scholarly journals Using GOME NO<sub>2</sub> satellite data to examine regional differences in TOMCAT model performance

2004 ◽  
Vol 4 (3) ◽  
pp. 2569-2613
Author(s):  
N. H. Savage ◽  
K. S. Law ◽  
J. A. Pyle ◽  
A. Richter ◽  
H. Nüß ◽  
...  
Keyword(s):  
North America ◽  
Case Studies ◽  
Biomass Burning ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Regional Differences ◽  
Model Development ◽  
Vertical Mixing ◽  
Model Performance ◽  
Model Treatment

Abstract. This paper compares column measurements of NO2 made by the GOME instrument on ERS-2 to model results from the TOMCAT global CTM. The overall correlation between the model and observations is good (0.79 for the whole world, and 0.89 for north America) but the modelled columns are too large over polluted areas (gradient of 1.4 for North America and 1.9 for Europe). NO2 columns in the region of outflow from North America into the Atlantic seem too high in winter in the model compared to the GOME results, whereas the modelled columns are too small off the coast of Africa where there appear to be biomass burning plumes in the satellite data. Several hypotheses are presented to explain these discrepancies. Weaknesses in the model treatment of vertical mixing and chemistry appear to be the most likely explanations. It is shown that GOME and other satellite data will be of great value in furthering our understanding of atmospheric chemistry and in targeting and testing future model development and case studies.

scholarly journals Using GOME NO<sub>2</sub> satellite data to examine regional differences in TOMCAT model performance

2004 ◽  
Vol 4 (7) ◽  
pp. 1895-1912 ◽  
Author(s):  
N. H. Savage ◽  
K. S. Law ◽  
J. A. Pyle ◽  
A. Richter ◽  
H. Nüß ◽  
...  
Keyword(s):  
North America ◽  
Biomass Burning ◽  
Satellite Data ◽  
Regional Differences ◽  
Vertical Mixing ◽  
Model Performance ◽  
Model Treatment

Abstract. This paper compares column measurements of NO2 made by the GOME instrument on ERS-2 to model results from the TOMCAT global CTM. The overall correlation between the model and observations is good (0.79 for the whole world, and 0.89 for North America) but the modelled columns are larger than GOME over polluted areas (gradient of 1.4 for North America and 1.9 for Europe). NO2 columns in the region of outflow from North America into the Atlantic are higher in winter in the model compared to the GOME results, whereas the modelled columns are smaller off the coast of Africa where there appear to be biomass burning plumes in the satellite data. Several hypotheses are presented to explain these discrepancies. Weaknesses in the model treatment of vertical mixing and chemistry appear to be the most likely explanations.

scholarly journals Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 204
Author(s):  
Chamay Kruger ◽  
Willem Daniel Schutte ◽  
Tanja Verster
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Model Performance ◽  
Data Set ◽  
Pooled Data ◽  
External Data ◽  
First Case ◽  
Multivariate Prediction ◽  
Credit Data

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.

scholarly journals Multiscale observations of NH3 around Toronto, Canada

2020 ◽  
Author(s):  
Shoma Yamanouchi ◽  
Camille Viatte ◽  
Kimberly Strong ◽  
Dylan B. A. Jones ◽  
Cathy Clerbaux ◽  
...  
Keyword(s):  
Case Studies ◽  
Biomass Burning ◽  
Grid Point ◽  
Fine Particulate Matter ◽  
Regional Scale ◽  
Model Performance ◽  
In Situ Measurements ◽  
Coefficient Of Determination ◽  
Predictive Capacity

&lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;Ammonia (NH&lt;sub&gt;3&lt;/sub&gt;) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to monitor NH&lt;sub&gt;3&lt;/sub&gt;. This study examines long-term measurements of NH&lt;sub&gt;3&lt;/sub&gt; around Toronto, Canada, derived from three multiscale datasets: 16 years of total column measurements using ground-based Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and ten years of total columns from the Infrared Atmospheric Sounding Interferometer (IASI) sensor onboard the Metop satellites. These datasets were used to quantify NH&lt;sub&gt;3&lt;/sub&gt; temporal variabilities (trends, inter-annual, seasonal) over Toronto to assess the observational footprint of the FTIR measurements, and two case studies of pollution events due to transport of biomass burning plumes.&lt;/p&gt; &lt;p&gt;All three timeseries showed increasing trends in NH&lt;sub&gt;3&lt;/sub&gt; over Toronto: 3.34 &amp;#177; 0.44 %/year from 2002 to 2018 in the FTIR columns, 8.88 &amp;#177; 2.49 %/year from 2013 to 2017 in the surface in-situ data, and 8.78 &amp;#177; 0.84 %/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH&lt;sub&gt;3&lt;/sub&gt; columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was found for coincidence criterion of &amp;#8804; 50 km and &amp;#8804; 20 minutes, with r = 0.66 and a slope of 0.988 &amp;#177; 0.058. The FTIR column and in-situ measurements were standardized and correlated, with 24-day averages and monthly averages yielding correlation coefficients of r = 0.72 and r = 0.75, respectively.&lt;br&gt;FTIR and IASI were also compared against the GEOS-Chem model, run at 2&amp;#176; by 2.5&amp;#176; resolution, to assess model performance and investigate correlation of the model output with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (domain spanning from 35&amp;#176;N to 53&amp;#176;N, and 93.75&amp;#176;W to 63.75&amp;#176;W) resulted in r = 0.62, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r&lt;sup&gt;2&lt;/sup&gt; = 0.38, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r&lt;sup&gt;2&lt;/sup&gt; = 0.26, indicating that a finer spatial resolution is needed to adequately model the variability of NH&lt;sub&gt;3&lt;/sub&gt;. This study also examines two case studies of NH&lt;sub&gt;3&lt;/sub&gt; enhancements due to biomass burning plumes, in August 2014 and May 2016. In these events, enhancements in both the total columns and surface NH3, were observed.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;

The MARINA-Plastic model: Global river export of macro- and microplastics from over 10,000 sub-basins to coastal seas

2021 ◽  
Author(s):  
Maryna Strokal ◽  
Paul Vriend ◽  
Jikke van Wijnen ◽  
Carolien Kroeze ◽  
Tim van Emmerik
Keyword(s):  
North America ◽  
Solid Waste ◽  
Case Studies ◽  
Coastal Waters ◽  
Model Performance ◽  
Point Sources ◽  
Plastic Pollution ◽  
Plastic Model ◽  
River Export ◽  
The Impact

&lt;p&gt;Plastics are found in different sizes in many rivers and coastal waters worldwide. Our understanding of the sources of this plastic is poor. Quantitative, and spatially explicit data on plastic loads is needed to design effective plastic pollution reduction strategies. One way to gather such data is through modeling studies. To this end, we develop the MARINA-Plastic model for macro- and microplastic. The MARINA-Plastic model quantifies annual river export of macro- and microplastic by source from sub-basins to coastal waters of the world. The model runs for over 10,000 sub-basins and considers point (e.g., sewage systems) and diffuse (e.g., mismanaged solid waste on land) sources of plastics in rivers. We evaluate and validate the model using a &amp;#8220;building trust&amp;#8221; approach. Evaluation results indicate the robustness of the model performance.&lt;/p&gt;&lt;p&gt;Results of the MARINA-Plastic model show that approximately 10% of all sub-basins are, today, responsible for over 90% of macroplastic inputs to rivers globally. Asia and Africa are responsible for approximately 80% of the plastic export by rivers globally. Coastal waters of Asia and Africa are predominantly polluted with macroplastics from diffuse sources in terms of mass, whereas coastal waters of Europe and North America are predominantly polluted with microplastics from point sources. Middle- and downstream activities contribute largely to coastal water pollution with plastics for selected case studies. These case studies are six large rivers, of which the drainage areas are divided into up-, middle- and downstream sub-basins. These rivers are the Mississippi (North America), Amazon (South America), Danube (Europe), Niger (Africa), Nile (Africa), and the Yangtze (Asia) rivers. Our analysis shows that reducing plastic pollution in coastal waters requires improvement of the wastewater treatment in Europe and North America and solid waste management in Asia and Africa.&lt;/p&gt;&lt;p&gt;We show that the MARINA-Plastic model is applicable to get a better understanding of the sources and the spatial variability of the plastic pollution in rivers and coastal waters. The model allows to analyse the impact of upstream activities on downstream plastic pollution and to explore effects of environmental policies on plastics in waters. This information can help to develop effective solutions for reducing future plastic pollution.&lt;/p&gt;

