The simple two-dimensional parameterisation for Flux Footprint Predictions FFP

Abstract. Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to ecosystem or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FLUXNET, ICOS, NEON, or AMERIFLUX, and with increasing temporal range of observations from such towers (order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on Surface Layer theory and hence are of restricted validity for real case applications. To remedy such shortcomings, we present the two-dimensional Flux Footprint Parameterisation, FFP, based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent, but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus it can provide footprint estimates for a wide range of real case applications. The new footprint parameterisation requires inputs that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.

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A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)

Geoscientific Model Development ◽

10.5194/gmd-8-3695-2015 ◽

2015 ◽

Vol 8 (11) ◽

pp. 3695-3713 ◽

Cited By ~ 200

Author(s):

N. Kljun ◽

P. Calanca ◽

M. W. Rotach ◽

H. P. Schmid

Keyword(s):

Boundary Layer ◽

Regional Scale ◽

Particle Dispersion ◽

Real Case ◽

Observation System ◽

Two Dimensional ◽

Flux Footprint ◽

Flux Tower ◽

Wide Range ◽

Flux Measurements

Abstract. Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to local or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such towers (of the order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on surface layer theory and hence are of restricted validity for real-case applications. To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications. The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.

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Heat and Mass Transfer in an Incompressible Turbulent Boundary Layer

Journal of Heat Transfer ◽

10.1115/1.3449865 ◽

1972 ◽

Vol 94 (1) ◽

pp. 23-28 ◽

Cited By ~ 2

Author(s):

E. Brundrett ◽

W. B. Nicoll ◽

A. B. Strong

Keyword(s):

Heat Transfer ◽

Boundary Layer ◽

Mass Transfer ◽

Porous Plate ◽

Mixing Length ◽

Two Dimensional ◽

Transfer Data ◽

Incompressible Turbulent Boundary Layer ◽

Wide Range ◽

Incompressible Boundary

The van Driest damped mixing length has been extended to account for the effects of mass transfer through a porous plate into a turbulent, two-dimensional incompressible boundary layer. The present mixing length is continuous from the wall through to the inner-law region of the flow, and although empirical, has been shown to predict wall shear stress and heat transfer data for a wide range of blowing rates.

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A new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersion

Geoscientific Model Development ◽

10.5194/gmd-14-2205-2021 ◽

2021 ◽

Vol 14 (4) ◽

pp. 2205-2220

Author(s):

Matthias Faust ◽

Ralf Wolke ◽

Steffen Münch ◽

Roger Funk ◽

Kerstin Schepanski

Keyword(s):

Boundary Layer ◽

Particle Transport ◽

Dispersion Model ◽

Weather Forecast ◽

Forecast Model ◽

Particle Dispersion ◽

Particle Simulation ◽

High Temporal Resolution ◽

Atmospheric Particle ◽

Trajectory Models

Abstract. Trajectory models are intuitive tools for airflow studies. But in general, they are limited to non-turbulent, i.e. laminar flow, conditions. Therefore, trajectory models are not particularly suitable for investigating airflow within the turbulent atmospheric boundary layer. To overcome this, a common approach is handling the turbulent uncertainty as a random deviation from a mean path in order to create a statistic of possible solutions which envelops the mean path. This is well known as the Lagrangian particle dispersion model (LPDM). However, the decisive factor is the representation of turbulence in the model, for which widely used models such as FLEXPART and HYSPLIT use an approximation. A conceivable improvement could be the use of a turbulence parameterisation approach based on the turbulent kinetic energy (TKE) at high temporal resolution. Here, we elaborated this approach and developed the LPDM Itpas, which is coupled online to the German Weather Service's mesoscale weather forecast model COSMO. It benefits from the prognostically calculated TKE as well as from the high-frequency wind information. We demonstrate the model's applicability for a case study on agricultural particle emission in eastern Germany. The results obtained are discussed with regard to the model's ability to describe particle transport within a turbulent boundary layer. Ultimately, the simulations performed suggest that the newly introduced method based on prognostic TKE sufficiently represents the particle transport.

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An extended Kalman-filter for regional scale inverse emission estimation

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-29195-2011 ◽

2011 ◽

Vol 11 (10) ◽

pp. 29195-29249 ◽

Cited By ~ 3

Author(s):

D. Brunner ◽

S. Henne ◽

C. A. Keller ◽

S. Reimann ◽

M. K. Vollmer ◽

...

Keyword(s):

Kalman Filter ◽

Dispersion Model ◽

Regional Scale ◽

Estimation Method ◽

Particle Dispersion ◽

Montreal Protocol ◽

Correlated Noise ◽

Temporal Correlations ◽

Emission Estimation ◽

Hfc 152A

Abstract. A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated) noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to the Kyoto protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower in different countries. The highest emissions of the order of 1000 Mg yr−1 are estimated for Italy which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its ban under the Montreal Protocol. Emissions from France, however, were still rather large (near 1000 Mg yr−1) in the years 2006 and 2007 but strongly declined thereafter.

