Difference between revisions of "AIP AQ Scenario C: forecasts"

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[[GEOSS_AIP_AQ_Scenario |<Back to AQ Pilot Scenario Workspace]]
 
[[GEOSS_AIP_AQ_Scenario |<Back to AQ Pilot Scenario Workspace]]
  
==Scenario C: Forecasts==
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==GEOSS Architecture Implementation Pilot Air Quality Scenario C: Forecasts==
  
 
This scenario is focused on building capacity to predict Air Quality, including in (or focused on) places where ambient AQ monitoring is not available.  
 
This scenario is focused on building capacity to predict Air Quality, including in (or focused on) places where ambient AQ monitoring is not available.  
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(This page is based on the template [http://www.ogcnetwork.net/node/349 provided by GEOSS Architecture Workgroup].)
  
 
===Summary===
 
===Summary===

Revision as of 16:32, January 30, 2008

<Back to AQ Pilot Scenario Workspace

GEOSS Architecture Implementation Pilot Air Quality Scenario C: Forecasts

This scenario is focused on building capacity to predict Air Quality, including in (or focused on) places where ambient AQ monitoring is not available.

(This page is based on the template provided by GEOSS Architecture Workgroup.)

Summary

Provide a summary of the scenario and the community that the scenario supports.

Air pollution is a serious public health and environmental issue contributing to hundreds of thousands of premature deaths per year globally and increased respiratory illness and hospitalization. Air pollution also does not respect jurisdictional boundaries and is affected by sources and processes over local, regional, intercontinental, and global scales. Some of the most challenging pollutants to address, such as ground-level ozone and fine particles, are not emitted directly but are produced or transformed in the atmosphere from other pollutants through non-linear chemical and physical processes. Serious AQ problems exist in remote or undeveloped areas which often have little or no surface ambient sensors. Understanding transport requires knowledge of pollution patterns aloft and in remote areas.

Air quality forecasting allows decision makers to anticipate conditions contributing to poor AQ to mitigate (e.g. by reducing driving) or manage (e.g., suggesting sensitive people avoid exercise) the event. AQ forecasting is more challenging in regions where ambient monitors are sparse or don't exist. The goal of this scenario is to create systems that facilitate AQ forecasts from globally available data - satellites and models.

The scenario envisions a network of air quality information systems and tools that can be applied by an analyst to

  1. develop accurate 24-72 hr forecasts of surface air quality for a particular region and provide those forecasts to the public
  2. Bring information from multiple sources into the forecasting as new sources are deployed

Current AQ forecasting systems are fairly dependent on ambient monitoring. Moving to global systems, or systems deployable globally, requires new capabilities to bring in satellite and model data.

Related Questions:

  • What fraction of observed air pollution comes from local, regional, intercontinental, and global sources?
  • How will observed air pollution in a given location change as changes are made in local, regional, intercontinental, and global sources?
  • How well do current models predict the observed atmospheric composition in 4 dimensions?
  • Given only satellite observations for a given location, what is surface air quality?
  • What issues arise in moving forecast systems to very different geographical domains (i.e., mid-lats to tropics)?

Identify the specific decisions to be made.

AQ forecasts support a number of decisions:

  1. mitigation decisions to reduce local emissions during poor AQ events
  2. management decisions to reduce exposure by individuals, groups
  3. public health decisions to anticipate health impacts, outreach to affect populations, etc.
  4. monitoring decisions: ramp up monitoring to learn about health effects

Provide references for additional information.

Context and pre-conditions

Identify the actors in the scenario. Actors are any persons involved in the scenario.

  • Satellite data providers
  • Weather agencies - met data is essential for AQ forecasts
  • Environmental agencies - point agencies for AQ
  • State and local weather, environmental agencies (in some countries)
  • Public health agencies
  • Health sector
  • Media
  • Public

List, at a summary level, the specific information assumed to be available before the scenario begins.

Information availability varies greatly from region to region. Sensor density varies tremendously for meteorology; many regions have no AQ sensors. Oceans, other remote areas have more influence on some regions.

Satellite data coverage is also not equal everywhere:

  • geostationary satellites
  • denser coverage at high latitudes
  • ocean vs. land sensitivities
  • resource issues for data workup ??????? (are there examples of global AQ satellites with geographic differences in data availability)

List, at a summary level, the specific processing and collaboration functionality assumed needed in the scenario.

  • Convenient functionality to register smoke-relevant components in the GEOSS Registry
  • GEOSS-compatible smoke portal used and maintained (?) by the Smoke Community of Practice
  • Identification and accessing the relevant monitoring data (surface, upper air and satellite) using the publish-find-bind SOA protocol
  • Data sharing and integration functionality including (1) registry/catalog for finding resources (2) standard-based access to spatio-temporal data and metadata, workflow software for integrating Service Components
  • Harvesting the data sources from "other" agencies/organizations, Public contributions of first-hand reports, images, videos...
  • A workspace to support the activities of the Smoke Community of Practice, including communal resources on the specific smoke event (and smoke in general?), are assembled ... other Decision Support System functionality... ??
  • Ability to produce near-real-time reports that characterize the smoke event for the Public, AQ managers and Scientists.

Scenario Events

Elaborate the steps that result in the creation of decision support products developed in collaboration by the actors.

Use the table to identify the main sequence of events in the scenario, including alternative branch steps.

  • Develop connections between existing repositories of air quality related information and tools for integrated analysis of observational data and comparison to models. E.g. HTAP Data Network Proposal
  • Develop tools for analysis and assimilation of observational data into atmospheric models and other decision support systems in near real time for producing air quality forecasts. i.e. AIRNow, IDEA
  • Develop a virtual observatory for collaborative exploration of large events and ...



  1. Designated and voluntary observers will use a 'virtual observatory' to monitor the current aerosol 'weather' situation over their region of interest (e.g. North America, Central America, Europe, Asia? - do we need to spell out regions? ).
  2. The observers scan the spatial, temporal aerosol pattern on the real-time satellite images, surface monitors, as well as monitoring the electronic media and private citizen reports.
  3. Once an ‘interesting’ smoke event appears, the observers explore the pattern of other peripheral data sources such as weather pattern, trajectories and other monitors to ascertain the emergence of a smoke event.
  4. Throughout the event’s emergence, the observers share their observations and views on a shared virtual workspace, including the the deliberations whether to issue alert(s).
  5. Using standardized Common Notification Protocol), alert(s) are issued to different groups (Public, Regulatory, Science - see) that may need to act or who are interested in observing/participating in smoke monitoring or response action action.
  6. In response to the alert, more intense monitoring and just-in-time analysis is initiated. This includes additional sampling, high resolution targeted smoke source/dispersion modeling, harvesting of other real-time data resources etc.
  7. As the smoke event evolves, a virtual workgroup of analysts summarizes the smoke situation, including sources, transport, aerosol pattern, forecast and impact in a manner suitable for multiple user communities, Public, Regulatory and Science.
  8. Based on those reports and other input, during the smoke event, a multiplicity of decisions and actions are executed by Public, Regulatory and Scientific decision makers.
  9. Following the smoke event, the event is evaluated for its impact, consequences and possibly for detailed retrospective analysis.

Related Links

AQ Scenario B: Model - Data Synthesis