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Ali Omar (NASA)

Title: Head, Lidar Science Branch
Technical Focus Areas: Lidar Science, Atmospheric Composition, Applied Science, Air Quality & Weather, Airborne Science
Mission/ProjectCALIPSO, ACCP
Study Topics: Aerosols, Lidar Algorithms, Aerosol Typing

About:

Dr. Ali Omar is the Head of the Lidar Science Branch (E304) at NASA Langley Research Center. He oversees the development of new lidar systems, their maturation for airborne and space-based platforms and the development of algorithms for converting lidar measurements to geophysical parameters. In this capacity, he provides institutional resources, scientific and engineering expertise, and guidance to enable missions, research, and technology development and general support and guidance to 63 scientists and engineers in E304. Prior to his current assignment, Dr. Omar was detailed to NASA HQ and served as the Associate Program Manager for Health & Air Quality Applications where he oversaw project progress and the application of Earth science products to various decision-making activities. As the Mission Applications Representative for PACE responsible Atmosphere & Air Quality Applications, he was responsible for integrating applications into mission requirements in the early design Phase (pre-Phase A). As the Physical Scientist at NASA LaRC, Dr. Omar is science team member of the Cloud and Aerosol Lidar Infrared imager Pathfinder Spaceborne Observations (CALIPSO) mission and led the development and testing of aerosol extinction-to-backscatter-ratio, and aerosol subtyping algorithms for its space-borne measurements. He also serves as the Principal Investigator for the AERONET System at NASA LaRC. In 2016, Dr. Omar was elected Secretary of the American Geophysical Union (AGU) Global Environmental Change Section where he served 2 terms until January 2020, the AGU Macelwane Medal Committee for the 2020-2021 term. Dr. Omar is also a member of the NOAA Science Advisory Board Climate Working Group.

Publication Bibliography:

Select Publications:

  • The CALIPSO automated aerosol classification and lidar ratio selection algorithm AH Omar, DM Winker, MA Vaughan, Y Hu, CR Trepte… – Journal of Atmospheric and Oceanic Technology, 2009

  • Overview of the CALIPSO mission and CALIOP data processing algorithms DM Winker, MA Vaughan, A Omar, Y Hu, KA Powell… – Journal of Atmospheric and Oceanic Technology, 2009

  • The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance Z Liu, M Vaughan, D Winker, C Kittaka, B Getzewich, R Kuehn, A Omar, … – Journal of Atmospheric and Oceanic Technology, 2009

  • Fully automated analysis of space-based lidar data: An overview of the CALIPSO retrieval algorithms and data products MA Vaughan, SA Young, DM Winker, KA Powell, AH Omar, Z Liu, YX Hu, CA Hostetler… – Laser radar techniques for atmospheric sensing, 2004

  • Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements AH Omar, JG Won, DM Winker, SC Yoon, O Dubovik… – Journal of Geophysical Research: Atmospheres, 2005

  • CALIPSO lidar observations of the optical properties of Saharan dust: A case study of long‐range transport Z Liu, A Omar, M Vaughan, J Hair, C Kittaka, Y Hu… – Journal of Geophysical Research: Atmospheres, 2008

  • CALIPSO lidar observations of the optical properties of Saharan dust: A case study of long‐range transport Z Liu, A Omar, M Vaughan, J Hair, C Kittaka, Y Hu… – Journal of Geophysical Research: Atmospheres, 2008

  • Dust Lidar Ratios Retrieved from the CALIOP Measurements Using the MODIS AOD as a Constraint MH Kim, SW Kim, AH Omar – Remote Sensing, 2020

  • Plankton Aerosol, Cloud, ocean Ecosystem mission: atmosphere measurements for air quality applications AH Omar, M Tzortziou, O Coddington, LA Remer – Journal of Applied Remote Sensing, 2018

  • The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm K Man-Hae, AH Omar, JL Tackett, MA Vaughan… – Atmospheric Measurement Techniques, 2018

Professional Memberships:

  • AGU
  • AMS

Education:

  • PhD – Civil Engineering, University of Illinois, Champaign-Urbana
  • MS – Aeronautical Engineering, University of Illinois, Champaign-Urbana
  • BS – Aerospace Engineering, Saint Louis University

National/International Leadership:

  • NOAA Science Advisory Board Climate Working Group Member (2020- )
  • Secretary, AGU Global Environmental Change Section (2015-19)
  • AGU Macelwane Medal Committee for the 2020-2021 term

Hobbies/Interests:

Volleyball

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  • Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)
    Virginia DEQ, NASA and Penn State-NATIVE Enclosures (from right to left)

  • Ozone-sonde away.
    Ozone-sonde away.
  • About to lift.
    About to lift.
PurpleAir PA-II-SD Air Quality Sensor
Laser Particle Counters
Type (2) PMS5003
Range of measurement 0.3, 0.5, 1.0, 2.5, 5.0, & 10 μm
Counting efficiency 50% at 0.3μm & 98% at ≥0.5μm
Effective range
(PM2.5 standard)*
0 to 500 μg/m³
Maximum range (PM2.5 standard)* ≥1000 μg/m³
Maximum consistency error (PM2.5 standard) ±10% at 100 to 500μg/m³ & ±10μg/m³ at 0 to 100μg/m³
Standard Volume 0.1 Litre
Single response time ≤1 second
Total response time ≤10 seconds
Pressure, Temperature, & Humidity Sensor
Type BME280
Temperature range -40°F to 185°F (-40°C to 85°C)
Pressure range 300 to 1100 hPa
Humidity Response time (τ63%): 1 s
Accuracy tolerance: ±3% RH
Hysteresis: ≤2% RH


