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

From Federation of Earth Science Information Partners
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[[GEOSS_AIP_AQ_Scenario |<Back to AQ Pilot Scenario Workspace]]
 
[[GEOSS_AIP_AQ_Scenario |<Back to AQ Pilot Scenario Workspace]]
  
==Alternative AQ Scenario==
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==Scenario C: Forecasts==
  
This is an attempt to broaden the scope of the AQ scenario beyond fire events.  It is motivated by a concern that a focus on fire events will not lead to developments that are applicable to more generalized issues with respect to air quality assessment and forecasting.
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This scenario is focused on building capacity to predict Air Quality, including in (or focused on) places where ambient AQ monitoring is not available.  
  
 
===Summary===
 
===Summary===
 
====<font color="#000080">''Provide a summary of the scenario and the community that the scenario supports.''</font>====
 
====<font color="#000080">''Provide a summary of the scenario and the community that the scenario supports.''</font>====
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 aloft and over the ocean, etc.
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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.   
 
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.   
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The scenario envisions a network of air quality information systems and tools that can be applied by an analyst to  
 
The scenario envisions a network of air quality information systems and tools that can be applied by an analyst to  
 
# develop accurate 24-72 hr forecasts of surface air quality for a particular region and provide those forecasts to the public
 
# develop accurate 24-72 hr forecasts of surface air quality for a particular region and provide those forecasts to the public
# estimate how different control strategies (on local, regional, or international sources) would have affected air quality and public health in a particular region during past time periods (either episodes, seasons, or years) to inform regulatory officials and regulated entities.   
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# Bring information from multiple sources into the forecasting as new sources are deployed
  
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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. 
  
To understand and manage air quality, we need to integrate observational data for a variety of atmospheric constituents from different observational platforms (surface monitoring networks, satellites, sondes, ground-based remote sensors, aircraft, ...) with atmospheric chemistry models and information related to meteorology, emissions, exposure, and health impacts.  For air quality assessment and analysis of management strategies, this integration can be done using historical datasets.  For air quality forecasting to inform the public and episodic management decisions, it is necessary to perform this integration in near real time.
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Related Questions:
 
 
 
 
 
 
Tangent: 
 
Questions:
 
 
*What fraction of observed air pollution comes from local, regional, intercontinental, and global sources?   
 
*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 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?   
 
*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?   
 
*Given only satellite observations for a given location, what is surface air quality?   
 
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*What issues arise in moving forecast systems to very different geographical domains (i.e., mid-lats to tropics)?
Tangent: 
 
How do we link to CEOS ACC project on Fire/Aerosol described in para below?
 
 
 
The ACC is one of four Constellations proposed by CEOS to support the overall goals of the Group on Earth Observations (GEO) and to provide prototype space-based Earth-observation systems for GEOSS, the Global Earth Observation System of Systems. This project is one of three projects within the ACC demonstration projects. The goal of this prototype is to demonstrate that a constellation can be developed using several satellites synergistically to characterize the global distribution of fire occurrences and aerosol distributions.  The fire and aerosol distributions, in conjunction with air parcel trajectory models, can then be integrated to produce a view of current aerosol distributions along with a forecast product related to movement of large-scale aerosol events.  The  integration of these data sets can be used to produce routine and systematic forecast guidance for users to develop warnings on instances of potential degradation of air quality due to long-range transport of aerosols from widespread burning as well as from naturally occurring dust storms
 
  
 
====<font color="#000080">''Identify the specific decisions to be made.''</font>====
 
====<font color="#000080">''Identify the specific decisions to be made.''</font>====
  
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AQ forecasts support a number of decisions:
 +
# mitigation decisions to reduce local emissions during poor AQ events
 +
# management decisions to reduce exposure by individuals, groups
 +
# public health decisions to anticipate health impacts, outreach to affect populations, etc.
 +
# monitoring decisions: ramp up monitoring to learn about health effects
  
 
====<font color="#000080">''Provide references for additional information.''</font><br>====
 
====<font color="#000080">''Provide references for additional information.''</font><br>====
 
  
 
===Context and pre-conditions===
 
===Context and pre-conditions===
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====<font color="#000080">''Identify the actors in the scenario. Actors are any persons involved in the scenario.''</font>====
 
====<font color="#000080">''Identify the actors in the scenario. Actors are any persons involved in the scenario.''</font>====
  
The actors in the smoke scenario include data/information providers, data processors and specialists to deliver tailored information products to the public, AQ managers and scientists. A key set of actors are the actual decision makers ...who interact with the ??....  (this needs work [[User:Rhusar|Rhusar]])
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* Satellite data providers
 
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* Weather agencies - met data is essential for AQ forecasts
* National Environmental agency
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* Environmental agencies - point agencies for AQ
* National Meteorological agency
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* State and local weather, environmental agencies (in some countries)
* National Health agency (if separate from National Environmental agency)
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* Public health agencies
* National Land Management agency
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* Health sector
* National Space agency
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* Media
* Local Environmental agencies
 
* Local emergency personnel
 
* Consulting companies
 
* GEOSS Portal integrator? (took from Energy scenario)
 
* Local, Regional and National media (print, online, broadcast)
 
 
* Public
 
* Public
  
 
====<font color="#000080">''List, at a summary level, the specific information assumed to be available before the scenario begins. ''</font>====
 
====<font color="#000080">''List, at a summary level, the specific information assumed to be available before the scenario begins. ''</font>====
  
The behavior of smoke depends on many factors, including the fire’s size and location, the topography of the area and the weather. Smoke (PM2.5) from large wildland fires can be transported hundreds or thousands of kilometers to a forecast region.  Smoke events can increase the background levels of PM2.5, thus combining transported PM2.5 with locally-generated PM2.5 to produce a more severe episode. Depending on concentrations, this transported PM2.5 could trigger an exception event and/or degrade visibility.
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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.
 
