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Richard Ferrare (NASA)

Title: Research Scientist
Technical Focus Area: Atmospheric Composition, Lidar Science, Airborne Science
Mission/ProjectHSRL
Study Topics: aerosols

About:

Dr. Ferrare has over thirty years of experience in lidar remote sensing of atmospheric aerosols and water vapor. Starting in 1985 at NASA Goddard, he investigated aerosol optical and physical properties using satellite data and ground based lidar measurements. He led retrievals of aerosol backscattering and extinction profiles from the DOE Atmospheric Radiation Measurement Program (ARM) Southern Great Plains Raman Lidar, was Chair of the ARM Aerosol Working Group (2000-2004) and remains a member of the ARM Climate Research Facility Science Board. Dr. Ferrare served as PI on NASA and DOE ARM projects related to lidar measurements of aerosols and water vapor and is a past member of the CALIPSO science team. Dr. Ferrare has flown on NASA hurricane and severe weather research missions and designed flight strategies to acquire science measurements for these missions. He led deployment of the LaRC airborne HSRLs in multiple national and international field missions, and served as flight scientist for NASA aircraft during airborne field campaigns SEAC4RS (2013), ORACLES (2016), and ACEPOL (2017). He chaired the Aerosol Working Group for the future NASA Aerosol-Clouds-Ecosystem (ACE) satellite mission and is a member of the Science and Applications Leadership Team (SALT) on the Aerosol, Clouds, Convection, and Precipitation (ACCP) Mission Study.

Publication Bibliography:

Select Publications:

  • Thorsen, T. J., R. A. Ferrare, S. Kato, and D. M. Winker, 2020: Aerosol Direct Radiative Effect Sensitivity Analysis. J. Climate, 33, 6119–6139, https://doi.org/10.1175/JCLI-D-19-0669.1.

  • Thorsen, T., R. Ferrare, et al., 2017: The impact of lidar detection sensitivity on assessing aerosol direct radiative effects, Geophys. Res. Lett., 44, 9059–9067, doi:10.1002/ 2017GL074521.

  • McComiskey, A. and R.A. Ferrare, 2016: Aerosol Physical and Optical Properties and Processes in the ARM Program. Meteorological Monographs, 57, 21.1–21.17, doi: 10.1175/AMSMONOGRAPHS-D-15-0028.1.

  • Turner, D.D., J.E. Goldsmith, and R.A. Ferrare, 2016: Development and Applications of the ARM Raman Lidar. Meteorological Monographs, 57, 18.1–18.15, doi: 10.1175/AMSMONOGRAPHS-D-15-0026.1.

  • Burton, S. P., R. A. Ferrare, et al. “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask.” Atm. Meas. Techn. 6: 1397-1412, 2013.

  • Burton, S. P., R. A. Ferrare, C. A. Hostetler, J. W. Hair, R. R. Rogers, M. D. Obland, C. F. Butler, A. L. Cook, D. B. Harper, and K. D. Froyd, Aerosol Classification of Airborne High Spectral Resolution Lidar Measurements – Methodology and Examples, Atmospheric Measurement Techniques, 5(1), 73-98, doi: 10.5194/amt-5-73-2012, 2012.

  • Burton, S.P., R.A. Ferrare, C.A. Hostetler, J.W. Hair, C. Kittaka, M.A. Vaughan, M.D. Obland, R.R. Rogers, A.L. Cook, D.B. Harper, and L. A. Remer, Using Airborne High Spectral Resolution Lidar Data to Evaluate Combined Active Plus Passive Retrievals of Aerosol Extinction Profiles, J. Geophys. Res., 115, doi:10.1029/2009JD012130, 2010.

  • Hair, J. W., C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R., Izquierdo, F. E. Hovis, 2008: Airborne High Spectral Resolution Lidar for Profiling Aerosol Optical Properties, Applied Optics, 47,doi: 10.1364/AO.47.006734

  • Ferrare, R., G. Feingold, S. Ghan, J. Ogren, B. Schmid, S. E. Schwartz, and P. Sheridan, Preface to special section: Atmospheric Radiation Measurement Program May 2003 Intensive Operations Period examining aerosol properties and radiative influences, J. Geophys. Res., 111, D05S01, doi:10.1029/2005JD006908, 2006.

  • Ferrare, R.A., D. D. Turner, L.A. Heilman, W. Feltz, O. Dubovik, and T. Tooman, Raman Lidar Measurements of the Aerosol Extinction-to-Backscatter Ratio Over the Southern Great Plains, J. Geophys. Res., 106, 20333-20347, 2001.

  • Ferrare, R.A.,et al., Comparisons of LASE, aircraft, and satellite measurements of aerosol optical properties and water vapor during TARFOX, J. Geophys. Res., 105, 9935-9947, 2000.

  • Ferrare, R.A., S.H. Melfi, D.N. Whiteman, K.D. Evans, and R. Leifer, Raman Lidar Measurements of Aerosol Extinction and Backscattering – Part 1: Methods and Comparisons, J. Geophys. Res., 103, No. D16, 19,663-19,672, 1998.

Awards:

  • NASA Distinguished Service Medal (2013)
  • NASA Exceptional Service Medal (2008)
  • NASA Center Team Awards (2007- 2010)
  • 6 NASA Group Achievement Awards (1999-2016)

Education:

  • Ph.D. Meteorology, University of Maryland (Advisor: R. Hudson), 1992-1997
  • M.S. Meteorology, University of Wisconsin (Advisors: J. Weinman, E. Eloranta), 1984-1986
  • B.S. Physics, Penn State University, 1978-1982

Professional Memberships:

  • AGU
  • AMS

Hobbies/Interests:

Golf

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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.

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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.

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