Report, June 2002 Evaluation of the ASOS Light Scattering Network Submitted by Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis Submitted to James F. Meagher NOAA Aeronomy Laboratory R/AL Boulder Colorado

The ASOS Visibility Sensor The ASOS visibility sensor is a forward scattering instrument

The ASOS visibility sensor is a forward scattering instrument

ASOS Stations from FAA, NWS and Archived at NCDC For this analysis data for 220 stations were available from the NCDC archive These ASOS sites are mostly NWS sites, uniformly distributed over the country (Imagine if we could get the entire set, including the DOD sites, not listed)

For this analysis data for 220 stations were available from the NCDC archive

These ASOS sites are mostly NWS sites, uniformly distributed over the country

(Imagine if we could get the entire set, including the DOD sites, not listed)

Duplicate Sensors: Good Sites At several duplicate sites the 2-sensor correlation is excellent and the absolute values also match. This indicates that the scattering sensor per se has high precision and temporal stability. Dallas-FW, TX San Diego, CA Erie, PA Houston, TX

At several duplicate sites the 2-sensor correlation is excellent and the absolute values also match.

This indicates that the scattering sensor per se has high precision and temporal stability.

Duplicate Sensors: Mediocre Sites Some sites (e.g. Tulsa, OK) show very high correlation between the sensors but they are off by a factor. Other sites indicate poorer correlation and a significant offset. Tulsa, OK Atlanta, GA

Some sites (e.g. Tulsa, OK) show very high correlation between the sensors but they are off by a factor.

Other sites indicate poorer correlation and a significant offset.

Duplicate Sensors: Poor Sites Duplicate sensors at some sites show significant deviation in scale and offset. The nature of these deviations indicate poor instrument calibration maintenance for the ASOS visibility sensors. Albuquerque, NM Albuquerque, NM Duluth, MN

Duplicate sensors at some sites show significant deviation in scale and offset.

The nature of these deviations indicate poor instrument calibration maintenance for the ASOS visibility sensors.

Three Sensor Comparison At 7 NWS Sites, there are 3 ASOS visibility sensors which allow more detailed sensor evaluation. Both at Cleveland, OH and Hartford, CT Bext1 and Bext3 show excellent correlation, R 2 =0.99 On the other hand, the Bext1 and Bext2 correlation is poor. This indicates that the Bext2 sensor either produces bad data or it is located at a site with significantly different Bext Cleveland, OH Hartford, CT

At 7 NWS Sites, there are 3 ASOS visibility sensors which allow more detailed sensor evaluation.

Both at Cleveland, OH and Hartford, CT Bext1 and Bext3 show excellent correlation, R 2 =0.99

On the other hand, the Bext1 and Bext2 correlation is poor. This indicates that the Bext2 sensor either

produces bad data or

it is located at a site with significantly different Bext

Three Sensor Comparison New York JFK New York La Guardia

ASOS Bext Threshold: 0.05 km(-1) The Bext values below 0.05 km-1 are reported as 0.05. For Koschmieder coeff K=3.9, this threshold VR=78km(~ 50 mile); for K=2 VR=40km(~25mi) In the pristine SW US, the ASOS threshold distorts the data Over the East and West, the ASOS signal is well over the threshold most of the time

The Bext values below 0.05 km-1 are reported as 0.05.

For Koschmieder coeff K=3.9, this threshold VR=78km(~ 50 mile); for K=2 VR=40km(~25mi)

In the pristine SW US, the ASOS threshold distorts the data

Over the East and West, the ASOS signal is well over the threshold most of the time

Sensor

Evidence of ASOS Data Problems Occasionally, the Bext values are replaced the threshold value of 0,05 km-1. The ASOS data for Temperature and Dewpoint appear to be erratic for some stations The problems include constant values, spikes and rapid step changes.

Occasionally, the Bext values are replaced the threshold value of 0,05 km-1.

Typical Diurnal Pattern of Bext, Temperature and Dewpoint Typically, Bext shows a strong nighttime peak due to high relative humidity. Most of the increase is due to water absorption by hygroscopic aerosols. At RH >90% , the aerosol is mostly water At RH < 90%, the Bext is mostly influenced by the dry aerosol content; the RH effect can be corrected. Macon, GA, Jul 24, 2000

Typically, Bext shows a strong nighttime peak due to high relative humidity.

Most of the increase is due to water absorption by hygroscopic aerosols. At RH >90% , the aerosol is mostly water

At RH < 90%, the Bext is mostly influenced by the dry aerosol content; the RH effect can be corrected.

