Air Quality Forecasting - How accurate can it be?

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

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:

Click here to see all the FAQ entries
  • AQI Scale: What do the colors and numbers mean?
  • Using Statistical Distances for Real-time Sensor Networks Validation
  • Nitrogen Dioxyde (NO2) in our atmosphere
  • ກ່ຽວກັບການວັດແທກຄຸນນະພາບອາກາດ ແລະມົນລະພິດ:

    ກ່ຽວກັບລະດັບຄຸນນະພາບອາກາດ

    -ດັດຊະນີຄຸນນະພາບອາກາດ (AQI) ຄ່າLevels of Health Concern
    0 - 50ດີຄຸນນະພາບທາງອາກາດຖືກພິຈາລະນາເປັນທີ່ພໍໃຈ, ແລະມົນລະພິດທາງອາກາດກໍ່ມີຄວາມສ່ຽງຫນ້ອຍຫຼືບໍ່ມີຄວາມສ່ຽງ
    51 -100ປານກາງຄຸນນະພາບທາງອາກາດແມ່ນການຮັບຮອງ; ຢ່າງໃດກໍ່ຕາມ, ສໍາລັບບາງມົນລະພິດອາດມີຄວາມກັງວົນກ່ຽວກັບສຸຂະພາບໃນລະດັບປານກາງສໍາລັບຈໍານວນຜູ້ທີ່ມີຄວາມເຄັ່ງຕຶງທີ່ຜິດປົກກະຕິກັບການມົນລະພິດທາງອາກາດ.
    101-150Unhealthy
    for sensitive groups
    ສະມາຊິກຂອງກຸ່ມທີ່ມີຄວາມອ່ອນໄຫວອາດຈະມີຜົນກະທົບຕໍ່ສຸຂະພາບ ປະຊາຊົນທົ່ວໄປບໍ່ໄດ້ຮັບຜົນກະທົບ.
    151-200ບໍ່ມີສຸຂະພາບດີທຸກໆຄົນອາດຈະເລີ່ມປະສົບຜົນກະທົບດ້ານສຸຂະພາບ; ສະມາຊິກຂອງກຸ່ມທີ່ມີຄວາມອ່ອນໄຫວອາດຈະມີຜົນກະທົບດ້ານສຸຂະພາບທີ່ຮ້າຍແຮງຫຼາຍ
    201-300Very
    Unhealthy
    ການເຕືອນໄພດ້ານສຸຂະພາບຂອງສະພາບສຸກເສີນ. ປະຊາກອນທັງຫມົດແມ່ນມັກຈະໄດ້ຮັບຜົນກະທົບ.
    300+ອັນຕະລາຍເຕືອນດ້ານສຸຂະພາບ: ທຸກໆຄົນອາດຈະມີຜົນກະທົບທາງສຸຂະພາບທີ່ຮ້າຍແຮງກວ່າເກົ່າ

    ເພື່ອຮູ້ເພີ່ມເຕີມກ່ຽວກັບຄຸນນະພາບອາກາດ ແລະມົນລະພິດ, ໃຫ້ກວດເບິ່ງ ຫົວຂໍ້ຄຸນນະພາບອາກາດຂອງ wikipedia ຫຼື ຄູ່ມື airnow ກ່ຽວກັບຄຸນນະພາບອາກາດ ແລະສຸຂະພາບຂອງເຈົ້າ .

    ສໍາລັບຄໍາແນະນໍາດ້ານສຸຂະພາບທີ່ເປັນປະໂຫຍດຫຼາຍຂອງທ່ານຫມໍປັກກິ່ງ Richard Saint Cyr MD, ກວດເບິ່ງ blog www.myhealthbeijing.com .


    ແຈ້ງການນໍາໃຊ້: ຂໍ້ມູນຄຸນນະພາບອາກາດທັງຫມົດແມ່ນບໍ່ຖືກຕ້ອງໃນເວລາທີ່ພິມເຜີຍແຜ່, ແລະເນື່ອງຈາກການຮັບປະກັນຄຸນນະພາບຂອງຂໍ້ມູນເຫຼົ່ານີ້ອາດຈະຖືກປັບປຸງ, ໂດຍບໍ່ມີຂໍ້ສັງເກດ, ໃນທຸກເວລາ. ໂຄງການດັດຊະນີທາງອາກາດໂລກໄດ້ໃຊ້ທັກສະແລະການດູແລທີ່ສົມເຫດສົມຜົນທັງຫມົດໃນການລວບລວມເນື້ອໃນຂອງຂໍ້ມູນນີ້ແລະພາຍໃຕ້ສະພາບການໃດກໍ່ຕາມທີມງານຂອງໂຄງການໂລກຂອງອາກາດທາງໂລກຫຼືຕົວແທນຂອງມັນຈະຮັບຜິດຊອບໃນສັນຍາ, ຄວາມເສຍຫາຍຫຼືຄວາມເສຍຫາຍໃດໆ, ເກີດຂື້ນໂດຍກົງຫຼືໂດຍທາງອ້ອມຈາກການສະຫນອງຂໍ້ມູນນີ້.



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