Surface Flux Model Research Articles

Improved accuracy of continuum surface flux models for metal additive manufacturing melt pool simulations

Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.

The Advective Flux Transport Model: Improving the Far Side with Active Regions Observed by STEREO 304 Å

Observations of the Sun’s photospheric magnetic field are often confined to the Sun–Earth line. Surface flux transport (SFT) models, such as the Advective Flux Transport (AFT) model, simulate the evolution of the photospheric magnetic field to produce magnetic maps over the entire surface of the Sun. While these models are able to evolve active regions that transit the near side of the Sun, new far-side side flux emergence is typically neglected. We demonstrate a new method for creating improved maps of the magnetic field over the Sun’s entire photosphere using data obtained by the Solar TErrestrial RElations Observatory (STEREO) mission. STEREO He ii 304 Å intensity images are used to infer the time, location, and total unsigned magnetic flux of far-side active regions. We have developed an automatic detection algorithm for finding and ingesting new far-side active region emergence into the AFT model. We conduct a series of simulations to investigate the impact of including active region emergence in AFT, both with and without data assimilation of magnetograms. We find that while He ii 304 Å can be used to improve surface flux models, care must taken to mitigate intensity surges from flaring events. We estimate that during Solar Cycle 24's maximum (2011–2015), 4–6 × 1022 Mx of flux is missing from SFT models that do not include far-side data. We find that while He ii 304 Å data alone can be used to create synchronic maps of photospheric magnetic field that resemble the observations, it is insufficient to produce a complete picture without direct magnetic observations from magnetographs.

A high-resolution monitoring approach of urban CO2 fluxes. Part 2 – surface flux optimisation using eddy covariance observations

Achieving climate neutrality by 2050 requires ground-breaking technological and methodological advancements in climate change mitigation planning and actions from local to regional scales. Monitoring the cities' CO2 emissions with sufficient detail and accuracy is crucial for guiding sustainable urban transformation. Current methodologies for CO2 emission inventories rely on bottom-up (BU) approaches which do not usually offer information on the spatial or temporal variability of the emissions and present substantial uncertainties. This study develops a novel approach which assimilates direct CO2 flux observations from urban eddy covariance (EC) towers with very high spatiotemporal resolution information from an advanced urban BU surface flux model (Part 1 of this study, Stagakis et al., 2023) within a Bayesian inversion framework. The methodology is applied to the city centre of Basel, Switzerland (3 × 3 km domain), taking advantage of two long-term urban EC sites located 1.6 km apart. The data assimilation provides optimised gridded CO2 flux information individually for each urban surface flux component (i.e. building heating emissions, commercial/industrial emissions, traffic emissions, human respiration emissions, biogenic net exchange) at 20 m resolution and weekly time-step. The results demonstrate that urban EC observations can be consistently used to improve high-resolution BU surface CO2 flux model estimations, providing realistic seasonal variabilities of each flux component. Traffic emissions are determined with the greatest confidence among the five flux components during the inversions. The optimised annual anthropogenic emissions are 14.7 % lower than the prior estimate, the human respiration emissions have decreased by 12.1 %, while the biogenic components transformed from a weak sink to a weak source. The root-mean-square errors (RMSEs) of the weekly comparisons between EC observations and model outputs are consistently reduced. However, a slight underestimation of the total flux, especially in locations with complex CO2 source/sink mixture, is still evident in the optimised fluxes.

Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain

Abstract. Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations.

New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model.

