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STAR and NPOESS

STAR and NPOESS. Presented by Andrew Heidinger. Requirement, Science, and Benefit. Mission Goals Advancing space-based data collection capabilities and associated platforms and systems Advancing in situ and surface-based data collection capabilities and associated platforms and systems

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STAR and NPOESS

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  1. STAR and NPOESS Presented by Andrew Heidinger

  2. Requirement, Science, and Benefit Mission Goals Advancing space-based data collection capabilities and associated platforms and systems Advancing in situ and surface-based data collection capabilities and associated platforms and systems Overall observing systems architecture design Data management, associated visualization technology and models, and related high performance computing and communication Science How can we best exploit NPOESS’s many EOS capabilities in real-time for NESDIS customers? How do we ensure continuity of POES climate records (1978-2020) with those from NPOESS? How best to exploit the combination of NPOESS with the evolving geostationary assets? Benefits NPOESS provides proven NASA EOS capabilities in many remote sensing areas. Examples: NWP – Availability of hyperspectral IR and microwave sounder data for assimilation. Air Quality – Real-time estimation of aerosol properties over land. Department of Defense – Improved high resolution imagery and an improved Day/Night band. Forecasters – decreased latency of NPOESS data through a more distributed data-downloading network (Safety-Net). 2

  3. Challenges and Path Forward Continuing Science Challenges Constructing continuous climate records. Generating VIIRS products (Winds, Cloud Heights) in the absence of IR absorption channels. Combining NPOESS with the available geostationary capabilities. Next steps Work within NDE to ensure NESDIS customers receive the high quality products they expect. Tap into available research opportunities to develop products not envisioned in the baseline set of products provided by industry to NESDIS. Further expand our collaboration with EUMETSAT to explore seamless products from the METOP/NPOESS platforms. Support all training and outreach opportunities. • NPOESS information furthers each of NOAA’s mission goals • STAR supports the NPOESS program in many ways 3

  4. NPOESS supports all Mission Goals SST Clouds + Precip. Ocean Color Soundings Aerosols 4

  5. STAR supports all NPOESS Sensors ATMS – Advanced Technology Microwave Sounder CrIS – Cross-track Infrared Sounder MIS – Microwave Imager/Sounder OMPS – Ozone Mapping and Profiler Suite VIIRS – Visible/Infrared Imager/Radiometer Suite ✔ ✔ ✔ ✔ ✔ 5

  6. Contrast in STAR’s Role in GOES-R and NPOESS STAR’s Role in GOES-R STAR lead AWG to develop algorithms and validation systems for GOES-R STAR and its CI’s played dominant role in GOES-R Risk Reduction (Research) including Hyperspectral studies and ABI band selection. STAR’s Initial Role in NPOESS STAR has developed and/or led the transition of all operational POES products. This included algorithm and processing system development and all calibration and validation activities. Under the NPOESS framework, STAR no longer develops the official NPOESS products. STAR scientists now work under the management of the IPO to evaluate and assess the NGST algorithms. The NPOESS contractor (NGST) maintains responsibility for the NPOESS algorithms and the processing system. Due to restrictions imposed during the mission competition, STAR and the other government agencies had little ability to tailor spectral characteristics of NPOESS sensors for NESDIS applications (polar winds). 6

  7. STAR’s Evolving Role in NPOESS In recognition that the baseline NGST products will not fully fulfill the expectations of NESDIS’s customers, NESDIS initiated the NPOESS Data Exploitation Project (NDE). NDE has selected several products as NOAA unique products and will generate these within NESDIS. STAR scientists are developing the algorithms for the NOAA Unique Products Legacy AVHRR SST (A. Ignatov) ATMS products (S. Boukabara) Products from CrIS/ATMS (C. Barnet, M. Goldberg and W. Wolf) NDE and STAR will also make tailored products from the baseline NPOESS products to match the format and resolution requirements of current NESDIS customers. NetCDF4 to BUFR/GRIB converter (W. Wolf) 7

  8. STAR’s Leadership within NPOESS • The IPO has looked to STAR for technical leadership throughout • Larry Flynn has led the Ozone OAT. • Andrew Heidinger led the VIIRS OAT. • Paul Menzel led the Sounding and VIIRS OAT. • Istvan Laszlo served on the Aerosol Polarimeter Sensor (APS) OAT and NPP/NPOES CAL/VAL teams. • Chris Barnet now leads the official IPO sponsored Sounder Cal/Val Team. • Ivan Csiszar leads on IPO-sponsored and NASA EOS/NPP efforts that are developing VIIRS fire algorithms. • Many STAR scientists and contractors also participate as members of the IPO OAT and CAL/VAL teams (Ivan Csiszar, Michael Ondrusek, Jeff Key, Bob Yu, Peter Romanov, Menghua Wang, Alexander Ignatov) 8

