An Introduction to the Use of Satellites, Models and In-Situ Measurements for Air Quality and Climate Applications Richard Kleidman Session 1 An Overview March 9, 2015

What should you expect to learn from these webinars? A general idea of how satellites, models and in-situ data are used to learn about atmospheric aerosols and particulate matter. Some details about the strengths and weaknesses of the individual components of this system. A general idea of the uses of satellite imagery and where to find images. What kinds of additional information you need to make use of the parts of this system.

What should you not expect as a result of these presentations? The capability to manipulate data or do research using satellite remote sensing data. A complete knowledge of all of the atmospheric satellite products and web tools.

Today’s Presentation 1.An introduction to your presenter (that’s me) and AirPhoton 2.What will be covered in the four sessions and why 3.The elements of a remote sensing system – how they work together and their strengths and weaknesses 4.What is the SPARTAN network and how does it help us.

Note: Parts of this presentation were originally created by and for the NASA ARSET program. Those slides may have been modified by Richard Kleidman of AirPhoton LLC for this training. Several slides were also obtained from presentations available at the SPARTAN network Website:

The Elements of A Combined Remote Sensing System Models Satellite Data In-Situ Measurements

The Elements of A Combined Remote Sensing System Models Satellite Data In-Situ Measurements What are the strengths and weaknesses of each element?

The Elements of A Combined Remote Sensing System Models Satellite Data In-Situ Measurements How do each of the elements improve the other?

The Elements of A Combined Remote Sensing System Models Satellite Data In-Situ Measurements How are these combined to form a unified system?

What is our area of concern?

Climate Air Quality

What is our area of concern? Air Quality Climate

Some Things We Want to Know About Aerosols And Particulate Matter Where are they? How much is present? Now Historical record Can we forecast their future presence? What are their properties? Chemical Physcial How to they interact with solar radition

Some Things We Want to Know About Aerosols And Particulate Matter Where are they? Spatial Distribution - horizontal - vertical Climate Public Health

Some Things We Want to Know About Aerosols And Particulate Matter Where are they? Climate Public Health Vertical distribution Heating Human Exposure Cloud Processes - Are they where people are?

Some Things We Want to Know About Aerosols And Particulate Matter Where are they? Climate Public Health Horizontal distribution Transport Sources and sinks Natural or Man Made (Anthropogenic)? - Can we take action?

Some Things We Want to Know About Aerosols And Particulate Matter How much is present? (Where are they?) Climate Public Health Mass – total, per unit volume, per unit area? Trends - now vs historical record Can we see the effect on a changing planet? Can we see health effects over time on a target population?

Some Things We Want to Know About Aerosols and Particulates Sources and sinks Concentrations at the ground Exposure Estimates Human Esposure Instrument exposure estimates Acute exposure Long term records Air quality forecasts Concentrations in specific locations

Why Use Remote Satellite Sensing Data? – Ground Monitors - Satellite (MODIS) Retrieval Locations White Areas – No Data (Most likely due to clouds) Spatial Coverage!

Spatial Coverage MODIS One Day Aerosol Product Coverage

Satellite Products for Air Quality Applications Particulate Pollution (dust, haze, smoke) - Qualitative: Visual imagery - Quantitative*: Atmospheric Column Products Fire Products: Fire locations or ‘hot spots’ Fire radiative power Trace Gases - Quantitative*: Column Products - Vertical profiles: mostly mid-troposphere - Some layer products

Some kinds of aerosol data available from satellite. Several satellites provide state-of-art aerosol measurements over global region on daily basis Spring Summer Fall Winter Haze & Pollution Pollution & dust Dust Biomass Burning Aerosol Optical Thickness

Aerosols Transported Across the Atlantic Global Coverage Helps Us to Estimate Transport and Source Regions

Earth Satellite Observations Advantages –Provides coverage where there are no ground monitors –Synoptic and trans-boundary view (time and space) –Visual context –Qualitative assessments and indications of long range transport –Adds value when combined with surface monitors and models Public Health

MODIS (on Terra) Hindered by clouds Can’t measure over snow and ice Low Earth Orbit (705km) Limitations of Satellite Measurements Current satellites provide global coverage but very limited information on the temporal nature of pollution

Can we infer surface aerosol mass from column-integrated AOD? Even Distribution Veritcal Discrimination/Surface Sensitivity

Boundary layer depth Stratospheric Burden Long-range Transport of Pollution Aloft Relative Humidity Surface Albedo Pixel Size Challenges for inferring surface aerosol mass from column-integrated AOD: Veritcal Discrimination/Surface Sensitivity

