Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on October 28, 2011 to provide various atmospheric and land related environmental parameters to the operational user community. An automated method to identify smoke and dust plumes in the VIIRS imagery is important for a host of applications including air quality monitoring and forecasting. We developed an algorithm that uses spectral and spatial variability tests to determine smoke and dust aerosol indices. This algorithm has been tested on VIIRS granules (43 for dust and 23 for smoke) globally with known dust and smoke plume outbreaks and evaluated by comparing to Cloud Aerosol LIdar with Orthogonal Polarization (CALIOP) and AERosol Robotic NETwork (AERONET). Each VIIRS granule is a 86s data capture with 48 scan lines, 3000 km swath width, and 750 m to 1.2 km pixel resolution. In this study, first by taking advantage of the strong spectral dependence of the absorption by smoke/dust at shorter wavelengths, such as 410 and 440nm, an index, named as Dust aerosol Index (DAI) was developed to detect smoke and dust. Secondly, due to that fact the particle size of dust is considerably larger than that of smoke, consequently leading to a strong signal at shortwave IR for dust but not for smoke, another index, named as non-dust aerosol index (NDAI) was developed to separate smoke from the dust. and smoke. Comparisons with AERONET observations and CALIOP VFM product indicated that the accuracy of aerosol detection (smoke and dust) is found to be 75% over land and 82% over ocean. 2. Introduction 3. Physical base of the detection algorithm 1. Abstract In this poster, we present the, (1) an algorithm to detect smoke/dust by using VIIRS observations at deep-blue, blue and Shortwave IR bands; (2) examples of smoke/dust events detected with the developed algorithm; (3) Validations with ground-based (AERONET) measurements; (4) Comparisons with CALIOP Vertical Feature Mask Product, and (5) summary 3. Examples of smoke/dust detections6. Comparisons with AERONET Observations 8. Summary 1.A smoke/dust detection algorithm based on observations from deep-blue and shortwave-IR developed for MODIS to has been adapted for VIIRS. The developed algorithm is simple, fast, and easy to be implemented operationally. 2.Validation against AERONET observations and CALIOP VFM products indicated that accuracy and POCD for dust and smoke detection can be as high as 80% and 75%, respectively 3.Additional algorithm enhancements to minimize false positives by improving turbidity test, bright pixel index test etc. will be carried out in the future. Disclaimer: The views, opinions, and findings contained in this work are those of the authors and should not be interpreted as an official NOAA or US Government position, policy, or decision. Algorithm to Detect Dust and Smoke in Suomi-NPP VIIRS Imagery Pubu Ciren (1) and Shobha Kondragunta (2) (1). I.M. Systems Group, Inc. (2). NOAA/NESDIS/STAR Figure 2. The observed ratio of reflectance at 412nm to that at 440nm as a function of reflectance at 412nm (a), and the reflectance at 2130 nm v.s. that at 412nm for smoke (red), dust (orange) and clear (blue) pixels. Figure 1. Ratio of Reflectance at 412nm to that at 445nm as a function of viewing zenith angle. red solid line is the sun glint angle. Figure 3. Simulated DAI as a function of Viewing zenith angle for dust over a). Desert, b). Ocean, and c). Vegetation surface with an optical depth changed from 0.0 to 2.0. Solid, dot-dashed and dotted lines are for solar zenith angle of 10 o, 30 o and 50 o, respectively. matchups with AERONET Measurements : 1.Spatially, a circle centered at AERONET station site with a radius of 25 km was selected, and if more than half of the cloud-free pixels within this circle were identified as dusty, then the area within the 25 km radius is considered as dusty. 