Ammonia surface flux is bidirectional; that is, net flux can be either upward or downward. In fertilized agricultural croplands and grasslands there is usually more emission than deposition especially in midday during warmer seasons. In North America, most of the ammonia emissions are from agriculture with a significant fraction of that coming from fertilizer. A new bidirectional ammonia flux modeling system has been developed in the Community Multiscale Air Quality (CMAQ) model, which has close linkages with the Environmental Policy Integrated Climate (EPIC) agricultural ecosystem model. Daily inputs from EPIC are used to calculate soil ammonia concentrations that are combined with air concentrations in CMAQ to calculate bidirectional surface flux. The model is evaluated against surface measurements of NH3 concentrations, NH4+ and SO42− aerosol concentrations, NH4+ wet deposition measurements, and satellite retrievals of NH3 concentrations. The evaluation shows significant improvement over the base model without bidirectional ammonia flux. Comparisons to monthly average satellite retrievals show similar spatial distribution with the highest ammonia concentrations in the Central Valley of California (CA), the Snake River valley in Idaho, and the western High Plains. In most areas the model underestimates, but in the Central Valley of CA, it generally overestimates ammonia concentration. Case study analyses indicate that modeled high fluxes of ammonia in CA are often caused by anomalous high soil ammonia loading from EPIC for particular crop types. While further improvements to parameterizations in EPIC and CMAQ are recommended, this system is a significant advance over previous ammonia bidirectional surface flux models.

A Parameterization Scheme for Air–Sea Surface Interface Fluxes: Design and Stand-Alone Experiments

Abstract Observations show that sea surface temperature (SST) can vary up to several degrees in a day and sea surface energy fluxes up to a few hundreds of watts per square meter. For synoptic- and subsynoptic-scale atmospheric modeling, there remains a need for the parameterization of air–sea surface interaction using simple schemes. In this paper, such a simple scheme, Atmosphere–Ocean Surface Interaction Scheme (AOSIS), is presented so that the short time variations in SST and energy fluxes can be estimated using a small number of atmospheric and oceanic bulk quantities. The scheme consists of three components: a two-layer ocean temperature model, a wind-wave model, and a surface flux model. Numerical experiments show that the scheme performs well in simulating SST and the air–sea exchanges. Relative to other schemes, AOSIS shows the following improvements: 1) it simulates SST and the cool-skin and warm-layer effect of the ocean mixed layer without the input of ocean bulk temperature of the mixed layer as a prior condition, which is required by most one-layer models; 2) the depth of the ocean mixed layer is allowed to vary according to surface wind stress and buoyancy flux; and 3) a method for computing ocean surface roughness length is proposed, which accounts for the aerodynamic effect of wind-generated waves. For experimental studies, AOSIS can be used in stand-alone mode for the calculation of SST through a small number of bulk measurements. AOSIS can also be used as an interface between the atmosphere and ocean models to be coupled together.

Evaporation Study for Real Soils Based on HYPROP Hydraulic Functions and Micro‐CT‐Measured Pore‐Size Distribution

Core Ideas We studied Stage 1 and Stage 2 evaporation for real soils. For hydraulic functions, HYPROP data and for pore‐size distribution micro‐CT data were used. An IPE model describes for Stage 1 evaporation, desorption theory for Stage 2 evaporation We present an analytical solution of the 2D steady‐state diffusion equation. Evaporation—a key process for water exchange between soil and atmosphere—is controlled by convective and diffusive surface fluxes that determine the functional time dependence of the evaporation rate e(t). Recent studies demonstrated that only a pore‐scale surface flux model can capture the correct e(t) curve. These studies also showed that a realistic estimate of the hydraulically connected region (HCR) of the pore‐size distribution (PSD) is crucial for coupling surface flux to internal water flux. Since previous studies were often based on natural sands and glass beads, the main focus of our study was to test these conclusions for real soils. Therefore, we investigated the evaporation process within undisturbed soil columns of a sandy soil and loamy sand and measured the hydraulic functions via HYPROP experiments (a system to measure hydraulic properties using the evaporation method). Based on the isolated pore evaporation (IPE) model using a discretized form of the PSD, we developed a continuous IPE model and applied it to our experiments. Because the PSD plays a central role in the IPE model, we determined the PSD of the loamy sand soil via X‐ray microtomography (μCT) for pores >19 μm. The consistency of the experimental data, i.e., (i) the retention curve for deriving the HCR of the pore size distribution, (ii) the unsaturated hydraulic conductivity for calculating the characteristic lengths of the evaporation process, and (iii) the high accuracy of the mass loss data strongly support the HYPROP method for this kind of complex evaporation experiment. The continuous IPE model describes the characteristic Stage 1 behavior well (functional form of the evaporation rate and length of Stage 1) for both soil types if a realistic HCR estimate is used that (i) is derived from a characteristic length analysis estimating the lower boundary of the HCR and (ii) the upper range of the HCR is based on the true PSD derived from μCT data.

