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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    대기 품질 및 환경 오염 측정에 관하여 :

    대기 품질 지수 단계에 대하여

    AQI지수구분구간의미
    0 - 50좋음대기오염 관련 질환자군에서도 영향이 유발되지 않을 수준
    51 -100보통환자군에게 만성 노출시 경미한 영향이 유발될 수 있는 수준
    101-150민감군영향환자군 및 민감군에게 유해한 영향이 유발될 수 있는 수준
    151-200나쁨환자군 및 민감군(어린이, 노약자 등)에게 유해한 영향 유발, 일반인도 건강상 불쾌감을 경험할 수 있는 수준
    201-300매우나쁨환자군 및 민감군에게 급성 노출시 심각한 영향 유발, 일반인도 약한 영향이 유발될 수 있는 수준
    300+위험환자군 및 민감군에게 응급 조치가 발생되거나, 일반인에게 유해한 영향이 유발될 수 있는 수준
    (Reference: see airkorea.or.kr)

    대기 품질과 오염에 대해 더 많은 것을 알아보려면 위키피디아의 공기품질 문서(영어)을 보거나 공기 품질과 건강에 대한 AirNow 가이드(영어)를 참조해보세요.

    베이징 의사 Richard Saint Cyr MD의 건강 관련 팁을 보려면 www.myhealthbeijing.com 에 블로그를 확인하세요.


    Usage Notice: All the Air Quality data are unvalidated at the time of publication, and due to quality assurance these data may be amended, without notice, at any time. The World Air Quality Index project has exercised all reasonable skill and care in compiling the contents of this information and under no circumstances will the World Air Quality Index project team or its agents be liable in contract, tort or otherwise for any loss, injury or damage arising directly or indirectly from the supply of this data.



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    Temperature unit:
    Celcius