Applica'ons of Geosta'onary Aerosol Retrievals on PM2.5 Forecas'ng: Increased Poten'al from GOES-15

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1 Applica'ons of Geosta'onary Aerosol Retrievals on PM2.5 Forecas'ng: Increased Poten'al from GOES-15 Yonghua Wu, Sam Lightstone, Fred Moshary, Barry Gross Op;cal Remote Sensing Lab City College of New York See the following posters: 1) Con&nental Transport of Wildfire Smoke and Impact on Air Quality observed by ground-based and satellite sensors in New York 2) Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM 2.5 in New York

2 Connec'ons between Transported Smoke Plumes and PM25 In mul;ple cases, mixing down of aloh transported smoke plumes have resulted in strong increases in PM25 which were observed dynamically using a combina;on of Lidars and Ceilometers located at CCNY The PM 2.5 concentra;ons indicate coincident increase from 5- to 20~30 μg/ m 3 and good correla;on (R=0.8) in the NYC urban and upwind rural area which implies the regional transport. At the upwind rural Pinnacle site, the PM2.5 and CO indicate consistent enhancement and strong correla;on (R= ) which demonstrates that PM25 enhancement is due to the smoke-associated transport. The wildfires sources and smoke transport path are demonstrated from the satellite and HYSPLIT product

3 Op'cal Ver'cal Observa'ons of Mix-Down AloL aerosol plumes and mixing down into PBL (smoke ex;nc;on: 0.1~0.3 k-1 at 532-nm)

4 PM25 Forecas'ng Previous Slides show strong smoke plumes have significant satellite AOD signatures which can be used to iden;fy plume transport into our area. Differen;al NN approach can be used to help forecast such events in comparison to exis;ng CMAQ forecasts although ini;al increase is ohen overshot. Using Satellite AOD (MODIS-OMI), we can iden;fy plume AOD pixels which can be transported using HYSPLIT forward trajectories over the NY region to es;mate poten;al enhanced PM25 intrusion PM µg/m NN and CMAQ vs Airnow (hourly) NN is released at 2100UTC with a 24hr forecast NYC CMAQ Release time =06Z CMAQ Bias Corrected (NYS AVG) Neural Network Airnow 0 May 20 May 22 May 24 May 26 May 28 May 30 Jun 01 Jun 03 Jun 05

5 Explore use of Strong Smoke Plume AOD as forecast factor Run ensemble forward trajectories for different ver;cal heights within realis;c plume heights and determine which trajectories lie within +0.25/ degree of NYC and bin into transport ;me with ver;cal mixing into the PBL (<3km) Calculate the weighted average of the AOD bins, and project the AOD forecast onto the PM forecast using a reasonable scale factor (PM25 ~ 50*AOD)

6 Preliminary Results (May 25-May )

7 Conclusions and Future Work Use of MODIS high AOD + Transport can poten;ally iden;fy likelihood of strong smoke events that can be used in a transport model to iden;fy poten;al PM25 mix down events. Unfortunately, MODIS data has strong cloud contamina;on and OMI footprint is poor so actual points that are valid for transport are ohen very sparse Future GOES-15 (Geosta;onary with MODIS like channels) + Enhanced OMI (TROPOMI) offer drama;c enhancements of spa;al / temporal resolu;on and data frequency allowing beher forecast poten;al.

8 Paper Solicita;on: Special Issue Journal of Applied Remote Sensing (JARS) Suggested topics may include but are not limited to Observa;onal and algorithmic approaches to quan;fying pollutant gases including nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ammonia (NH 3 ), carbon monoxide (CO), methane (CH 4 ), carbon dioxide (CO 2 ), ozone (O 3 ) etc. Retrieval of aerosol op;cal proper;es including aerosol op;cal depth (AOD), angstrom exponent (AE), albedo and par;cle modes, and the use of these products to beher quan;fy PM 2.5 and/or PM 10 levels Use of integrated remote sensing tools to beher constrain and improve current atmospheric pollu;on modeling methodologies Assimila;on of air quality remote sensing retrieval products and profiles into exis;ng opera;onal meteorological chemical transport models Innova;ve use of GIS and and/or machine learning tools like source appor;onment to beher integrate remote sensing data for a beher understanding of pollu;on sources, transport mechanisms, and poten;al health effects Improved tracking and quan;fica;on of both natural and anthropogenic pollutant plumes Reports and assessments of nextgenera;on air pollu;on sensors such as TEMPO (Tropospheric Emissions: Monitoring of Pollu;on), TROPOMI (TROPOspheric Monitoring Instrument) and IASI-NG (Infrared Atmospheric Sounding Interferometer Next Genera;on) Characteriza;on of temporal and spa;al air pollu;on trends and coupling at local, regional, and global scales Novel sensor designs and applica;ons Regional studies in stressed environments.