Diurnal Cycle of Relative Humidity and Bext The diurnal RH cycle causes the high Bext values in the misty morning hours The shape of the RH-dependence is site (aerosol) dependent – needs work Relative Humidity Bext

The diurnal RH cycle causes the high Bext values in the misty morning hours

The shape of the RH-dependence is site (aerosol) dependent – needs work

Adopted RH Correction Curve (To be validated for different locations/seasons) The ASOS Bext value are filtered for high humidity Values at RH >= 80% is not used Later we will try to push the RH correction to 90%) The Bext is also corrected for RH: RHCorrBext = Bext/RHFactor RH is calculated from T – Temperature, deg C and D – Dewpoint, deg C RH = 100*((112-(0.1*T)+D)/(112+(0.9*T))) 8

The ASOS Bext value are filtered for high humidity

Values at RH >= 80% is not used

Later we will try to push the RH correction to 90%)

The Bext is also corrected for RH: RHCorrBext = Bext/RHFactor

Seasonal Average Diurnal Bext Pattern For each minute of the day, the data were averaged over June, July and August, 2000 Average Bext was calculated for Raw, as reported For data with RH < 90% RH < 90% and RH Corrected Based on the three values, the role of water can be estimated for each location

For each minute of the day, the data were averaged over June, July and August, 2000

Average Bext was calculated for

Raw, as reported

For data with RH < 90%

RH < 90% and RH Corrected

Based on the three values, the role of water can be estimated for each location

Location of ASOS and Nearby Hourly PM2.5 Sites There are no co-located ASOS and PM2.5 sites The stations are not co-located but in the same city Hourly PM2.5 data are compared to the filtered and RH-corrected one minute Bext

There are no co-located ASOS and PM2.5 sites

The stations are not co-located but in the same city

Hourly PM2.5 data are compared to the filtered and RH-corrected one minute Bext

ASOS-Hourly PM2.5 Islip Long Island, NY

Combinations of ASOS with Other Data ASOS & Satellite Data Fusion: New information on aerosol height ASOS & Satellite: High space-time coverage (e.g. smoke disasters) ASOS as PM25 Surrogate: Better exposure assessment

ASOS & Satellite Data Fusion: New information on aerosol height

ASOS & Satellite: High space-time coverage (e.g. smoke disasters)

ASOS as PM25 Surrogate: Better exposure assessment

July 2020 Quebec Smoke Event Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002 – PM2.5 time series for New England sites. Note the high values at White Face Mtn. Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

Superposition of ASOS visibility data (NWS) and SeaWiFS reflectance data for July 7, 2002

PM2.5 time series for New England sites. Note the high values at White Face Mtn.

Micropulse Lidar data for July 6 and July 7, 2002 - intense smoke layer over D.C. at 2km altitude.

GLAS Satellite Lidar ( Geoscience Laser Altimeter System) First satellite lidar for continuous global observations of Earth California Fires, Oct 7, 2003

Data Fusion: ASOS Bext, SeaWiFS, TOMS

Smoke Pattern: Fusion of Elevation and Reflectance

Model-Data Smoke

Idaho Smoke and PM2.5

SeaWiFS AOT and PM2.5

Asian Dust Cloud over N. America On April 27, the dust cloud rolled into North America. The regional average PM10 increased to 65  g/m 3 In Washington State, PM10 exceeded 100  g/m 3 Reg. Avg. PM10 100  g/m 3 Hourly PM10

PM2.5 Station Sampling Zones Every location on the map is assigned to the closest monitoring station . At the boundaries the distance to two stations is equal. Following the above rules, the ‘sampling zone’ surrounding each site is a polygon . The area (km 2 ) of each polygon is calculated in ArcView.

Every location on the map is assigned to the closest monitoring station .

At the boundaries the distance to two stations is equal.

Following the above rules, the ‘sampling zone’ surrounding each site is a polygon .

The area (km 2 ) of each polygon is calculated in ArcView.

Census Tract Population The population data used for determining a station’s population is from ESRI’s census tract file with estimated 1999 populations. The centroid of each census tract is associated with a station area. The census tract populations for all centroids that fall within a station’s area are summed.

The population data used for determining a station’s population is from ESRI’s census tract file with estimated 1999 populations.

The centroid of each census tract is associated with a station area.

The census tract populations for all centroids that fall within a station’s area are summed.

Surrogate Aided Interpolation Fine Mass Concentrations 1/r2 Interpolation Extinction Coefficient 1/r2 Interpolation Fine Mass Bext 1/r2 Interpolation Bext Aided FM = Fine Mass Bext x Bext 1991-1995 Summer 1991-1995 Summer 1991-1995 Summer 1991-1995 Summer

Estimated Ozone Concentrations , 1991-1995

Barrier Aided Estimation Vertical Flow Barriers (Scale Height) Horizontal Flow Barriers (Mountains) Pollutants are “trapped” in valleys while mountain tops have low pollutant concentrations

Vertical Flow Barriers (Scale Height)

Horizontal Flow Barriers (Mountains)

PM10 in California Without Barriers With Barriers AIRS PM10 data (1994-1996) Sierra Nevada Mountains are clearly visible with barrier aided estimation

