Surface Reflectivity from OMI: Effects of snow on OMI NO 2 retrievals Gray O’Byrne 1, Randall Martin 1,2, Joanna Joiner 3, Edward A. Celarier 3 1 Dalhousie.

Slides:



Advertisements
Similar presentations
Inferring SO 2 and NO x Emissions from Satellite Remote Sensing Randall Martin with contributions from Akhila Padmanabhan, Gray O’Byrne, Sajeev Philip.
Advertisements

Aircraft GC 2006 ems GC Streets ems East Asian contrib Lightning contrib Aircraft GC 2006 ems GC Streets ems No Asian No Lightning Long-range transport.
A New A-Train Collocated Product : MODIS and OMI cloud data on the OMI footprint Brad Fisher 1, Joanna Joiner 2, Alexander Vasilkov 1, Pepijn Veefkind.
1 A Temporally Consistent NO 2 data record for Ocean Color Work Wayne Robinson, Ziauddin Ahmad, Charles McClain, Ocean Biology Processing Group (OBPG)
Space-Based Constraints on Lightning NOx Emissions Randall V. Martin 1,2, Bastien Sauvage 1, Ian Folkins 1, Christopher Sioris 2,3, Christopher Boone 4,
Simulation of Absorbing Aerosol Index & Understanding the Relation of NO 2 Column Retrievals with Ground-based Monitors Randall Martin (Dalhousie, Harvard-Smithsonian)
GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.
Quantifying uncertainties of OMI NO 2 data Implications for air quality applications Bryan Duncan, Yasuko Yoshida, Lok Lamsal, NASA OMI Retrieval Team.
15% 1. ABSTRACT We show results from joint TES-OMI retrievals for May, We combine TES and OMI data by linear updates from the spectral residuals.
Integrating satellite observations for assessing air quality over North America with GEOS-Chem Mark Parrington, Dylan Jones University of Toronto
Retrieval of SO 2 Vertical Columns from SCIAMACHY and OMI: Air Mass Factor Algorithm Development Chulkyu Lee, Aaron van Dokelaar, Gray O’Byrne: Dalhousie.
Satellite-based Global Estimate of Ground-level Fine Particulate Matter Concentrations Aaron van Donkelaar1, Randall Martin1,2, Lok Lamsal1, Chulkyu Lee1.
Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.
1 Surface nitrogen dioxide concentrations inferred from Ozone Monitoring Instrument (OMI) rd GEOS-Chem USERS ` MEETING, Harvard University.
Gloudemans 1, J. de Laat 1,2, C. Dijkstra 1, H. Schrijver 1, I. Aben 1, G. vd Werf 3, M. Krol 1,4 Interannual variability of CO and its relation to long-range.
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.
Overview of Boundary Layer including Surface Science (BLiSS) Activities in Canadian Universities & Some Emerging Remote Sensing Capabilities Randall Martin.
Surface Reflectivity from OMI using MODIS to Eliminate Clouds: Effects of Snow on UV-Vis Trace Gas Retrievals Gray O’Byrne, 1 Randall V. Martin, 1,2 Aaron.
Satellite Remote Sensing of Global Air Pollution
Folkert Boersma, D. Jacob, R. Park, R. Hudman – Harvard University H. Eskes, P. Veefkind, R. van der A, P. Levelt, E. Brinksma – KNMI A. Perring, R. Cohen,
Indirect Validation of Tropospheric Nitrogen Dioxide Retrieved from the OMI Satellite Instrument: Insight into the Seasonal Variation of Nitrogen Oxides.
Cloud algorithms and applications for TEMPO Joanna Joiner, Alexander Vasilkov, Nick Krotkov, Sergey Marchenko, Eun-Su Yang, Sunny Choi (NASA GSFC)
Algorithms and chemical data assimilation activities at Environment Canada Chris McLinden Air Quality Research Division, Environment Canada 2 nd TEMPO.
Retrieving Snowpack Properties From Land Surface Microwave Emissivities Based on Artificial Neural Network Techniques Narges Shahroudi William Rossow NOAA-CREST.
Heidy Plata 1, Ezinne Achinivu 1, Szu-Ting Chou 1, Sheryl Ehrman 1, Dale Allen 2, Kenneth Pickering 2♦, Thomas Pierce 3, James Gleason 3 1 Department of.
OMI total-ozone anomaly and its impact on tropospheric ozone retrieval Jae Kim 1, Somyoung Kim 1, K. J. Ha 1, and Mike Newchurch Department of Atmospheric.
Surface Reflectivity from OMI: Effects of Snow on OMI NO 2 Gray O’Byrne 1, Randall Martin 1,2, Aaron van Donkelaar 1, Joanna Joiner 3, Edward A. Celarier.
Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie.
Applications of Satellite Remote Sensing to Estimate Global Ambient Fine Particulate Matter Concentrations Randall Martin, Dalhousie and Harvard-Smithsonian.
