scholarly journals Optimisation of a Numerical Model to Simulate the Dispersion and Chemical Transformations Within the Oxides of Nitrogen/Ozone System as Traffic Pollution Enters an Urban Greenspace

2021 ◽  
Author(s):  
Dewi Komalasari ◽  
Zongbo Shi ◽  
Roy M. Harrison
Keyword(s):  
Meteorological Data ◽  
Model Performance ◽  
Traffic Emissions ◽  
Chemical Transformations ◽  
Oxides Of Nitrogen ◽  
Atmospheric Measurements ◽  
Urban Greenspace ◽  
Diffusion Tubes ◽  
Deposition Processes ◽  
The University

AbstractUrban greenspace has many health benefits, including cleaner air than the surrounding streets. In this study, a detailed exercise has been conducted to measure concentrations of NO/NO2/NOx and O3 within an urban greenspace, the University of Birmingham campus, using continuous analysers, as well as transects of NO2 measured with diffusion tubes. Concentrations have been simulated using the ADMS-Roads model which has been optimised initially using NOx concentrations for traffic emissions on surrounding roads, background concentrations, and meteorological data considering four candidate sites. Optimisation for prediction of NO2 shows the critical importance of the NO2:NOx ratio in traffic emissions, for which a derivation from atmospheric measurements is consistent with a value derived from optimisation of the model fit to roadside data. After optimisation, the model gives an excellent fit to continuous data measured at roadside. Comparison of model predictions with transects of NO2 across the greenspace also show generally good model performance. The incorporation of dry deposition processes for the nitrogen oxides into the model leads to a reduction of less than 1% in predicted concentrations, leading to the conclusion that the cleaner air within urban greenspace is primarily the result of dispersion rather than deposition processes.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

2014 ◽  
Vol 14 (23) ◽  
pp. 32233-32323 ◽  
Author(s):  
M. Bocquet ◽  
H. Elbern ◽  
H. Eskes ◽  
M. Hirtl ◽  
R. Žabkar ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Hybrid Methods ◽  
Model Performance ◽  
Chemical Data ◽  
Current Status ◽  
Model Parameters ◽  
Chemical Transformations ◽  
Future Prospects ◽  
Main Challenge

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

Advances in the Ocean Observing System in the Gulf of Maine: Technical Capabilities and Scientific Results

2011 ◽  
Vol 45 (1) ◽  
pp. 85-97 ◽  
Author(s):  
Neal R. Pettigrew ◽  
C. Patrick Fikes ◽  
M. Kate Beard
Keyword(s):  
Gulf Of Maine ◽  
Meteorological Data ◽  
Coast Guard ◽  
Remote Access ◽  
Resource Managers ◽  
Salinity Anomaly ◽  
Natural Resource Managers ◽  
Scientific Results ◽  
The University ◽  
Ocean Observing

AbstractThe Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS), which began in 2008, includes the University of Maine’s comprehensive data buoy array in the Gulf of Maine (GoM). The University of Maine buoy system started in 2001 as part of the Gulf of Maine Ocean Observing System (GoMOOS). The buoys provide a wide variety of oceanographic and marine meteorological data in real time to scientists, environmentalists, the National Weather Service, the U.S. Coast Guard and Canadian Coast Guard, educators, regional natural resource managers, the GoM fishing and maritime industries, and the general public. The GoM observing system is presently undergoing a redesign of the buoy control system to enhance remote access and reduce operational costs. The enhancements will allow remote trouble-shooting and reprogramming of the buoys and subsurface sensors. The system will also accommodate sensors from other research groups and allow them post-deployment control without assistance from our buoy group.Over the near-decade of operation, the system has revealed marked seasonal and interannual variability of the circulation and physical properties of the GoM. In the fall of 2004 to spring of 2005, Doppler currents measured an outflow of deep salty slope waters that suggest a regime shift in the inflow and outflow of transports through the Northeast Channel. During the same period, a salinity anomaly event lowered salinity throughout the GoM by roughly 2 psu by the winter of 2005. In following years, the previously unusual slope outflow and reduced salinity have often reoccurred.

Small-scale processes with large-scale impacts: Investigating canopy structure controls on energy fluxes to the forest snowpack

2020 ◽  
Author(s):  
Giulia Mazzotti ◽  
Richard Essery ◽  
Johanna Malle ◽  
Clare Webster ◽  
Tobias Jonas
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Meteorological Data ◽  
Canopy Structure ◽  
Model Performance ◽  
Turbulent Fluxes ◽  
Temporal Variations ◽  
Small Scale ◽  
Forest Stands

<p>Forest canopies strongly affect snowpack energetics during wintertime. In discontinuous forest stands, spatio-temporal variations in radiative and turbulent fluxes create complex snow distribution and melt patterns, with further impacts on the hydrological regimes and on the land surface properties of seasonally snow-covered forested environments.</p><p>As increasingly detailed canopy structure datasets are becoming available, canopy-induced energy exchange processes can be explicitly represented in high-resolution snow models. We applied the modelling framework FSM2 to obtain spatially distributed simulations of the forest snowpack in subalpine and boreal forest stands at high spatial (2m) and temporal (10min) resolution. Modelled sub-canopy radiative and turbulent fluxes were compared to detailed meteorological data of incoming irradiances, air and snow surface temperatures. These were acquired with novel observational systems, including 1) a motorized cable car setup recording spatially and temporally resolved data along a transect and 2) a handheld setup designed to capture temporal snapshots of 2D spatial distributions across forest discontinuities.</p><p>The combination of high-resolution modelling and multi-dimensional datasets allowed us to assess model performance at the level of individual energy balance components, under various meteorological conditions and across canopy density gradients. We showed which canopy representation strategies within FSM2 best succeeded in reproducing snowpack energy transfer dynamics in discontinuous forests, and derived implications for implementing forest snow processes in coarser-resolution models.</p>

scholarly journals The University of South Alabama Mesonet and Coastal Observing System: A Technical and Statistical Overview