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Identification of Aerosol Sources in Siberia and Study of Aerosol Transport at Regional Scale by Airborne and Space-Borne Lidar Measurement

EPJ Web of Conferences ◽

10.1051/epjconf/202023702014 ◽

2020 ◽

Vol 237 ◽

pp. 02014

Author(s):

Antonin Zabukovec ◽

Gérard Ancellet ◽

Jacques Pelon ◽

J.D. Paris ◽

Iogannes E. Penner ◽

...

Keyword(s):

Forest Fires ◽

Dispersion Model ◽

Regional Scale ◽

Particle Dispersion ◽

Airborne Lidar ◽

Orthogonal Polarization ◽

Lidar Measurement ◽

Imaging Spectrometer ◽

Modis Aod ◽

Moderate Resolution

Airborne lidar measurements were carried out over Siberia in July 2013 and June 2017. Aerosol optical properties are derived using the Lagrangian FLEXible PARTicle dispersion model (FLEXPART) simulations and Moderate Resolution Imaging Spectrometer (MODIS) AOD. Comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products is used to validate the CALIOP aerosol type identification above Siberia. Two case studies are discussed : a mixture of dust and pollution from Northern Kazakhstan and smoke plumes from forest fires. Comparisons with the CALIOP backscatter ratio show that CALIOP algorithm may overestimate the LR for a dusty mixture if not constrained by an independent AOD measurement.

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Top-down Support of Swiss non-CO2 Greenhouse Gas Emissions Reporting to UNFCCC

10.5194/egusphere-egu2020-17176 ◽

2020 ◽

Author(s):

Stephan Henne ◽

Martin K. Vollmer ◽

Martin Steinbacher ◽

Markus Leuenberger ◽

Frank Meinhardt ◽

...

Keyword(s):

Nitrous Oxide ◽

Radiative Forcing ◽

Dispersion Model ◽

Regional Scale ◽

Particle Dispersion ◽

Mitigation Measures ◽

Ratio Method ◽

Top Down ◽

Bottom Up ◽

Halogenated Compound

Globally, emissions of long-lived non-CO2 greenhouse gases (GHG; methane, nitrous oxide and halogenated compounds) account for approximately 30 % of the radiative forcing of all anthropogenic GHG emissions. In industrialised countries, &#8216;bottom-up&#8217; estimates come with relatively large uncertainties for anthropogenic non-CO2 GHGs when compared with those of anthropogenic CO2. 'Top-down' methods on the country scale offer an independent support tool to reduce these uncertainties and detect biases in emissions reported to the UNFCCC. Based on atmospheric concentration observations these tools are also able to detect the effectiveness of emission mitigation measures on the long term.Since 2012 the Swiss national inventory reporting (NIR) contains an appendix on 'top-down' studies for selected halogenated compound. Subsequently, this appendix was extended to include methane and nitrous oxide. Here, we present these updated (2020 submission) regional-scale (~300 x 200 km2) atmospheric inversion studies for non-CO2 GHG emission estimates in Switzerland, making use of observations on the Swiss Plateau (Berom&#252;nster tall tower) as well as the neighbouring mountain-top sites Jungfraujoch and Schauinsland.We report spatially and temporally resolved Swiss emissions for CH4 (2013-2019), N2O (2017-2019) and total Swiss emissions for hydrofluorocarbons (HFCs) and SF6 (2009-2019) based on a Bayesian inversion system and a tracer ratio method, respectively. Both approaches make use of transport simulations applying the high-resolution (7 x 7 km2) Lagrangian particle dispersion model (FLEXPART-COSMO). We compare these 'top-down' estimates to the 'bottom-up' results reported by Switzerland to the UNFCCC. Although we find good agreement between the two estimates for some species (CH4, N2O), emissions of other compounds (e.g., considerably lower 'top-down' estimates for HFC-134a) show larger discrepancies. Potential reasons for the disagreements are discussed. Currently, our 'top-down' information is only used for comparative purposes and does not feed back into the 'bottom-up' inventory.

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Evaluation of the Lagrangian particle dispersion model DIPCOT against data from wind tunnel simulations of quasi two-dimensional turbulent flow

International Journal of Environment and Pollution ◽

10.1504/ijep.2005.007389 ◽

2005 ◽

Vol 24 (1/2/3/4) ◽

pp. 114 ◽

Cited By ~ 2

Author(s):

Efstratios Davakis ◽

Spyros Andronopoulos ◽

George A. Sideridis ◽

Eleftherios G. Kastrinakis ◽

Stavros G. Nychas ◽

...

