Minkowski Sums of Periodic Surface Models

2009 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Crystal Packing ◽  
Surface Model ◽  
Implicit Surface ◽  
Periodic Surface ◽  
Construction Methods ◽  
Periodic Surfaces ◽  
Nano Structures ◽  
Surface Models ◽  
Minkowski Sums ◽  
Volume Translation

Recently we proposed a periodic surface model to assist geometric construction in computer-aided nano-design. This implicit surface model helps create super-porous nano structures parametrically and support crystal packing. In this paper, we study construction methods of Minkowski sums for periodic surfaces. A numerical approximation approach based on the Chebyshev polynomials is developed and can be applied in both surface normal direction matching and volume translation formulations.

Degree Operations on Periodic Surfaces

2007 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Surface Model ◽  
Implicit Surface ◽  
Periodic Surface ◽  
Degree Elevation ◽  
Periodic Surfaces ◽  
Computer Aided ◽  
Particle Aggregates

In previous work, a periodic surface model for computer-aided nano-design (CAND) was developed. This implicit surface model can construct Euclidean and hyperbolic nano geometries parametrically and represent morphologies of particle aggregates and polymers. In this paper, we study the characteristics of degree elevation and reduction based on a generalized periodic surface model. Methods of degree elevation and reduction operations are developed in order to support multi-resolution representation and model exchange.

Feature-Based Crystal Construction in Computer-Aided Nano-Design

2008 ◽  
Author(s):  
Cheng Qi ◽  
Yan Wang
Keyword(s):  
Material Design ◽  
Surface Model ◽  
Periodic Surface ◽  
Periodic Surfaces ◽  
Nano Structures ◽  
Rapid Construction ◽  
Computer Aided ◽  
Feature Based ◽  
Basic Features ◽  
Complex Crystal

Providing nano engineers and scientists efficient and easy-to-use tools to create geometry conformations that have minimum energies is highly desirable in material design. Recently we developed a periodic surface model to assist the construction of nano structures parametrically for computeraided nano-design. In this paper, we present a feature-based approach for crystal construction. The proposed approach starts to create models of basic features by the aide of periodic surfaces followed by operations between basic features. The goal is to introduce a rapid construction method for complex crystal structures.

3D Fractals From Periodic Surfaces

2010 ◽  
Author(s):  
Yan Wang
Keyword(s):  
Porous Silicon ◽  
Iterated Function Systems ◽  
Surface Model ◽  
Porous Structures ◽  
Implicit Surface ◽  
Periodic Surface ◽  
Irregular Structures ◽  
Periodic Surfaces ◽  
Iterated Function ◽  
3D Fractals

Fractals are ubiquitous as in natural objects and have been applied in designing porous structures such as micro antenna and porous silicon. The seemingly complex and irregular structures can be generated based on simple principles. In this paper, we present three approaches to construct 3D fractal geometries using a recently proposed periodic surface model. By applying iterated function systems to the implicit surface model in the Euclidean or parameter space, 3D fractals can be constructed efficiently. Porosity is also proposed as a metric in fractal design.

scholarly journals Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

2017 ◽  
Vol 18 (3) ◽  
pp. 897-915 ◽  
Author(s):  
Jennifer L. Jefferson ◽  
Reed M. Maxwell ◽  
Paul G. Constantine
Keyword(s):  
Land Surface ◽  
Stomatal Resistance ◽  
Hydrologic Model ◽  
Surface Model ◽  
Land Surface Models ◽  
Common Land Model ◽  
Surface Models ◽  
Input Parameters ◽  
Rate Of Photosynthesis ◽  
Quantities Of Interest

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Valérie Daux ◽  
Camille Risi ◽  
Jina Jeong ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Forest Ecosystems ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

<p>Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (Δ<sup>13</sup>C) and oxygen (δ<sup>18</sup>O) stable isotopes in six sites in boreal and temperate Europe.  The model simulates Δ<sup>13</sup>C (r = 0.31-0.80) and δ<sup>18</sup>O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with δ<sup>18</sup>O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, Δ<sup>13</sup>C and δ<sup>18</sup>O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.</p>

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

2013 ◽  
Vol 7 (3) ◽  
pp. 2333-2372
Author(s):  
E. Kantzas ◽  
M. Lomas ◽  
S. Quegan ◽  
E. Zakharova
Keyword(s):  
Land Surface ◽  
Field Data ◽  
Climate Models ◽  
Optimization Techniques ◽  
The Arctic ◽  
Surface Model ◽  
Land Surface Models ◽  
Snow Density ◽  
Data Set ◽  
Surface Models

Abstract. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

scholarly journals Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

2021 ◽  
Author(s):  
Mengyuan Mu ◽  
Martin De Kauwe ◽  
Anna Ukkola ◽  
Andy Pitman ◽  
Teresa Gimeno ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Model ◽  
Strong Impact ◽  
Water Fluxes ◽  
Land Surface Models ◽  
Groundwater Aquifer ◽  
Surface Models

<p>Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics - highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias; a more realistic initialisation of the groundwater aquifer state; higher vertical soil resolution informed by observed soil properties; and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ~150 W m<sup>-2</sup> in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.</p>

scholarly journals Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

2020 ◽  
Author(s):  
Yaqiong Lu ◽  
Xianyu Yang
Keyword(s):  
Crop Yield ◽  
Temporal Resolution ◽  
Land Surface ◽  
Crop Yields ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Climate Scenarios ◽  
Climate Data ◽  
Land Surface Models ◽  
Surface Models

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

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