  9. STAR’s Technical Impact on NPOESS STAR scientists played leading role in drafting the original requirements document (IORD) used to determine the NPOESS specifications. Many STAR concepts adopted by NGST. (i.e., NGST adopted from NESDIS a cloud typing algorithm, several cloud masking concepts and SST algorithms.) P. Menzel/ J. Key led the effort to get IR water vapor bands on VIIRS (may still occur on C3). I. Csiszar is involved in evaluating potential VIIRS sensor improvement for fire detection. 9

  10. STAR contributing to VIIRS Ocean EDR Product Calibration/Validation Plan 6 • Approach: • Develop an integrated cross-agency Cal/Val plan for the NPP products • Ensure consistency of NPP products with heritage satellite products • Construct a readiness program beginning with MODIS as a pathfinder to ocean calibration and validation for NPP and extending to C1 and C2 Satellite inter- comparisons, robustness, seasonal and product stability 5 Product validation and product long-term stability 4 Ocean Algorithm, stability evaluation and uncertainty 3 Tuning of algorithms and LUTS (Vicarious calibration and SDR feedback) 2 Not in order or priority Define the required in situ data stream for Cal/Val 1 Define a VIIRS Simulated Data Stream From: VIIRS Cal/Val Plan for Ocean Products, B. Arnone et al.

  11. Ocean EDR Product Calibration and Validation Plan for the VIIRS Sensor for Ocean Products VIIRS Cal/Val Plan developed for IPO by inter-agency government team (Navy, NOAA, NASA) to construct end-to-end sensor-to-product capability based on heritage capabilities STAR Ocean Color Activities: VIIRS Ocean Color Algorithm Evaluation, Data Processing & Analyses – Menghua Wang 1) Algorithm Evaluation and Development 2) Vicarious Calibration (VC) Technique Demonstration using MODIS Data 3) VIIRS Data Processing System and VIIRS Proxy Data Set VIIRS In situ Data for Vicarious Calibration and Validation - Michael Ondrusek 1) Historical/real time match up data set used for vicarious calibration 2) Vicarious calibration exclusion criteria 3) In situ data management group STAR Sea Surface Temperature and Clear-Sky Radiances Activities: VIIRS SST & CSR: Cal/Val & Monitor for Stability & Cross-Platform Consistency - Alexander Ignatov 1) Cal/Val VIIRS SST against in situ SSTs 2) Evaluate VIIRS SST against global Level 4 SST fields 3) Evaluate VIIRS CSR against Community Radiative Transfer Model simulations

  12. Example STAR-developed NPOESS Applications: Generating NPOESS-analogs on POES for NPOESS Readiness • STAR algorithms running on POES/AVHRR already generate many of the NPOESS/VIIRS cloud algorithms using analogous approaches. • At CIMSS, these data are fed into the AWIPS data stream through the NWS Proving Ground Project. Our goal is prepare forecasters for NPOESS/VIIRS products. Products also served via Google Earth. False Color Image Cloud Mask + SST Cloud Optical Depth 12

  13. Example of STAR-developed NPP/NPOESS CAL/VALSystem Monitoring VIIRS aerosol retrieval The evaluation system routinely evaluates performance of the VIIRS aerosol retrieval run with MODIS proxy inputs using ground measurements (AERONET) and independent satellite (MODIS) retrievals. Map of MODIS Collection 5 and VIIRS-like aerosol optical depth (AOD) difference for MODIS/Terra on July 1, 2008 Time series of daily AOD (top), bias (middle) and RMSE (bottom) over land (left) and water (right).

  14. Challenges and Path Forward Continuing Science Challenges Constructing continuous climate records. Generating VIIRS products (Winds, Cloud Heights) in the absence of IR absorption channels. Combining NPOESS with the available geostationary capabilities. Next steps Work within NDE to ensure NESDIS customers receive the high quality products they expect. Tap into available research opportunities to develop products not envisioned in the baseline set of products provided by industry to NESDIS. Further expand our collaboration with EUMETSAT to explore seamless products from the METOP/NPOESS platforms. Support all training and outreach opportunities. • NPOESS information furthers each of NOAA’s mission goals • STAR supports the NPOESS program in many ways 14

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