scholarly journals Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Comparison ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Strong Impact ◽  
Air Quality Model ◽  
Quality Model ◽  
Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

scholarly journals Predicting the tripartite network of mosquito-borne disease

2021 ◽  
Author(s):  
Tad Dallas ◽  
Sadie Jane Ryan ◽  
Ben Bellekom ◽  
Anna Claire Fagre ◽  
Rebecca Christofferson ◽  
...  
Keyword(s):  
North America ◽  
Predictive Models ◽  
Model Development ◽  
Model Performance ◽  
Vector Biology ◽  
Vector Transmission ◽  
Geographic Scope ◽  
Complex Interactions ◽  
Virus Interactions ◽  
Intermediate Vector

The potential for a pathogen to infect a host is mediated by traits of both the host and pathogen, as well as the complex interactions between them. Arthropod-borne viruses (arboviruses) require an intermediate vector, introducing an additional compatibility layer. Existing predictive models of host-virus networks rarely incorporate the unique aspects of vector transmission, instead treating vector biology as a hidden, unobserved layer. We explore two possible extensions to address this: first, we added vector traits into predictions of the bipartite host-virus network; and second, we used host, vector, and virus traits to predict the tripartite host-vector-virus network. We tested both approaches on mosquito-borne flaviviruses of mammals. Using host-virus models, we find that the inclusion of vector traits may improve inference in some cases, while viral traits proved to be the most important for model performance. Further, we found that it was possible, though quite difficult, to predict full tripartite (host-vector-virus) links. Both approaches are interesting avenues for further model development, but our results keenly underscore a need to collect more comprehensive datasets to characterize arbovirus ecology, across a wide and less biased geographic scope, especially outside of North America, and to better identify molecular traits that underpin host-vector-virus interactions.

scholarly journals Emission factors for open and domestic biomass burning for use in atmospheric models

2011 ◽  
Vol 11 (9) ◽  
pp. 4039-4072 ◽  
Author(s):  
S. K. Akagi ◽  
R. J. Yokelson ◽  
C. Wiedinmyer ◽  
M. J. Alvarado ◽  
J. S. Reid ◽  
...  
Keyword(s):  
Gas Phase ◽  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Emission Factor ◽  
Standard Form ◽  
Model Performance ◽  
Future Research ◽  
Atmospheric Models ◽  
Carbonaceous Particles ◽  
Global Estimate

Abstract. Biomass burning (BB) is the second largest source of trace gases and the largest source of primary fine carbonaceous particles in the global troposphere. Many recent BB studies have provided new emission factor (EF) measurements. This is especially true for non-methane organic compounds (NMOC), which influence secondary organic aerosol (SOA) and ozone formation. New EF should improve regional to global BB emissions estimates and therefore, the input for atmospheric models. In this work we present an up-to-date, comprehensive tabulation of EF for known pyrogenic species based on measurements made in smoke that has cooled to ambient temperature, but not yet undergone significant photochemical processing. All EFs are converted to one standard form (g compound emitted per kg dry biomass burned) using the carbon mass balance method and they are categorized into 14 fuel or vegetation types. Biomass burning terminology is defined to promote consistency. We compile a large number of measurements of biomass consumption per unit area for important fire types and summarize several recent estimates of global biomass consumption by the major types of biomass burning. Post emission processes are discussed to provide a context for the emission factor concept within overall atmospheric chemistry and also highlight the potential for rapid changes relative to the scale of some models or remote sensing products. Recent work shows that individual biomass fires emit significantly more gas-phase NMOC than previously thought and that including additional NMOC can improve photochemical model performance. A detailed global estimate suggests that BB emits at least 400 Tg yr−1 of gas-phase NMOC, which is almost 3 times larger than most previous estimates. Selected recent results (e.g. measurements of HONO and the BB tracers HCN and CH3CN) are highlighted and key areas requiring future research are briefly discussed.

