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NPP ATMS Engineer Delivery Unit (EDU) Calibration Accuracy

2 nd Quarter SCMD Highlights. NPP ATMS Engineer Delivery Unit (EDU) Calibration Accuracy. ORA analyzed the ATMS EDU data obtained from NGST and confirmed all ATMS calibration accuracy meet the instrument specification ATMS calibration accuracy is comparable to NOAA AMSU

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NPP ATMS Engineer Delivery Unit (EDU) Calibration Accuracy

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  1. 2nd Quarter SCMD Highlights NPP ATMS Engineer Delivery Unit (EDU) Calibration Accuracy • ORA analyzed the ATMS EDU data obtained from NGST and confirmed all ATMS calibration accuracy meet the instrument specification • ATMS calibration accuracy is comparable to NOAA AMSU • ORA analysis will continue to obtain non-linearity calibration required for ATMS • This study is performed by Dr. Tsan Mo in Sensor Physics Branch ATMS will continue quality observations established by AMSU Impact : High quality observations for weather and climate applications Benefit :

  2. 2nd Quarter SCMD Highlights NPP ATMS Engineer Delivery Unit (EDU) Calibration Accuracy • Definition: Difference between scene PRT temperature and scene brightness temperature, • Specification: T = 0.83K for Ch. 1-2, T = 0.67K for Ch. 3-15, and T = 0.95 K for Ch. 16-22 Impact : ATMS will continue quality observations established by AMSU High quality observations for weather and climate applications Benefit :

  3. ORA Milestone: Radiance Products and Atmospheric Sounding from Advanced IR and microwave cloud clearing risk reduction: develop cloud clearing via forecast model Accomplishments The system to ingest the AVN and the ECMWF forecast models and compute and infrared clear state radiance estimate was developed and tested on AIRS science team focus days. The methodology and a comparison of AMSU versus AVN cloud clearing systems was presented at AIRS science team meetings (the funding agent in this case) in Dec. 2004 and Feb. 2005. ECMWF was used as a reference state. AIRS Science Team agreed to implement the capability in version 5.0 (summer 2005), but they rejected the idea of using any forecast model in the retrieval system unless it is absolutely necessary (i.e., AMSU fails on Aqua). We developed and presented an alternative method, based on a eigenvector regression trained on partially cloudy AIRS radiances to the AIRS science team in Nov. 2004 and it was also adopted for installation into the team algorithm for v5.0. This algorithm generated a lot of excitement for the potential of infrared-only retrievals (i.e., could result in higher spatial resolution products and could be used with GOES-R, etc.). Problems meeting the objectives of this task. We have been unable to hire a qualified person for this position. At this stage it hasn’t been a negative impact since JPL will handle the installation of the algorithms and the accomplishments to date have been handled by C.Barnet. The near future milestones of this project will be impacted if we do not find a qualified person soon.

  4. ORA Milestone: Carbon Cycle: submit publication on CO2 & T(p) separability • Accomplishments • Much of the work has been done; however, the separability issue is more complex than we anticipated so a publication is still in work • Simulation experiments were performed to understand how temperature and CO2 are intertwined in our spectrum and to develop techniques to unravel them. • Analysis of AIRS temperature biases w.r.t. 100,000 co-located radiosondes shows correlation of our temperature biases with CO2. • This led to the development of a new error term in our co-variance matrix that has performed very well in simulation. • Three presentations on this topic were given • One presentation at the OSA HISE meeting in Feb. 2005, with 3 page conference proceeding publication. • Two presentations at the Orbiting Carbon Observatory (OCO) science team meeting in Mar. 2005. • We plan to submit a paper for the peer reviewed literature by 4Q FY05; however, a significant effort is still required. • We are processing and analyzing our CO2 product biases w.r.t. aircraft measurements of CO2 by NOAA/CMDL • We are processing and analyzing our CO2 product biases w.r.t. CO2 measured on Japanese commercial aircraft flights using AIRS clear radiances using a single footprint (15 km) retrievals. • We are processing and analyzing 18 months of global gridded AIRS radiances.

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