A new sub-grid surface mass balance and flux model for continental-scale ice sheet modelling: testing and last glacial cycle

Abstract. To investigate ice sheet evolution over the timescale of a glacial cycle, 3-D ice sheet models (ISMs) are typically run at "coarse" grid resolutions (10–50 km) that do not resolve individual mountains. This will introduce to-date unquantified errors in sub-grid (SG) transport, accumulation and ablation for regions of rough topography. In the past, synthetic hypsometric curves, a statistical summary of the topography, have been used in ISMs to describe the variability of these processes. However, there has yet to be detailed uncertainty analysis of this approach. We develop a new flow line SG model for embedding in coarse resolution models. A 1 km resolution digital elevation model was used to compute the local hypsometric curve for each coarse grid (CG) cell and to determine local parameters to represent the hypsometric bins' slopes and widths. The 1-D mass transport for the SG model is computed with the shallow ice approximation. We test this model against simulations from the 3-D Ice Sheet System Model (ISSM) run at 1 km grid resolution. Results show that none of the alternative parameterizations explored were able to adequately capture SG surface mass balance and flux processes. Via glacial cycle ensemble results for North America, we quantify the impact of SG model coupling in an ISM. We show that SG process representation and associated parametric uncertainties, related to the exchange of ice between the SG and CG cells, can have significant (up to 35 m eustatic sea level equivalent for the North American ice complex) impact on modelled ice sheet evolution.

Multiscale modeling of electron beam and substrate interaction: a new heat source model

An electron beam is a widely applied processing tool in welding and additive manufacturing applications. The heat source model of the electron beam acts as the basis of thermal simulations and predictions of the micro-structures and mechanical properties of the final products. While traditional volumetric and surface heat flux models were developed previously based on the observed shape of the molten pool produced by the beam, a new heat source model with a physically informed foundation has been established in this work. The new model was developed based on Monte Carlo simulations performed to obtain the distribution of absorbed energy through electron-atom collisions for an electron beam with a kinetic energy of 60 keV hitting a Ti---6Al---4V substrate. Thermal simulations of a moving electron beam heating a solid baseboard were conducted to compare the differences between the new heat source model, the traditional surface flux model and the volumetric flux model. Although the molten pool shapes with the three selected models were found to be similar, the predicted peak temperatures were noticeably different, which will influence the evaporation, recoil pressure and molten pool dynamics. The new heat source model was also used to investigate the influence of a static electron beam on a substrate. This investigation indicated that the new heat source model could scientifically explain phenomena that the surface and volumetric models cannot, such as eruption and explosion during electron beam processing.

Soil carbon content and relative abundance of high affinity H2-oxidizing bacteria predict atmospheric H2 soil uptake activity better than soil microbial community composition

Soil–atmosphere exchange of H2 is controlled by gas diffusion and the microbial production and oxidation activities in soil. Among these parameters, the H2 oxidation activity catalyzed by soil microorganisms harboring high affinity hydrogenase is the most difficult variable to parameterize because it is influenced by many unknown edaphic factors that shape microbial community structure and function. Here we seek to formulate a model combining microbiological and physicochemical variables to predict the H2 oxidation rate (u) in soil. Soil sample replicates collected from a grassland and three forests exhibited different H2 oxidation potentials. We examined the microbial community structure based on ribotyping analysis, the relative abundance of high affinity H2-oxidizing bacteria (HOB) estimated by qPCR and soil physicochemical characteristics as predictors for u. A single linear regression parameterized by total carbon content and a multiple linear regression using total carbon content and HOB relative abundance in soil explained 66 and 92% of the variance in u, respectively. Microbial community composition based on 16S rRNA gene pyrosequencing profiles was not a reliable predictor for u. Indeed, we found that HOB are members of the rare biosphere, comprising less than 1% of total bacteria as estimated by qPCR. We confirmed this relationship of u with total carbon content and HOB by an independent soil survey of 14 samples collected from maize monocultures, grasslands, deciduous forests and larch plantations. Observations made from both soil surveys thus were combined to build a predictive model for u parameterized with total carbon content and HOB relative abundance. Our results show that molecular biogeochemistry is a potential approach to improve performance of classical H2 surface flux models which estimate u empirically without considering variation in HOB distribution and activity in soil.