2010 ◽  
Vol 27 (9) ◽  
pp. 1417-1439 ◽  
Author(s):  
Sytske K. Kimball ◽  
Madhuri S. Mulekar ◽  
Shailer Cummings ◽  
Jack Stamates
Keyword(s):  
Weather Forecasting ◽  
Meteorological Data ◽  
Future Research ◽  
Sensor Failure ◽  
North Central ◽  
The North ◽  
System A ◽  
The University ◽  
Mexico Coast ◽  
Good Agreement

Abstract The University of South Alabama Mesonet consists of 26 sites across the north-central Gulf of Mexico coast. Although the original purpose of the mesonet was monitoring landfalling tropical systems, meteorological data are collected and disseminated every 5 min year-round to serve a multitude of purposes, including weather forecasting, education, and research. In this paper a statistical analysis and like-sensor comparison demonstrates that variables, measured by different sensor types or by sensors at different heights, correlate well. The benefits of sensor redundancy are twofold, offering 1) backup sensors in the case of sensor failure during severe weather and 2) the ability to perform a large number of internal consistency checks for quality control purposes. An oceanographic compliment to the University of South Alabama Mesonet system, which was deployed by NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) to measure surface waves and ocean currents in an area south of Mobile, Alabama, is described. A preliminary comparison of mesonet wind data and ocean wave data show good agreement, offering promising opportunities for future research.

The Combustion Process in the Compression-Ignition Engine

1938 ◽  
Vol 138 (1) ◽  
pp. 415-493 ◽  
Author(s):  
J. W. Drinkwater ◽  
A. C. Egerton
Keyword(s):  
Chemical Reactions ◽  
Sampling Method ◽  
Combustion Process ◽  
Cylinder Wall ◽  
Compression Ignition ◽  
Oxides Of Nitrogen ◽  
Compression Ignition Engine ◽  
Engine Cylinder ◽  
Compression And Expansion ◽  
The University

The object of the work, which was carried out in the University Engineering Laboratory, Oxford, was to investigate the combustion process in a compression-ignition engine by determining the extent of the chemical reactions of the fuel and air at various stages during the compression and expansion strokes. The results of the tests are illustrated by several curves showing the percentage volumes of the constituent gases in the engine cylinder at different points in the cycle. Various inferences are drawn concerning the combustion in this type of engine. Attempts were made to determine the concentration of aldehydes in the gases, but the tests showed that the amount was less than anticipated. Oxides of nitrogen were detected, and considered to have an influence upon cylinder wall corrosion. It is suggested that there is scope for further work using the sampling method for investigating combustion problems in engines under running conditions.

scholarly journals Investigation of global nitrate from the AeroCom Phase III experiment

2017 ◽  
Author(s):  
Huisheng Bian ◽  
Mian Chin ◽  
Didier A. Hauglustaine ◽  
Michael Schulz ◽  
Gunnar Myhre ◽  
...  
Keyword(s):  
Wet Deposition ◽  
Model Performance ◽  
Sea Salt ◽  
Phase Iii ◽  
Chemical Production ◽  
Magnitude Distribution ◽  
Ground Station ◽  
Vertical Distributions ◽  
Atmospheric Nitrate ◽  
Deposition Processes

Abstract. An assessment of global nitrate and ammonium aerosol based on simulations from nine models participating in the AeroCom Phase III study is presented. A budget analyses was conducted to understand the typical magnitude, distribution, and diversity of the aerosols and their precursors among the models. To gain confidence on model performance, the model results were evaluated with various observations globally, including ground station measurements over North America, Europe, and East Asia for tracer concentrations and dry and wet depositions, as well as with aircraft measurements in the Northern Hemisphere mid-high latitudes for tracer vertical distributions. Given the unique chemical and physical features of the nitrate occurrence, we further investigated the similarity and differentiation among the models by examining: (1) the pH-dependent NH3 wet deposition; (2) the nitrate formation via heterogeneous chemistry on the surface of dust and sea-salt particles; and (3) the nitrate coarse mode fraction (i.e., coarse/total). It is found that HNO3, which is simulated explicitly based on full O3–HOx–NOx–aerosol chemistry by all models, differs by up to a factor of 9 among the models in its global tropospheric burden. This partially contributes to a large difference in NO3−, whose atmospheric burden differs by up to a factor of 13. Analyses at the process level show that the large diversity in atmospheric burdens of NO3−, NH3, and NH4+ is also related to deposition processes. Wet deposition seems to be the dominant process in determining the diversity in NH3 and NH4+ lifetimes. It is critical to correctly account for contributions of heterogeneous chemical production of nitrate on dust and sea-salt, because this process overwhelmingly controls atmospheric nitrate production (typically > 80 %) and determines the coarse and fine mode distribution of nitrate aerosol.

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