1 Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument Lok Lamsal and Randall Martin with contributions.
Using Satellite Remote Sensing to Estimate Global Outdoor Air Pollution Exposure Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar,
Application of Satellite Observations for Timely Updates to Bottom-up Global Anthropogenic NO x Emission Inventories L.N. Lamsal 1, R.V. Martin 1,2, A.
Characterization of Aerosols using Airborne Lidar, MODIS, and GOCART Data during the TRACE-P (2001) Mission Rich Ferrare 1, Ed Browell 1, Syed Ismail 1,
1 Inferring Ground-level Nitrogen Dioxide Concentrations from OMI Martin Steinbacher, Empa Edward Celarier, SGT Inc. Eric Bucsela, NASA GSFC.
Intercomparison of OMI NO 2 and HCHO air mass factor calculations: recommendations and best practices A. Lorente, S. Döerner, A. Hilboll, H. Yu and K.
Space-based Constraints on Global SO 2 Emissions and Timely Updates for NO x Inventories Randall Martin, Dalhousie and Harvard-Smithsonian Chulkyu Lee,
Use of space-based tropospheric NO 2 observations in regional air quality modeling Robert W. Pinder 1, Sergey L. Napelenok 1, Alice B. Gilliland 1, Randall.
Constraints on the Production of Nitric Oxide by Lightning as Inferred from Satellite Observations Randall Martin Dalhousie University With contributions.
Retrieval of Vertical Columns of Sulfur Dioxide from SCIAMACHY and OMI: Air Mass Factor Algorithm Development, Validation, and Error Analysis Chulkyu Lee.
Improving Retrievals of Tropospheric NO 2 Randall Martin, Dalhousie and Harvard-Smithsonian Lok Lamsal, Gray O’Byrne, Aaron van Donkelaar, Dalhousie Ed.
1 Monitoring Tropospheric Ozone from Ozone Monitoring Instrument (OMI) Xiong Liu 1,2,3, Pawan K. Bhartia 3, Kelly Chance 2, Thomas P. Kurosu 2, Robert.
How accurately we can infer isoprene emissions from HCHO column measurements made from space depends mainly on the retrieval errors and uncertainties in.
Some Applications of Satellite Remote Sensing for Air Quality: Implications for a Geostationary Constellation Randall Martin, Dalhousie and Harvard-Smithsonian.
Evaluation of model simulations with satellite observed NO 2 columns and surface observations & Some new results from OMI N. Blond, LISA/KNMI P. van Velthoven,
Influence of Lightning-produced NOx on upper tropospheric ozone Using TES/O3&CO, OMI/NO2&HCHO in CMAQ modeling study M. J. Newchurch 1, A. P. Biazar.
Lok Lamsal, Nickolay Krotkov, Sergey Marchenko, Edward Celarier, William Swartz, Wenhan Qin, Alexander Vasilkov, Eric Bucsela, Dave Haffner 19 th OMI Science.
Folkert Boersma, D.J. Jacob, R.J. Park, R.C. Hudman – Harvard University H.J. Eskes, J.P. Veefkind, R.J. van der A, P.F. Levelt, E.J. Brinksma – KNMI A.
USE OF GEOS-CHEM BY SMITHSONIAN ASTROPHYSICAL OBSERVATORY AND DALHOUSIE UNIVERSITY Randall Martin Mid-July SAO Halifax, Nova Scotia.
Observing Air Quality from Space Randall Martin, Aaron van Donkelaar, Lok Lamsal, Chulkyu Lee, Carolyn Verduzco Undergraduate Science Conference 25 September.
Global Air Pollution Inferred from Satellite Remote Sensing Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from Aaron van Donkelaar,
Challenge the future Corresponding author: Delft University of Technology Collocated OMI DOMINO and MODIS Aqua aerosol products.
Xiaomeng Jin and Arlene Fiore
Quantifying uncertainties of OMI NO2 data
Randall Martin, Dalhousie and Harvard-Smithsonian
Harvard-Smithsonian Center for Astrophysics
An Improved Retrieval of Tropospheric Nitrogen Dioxide from GOME
Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.
Estimating Ground-level NO2 Concentrations from OMI Observations
Satellite Remote Sensing of Ozone-NOx-VOC Sensitivity
Development of Methods for Retrieval and Interpretation of TEMPO NO2 Columns for Top-down Constraints on NOx Emissions & NOy Deposition Randall Martin.
