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Satellite based estimates of surface visibility for state haze rule implementation planning Air Quality Applied Sciences Team 6th Semi-Annual Meeting (Jan.

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Presentation on theme: "Satellite based estimates of surface visibility for state haze rule implementation planning Air Quality Applied Sciences Team 6th Semi-Annual Meeting (Jan."— Presentation transcript:

1 Satellite based estimates of surface visibility for state haze rule implementation planning Air Quality Applied Sciences Team 6th Semi-Annual Meeting (Jan 15-17, 2014, Rice University, Houston, TX 2013 AQAST Tiger Team Project: Under support from the NOAA GOES-R Algorithm Working Group NESDIS has developed a satellite based visibility retrieval that uses MODIS Aerosol Optical Depth retrievals and GFS forecast data as input and is based on regression against surface extinction measurements from the NWS Automated Surface Observing System (ASOS). Under this AQAST Tiger Team Project we combine this satellite retrieval with IMPROVE network measurements to assess the utility for use in state haze rule planning Brad Pierce, NOAA NESDIS Jason Brunner, UW/CIMSS Allen Lenzen, UW/SSEC

2 Aerosol Visibility algorithm uses a weighted mean of a first guess visibility (V=3.0/(  /z pbl ) and a multi-variate linear regression based on NWS ASOS surface visibility. Meteorological predictors are obtained from the NCEP Global Forecasting System (GFS) Numerical Weather Prediction (NWP) model: 1)relative humidity at the top of the PBL 2)2m relative humidity 3)PBL mean relative humidity 4)PBL lapse rate 5)PBL depth 6)2m temperature 7)temperature at the top of the PBL 8)height of the PBL above sea level (including topography) Measurement Accuracy Clear (vis ≥ 30 km) Moderate (10 ≤ Vis < 30 km) Low (2 km ≤ vis < 10 km); Poor (vis < 2 km) under the conditions of clear up through clouds of only layer Correct classification 80% GOES-R Advanced Baseline Imager (ABI) Visibility Algorithm

3 Visibility Validation Histogram – Entire USA

4 Visibility Validation Monthly Mean – Entire USA

5 deciview haze index (dv) is dv = 10 ln (bext / 10 Mm-1) (bext expressed in Mm-1) Visual Range = 3.91/bext (bext expressed in km-1) Comparison With IMPROVE data WMO Visibility = 3./bext (bext expressed in km-1) 60km 30km 10km 16dv 25dv 36dv 2km 52dv ABI Visibility Classes “Clear” “Moderate” “Low”

6 MODIS Terra and Aqua Visibility retrievals binned into 0.25x0.25 degree bins and aggregated for each month Require 180 (10km) retrievals per bin each month.

7 GOES WFABBA fire detections

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9 Version 5 MODIS Aqua Visibility Retrieval (dv) Improve Visibility (dv)

10 dv=10ln(  /10)=10ln(  /z/10)  = ±0.05 ±0.15 (MODIS ATBD) MODIS limit of detection: dv min =10ln(  /1.e-3/10.)=16 assuming z=1km Version 5 MODIS Aqua Visibility Retrieval (dv) Improve Visibility (dv)

11 Version 5 MODIS Aqua Visibility Retrieval (dv) Improve Visibility (dv) Establish regression between Improve and MODIS Visibility retrieval to remove bias

12 Version 5 MODIS Aqua Visibility Retrieval (dv) Improve Visibility (dv) Improve regression applied

13 Version 5 MODIS Terra Visibility Retrieval (dv) Improve Visibility (dv) Improve regression applied

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16 Colorado, Arizona, New Mexico wildfires

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19 Pains Bay Fire, Alligator River National Wildlife Refuge, NC (45,294 acres) Honey Prairie Fire, Okefenokee National Wildlife Refuge, GA, (283,673 acres)

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23 Las Conchas Fire, Bandelier National Monument, NM (154,349 acres )

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25 Conclusions: MODIS based Visibility retrievals have been generated for ( currently under way) and will be delivered to the EPA Remote Sensing Information Gateway (RSIG) for use by AQ management to support regional haze rule planning activities The MODIS based visibility retrievals have the most skill (with respect to NWS ASOS visibility) during June-September and some skill during the winter January-March Monthly mean MODIS based visibility retrieval shows relatively high correlations with monthly mean IMPROVE measurements but has a high bias consistent with the limit of detection for MODIS aerosol optical depth The high bias can be corrected by applying a IMPROVE based regression to the MODIS visibility retrievals Analysis of MODIS visibility retrievals shows that wild-fires contribute significantly to the highest frequency of reduced visibility over the continental US during the summer of Once GOES-R is launched in 2016, ABI will provide MODIS quality aerosol optical depth at hourly intervals which will significantly improve our ability to monitor surface visibility from space


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