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Harmonization of aerosol Assimilation Models and Retrievals (HAMR)

Project Lead: Oleg Dubovik

Both climate models and remote sensing algorithms are attempting to provide accurate modeling of microphysical and optical properties of atmospheric aerosol. At the same time, the focus of these algorithms have differences. Climate models focus on realizing the most accurate chemical transport of aerosols by accounting for chemical and physical atmospheric processes, while RS is focused on developing the most accurate models of aerosol optical properties possible to produce results that are consistent with radiometric observations. Therefore, there are some differences in representation of aerosol properties by climate models and remote sensing. For instance, remote sensing techniques are more accurate than climate models at modeling aerosol optical properties and radiation effects, but satellites in low Earth orbits only provide daily ‘snapshots’ (at best) at any given location. Climate models can provide the spatial and temporal variability of aerosol components (or species) between the snapshots.

The objective of HAMR is to harmonize aerosol representations connecting climate models and remote sensing. Several complimentary positive outcomes can be expected from these efforts. First, climate models can certainly benefit from the experience accumulated by remote sensing for improving optical properties of aerosols. Second, the refining of climate models relies heavily on aerosol assimilation models; therefore, aligning climate models with remote sensing may certainly affect very positively the efficiency of aerosol assimilation models. Third, the harmonization of climate models and remote sensing can significantly improve the efficiency of remote sensing approaches that attempt to use climate models data as a priori constraints. Indeed, the aerosol properties are very complex and practically no single remote sensing approach has sufficient information content to reliably retrieve the full state vector of aerosols that are generally considered by climate models. Correspondingly, climate model data is one of most evident sources of a priori information for remote sensing techniques. Thus, HAMR is expected to significantly improve the consistency of climate models and remote sensing and notably enhance the efficiency and accuracy of both aerosol assimilation models and remote sensing.

We seek discussion and collaborations in the following areas:
+ How can we facilitate comparisons of modeled aerosol species to remote sensing of aerosol type?
+ How can remote sensing observations be configured for easy assimilation by climate models?
+ How can climate model output of aerosol species and/or types be implemented as a viable constraint for remote sensing retrievals?

  • CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ
    CAPABLE/CRAVE Full Site Photo from left to right site enclosures: 1196A NASA LaRC, MPLnet, Virginia DEQ

  • NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison
    NASA LaRC NAST-I and HU ASSIST side-by-side for intercomparison

  • 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.