Sustainability assessment model for heritage buildings

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abobakr Al-Sakkaf ◽  
Ashutosh Bagchi ◽  
Tarek Zayed ◽  
Sherif Mahmoud
Keyword(s):  
Case Studies ◽  
Rating Scale ◽  
Sustainability Assessment ◽  
Model Development ◽  
Assessment Model ◽  
Equal Weight ◽  
Indoor Environmental Quality ◽  
Regional Variations ◽  
Content Type ◽  
Heritage Buildings

PurposeThe purpose of this research is to focus on the evaluation of heritage buildings' sustainability. BIM modeling was necessary for the design of the sustainability assessment model for Heritage Buildings (SAHB). Using ArchiCAD®, energy simulations were performed for two case studies (Murabba Palace, Saudi Arabia, and Grey Nuns Building, Canada), and the developed model was validated through sensitivity analysis.Design/methodology/approachHeritage buildings (HBs) are unique and must be preserved for future generations. This article focuses on a sustainability assessment model and rating scale for heritage buildings in light of the need for their conservation. Regional variations were considered in the model development to identify critical attributes whose corresponding weights were then determined by fuzzy logic. Data was collected via questionnaires completed by Saudi Arabian and Canadian experts, and Fuzzy TOPSIS was also applied to eliminate the uncertainties present when human opinions are involved.FindingsResults showed that regional variations were sufficiently addressed through the multi-level weight consideration in the proposed model. Comparing the nine identified factors that affect the sustainability of HBs, energy and indoor environmental quality were of equal weight in both case studies.Originality/valueThis study will be helpful for the design of a globally applicable sustainability assessment model for HBs. It will also enable decision-makers to prepare maintenance plans for HBs.

A case study review of wood ash land application programs in North America

TAPPI Journal ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 111-120
Author(s):  
ILICH LAMA ◽  
DEREK SAIN
Keyword(s):  
North America ◽  
Case Studies ◽  
Soil Quality ◽  
Management Practices ◽  
Agricultural Land ◽  
Forest Products ◽  
Wood Ash ◽  
Land Application ◽  
Agricultural Application ◽  
Application Programs

Several regulatory agencies and universities have published guidelines addressing the use of wood ash as liming material for agricultural land and as a soil amendment and fertilizer. This paper summarizes the experiences collected from several forest products facility-sponsored agricultural application programs across North America. These case studies are characterized in terms of the quality of the wood ash involved in the agricultural application, approval requirements, recommended management practices, agricultural benefits of wood ash, and challenges confronted by ash generators and farmers during storage, handling, and land application of wood ash. Reported benefits associated with land-applying wood ash include increasing the pH of acidic soils, improving soil quality, and increasing crop yields. Farmers apply wood ash on their land because in addition to its liming value, it has been shown to effectively fertilize the soil while maintaining soil pH at a level that is optimal for plant growth. Given the content of calcium, potassium, and magnesium that wood ash supplies to the soil, wood ash also improves soil tilth. Wood ash has also proven to be a cost-effective alternative to agricultural lime, especially in rural areas where access to commercial agricultural lime is limited. Some of the challenges identified in the review of case studies include lengthy application approvals in some jurisdictions; weather-related issues associated with delivery, storage, and application of wood ash; maintaining consistent ash quality; inaccurate assessment of required ash testing; potential increased equipment maintenance; and misconceptions on the part of some farmers and government agencies regarding the effect and efficacy of wood ash on soil quality and crop productivity.

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