scholarly journals Atmospheric CO<sub>2</sub> observations and models suggest strong carbon uptake by forests in New Zealand

2016 ◽  
Author(s):  
K. Steinkamp ◽  
S. E. Mikaloff Fletcher ◽  
G. Brailsford ◽  
D. Smale ◽  
S. Moore ◽  
...  
Keyword(s):  
New Zealand ◽  
Process Model ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Atmospheric Dispersion ◽  
Temporal Distribution ◽  
Inversion Method ◽  
Back Trajectory ◽  
Dispersion Modeling ◽  
Limited Area

Abstract. A regional atmospheric inversion method has been developed to determine the spatial and temporal distribution of CO2 sinks and sources across New Zealand for 2011–2013. This approach infers air-sea and air-land CO2 fluxes from measurement records, using back-trajectory simulations from the Numerical Atmospheric dispersion Modeling Environment (NAME) Lagrangian dispersion model, driven by meteorology from the New Zealand Limited Area Model (NZLAM) weather prediction model. The inversion uses in situ measurements from two fixed sites, Baring Head on the southern tip of New Zealand’s North Island (41.408°S, 174.871°E) and Lauder from the central South Island (45.038°S, 169.684°E), and ship board data from monthly cruises between Japan, New Zealand and Australia. A range of scenarios is used to assess the sensitivity of the inversion method to underlying assumptions, and to ensure robustness of the results. The results indicate a strong seasonal cycle in terrestrial land fluxes from the South Island of New Zealand, especially in western regions covered by indigenous forest, suggesting higher photosynthetic and respiratory activity than is evident in the current a priori land process model. On the annual scale, the terrestrial biosphere in New Zealand is estimated to be a net CO2 sink, removing 98 (±37) Tg CO2 yr−1 from the atmosphere on average during 2011–2013. This sink is much larger than the reported 27 Tg CO2 yr−1 from the national inventory for the same time period. The difference can be partially reconciled when factors related to forest and agricultural management and exports, fossil fuel emission estimates, hydrologic fluxes, and soil carbon change are considered, but some differences are likely to remain.

scholarly journals Atmospheric CO<sub>2</sub> observations and models suggest strong carbon uptake by forests in New Zealand

2017 ◽  
Vol 17 (1) ◽  
pp. 47-76 ◽  
Author(s):  
Kay Steinkamp ◽  
Sara E. Mikaloff Fletcher ◽  
Gordon Brailsford ◽  
Dan Smale ◽  
Stuart Moore ◽  
...  
Keyword(s):  
New Zealand ◽  
Process Model ◽  
Dispersion Model ◽  
Weather Prediction ◽  
A Priori ◽  
Atmospheric Dispersion ◽  
Temporal Distribution ◽  
Inversion Method ◽  
Back Trajectory ◽  
Limited Area

Abstract. A regional atmospheric inversion method has been developed to determine the spatial and temporal distribution of CO2 sinks and sources across New Zealand for 2011–2013. This approach infers net air–sea and air–land CO2 fluxes from measurement records, using back-trajectory simulations from the Numerical Atmospheric dispersion Modelling Environment (NAME) Lagrangian dispersion model, driven by meteorology from the New Zealand Limited Area Model (NZLAM) weather prediction model. The inversion uses in situ measurements from two fixed sites, Baring Head on the southern tip of New Zealand's North Island (41.408° S, 174.871° E) and Lauder from the central South Island (45.038° S, 169.684° E), and ship board data from monthly cruises between Japan, New Zealand, and Australia. A range of scenarios is used to assess the sensitivity of the inversion method to underlying assumptions and to ensure robustness of the results. The results indicate a strong seasonal cycle in terrestrial land fluxes from the South Island of New Zealand, especially in western regions covered by indigenous forest, suggesting higher photosynthetic and respiratory activity than is evident in the current a priori land process model. On the annual scale, the terrestrial biosphere in New Zealand is estimated to be a net CO2 sink, removing 98 (±37) Tg CO2 yr−1 from the atmosphere on average during 2011–2013. This sink is much larger than the reported 27 Tg CO2 yr−1 from the national inventory for the same time period. The difference can be partially reconciled when factors related to forest and agricultural management and exports, fossil fuel emission estimates, hydrologic fluxes, and soil carbon change are considered, but some differences are likely to remain. Baseline uncertainty, model transport uncertainty, and limited sensitivity to the northern half of the North Island are the main contributors to flux uncertainty.