Kelly Chance Smithsonian Astrophysical Observatory
6th TEMPO Science Team Meeting
Chris Sioris Kelly Chance
Chris Sioris Kelly Chance
Retrieval of SO2 Vertical Columns from SCIAMACHY and OMI: Air Mass Factor Algorithm Development and Validation Chulkyu Lee, Aaron van Dokelaar, Gray O’Byrne:
Daniel Jacob Paul Palmer Mathew Evans Kelly Chance Thomas Kurosu
2019 TEMPO Science Team Meeting
Randall Martin Mid-July
Presentation transcript:

Surface Reflectivity from OMI: Effects of snow on OMI NO 2 retrievals Gray O’Byrne 1, Randall Martin 1,2, Joanna Joiner 3, Edward A. Celarier 3 1 Dalhousie University 2 Harvard-Smithsonian Center for Astrophysics 3 NASA Goddard Space Flight Center Locating Cloud Free OMI scenes We use the MODIS/Aqua cloud mask to determine the presence of clouds within the OMI field of view. Using MODIS to screen for clouds ensures that an OMI scene is cloud free even when surface reflectivity is unknown. We account for horizontal displacement of the clouds during the time between the MODIS and OMI overpass (~12 minutes). MODIS Cloud Mask Potential Transport Cloud Free OMI Scenes! Snow-Free Surface Reflectivity We filter the Lambertian-Equivalent Reflectivity (LER) retrieved from OMI [Joiner and Vasilikov, 2006] to exclude clouds and aerosols as determined by MODIS. Deserts are more reflective then vegetation and ocean. White space indicates persistent cloud. Surface Reflectivity of Seasonal Snow Cover (LER) This is the surface reflectivity we measure for scenes that are both cloud free (as determined by MODIS) and snow covered, as flagged in the OMI product according to the NISE dataset. Surface reflectivity over snow varies considerably from the value of 0.6 that is typically used in current OMI retrievals. Annual Mean Surface Reflectivity (LER) at 354nm We compare our results to the climatology of surface reflectivity from OMI (OMLER) of Kleipool et. al [2008]. Our product is lower by ~0.01 over land with the exception of Africa and the Middle-East where residual aerosol contamination is expected in our product. In these dusty regions our product is higher by ~0.005 on average. Differences due to seasonal snow cover are evident in the northern hemisphere. Some isolated regions such as the Aral Sea, Lake Eyre (Australia) and Salt Lake (US) are up to 0.2 higher in our product. Above minus OMLER (354nm) Effect on OMI NO 2 – Challenges with Clouds and Snow Here we compare the OMI NO2 product [Bucsela et al., 2006] over the cities of Calgary and Edmonton (in the highly reflective region in South-Central Canada) for three different snow-on-ground categories. The current fixed surface reflectivity for snow used in OMI cloud and NO 2 retrievals leads to trends of increasing NO 2 with snow and cloud. Errors in the a priori surface reflectance will introduce errors in the OMI (O2-O2) cloud fraction retrieval, so we make the distinction between reported and real cloud fractions. Fractional Bias in tropospheric NO 2 Over Snow-Covered Lands All Scenes With Cloud Mask The top panel shows the bias between the OMI NO 2 columns for cloudless scenes (determined by MODIS) retrieved with our snow-covered surface reflectivity versus columns calculated with the surface reflectivity (and cloud data) used in the current OMI NO 2 product. The error in the a priori surface reflectance leads to non- zero cloud fractions being reported for these cloudless scenes. In practice scenes with high cloud fractions are often masked to ensure good sensitivity to the boundary layer. The bottom panel shows the bias once scenes with OMI cloud fractions greater then 0.3 are removed. Most positive biases are removed, but negative biases remain unchanged. This work was supported by the Canadian Foundation for Climate and Atmospheric Science. The authors would like to acknowledge Jim Gleason for his helpful comments. References: Bucsela, E. J., E. A. Celarier, M. O. Wenig, J. F. Gleason, J. P. Veefkind, K. F. Boersma, and E. J. Brinksma (2006), Algorithm for NO2 vertical column retrieval from the ozone monitoring instrument, IEEE Trans. Geosci. Remote Sens., 44(5), Joiner, J. and A. P. Vasilkov (2006), First results from the OMI rotational Raman scattering cloud pressure algorithm, IEEE Trans. Geosci. Remote Sens., 44(5), Kleipool, Q. L., M. R. Dobber, J. F. de Haan, and P. F. Levelt (2008), Earth surface reflectance climatology from 3 years of OMI data, Journal of Geophysical Research-Atmospheres, 113(D18). Mean NO 2 Column (molec/cm 2 ) Reported OMI Cloud Fraction Winter OMI NO 2 over Calgary & Edmonton Retrievals of NO 2 from the OMI/Aura satellite instrument are being widely applied to improve understanding of air quality and NOx emissions. OMI NO 2 retrievals depend on information about surface reflectivity. We use observations from the MODIS/Aqua satellite instrument, which flies 12 minutes ahead of OMI, to exclude clouds from OMI observations and determine surface reflectivity for cloud-free conditions. The resultant dataset is used to evaluate surface reflectivity inferred from other techniques, and to assess the implications for OMI NO 2 retrievals over snow. Summary