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ASOS Stations from FAA, NWS and Archived at NCDC

Progress Report February, 2002 Evaluation of the Light Scattering Data from the ASOS Network Submitted by Rudolf B. Husar Center for Air Pollution Impact and Trend Analysis February 18, 2002 Submitted to James F. Meagher NOAA Aeronomy Laboratory R/AL Boulder Colorado.

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ASOS Stations from FAA, NWS and Archived at NCDC

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  1. Progress Report February, 2002Evaluation of the Light Scattering Data from the ASOS NetworkSubmitted byRudolf B. HusarCenter for Air Pollution Impact and Trend AnalysisFebruary 18, 2002Submitted to James F. MeagherNOAA Aeronomy Laboratory R/ALBoulder Colorado

  2. 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)

  3. Data Quality Assessment • Co-located ASOS Sensors – absolute calibration • Lower detection limit (0.05 km-1, 50 km visual range) • Random sensor malfunctions • File format problems

  4. Comparison of Sites with Duplicate ASOS Sensors • Co-located ASOS sensors are installed at different runways of the same airport. • Dual ASOS sensors (55) are distributed uniformly over the 800+ station network • Triple sensors are particularly useful for sensor calibration and consistency checking

  5. Duplicate Sensors: Good Sites Dallas-FW, TX Erie, PA • 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. San Diego, CA Houston, TX

  6. Tulsa, OK Atlanta, GA 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.

  7. Albuquerque, NM Duluth, MN Albuquerque, NM 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.

  8. Three Sensor Comparison Cleveland, OH • 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, R2=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 Hartford, CT

  9. Three Sensor Comparison New York JFK • At New York JKF airport, New York La Guardia

  10. Washington, Dullas Philadelphia, PA

  11. Comparison of Sites with Duplicate ASOS Sensors At the St. Louis Lambert airport, the Bext from the two ASOS sensors track well (right, top) However, the absolute magnitude of the Bext values differ by a factor When the ASOS sensor values are multiplied by a 1.66, the two signals are virtually identical (right, bottom) This indicates that at this site, the absolute calibration of the sensors differs by a factor of 1.66

  12. Bext Comparison at other Duplicate Sites The signal pattern of adjacent sensors (at different runways) is consistent. However, the calibration differences between sensors may be up to a factor two. More rigorous calibration could reduce the calibration error.

  13. 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

  14. Evidence of ASOS Data Problems The ASOS data for Temperature and Dewpoint appear to be erratic for some stations The problems include constant values, spikes and rapid step changes.

  15. Data Problems: Bad data records • The main data reading problems are due to bad records • Some records for some stations are not fixed length • Cause of the bad data records need to be identified

  16. ASOS Data Pattern Analysis • Diurnal Cycle • RH Dependence of Bext • ASOS Bext – PM2.5 Relationship

  17. 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

  18. Diurnal Cycle of Relative Humidity and Bext Relative Humidity 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 Bext

  19. Adopted RH Correction Curve(To be validated for different locations/seasons) RH is calculated fromT – 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

  20. 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

  21. 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

  22. ASOS-Hourly PM2.5 Allentown, PA

  23. ASOS-Hourly PM2.5 Des Moines, IO

  24. ASOS-Hourly PM2.5 Grand Rapids, MI • In Grand Rapids, MI, July, the relationship is good. • Occasional spikes of Bext are probably weather events not adequately filtered

  25. ASOS-Hourly PM2.5 Islip Long Island, NY

  26. ASOS-Hourly PM2.5 Toledo, OH

  27. ASOS-Hourly PM2.5 San Diego, CA

  28. ASOS-Hourly PM2.5 Islip Long Island, NY

  29. Summary – Tentative Conclusions • There are data for at least 220 Weather Service ASOS stations • Format problems with the data files forced us to discard 30-40 % of readings • To use the ASOS data as PM2.5 surrogate, RH filtering and correction can be applied. These procedures need more calibration. • Comparison of RH-filtered and corrected Bext with hourly PM2.5 at several cities is most encouraging. • ASOS will be a meaningful PM surrogate of PM2.5 concentration estimates with high time and spatial resolution over the Eastern US. For the pristine Southwest, the utility of ASOS is questionable. • The next steps will focus on further comparisons and calibrations.

  30. ASOS-SeaWiFS Satellite Data Comparison • This section is incomplete • Contains only SeaWiFS satellite data illustration for October 2000 (Smoke over the SE US)

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