scholarly journals Diagnosing the Surface Layer Parameters for Dispersion Models within the Meteorological-to-Dispersion Modeling Interface

2010 ◽  
Vol 49 (2) ◽  
pp. 221-233 ◽  
Author(s):  
M. Sofiev ◽  
E. Genikhovich ◽  
P. Keronen ◽  
T. Vesala
Keyword(s):  
Surface Layer ◽  
Weather Forecasting ◽  
Dispersion Model ◽  
Temporal Correlation ◽  
Complex Surface ◽  
Dispersion Modeling ◽  
Limited Area ◽  
Dispersion Models ◽  
Absolute Flux ◽  
The Absolute

Abstract The problem of providing dispersion models with meteorological information from general atmospheric models used, for example, for weather forecasting is considered. As part of a generalized meteorological-to-dispersion model interface, a noniterative scheme diagnosing the surface layer characteristics from wind, temperature, and humidity profiles was developed. The scheme verification included long-term comparison with data of meteorological masts at Cabauw, the Netherlands, and Hyytiälä, Finland. The algorithm compatibility and consistency with the High-Resolution Limited-Area Model (HIRLAM) was also checked, as this model is routinely used as a meteorological driver for the Air Quality and Emergency Modeling System (SILAM). The comparison with Cabauw mast data showed a good quantitative agreement between observed and diagnosed heat and momentum fluxes: the temporal correlation coefficient was ∼0.8, bias was less than 10% of the absolute flux levels, regression slope deviated from unity for less than 20% with the intercept being less than 10% of the absolute flux values, and so on. In the case of complex surface features (Hyytiälä mast in forest) the scheme proved to be robust with large deviations appearing only if the input profile data were taken outside the constant-flux layer. Comparison with the HIRLAM model showed qualitatively good agreement but also highlighted several differences between the goals, standards, and methodologies of meteorological and dispersion models. The scheme was implemented in SILAM, which served as the development platform.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

Global and local sensitivity analysis of the Emission Dispersion Model input parameters

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samia Chettouh
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Dispersion Modeling ◽  
Content Type ◽  
Initial Concentration ◽  
Model Input ◽  
Input Parameters ◽  
Industrial Fire

PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

Air Dispersion Model to Forecast Pollutant Concentration Around Thermal Power Plants

2006 ◽  
Author(s):  
Jose´ I. Huertas ◽  
Mauricio Y. Carmona ◽  
Diego Moreno
Keyword(s):  
Power Plants ◽  
Dispersion Model ◽  
Thermal Power ◽  
Atmospheric Dispersion ◽  
Pollutant Concentration ◽  
Dispersion Modeling ◽  
Thermal Power Plants ◽  
Pollution Dispersion ◽  
Concentration Levels ◽  
Monitoring Stations

The Mexican environmental authority requires that thermal power plants operate 3 or 4 air quality monitoring stations around its main stack to ensure that pollutant concentration levels are always below the maximum allowable. However the high cost of these stations and the cost of their maintenance have made this regulation economically unreasonable. It has been proposed to reduce the number of monitoring stations to one and substitute the other stations by an accurate atmospheric dispersion model that allows the permanent surveillance of the surface pollutant concentration levels around the thermoelectric power plants. CALPUFF, an advanced air pollution dispersion modeling system was implemented for the special case of the Mexican thermal power plants. Experimental work was conducted to verify the correct implementation of the model. This paper describes the main results obtained during the development of this work.

AIRCITY: A Very High Resolution Atmospheric Dispersion Modeling System for Paris

2014 ◽  
Author(s):  
J. Moussafir ◽  
C. Olry ◽  
M. Nibart ◽  
A. Albergel ◽  
P. Armand ◽  
...  
Keyword(s):  
Air Quality ◽  
Parallel Machines ◽  
Road Traffic ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Parallel Architecture ◽  
Pollutant Dispersion ◽  
Particle Dispersion ◽  
Dispersion Modeling ◽  
The European Union