Evidence of spray‐mediated air‐sea enthalpy flux within tropical cyclones

It has long been conjectured that spray ejected from the high‐wind ocean surface enhances air/sea enthalpy fluxes, but a lack of observational data, particularly at wind speeds exceeding 20 m s−1, has prevented either confirmation or refutation of this hypothesis. The current study has two aims: first, to provide an estimate of surface enthalpy fluxes obtained from dropsonde data and second, to provide evidence of spray‐mediated enthalpy transfer. These are accomplished first by assuming that Monin‐Obukhov similarity is satisfied throughout the bottom 100 m of the high‐wind boundary layer, then by focusing on the enthalpy flux HK rather than its transfer coefficient CK. The scaling of HKwith wind speed in observational data sets reveals similarities to spray‐mediated fluxes predicted by a newly developed surface flux model, in contrast with measurements made in a laboratory setting. This behavior supports the claim that surface enthalpy fluxes are dominated by spray within tropical cyclones.

Development and evaluation of an ammonia bidirectional flux parameterization for air quality models

AbstractAmmonia is an important contributor to particulate matter in the atmosphere and can significantly impact terrestrial and aquatic ecosystems. Surface exchange between the atmosphere and biosphere is a key part of the ammonia cycle. New modeling techniques are being developed for use in air quality models that replace current ammonia emissions from fertilized crops and ammonia dry deposition with a bidirectional surface flux model including linkage to a detailed biogeochemical and farm management model. Recent field studies involving surface flux measurements over crops that predominate in North America have been crucial for extending earlier bidirectional flux models toward more realistic treatment of NH3 fluxes for croplands. Comparisons of the ammonia bidirection flux algorithm to both lightly fertilized soybeans and heavily fertilized corn demonstrate that the model can capture the magnitude and dynamics of observed ammonia fluxes, both net deposition and evasion, over a range of conditions with overall biases on the order of the uncertainty of the measurements. However, successful application to the field experiment in heavily fertilized corn required substantial modification of the model to include new parameterizations for in‐soil diffusion resistance, ground quasi‐laminar boundary layer resistance, and revised cuticular resistance that is dependent on in‐canopy NH3 concentration and RH at the leaf surface. This new bidirectional flux algorithm has been incorporated in an air quality modeling system, which also includes an implementation of a soil nitrification model.

Surface Flux Modeling for Air Quality Applications

For many gasses and aerosols, dry deposition is an important sink of atmospheric mass. Dry deposition fluxes are also important sources of pollutants to terrestrial and aquatic ecosystems. The surface fluxes of some gases, such as ammonia, mercury, and certain volatile organic compounds, can be upward into the air as well as downward to the surface and therefore should be modeled as bi-directional fluxes. Model parameterizations of dry deposition in air quality models have been represented by simple electrical resistance analogs for almost 30 years. Uncertainties in surface flux modeling in global to mesoscale models are being slowly reduced as more field measurements provide constraints on parameterizations. However, at the same time, more chemical species are being added to surface flux models as air quality models are expanded to include more complex chemistry and are being applied to a wider array of environmental issues. Since surface flux measurements of many of these chemicals are still lacking, resistances are usually parameterized using simple scaling by water or lipid solubility and reactivity. Advances in recent years have included bi-directional flux algorithms that require a shift from pre-computation of deposition velocities to fully integrated surface flux calculations within air quality models. Improved modeling of the stomatal component of chemical surface fluxes has resulted from improved evapotranspiration modeling in land surface models and closer integration between meteorology and air quality models. Satellite-derived land use characterization and vegetation products and indices are improving model representation of spatial and temporal variations in surface flux processes. This review describes the current state of chemical dry deposition modeling, recent progress in bi-directional flux modeling, synergistic model development research with field measurements, and coupling with meteorological land surface models.

Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales

Lack of good quality satellite images because of cloud contamination or long revisit time severely degrades predictions of evapotranspiration (ET) time series at watershed/regional scales from satellite‐based surface flux models. We integrate the feedback model developed by Granger and Gray (the GG model) with the Surface Energy Balance Algorithm for Land (SEBAL), with the objective to generate ET time series of high spatial resolution and reliable temporal distribution at watershed scales. First, SEBAL is employed to yield estimates of ET for the Baiyangdian watershed in a semihumid climatic zone in north China on cloud‐free days, where there exists the complementary relationship (CR) between actual ET and pan ET. These estimates constitute input to the GG model to inversely derive the relationship between the relative evaporation and the relative drying power of the air. Second, the modified GG model is used to yield ET time series on a daily basis simply by using routine meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) albedo and leaf area index products. Results suggest that the modified GG model that has incorporated remotely sensed ET can effectively extend remote sensing based ET to days without images and improve spatial representation of ET at watershed scales. Utility of the evaporative fraction method and the crop coefficients approaches to extrapolate ET time series depends largely on the number and interval of good quality satellite images. Comparison of ET time series from the two techniques and the proposed integration method for days with daily net radiation larger than 100 W m−2 and corresponding pan ET clearly shows that only the integration method can exhibit an asymmetric CR at the watershed scale and daily time scale. Validation performed using hydrologic budget calculations indicate that the proposed method has the highest accuracy in terms of annual estimates of ET for both watersheds in north China.

Estimating regional carbon exchange in New England and Quebec by combining atmospheric, ground-based and satellite data

We derive regional-scale (∼104 km2) CO2 flux estimates for summer 2004 in the northeast United States and southern Quebec by assimilating extensive data into a receptor-oriented model-data fusion framework. Surface fluxes are specified using the Vegetation Photosynthesis and Respiration Model (VPRM), a simple, readily optimized biosphere model driven by satellite data, AmeriFlux eddy covariance measurements and meteorological fields. The surface flux model is coupled to a Lagrangian atmospheric adjoint model, the Stochastic Time-Inverted Lagrangian Transport Model (STILT) that links point observations to upwind sources with high spatiotemporal resolution. Analysis of CO2 concentration data from the NOAA-ESRL tall tower at Argyle, ME and from extensive aircraft surveys, shows that the STILT– VPRM framework successfully links model flux fields to regionally representative atmospheric CO2 data, providing a bridge between ‘bottom-up’ and ‘top-down’ methods for estimating regional CO2 budgets on timescales from hourly to monthly. The surface flux model, with initial calibration to eddy covariance data, produces an excellent a priori condition for inversion studies constrained by atmospheric concentration data. Exploratory optimization studies show that data from several sites in a region are needed to constrain model parameters for all major vegetation types, because the atmosphere commingles the influence of regional vegetation types, and even high-resolution meteorological analysis cannot disentangle the associated contributions. Airborne data are critical to help define uncertainty within the optimization framework, showing for example, that in summertime CO2 concentration at Argyle (107 m) is ∼0.6 ppm lower than the mean in the planetary boundary layer.

Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA)

Accurate estimation of surface energy fluxes from space at high spatial resolution has the potential to improve prediction of the impact of land-use changes on the local environment and to provide a means to assess local crop conditions. To achieve this goal, a combination of physically based surface flux models and high-quality remote-sensing data are needed. Data from the ASTER sensor are particularly well-suited to the task, as it collects high spatial resolution (15–90 m) images in visible, near-infrared, and thermal infrared bands. Data in these bands yield surface temperature, vegetation cover density, and land-use types, all critical inputs to surface energy balance models for assessing local environmental conditions. ASTER is currently the only satellite sensor collecting multispectral thermal infrared images, a capability allowing unprecedented surface temperature estimation accuracy for a variety of surface cover types. Availability of ASTER data to study surface energy fluxes allows direct comparisons against ground measurements and facilitates detection of modeling limitations, both possible because of ASTER's higher spatial resolution. Surface energy flux retrieval from ASTER is demonstrated using data collected over an experimental site in central Iowa, USA, in the framework of the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This experiment took place during the summer of 2002 in a study of heterogeneous agricultural croplands. Two different flux estimation approaches, designed to account for the spatial variability, are considered: the Two-Source Energy Balance model (TSEB) and the Surface Energy Balance Algorithm or Land model (SEBAL). ASTER data are shown to have spatial and spectral resolution sufficient to derive surface variables required as inputs for physically based energy balance modeling. Comparison of flux model results against each other and against ground based measurements was promising, with flux values commonly agreeing within ∼50 W m − 2 . Both TSEB and SEBAL showed systematic agreement and responded to spatially varying surface temperatures and vegetation densities. Direct comparison against ground Eddy Covariance data suggests that the TSEB approach is helpful over sparsely vegetated terrain.

Measuring fluxes of trace gases at regional scales by Lagrangian observations: Application to the CO2 Budget and Rectification Airborne (COBRA) study

We present a general framework for designing and analyzing Lagrangian‐type aircraft observations in order to measure surface fluxes of trace gases on regional scales. Lagrangian experiments minimize uncertainties due to advection by measuring tracer concentrations upstream and downstream of the study region, assuring that observed concentration changes represent fluxes within the region. The framework includes (1) a receptor‐oriented model of atmospheric transport, including turbulent dispersion, (2) an upstream tracer boundary condition, (3) a surface flux model that predicts the distribution of tracer fluxes in time and space, and (4) a Bayesian inverse analysis that combines a priori information with observations to yield optimal estimates of tracer fluxes by the flux model. We use a receptor‐oriented transport model, the Stochastic Time‐Inverted Lagrangian Transport (STILT) model, to simulate ensembles of particles representing air parcels transported backward in time from an observation point (receptor), linking receptor concentrations with upstream locations and surface inputs. STILT provides the capability to forecast flight tracks for Lagrangian experiments in the presence of atmospheric shear and dispersion. STILT may be used to forecast flight tracks that sample the upstream tracer boundary condition, or to analyze the data and provide optimized parameters in the surface flux model. We present a case study of regional scale surface CO2 fluxes using data over the United States obtained in August 2000 in the CO2 Budget and Rectification Airborne (COBRA‐2000) study. STILT forecasts were obtained using the National Centers for Environmental Prediction Eta model to plan the flight tracks. Results from the Bayesian inversion showed large reductions in a priori errors for estimates of daytime ecosystem uptake of CO2, but constraints on nighttime respiration fluxes were weaker, due to few observations of CO2 in the nocturnal boundary layer. Derived CO2 fluxes from the influence‐following analysis differed notably from estimates using a conventional one‐dimensional budget (“Boundary Layer Budget”) on a typical day, due to time‐variable contributions from forests and croplands. A critical examination of uncertainties in the Lagrangian analyses revealed that the largest uncertainties were associated with errors in forecasting the upstream sampling locations and with aggregation of heterogeneous fluxes at the surface. Suggestions for improvements in future experiments are presented.