Keyword(s):

Wind Tunnel ◽

Turbulent Flow ◽

Dispersion Model ◽

Particle Dispersion ◽

Two Dimensional ◽

Lagrangian Particle

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Employing Spectral Analysis to Obtain Dispersion Parameters in an Atmospheric Environment Driven by a Mesoscale Downslope Windstorm

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182413027 ◽

2021 ◽

Vol 18 (24) ◽

pp. 13027

Author(s):

Cinara Ewerling da Rosa ◽

Michel Stefanello ◽

Silvana Maldaner ◽

Douglas Stefanello Facco ◽

Débora Regina Roberti ◽

...

Keyword(s):

Boundary Layer ◽

Planetary Boundary Layer ◽

Diffusion Theory ◽

Dispersion Model ◽

Atmospheric Dispersion ◽

Particle Dispersion ◽

Atmospheric Environment ◽

Dispersion Parameters ◽

Simulated Concentration ◽

Four Levels

Considering the influence of the downslope windstorm called “Vento Norte” (VNOR; Portuguese for “North Wind”) in planetary boundary layer turbulent features, a new set of turbulent parameterizations, which are to be used in atmospheric dispersion models, has been derived. Taylor’s statistical diffusion theory, velocity spectra obtained at four levels (3, 6, 14, and 30 m) in a micrometeorological tower, and the energy-containing eddy scales are used to calculate neutral planetary boundary layer turbulent parameters. Vertical profile formulations of the wind velocity variances and Lagrangian decorrelation time scales are proposed, and to validate this new parameterization, it is applied in a Lagrangian Stochastic Particle Dispersion Model to simulate the Prairie Grass concentration experiments. The simulated concentration results were shown to agree with those observed.

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Planning for LOTOS: A New LOwer Troposphere Observing System

10.5194/egusphere-egu2020-19600 ◽

2020 ◽

Author(s):

Terry Hock ◽

Tammy Weckwerth ◽

Steve Oncley ◽

William Brown ◽

Vanda Grubišić ◽

...

Keyword(s):

Boundary Layer ◽

Real Time ◽

Sensor Network ◽

Regional Scale ◽

Lower Troposphere ◽

Integrated System ◽

Lower Atmosphere ◽

Time Data ◽

Wide Range ◽

Real Time Data

The National Center for Atmospheric Research Earth Observing Laboratory (EOL) proposes to develop the LOwer Troposphere Observing System (LOTOS), a new integrated sensor network that offers the potential for transformative understanding of the lower atmosphere and its coupling to the Earth's surface.&#160;&#160;The LOTOS sensor network is designed to allow simultaneous and coordinated sampling both vertically, through the atmospheric planetary boundary layer, and horizontally, across the surrounding landscape, focusing on the land-atmosphere interface and its coupling with the overlying free troposphere. The core of LOTOS will be a portable integrated network of up to five nodes, each consisting of a profiling suite of instruments surrounded by up to fifteen flux measuring towers. LOTOS will provide an integrated set of measurements needed to address outstanding scientific challenges related to processes within the atmospheric surface layer, boundary layer, and lower troposphere. LOTOS will also enable novel quantification of exchanges of biogeochemical and climate-relevant gases from microscale up to regional scale.&#160;&#160;LOTOS&#8217; uniqueness lies in its ability to simultaneously sample both horizontally and vertically as an integrated system, but also in its flexibility to be easily relocated as a portable field-deployable system suitable for addressing a wide range of research needs. LOTOS will provide real-time data quality control, combine measurements from a variety of sensors into integrated data products, and provide real-time data displays. It is envisioned that LOTOS will become part of the deployable NSF Lower Atmosphere Observing Facilities (LAOF) and thus be available to a broad base of NSF users from both observational and modeling communities. LOTOS offers the potential for transformative understanding of the Earth and its atmosphere as a coupled system. This presentation will describe the background, motivation, plan, and timeline for the LOTOS&#8217; proposed development.

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Estimating regional-scale methane flux and budgets using CARVE aircraft measurements over Alaska

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-185-2018 ◽

2018 ◽

Vol 18 (1) ◽

pp. 185-202 ◽

Cited By ~ 8

Author(s):

Sean Hartery ◽

Róisín Commane ◽

Jakob Lindaas ◽

Colm Sweeney ◽

John Henderson ◽

...

Keyword(s):

Soil Temperature ◽

Dispersion Model ◽

Linear Regression Analysis ◽

Regional Scale ◽

Methane Flux ◽

Particle Dispersion ◽

Environmental Drivers ◽

Study Region ◽

Ch4 Emissions ◽

Soil Temperature And Moisture

Abstract. Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

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