 
Air quality forecasts provide the public with air quality information with which they can make daily lifestyle decisions to protect their health. This information allows people to take precautionary measures to avoid or limit their exposure to unhealthy levels of air quality.  Pre-determined safe / unhealthy / hazardous levels of pollutants (from federal/national air quality standards such as the United States [http://www.epa.gov/air/criteria.html NAAQS], [http://www.acgih.org/TLV/ Threshold Limit Values] (TLVs), etc) and standard descriptions of hazards and effects from PM2.5 help provide context for decision-makers and the public from PM2.5 concentrations or a standardized health index such as the [http://www.airnow.gov/index.cfm?action=static.aqi Air Quality Index].
 
  
Information available before scenario begins (via GEOSS portals)
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Satellite data coverage is also not equal everywhere:
* Meteorological data
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* geostationary satellites
** Observed and forecasted surface meteorological data (such as temperature, surface wind speed and direction, humidity)
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* denser coverage at high latitudes
** Observed and forecasted aloft large-scale (1000 km or more) atmospheric parameters (such as 850 and 500 millibar heights, temperature, wind speed, dew point)
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* ocean vs. land sensitivities
** HYSPLIT trajectories (NAM/NDAS Models (40km) - forward trajectories can be used to estimate the transport direction and potential time the smoke or dust might enter a particular forecast region
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* resource issues for data workup ??????? (are there examples of global AQ satellites with geographic differences in data availability)
* Geographical data
 
** land use for knowing affected and forecast areas
 
** demographic data for understanding impacted population: different age groups have different sensitivities to poor AQ, for example
 
** fuel type - important input for smoke models [http://www.blueskyrains.org/ BlueSky RAINS]
 
* Air Quality information
 
** Particle pollution ground observations (for [http://www.airnow.gov/ United States] and [http://www.ns.ec.gc.ca/airquality/query/ Canada], EEA?)
 
** Air quality forecasts for particle pollution areas (for United States, [http://www.airnow.gov/ AIRNow], [http://www.weatheroffice.gc.ca/forecast/textforecast_e.html/ Canada], Europe – EEA)
 
** Established air quality programs which issue public alerts
 
* Air Quality Numerical Forecast Models?
 
** [http://www.arl.noaa.gov/smoke/forecast.html NOAA Smoke Forecast Tool]
 
** [http://www.blueskyrains.org/ BlueSky RAINS]
 
** private numerical models ([http://www.baronams.com/projects/SECMEP/index.html Baron Advanced Meteorological Systems])
 
* Satellite data
 
** graphical satellite data, (true color and/or aerosol optical depth (AOD) imagery)  MODIS Rapid Response Team, NASA Goddard Space Flight Center (MODIS instrument on board the Terra or Aqua satellite)
 
** GOES Aerosol and Smoke Product (GASP). Geostationary Operational Environmental Satellite East (GOES-12)  NOAA Satellite and Information Service
 
** NOAA fire locations - [http://www.ssd.noaa.gov/PS/FIRE/hms.html Hazard Mapping System Fire and Smoke Product]
 
** [http://www.nifc.gov/ National Interagency Fire Center] with real-time and historical fire data and statistics
 
** [http://fire.boi.noaa.gov/ National Weather Service Fire Weather]
 
** NOAA’s Air Resources Laboratory – [http://www.arl.noaa.gov/smoke/ Wildfire/Forest Fire Smoke Forecasting ]
 
** Measurements of Pollution in the Troposphere ([http://www.eos.ucar.edu/mopitt/ MOPITT]) sensor on NASA's Terra satellite (22-km horizontal resolution) measurements in the lower part of the atmosphere - global
 
* Specific processing
 
** collection / QC of ambient air quality data by an air quality data management system
 
** local or regional air quality forecast generated by numerical model (requiring air quality and meteorological data) or human forecaster
 
** integrating air quality data, forecasts with available satellite data products
 
** develop context and understanding of air quality conditions and forecasts for the event
 
** develop and issue communication piece to decision-makers and/or public
 
** distribute data/information through established channels
 
* GUI development and GEOSS portal integration
 
** Exploit Web service description (GEOSS portal integrator)
 
** Build GUI (GEOSS portal integrator)  
 
** map / image production for release to media, public
 
** distribute data/information/graphics
 
** ?
 
  
 
====<font color="#000080">''List, at a summary level, the specific processing and collaboration functionality assumed needed in the scenario.''</font>====
 
====<font color="#000080">''List, at a summary level, the specific processing and collaboration functionality assumed needed in the scenario.''</font>====

Revision as of 16:12, January 30, 2008

<Back to AQ Pilot Scenario Workspace

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.

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

Alternative AQ Scenario