On comparison of modeled surface flux variations to aircraft observations

Evaluation of models of air–surface exchange is facilitated by an accurate match of areas simulated with those seen by micrometeorological flux measurements. Here, spatial variations in fluxes estimated with the parameterized subgrid-scale surface (PASS) flux model were compared to flux variations seen aboard aircraft above the Walnut River Watershed (WRW) in Kansas. Despite interference by atmospheric eddies, the areas where the modeled sensible and latent heat fluxes were most highly correlated with the aircraft flux estimates were upwind of the flight segments. To assess whether applying a footprint function to the surface values would improve the model evaluation, a two-dimensional correlation distribution was used to identify the locations and relative importance of contributing modeled surface pixels upwind of each segment of the flight path. The agreement between modeled surface fluxes and aircraft measurements was improved when upwind fluxes were weighted with an optimized footprint parameter φ, which can be estimated from wind profiler data and surface eddy covariance. Variations of the flight-observed flux were consistently greater than those modeled at the surface, perhaps because of the smoothing effect of using 1 km pixels in the model. In addition, limited flight legs prevented sufficient filtering of the effects of atmospheric convection, possibly accounting for some of the more prominent changes in fluxes measured along the flight paths.

Retrieval of Flow Structures in a Convective Boundary Layer Using an Adjoint Model: Identical Twin Experiments

The applicability of a four-dimensional variational data assimilation (4DVAR) technique to retrieval of microscale turbulent structures in a convective boundary layer is assessed. Two new features are implemented into the existing 4DVAR model: a height-dependent eddy viscosity and a surface flux model. The identical twin experiments approach is adopted to utilize the model itself to generate 13 instantaneous three-dimensional radial velocity datasets uniformly spanning 5 min. An ideal experiment, using these datasets as the observations, is first tested. After 400 iterations, the resulting correlation coefficients between retrieved and exact data are 0.99 for velocity and 0.97 for temperature fields. To emulate the lidar scanning feature, the 13 three-dimensional datasets are used to construct two volume scan datasets with each horizontal data slice taken from different instantaneous datasets. Using these data as the input, the correlation coefficients for horizontal, spanwise, and vertical velocity fluctuations and temperature can still reach 0.97, 0.97, 0.94, and 0.72 after 400 iterations. Addition of a surface flux model improves retrieval quality. Allowing height-dependent eddy viscosity and diffusivity does not improve retrieval quality, whereas doubling the value of eddy diffusivity improves retrieval quality. Implementation of temporal and spatial smoothness penalty functions significantly improves retrieval quality in the presence of various sources of error.

Physical aspects of the weather generator

One task in B ahc is to develop a physical understanding of the weather generator (WG). The core of the WG concept is scale interaction, represented by upscaling and downscaling, between the gridscale atmospheric fields and the subgrid-scale hydrological and ecosystem patterns. This paper considers scale interaction in terms of the convective (=latent plus sensible heat) and rain fluxes. Earth's surface and atmosphere are coupled through these fluxes. However, horizontal scale interaction in the free atmosphere is physically different from that at the surface. Upscaling atmospheric fluxes generates new sub-gridscale fluxes at the lower grid resolution, caused by the gridscale fluxes resolved at the higher grid resolution. Downscaling has to recover these secondary circulations, usually through mesoscale submodels. The same gridscale eddy effect is zero for surface fluxes because there is no cross-surface mass flux of air. The atmospheric fluxes on the various scales are quantified here with an atmospheric diagnostic model (D iamod) coupled to a surface flux model (S urfmod). Two convectively active periods over Europe (one disturbed, one undisturbed) are considered. Upscaling from 100 to 1000 km horizontal resolution generates additional grid-scale eddy fluxes. However, these amount, in both cases, to just 10% of the diagnosed fluxes that are sub-gridscale at 100 km; this suggests that the additional gridscale eddy effect may be negligible in convective situations. We further apply a simple downscaling recipe for disturbed periods (scale the rain flux profiles with the observed surface rain) and for undisturbed periods (scale the convective flux profiles with the observed surface latent plus sensible heat flux). At the Earth's surface, we study the upscaling–downscaling mechanism, not with diagnosed, but with modeled latent and sensible heat fluxes. With a simplified version of S urfmod, plus the probability density function of soil moisture, we reproduce the fatal impact of taking mean soil moisture for calculating mean evaporation: evaporation can be underestimated by 25% in dry situations and overestimated by 10% in moist situations. We demonstrate how to completely remove this bias. The technique presented here can be generalized to a wide class of deterministic and statistical models and offers a rational framework for the aggregation problem and the WG problem in general.