NESCAUM Tools in support of Inter-RPO Data Analysis Workgroup Performed by CAPITA & Sonoma Technology, Inc F ast A erosol S ensing T ools for N atural E vent T racking FASTNET Project Synopsis Haze levels should be reduced to the ‘natural conditions’ by 2064. The space, time, composition features of natural aerosols are not known This long-term project goal is to better characterize the natural haze conditions Focus is on detailed analysis of major natural events, e.g. forest fires and windblown dust FASTNET is primarily a tools development project for data access, archiving and anlysis This, first year pilot project focuses on demonstrating the feasibility and utility of FASTNET

NESCAUM

Tools in support of Inter-RPO Data Analysis Workgroup

Performed by

CAPITA & Sonoma Technology, Inc

Real-Time Aerosol Watch (RAW) RAW is an open communal facility to study non-industrial (e.g. dust and smoke) aerosol events , including detection, tracking and impact on PM and haze. RAW output will be directly applicable, to public health protection, Regional Haze rule, SIP and model development as well as toward stimulating the scientific community. The main asset of RAW is the community of data analysts, modelers, managers and others participating in the production of actionable knowledge from observations, models and human reasoning The RAW community will be supported by a networking infrastructure based on open Internet standards (web services) and a set of web-tools evolving under the umbrella of Fast Aerosol Sensing Tools for Natural Event Tracking (FASTNET) . Initially, FASTNET is composed of the Community Website for open community interaction, the Analysts Console for diverse data access and the Managers Console for AQ management decision support.

Data Federation Concept and the FASNET Network Schematic representation of data sharing in a federated information system. Based on the premise that providers expose part of their data (green) to others Schematics of the value-adding network proposed for FASTNET Components embedded in the federated value network

Information ‘Refinery’ Value Chain (Taylor, 1975) Organizing Grouping Classifying Formatting Displaying Analyzing Separating Evaluating Interpreting Synthesizing Judging Options Quality Advantages Disadvantages Deciding Matching goals, Compromising Bargaining Deciding e.g. CIRA VIEWS e.g. Langley IDEA RAW System e.g. WG Summary Rpt e.g. RPO Manager Informing Knowledge Action Productive Knowledge Information Data

Interactive Virtual Workgroup Websites July 2002 Quebec Smoke

Air Quality Analysts Console, AQAC Implemented using Distributed Voyager Technologies AQAC is a set of web-pages for one-stop access to current PM monitoring data It taps into the on-line data services of EPA and RPOs, NASA, and NOAA and other providers AQAC the emphasizes timeliness and inclusiveness with some data integration Provides tools for dynamic data connections, space-time overlays and some analysis AQAC is implemented using the CAPITA Distributed Voyager infrastructure and tools.

Real-time PM Monitoring Console Example Views – Selected from Dozens of spatial, temporal, height cross-sections wind direction back trajectories temperature NAAPS model PM/Bext time series Bext contours PM2.5 contours web cam satellite image

Air Quality Managers Console, ACMC (STI, Prototype) Managers Console helps PM managers make decisions during aerosol events. AQMC delivers a subset of the relevant PM data through a simple interface It includes event summary reports prepared by the ACAC Virtual workgroups The Analysts and Managers Consoles will issues alerts and triggers The Manager Console will be developed by STI with links to AIRNow

Managers Console helps PM managers make decisions during aerosol events.

AQMC delivers a subset of the relevant PM data through a simple interface

It includes event summary reports prepared by the ACAC Virtual workgroups

The Analysts and Managers Consoles will issues alerts and triggers

The Manager Console will be developed by STI with links to AIRNow

Data Analysis and Decision Support Retrospective Anal. Months-years Now Analysis Days Predictive Analysis Days-years Data Sources & Types All the Real-Time data + NPS IMPROVE Aer. Chem. EPA Speciation EPA PM10/PM2.5 EPA CMAQ Full Chem. Model EPA PM2.5Mass NWS ASOS Visibility, WEBCAMs NASA MODIS, GOES, TOMS, MPL NOAA Fire, Weather & Wind NAAPS MODEL Simulation NAAPS MODEL Forecast NOAA/EPA CMAQ? Data Analysis Tools & Methods Full chemical model simulation Diagnostic & inverse modeling Chemical source apportionment Multiple event statistics Spatio-temporal overlays Multi-sensory data integration Back & forward trajectories, CATT Pattern analysis Emission and met. forecasts Full chemical model Data assimilation Parcel tagging, tracking Communication Collab. & Coord. Methods Tech Reports for reg. support Peer reviewed scientific papers Science-AQ mgmt. interaction Reconciliation of perspectives Analyst and managers consoles Open, inclusive communication Data assimilation methods Community data & idea sharing Open, public forecasts Model-data comparison Modeler-data analyst comm. Analysis Products Quantitative natural aer. concr. Natural source attribution Comparison to manmade aer. Current Aerosol Pattern Evolving Event Summary Causality (dust, smoke, sulfate) Future natural emissions Simulated conc. pattern Future location of high conc. Decision Support Jurisdiction: nat./manmade State Implementation Plans, (SIP) PM/Haze Crit. Documents, Regs Jurisdiction: nat./manmade Triggers for management action Public information & decisions Statutory & policy changes Management action triggers Progress tracking