2.Temporally, valid AERONET measurements within ±15 min of satellite over-passing time were averaged 3.dust criterion, i.e., AOD at 1020 nm >0.3 and AE between 440 nm and 870 nm <0.6, was used to classify AERONET observations as dust Table 2. Performance Metrics derived from the collocated VIIRS dust detection with AERONET observations DAI = 100*[log 10 (R 412nm /R 445nm )-log10(R ’ 412nm /R ’ 445nm )] NDAI = -10*[log 10 (R 412nm /R 2.25um )] Dust Aerosol Index (DAI): Non-Dust Aerosol Index (NDAI): R 412 /R 445 6S Simulations:  MODIS C5 dust aerosol model is used Desert, vegetation, ocean BRDF with easterly wind speed of 6 m/s are used to represent surfaces in 6S  DUST reduces the contrast between 412nm and 445 nm as absorption by dust increases with the decreasing wavelength.  Smoke and dust have the same effect in terms of reduction of the contrast between 412nm to 440nm as a result of the increased absorption at a shorter wavelength, as shown in Figure 2a.  Difference in particle size enables us to pick-out the smoke by introducing short-wave IR channel (2.13 µm) as shown in Figure 2b. DAI after cloud screening NDAI after cloud screening Sunglint flag Dust flag Final dust flag Smoke plume and a streak of dust plume in the VIIRS RGB image on June 11, 2012 over west coast of Africa Smoke/dust detection algorithm identifies the smoke plumes (pink-red) and the streak of dust plume (yellow-brown) smoke dust VIIRS fire hot spots and visible smoke in the RGB image on August 3, 2014 Over west coast of U.S. VIIRS smoke detection algorithm identifies the smoke plumes including the one removed from fire hot spots Dust over land: September 1, 2014 Dust over water: December 14, 2013 OMPS Absorbing Aerosol Index July 16, 2014 Global VIIRS smoke/dust detection Figure 4. Illustration of VIIRS smoke/dust detection from DAI and NDAI 7. Comparisons with CALIOP VFM products Granule Accuracy (%) POD (%) FAR (%) d _t d _t d _t d _t Granule Accuracy (%) POD (%) FAR (%) d _t d _t d _t d _t d _t Land Water to 12LandWater Accuracy (%) POCD (%) POFD (%) Global Granules for the year of 2013 Granules with dust YesNo YesAB NoCD VIIRS Statistics metrics: Accuracy* = (A+D)/(A+B+C+D) POCD = A/(A+C) POFD = B/(A+B) Dust/smoke detection algorithm run on VIIRS data for the entire year of VIIRS smoke and dust detection matchups with CALIOP Vertical Feature Mask (VFM), spatially by 3 x 3 pixels around VIIRS pixel and temporally with 2 minutes. Truth data Stations True positive False positive True negative False negative AccuracyPOCDPOFD Banizoumbou Darkar IER_Cinzana Solar_Village Capo_Verde Cape_San_Juan Over 401 AERONET stations AccuracyPOCDPOFD Year of to 12LandWater Accuracy (%) POCD (%) POFD (%) Smoke Dust SurfaceConditionDetection Land VCM: Cirrus test is true and no snow/ice, STD Ref 412nm >0.01, Ref 412nm ≥0.5, bright surfaceNot smoke/dust DAI ≥ 11.5 and NDAI > 0.0Dust DAI ≥ 5.0 and NDAI ≤ -3.0Thin Smoke DAI ≥ 9.0 and NDAI ≤ -2.0, 0.4 >Ref 412nm >0.2,Thick Smoke Water VCM: Cirrus test is true and no snow/ice, STD Ref865nm >0.003, Turbid water test is true,Not smoke/dust DAI ≥ 4.0 and NDAI > and, Ref 2250nm < 0.1Dust DAI ≥ 4.5 and NDAI ≤ 10.0 and no Dust Thin Smoke DAI ≥ 9.0 and NDAI ≤ -4.0Thick Smoke 375m 330m 5km CALIPSO VIIRS pixels Figure 6. Schematic illustration of match-ups between CALIOP and VIIRS observations Table 1. Description of the developed VIIIRS Smoke/Dust Detection algorithm Figure 5. Global Smoke/Dust Aerosol Index from VIIRS smoke/dust detection Algorithm VS. OMPS Absorbing Aerosol Index