Air Quality Forecasting - How accurate can it be?

Posted on March 23rd 2015
Share: aqicn.org/faq/2015-03-23/air-quality-forecasting-how-accurate-can-it-be


STRONG LAPSE CONDITION (LOOPING)

WEAK LAPSE CONDITION (CONING)

INVERSION CONDITION (FANNING)

Examples of Atmospheric Stability (attribution)
In weather prediction, forecast models are used to predict future states of the atmosphere, based on how the climate system evolves with time from an initial state. While the forecast models are quite complex (and do require strong scientific and engineering capabilities), the science of analyzing those forecast models, and verifying their accuracy, by comparing actual empirical observations to predicted values, is quite straightforward.

For the domain of Air Quality, just like for weather prediction, it is possible to define models used to predict the future set of atmospheric pollution. There are actually plenty of such models, often referred as Atmospheric Dispersion Modeling. And just like weather prediction, the same concept of accuracy analysis can be applied to Atmospheric pollution predictions. This article is the first of a series on Air Quality forecasting.

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PM2.5 air pollution forecast is already available on the World Air Quality Index project for Asia, Europe as well as the whole world. But the data used to feed the forecast models is mostly based on satellite observation (see this article) rather than terrestrial stations readings. Using satellite data has the advantage of being able to cover any part of the globe, including oceans, provided there are no cloud. But, on the other hand, satellite data is also inherently less accurate, and only available twice a day, compared to 24 times (every hour) for terrestrial observations. Considering the dynamics of Air Pollution in Asia, having only two readings a day might introduced a significant true forecast error in the prediction, following Rosanne Cole's classification[2]:
An observed forecast error may contain data errors of two kinds: (1) measurement errors in the data used to construct the forecast and (2) measurement error in the realized value. Data errors of the first kind will be a component of the true forecast error
Error of type 2 are related to the dispersion model used for the forecast. Since different models are used for the different countries and continents (this is currently the case for the World Air Quality Index project), the accuracy analysis needs to be done for each of the model. So, to start with, this article will focus on the model used for the Asian continent. In later post, we will extend the analysis to more continents.

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Back to the initial question about the forecast accuracy, one last item to be considered in the analysis in the how far in advance the forecast in computer. The less in the advance, the more accurate the model is likely to be. So, just to start with, the following analysis graphs are based on "day +1" forecast (e.g. forecast for the next day, or if you are a Tuesday, then the forecast is for the Wednesday).

There are several ways of representing the accuracy, the most obvious one being a simple number representing percentage of forecast matching the actual observation. But because we do believe that graphic visualization are much more powerful than numbers, we prefer to present the superposed forecast/observation matching for several cities in Asia. The squares at the top are the empirical observations and the one at the bottom the foretasted values.


By checking all the graphs below, one can notice quite disappointing results for Guangzhou, Chengdu and South Korea... to the extent that the model in use for Asia could almost be disqualified for public usage. This is something that we will be writing in the second post of this series on Air Quality Forecasting.


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Forecast advance:


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Some interesting links for those interested to read about about forecast accuracy:


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  • Nitrogen Dioxyde (NO2) in our atmosphere
  • Ozone AQI Scale update
  • Kriging Interpolation

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    Sobre a medição da aualidade do ar e poluição

    Sobre os níveis de qualidade do ar

    -Valores do Índice de Qualidade do Ar (AQI)Níveis de preocupação de saúde
    0 - 50Boa0-50: Boa - A qualidade do ar é considerada satisfatória, a poluição do ar representa pouco ou nenhum risco
    51 -100Moderado50-100: Moderado - A qualidade do ar é aceitável; No entanto, para alguns poluentes pode haver um problema de saúde moderada para um número muito pequeno de pessoas que são mais sensíveis à poluição do ar.
    101-150Não Saudável para Grupos SensíveisMembros de grupos sensíveis podem ter efeitos na a saúde. O público em geral não é susceptível de ser afetado.
    151-200Não saudável150-200: Insalubre - Toda a população pode começar a sentir os efeitos na saúde; membros de grupos sensíveis podem apresentar efeitos mais sérios de saúde.
    201-300Muito Prejudical à Saúde200-300: Muito Insalubre - As advertências de saúde de situações de emergência. Toda a população é mais susceptível de ser afectada.
    300+Perigoso300+: Perigoso - alerta de saúde: todos podem experimentar efeitos mais graves para a saúde

    Para saber mais sobre Qualidade do Ar e Poluição, verifique o wikipedia Qualidade do Ar tópico ou o guia AIRNow a Qualidade do Ar e sua saúde.

    Para informações sobre saúde muito úteis em Pequim, procure Doutor Richard Saint Cyr MD, consulte www.myhealthbeijing.com blogue.


    Aviso de uso: Todos os dados da Qualidade do Ar não são validados no momento da publicação e, devido à garantia de qualidade, esses dados podem ser alterados, sem aviso prévio, a qualquer momento. O projeto Índice de Qualidade do Ar Mundial exerceu todas as habilidades e cuidados razoáveis na compilação do conteúdo desta informação e sob nenhuma circunstância o A equipe do projeto World Air Quality Index ou seus agentes podem ser responsabilizados em contrato, responsabilidade civil ou de outra forma por qualquer perda, lesão ou dano decorrente direta ou indiretamente do fornecimento desses dados.



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