the-atmosphere (TOA) energy loss associated with increased convection found by other observationally based studies. However, MERRA’s convectively produced clouds appear to deepen too soon as precipitation increases. Total fractional cloud cover variations appear somewhat weak compared to observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Evolution of the surface fluxes, convection, and TOA radiation is consistent with the “discharge–recharge” paradigm that posits the importance of lower-tropospheric moisture accumulation prior to the expansion of organized deep convection. The authors conclude that MERRA constitutes a very useful representation of intraseasonal variability that will support a variety of studies concerning radiative–convective–dynamical processes and will help identify pathways for improved moist physical parameterization in global models.

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