The AIRCITY project, partly funded by the European Union, is now successfully achieved. It aimed at developing a 4D innovative numerical simulation tool dedicated to the dispersion of traffic-induced air pollution at local scale on the whole urban area of PARIS. AIRCITY modeling system is based on PMSS (Parallel-Micro-SWIFT-SPRAY) software, which has been developed by ARIA Technologies in close collaboration with CEA and MOKILI. PMSS is a simplified CFD solution which is an alternative to micro-scale simulations usually carried out with full-CFD. Yet, AIRCITY challenge was to model the flow and pollutant dispersion with a 3 m resolution over the whole city of Paris covering a 14 km × 11,5 km domain. Thus, the choice was to run a mass-consistent diagnostic flow model (SWIFT) associated with a Lagrangian Particle Dispersion Model (SPRAY) on a massively parallel architecture. With a 3 m resolution on this huge domain, parallelization was applied to the computation of both the flow (by domain splitting) and the Lagrangian dispersion (management of particles is split over several processors). This MPI parallelization is more complex but gives a large flexibility to optimize the number of CPU, the available RAM and the CPU time. So, it makes possible to process arbitrarily large domains (only limited by the memory of the available nodes). As CEA operates the largest computing center in Europe, with parallel machines ranging from a few hundred to several thousand cores, the modeling system was tested on huge parallel clusters. More usual and affordable computers with a few tens of cores were also utilized during the project by ARIA Technologies and by AIRPARIF, the Regional Air Quality Management Board of Paris region, whose role was also to build the end-users requirements. These computations were performed on a simulation domain restricted to the hypercenter of Paris with dimensions around 2 km × 2 km (at the same resolution of 3 m). The focus was on the improvements needed to adapt simulation codes initially designed for emergency response to urban air quality applications: • Coupling with the MM5 / CHIMERE operational photochemical model at AIRPARIF (as the forecast background), • Turbulence generated by traffic / coupling with traffic model, • Inclusion of chemical reactions / Interaction with background substances (especially NO / NO2). Finally, in-depth validation of the modeling system was undertaken using both the routine air quality measurements in Paris (at four stations influenced by the road traffic) and a field experiment specially arranged for the project, with LIDARs provided by LEOSPHERE Inc. Comparison of PMSS and measurements gave excellent results concerning NO / NO2 and PM10 hourly concentrations for a monthly period of time while the LIDAR campaign results were also promising. In the paper, more details are given regarding the modeling system principles and developments and its validation. Perspectives of the project will also be discussed as AIRCITY system. The TRL must now be elevated from a demonstration to a robust and systematically validated modeling tool that could be used to predict routinely the air quality in Paris and in other large cities around the world.

scholarly journals Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Yang Yang ◽  
Phillip Andrews ◽  
Trevor Carey-Smith ◽  
Michael Uddstrom ◽  
Mike Revell
Keyword(s):  
New Zealand ◽  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Model Verification ◽  
Ensemble Prediction ◽  
Limited Area ◽  
The North ◽  
Rainfall Occurrence

This numerical weather prediction study investigates the effects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 different experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verification has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains significantly affect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifting due to the relatively low resolution of the model (~12 km) is a factor leading to this variability in heavy rainfall forecast skill. This study indicates that regional ensemble prediction with a suitably fine model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand.

Dispersion Modelling at the London VAAC 10 Years after the Eyjafjallajökull Ash Cloud

2020 ◽  
Author(s):  
Frances Beckett ◽  
Claire Witham ◽  
Susan Leadbetter ◽  
Ric Crocker ◽  
Helen Webster ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Lessons Learned ◽  
Volcanic Ash Cloud ◽  
Atmospheric Dispersion Models ◽  
Ash Cloud

&lt;p&gt;It has been 10 years since the ash cloud from the eruption of Eyjafjallaj&amp;#246;kull caused chaos to air traffic across Europe. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. Here we present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned.&lt;/p&gt;&lt;p&gt;Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep atmospheric convection and parameterizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Further, atmospheric dispersion models are driven by 3-dimensional meteorological data from Numerical Weather Prediction (NWP) models and the Met Office&amp;#8217;s upper air wind field data is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event affecting their region.&lt;/p&gt;

scholarly journals A Near-Real-Time Method for Estimating Volcanic Ash Emissions Using Satellite Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1573
Author(s):  
Rachel Pelley ◽  
David Thomson ◽  
Helen Webster ◽  
Michael Cooke ◽  
Alistair Manning ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
A Priori ◽  
Atmospheric Dispersion ◽  
Posteriori Probability ◽  
Inversion Method ◽  
Volcanic Plume ◽  
A Posteriori Probability ◽  
Satellite Retrievals ◽  
Atmospheric Dispersion Model

We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions.

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