Gupta, 2008 AOT-PM2.5 Relationship

Observation Frequency Polar orbiting satellites – observations per day per sensor Geostationary satellites – product quality is lacking in many locations - Polar observations - Geostationary observations

1.Temporal Coverage 2.Vertical Resolution of Pollutants 3.Lack of Near Surface Sensitivity 4.Lack of specific identification of pollutant type Earth Satellite Observations Limitations

In-Situ Measurements Direct, specific and accurate measurements of physical, optical and chemical properties High time resolution – seconds to minutes Lowest spatial resolution

In-Situ Measurements – Remote Sensors Satellite Data Information to construct the satellite algorithms and products - relationships between optical and phycial properties Data for validation of the satellite products

What is a model? Modelng, Assimilation and Reanalysis A representation of a physical reality. Although we use models for their predictive capabilities that is not how they are defined.

What do model systems do for us? Modelng, Assimilation and Reanalysis We use model system to help define current and future states of a physical reality based on mathematical and/orstatistical computations.

What do models do for us? Modelng, Assimilation and Reanalysis We use models to provide information where we have few or no measurements.

Spatial Resolution – Fair Resolution at Ground - Fair An Illustration of Model Pros and Cons

Model Inputs Data for initiation of model conditions, time steps and constraining results Data fields for statistical models Data for evaluation of model performance

The 2016 Scorecard SatellitesIn – SituModel Spatial Coverage Excellent (Not under clouds, over snow) Poor Good (Everywhere) Spatial Resolution Fair to Very Good Excellent Fair Temporal Coverage Fair to Poor Excellent Good - Excellent (Past,Present, Future) Vertical Resolution Poor (Lidar but very limited spatial coverage) Variable Good Mass at Ground Level Poor Good Fair Physical Properties Fair to Poor (Better over ocean) Excellent Good Aerosol Type and Species Chemical Properties Poor (Aerosols) Good (trace gases) Excellent

Satellite Data Current and Future Prospects Several New Missions in Development Products are improving spatial resolution. Near future launch of geosynchronous satellites will improve temporal resolution Several Missions Beyond Design Life Loss of Main Vertical Resolving Sensor

Geostationary Earth Orbit (36,000 km): Satellite appears stationary over one location on the Earth Greatly increases temporal coverage! Proposed Geostationary Satellites: TEMPO over North America Sentinel-4 over Europe GEMS over Asia Satellite Measurements of Aerosol

Models – Future Prospects Increased computing power Increased understanding of processes Increasing availability of satellite data Increasing availability of in-situ measurements Models

The Future Scorecard? SatellitesIn – SituModel Spatial Coverage Excellent (Not under clouds, over snow) Fair – Good? Good (Everywhere) Spatial Resolution Fair to Very Good Excellent Fair Temporal Coverage Good Excellent Good - Excellent (Past,Present, Future) Vertical Resolution Poor (Lidar but very limited spatial coverage) Variable Good Mass at Ground Level Poor Good Physical Properties Fair to Poor (Better over ocean) Excellent Good Aerosol Type and Species Chemical Properties Poor (Aerosols) Good (trace gases) Excellent

The Power of Combined Satellite – Model – Measurement Systems Combining Global Process Models with Satellite Data Combining Statistical Models, Satellite Data and Ground Measurements

The need for the best monitoring possible Driven by concern for human health

Fine Particulate Matter (PM 2.5 ): Tiny Particles with Major Health Implications Life expectancy increases ~ 7 months per 10 μg/m 3 decrease in long-term exposure 45

GEOS-Chem Provides crucial information on how to relate column measurements to mass at the ground. Customized Satellite Global Data Set Uses several Inputs What did the Dalhousie group do? Powerful Global Process Model

Method is Applied Globally Results Vary Regionally

The Combined Scorecard SatellitesIn – SituModelCombined Spatial Coverage Excellent Fair – Good? Good (Everywhere ) Excellent Temporal Coverage Good Excellent Good - Excellent Very Good - Excellent Vertical Resolution Poor Variable Good Very Good Mass at Ground Level Poor Good Very Good Physical Properties Fair to Poor Excellent Good Aerosol Type and Species Good Optical Properties Fair to Poor Excellent Chemical Properties Poor Good Excellent

Brauer M, Ammann M, Burnett R et al. GBD 2010 Outdoor Air Pollution Expert Group 2011 Submitted –under review Global Status of Particulate Monitoring (PM) Networks

Introducing SPARTAN The Surface PARTiculate mAtter Network 52

What is SPARTAN? The first global network of in situ PM 2.5 measurements in populated areas using the same instrumentation A network that will provide data to evaluate and enhance satellite remote sensing estimates 53

In – Situ Measurement Networks