Pandora capabilities

Instrument

Response

Parameter

Precision

Uncertainty

Range

Resolution

Pandora

~2min

Total Column O3, NO2, HCHO, SO2, H2O, BrO

0.01 DU

0.1 DU

 

 

Virginia Department of Environment Quality in-situ instrumentation

Instrument

Response

Parameter

Precision

Uncertainty

Thermo Scientific 42C (Molybdenum converter)
(VADEQ)

60 s

NO and NOx

50 pptv

3%

Teledyne API 200EU w/ photolytic converter
(EPA) PI-Szykman

20 s

NO2

50 pptv

 

Thermo Scientific 49C (VADEQ)

20 s

O3

1 ppbv

4%

Thermo Scientific 48i (VADEQ)

60 s

CO

40 ppbv

5%

Thermo Scientific 43i (VADEQ)

80 s

SO2

0.2 ppbv

5%

Thermo Scientific 1400AB TEOM (VADEQ)

600 s

PM2.5 (continuous)

µg/m3

1 3%

Thermo Scientific Partisol Plus 2025 (VADEQ)

24 hr

PM2.5 (filter-based FRM)- 1/3 days

 

 

BSRN-LRC-49
Large area view.
Latitude: 37.1038
Longitude: -76.3872
Elevation: 3 m Above sea level
Scenes: urban, marsh, bay, river and farm.

Legend

  • The inner red circle is a 20km CERES foot print centered on the BSRN-LRC site.
  • The pink circle represents a possible tangential 20km foot print.
  • The middle red circle represents the area in which a 20km foot print could fall and still see the site.
  • Yellow is a sample 40 deg off nadir foot print.
  • The outer red circle is the region which would be seen by a possible 40 deg off nadir foot print.
The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015) The BSRN-LRC sun tracker at the NASA Langley Research Center on a snowy day (02/20/2015)
CAPABLE-BSRN Google Site Location Image

Team Satellite Sensor G/L Dates Number of obs Phase angle range (°)
CMA FY-3C MERSI LEO 2013-2014 9 [43 57]
CMA FY-2D VISSR GEO 2007-2014
CMA FY-2E VISSR GEO 2010-2014
CMA FY-2F VISSR GEO 2012-2014
JMA MTSAT-2 IMAGER GEO 2010-2013 62 [-138,147]
JMA GMS5 VISSR GEO 1995-2003 50 [-94,96]
JMA Himawari-8 AHI GEO 2014- -
EUMETSAT MSG1 SEVIRI GEO 2003-2014 380/43 [-150,152]
EUMETSAT MSG2 SEVIRI GEO 2006-2014 312/54 [-147,150]
EUMETSAT MSG3 SEVIRI GEO 2013-2014 45/7 [-144,143]
EUMETSAT MET7 MVIRI GEO 1998-2014 128 [-147,144]
CNES Pleiades-1A PHR LEO 2012 10 [+/-40]
CNES Pleiades-1B PHR LEO 2013-2014 10 [+/-40]
NASA-MODIS Terra MODIS LEO 2000-2014 136 [54,56]
NASA-MODIS Aqua MODIS LEO 2002-2014 117 [-54,-56]
NASA-VIIRS NPP VIIRS LEO 2012-2014 20 [50,52]
NASA-OBPG SeaStar SeaWiFS LEO 1997-2010 204 (<10, [27-66])
NASA/USGS Landsat-8 OLI LEO 2013-2014 3 [-7]
NASA OCO-2 OCO LEO 2014
NOAA-STAR NPP VIIRS LEO 2011-2014 19 [-52,-50]
NOAA GOES-10 IMAGER GEO 1998-2006 33 [-66, 81]
NOAA GOES-11 IMAGER GEO 2006-2007 10 [-62, 57]
NOAA GOES-12 IMAGER GEO 2003-2010 49 [-83, 66]
NOAA GOES-13 IMAGER GEO 2006 11
NOAA GOES-15 IMAGER GEO 2012-2013 28 [-52, 69]
VITO Proba-V VGT-P LEO 2013-2014 25 [-7]
KMA COMS MI GEO 2010-2014 60
AIST Terra ASTER LEO 1999-2014 1 -27.7
ISRO OceanSat2 OCM-2 LEO 2009-2014 2
ISRO INSAT-3D IMAGER GEO 2013-2014 2

The NASA Prediction Of Worldwide Energy Resources (POWER) Project improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. The latest POWER version enhances its distribution systems to provide the latest NASA EO source data, be more resilient, support users more effectively, and provide data more efficiently. The update will include hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series now spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001.

The newly available hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file). One of POWER’s partners, Natural Resource Canada’s RETScreen™, will be simultaneously releasing a new version of its software, which will have integrated POWER hourly and daily ARD products. For our agroclimatology users, the ICASA (International Consortium for Agricultural Systems Applications standards) format for the crop modelers has been modernized.

POWER is releasing new user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones. The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. Updated methodology documentation and usage tutorials, as well as application developer specific pages, allow users to access to POWER Data efficiently.

+Visit the POWER Program Site to Learn More.