Search

This paper presents a study of haze in Singapore caused by biomass burning in Southeast Asia over the 6-year period from 2010 to 2015, using the Numerical Atmospheric-dispersion Modelling Environment (NAME), which is a Lagrangian dispersion model. The major contributing source regions to the haze are identified using forwards and backwards model simulations of particulate matter. The coincidence of relatively strong southeast monsoonal winds with increased biomass burning activities in the Maritime Continent create the main Singapore haze season from August to October (ASO), which brings particulate matter from varying source regions to Singapore. Five regions are identified as the dominating sources of pollution during recent haze seasons: Riau, Peninsular Malaysia, South Sumatra, and Central and West Kalimantan. In contrast, off-season haze episodes in Singapore are characterised by unusual weather conditions, ideal for biomass burning, and contributions dominated by a single source region (different for each event). The two most recent off-season haze events in mid-2013 and early 2014 have different source regions, which differ from the major contributing source regions for the haze season. These results challenge the current popular assumption that haze in Singapore is dominated by emissions/burning from only Indonesia. For example, it is shown that Peninsular Malaysia is a large source for the Maritime Continent off-season biomass burning impact on Singapore. The results demonstrate that haze in Singapore varies across year, season, and location and is influenced by local and regional weather, climate, and regional burning. Differences in haze concentrations and variation in the relative contributions from the various source regions are seen between monitoring stations across Singapore, on a seasonal as well as on an inter-annual timescale. This study shows that even across small scales, such as in Singapore, variation in local meteorology can impact concentrations of particulate matter significantly, and it emphasises the importance of the scale of modelling both spatially and temporally.

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.

Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite‐based active‐fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up‐to‐date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real‐time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

The Met Office has traditionally distributed the NAME atmospheric dispersion model and supporting meteorological data to a number of external organisations. CEDA's new JASMIN platform enables consolidation of resources by providing a centralised and supported NAME community service. Wider exploitation of the model is planned along with a web-tool for running historic forward and backward trajectories and a dataset of NAME outputs including trajectory plots. NCAS scientists can exploit this service for large scale batch runs supporting field campaigns as well as individual research applications., Previously curated at: http://cedadocs.ceda.ac.uk/958/ Event type: conference. The publish date on this item was its original completed date. This item was not refereed before the publication Main files in this record: ncas2013_jasmin_name_vfinal.pdf Item originally deposited with Centre for Environmental Data Analysis (CEDA) document repository by Mr Ag Stephens. Transferred to CEDA document repository community on Zenodo on 2022-11-24

The transient behaviour of natural convection within a horizontal cylindrical enclosure, subjected to a uniform and constant wall temperature, is investigated numerically and experimentally. Numerically the two dimensional instantaneous governing equations are solved through Chebyshev-Fourier spatial approximation. Two boundary condition configurations have been examined. In the first, considered to be an idealised configuration, the enclosure wall is neglected and the constant boundary temperature applied at the solid/fluid interface. The second, applies constant temperature boundary conditions on the outer surface of the enclosure wall, giving a conjugate configuration, as is the case in the experimental study. Numerically, both boundary conditions are considered over the Rayleigh number range of 10 4 < Ra < 108 for Prandtl numbers Pr - 0.71, 7.1 and 100. This is extended to Ra = 109 for Pr = 7.1 and 100 for the ideal and for Pr = 7.1 for the conjugate case. Experimental results are obtained for Ra = 1.4 x 1010 at Pr = 5.6. In order to achieve greater insight, of the transient features, extensive use is made of visualisation techniques both numerically and experimentally. The flows evolution for the ideal boundary condition case is found to be divisible into two time regimes. The early regime, extending over the conduction time period, scales as Ra-1/2 L2/α. The later regime, encompassing the convective flow, scales as Ra-0.24 L2/α. The magnitude of the average Nusselt number scales like Ra1/4. For the conjugate case the thermal conductivity of the wall limits the maximum heat transfer rate into the cylinder for all Ra. The time evolution of the conjugate boundary condition case has no simple scaling. For both boundary conditions, the flow structure demonstrates an asymptotic behaviour with increasing Pr. It is shown that the boundary layer possesses a two layer structure only over the very early time period. This is quickly superseded by a single layer, where both the thermal and dynamic boundary layers are the same thickness. For the ideal boundary condition case this single layer structure scales as Ra1/4. At large Ra, the transients within the core of the enclosure are found to evolve in an oscillatory manner due to internal wave motion. The structure of these waves is Pr dependant.