National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center GOES-R ABI Sounding Algorithm Development: “ABI+PHS”

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National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center GOES-R ABI Sounding Algorithm Development: “ABI+PHS” Approach and Processing of Cloudy Observations Stanislav Kireev 1 and William L. Smith 1,2 1 Center for Atmospheric Sciences, Hampton University, VA 2 University of Wisconsin - Madison, WI 8 th NOAA-CREST Annual Symposium, New York, 5-6 June, 2013

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center HU participates in NOAA GOES-R preparation and risk reduction activities (GOES-R ABI Algorithm Working Group) Two main focuses of the HU research:  Develop ABI+PHS approach to improve the accuracy of ABI retrievals;  Enhance retrieval algorithm with ability to process all-sky (clear and cloudy) observation conditions.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center ABI Full Disk Scan Figure credit: ITT Industries  The primary instrument on GOES-R satellite  Broad-band visible & IR spectrometer  Imaging Earth’s weather  High spatial and temporal resolution  Scheduled for flight in 2015  Retrieve products include: Air temperature Water vapor Ozone Surface properties Cloud properties … What is Advanced Baseline Imager (ABI):

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center 10 ABI IR Bands: 6 ABI Vis/Near-IR Bands: Polar Hyperspectral Satellite (PHS)  1000s of spectral channels => Higher actual retrieval SNR Higher vertical resolution Higher accuracy of retrieved products But…  Lower spatial resolution (12-15 km footprint)  Lower temporal resolution (twice per day over the same area)

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center The primary goal of ABI+PHS approach is to combine high spatial and temporal resolution of ABI observations with high vertically resolved and accurate retrievals that can be obtained with hyperspectral instrument. The combination of both instruments is especially important for observations of rapidly developing hazardous weather conditions (severe storms, hurricanes, flooding, tornadoes, etc.) Latest approaches to incorporate PHS soundings into ABI retrievals:  Temporal difference: X ABI+PHS (t 1 ) = X PHS (t 0 ) + X ABI (t 1 ) - X ABI (t 0 )  Center retrieval around PHS state: X ABI+PHS (t 1 ) - X PHS (t 0 ) = G [R ABI (t 1 ) – R ABI/PHS (t 0 )]

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Joint Airborne IASI Validation Experiment (JAIVEx): the perfect Cal/Val campaign to test ABI+PHS US-European collaboration focusing on the validation of radiance and geophysical products from MetOp-A (1 st advanced sounder in the Joint Polar Satellite System) Location/dates –Houston, TX, DOE ARM CART site, OK, Gulf of Mexico –14 Apr–4 May, 2007 Aircrafts –NASA WB-57 (NAST-I, NAST-M, S-HIS) –UK BAe (ARIES, MARSS, Deimos, SWS; dropsondes) Satellites –MetOp-A (IASI, AMSU, MHS, AVHRR, HIRS, GOME, SBUV, ACAT) –A-train (Aqua AIRS, AMSU, HSB, MODIS; Aura TES; CloudSat; and Calipso) BAe WB-57

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center JAIVEx case April 27 th, 2007: over CART site, nighttime (cloudy) JAIVEx case April 29 th, 2007 Over Gulf of Mexico, daytime (~clear) Background: IASI IR-imager; circles – IASI IFOVs; black line – NASA WB-57/NAST-I track Two JAIVEx Cal/Val Flights Selected for Analysis: CART site

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Horizontal cross-section of T AIR (P=850 mb), JAIVEx case Apr 27, Five panels in each row correspond to 5 laps of NAST-I flight. Retrievals are done for three instrument configuration. “ABI+PHS” retrieval is much closer to the “Truth” than “ABI only”.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Horizontal cross-section of relative humidity, P=500 mb, JAIVEx case Apr 27, Five panels in each row correspond to 5 laps of NAST-I flight. Retrievals are done for three instrument configuration.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Horizontal cross-section for surface temperature, JAIVEx clear case Apr 29, 2007.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center ALOSE-1 validation experiment DOE ARM CART site, OK Dec 11-14, 2012 IASI, CrIS, AIRS overpasses (4-5 hours time difference) are accompanied with ground-based observations (ASSIST, AERI, sondes). ABI radiances are simulated from all three hyperspectral instruments. IASI is chosen as referenced moment t 0 ; then ABI/CrIS and ABI/AIRS are used as ABI observations at moments t 1 and t 2. After, ABI+PHS and ABI only retrievals are compared with retrievals from full resolution CrIS and AIRS. ABI + PHS approach has a potential to improve the accuracy of ABI atmospheric soundings (but can not totally replace hyperspectral instruments!)

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Part II: Clouds: why are they so important? Monthly Averaged Global Cloud Fraction 2005 – 2013: Movie credit:

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Main features:  Two training sets of atmospheric states and corresponding radiances: clear and cloudy  Cloudy sets are divided to 9 bins depending on P CLD in mb pressure range;  Retrieved products: T(p), H 2 O(p), O 3 (p) Surface characteristics: T SFC,  SFC  Cloud parameters: P CLD, H CLD, T CLD,    = effective cloud emissivity Latest development:  Four methods for cloud bin classification: Residual fit of observed radiance with radiance EOFs; T-split CO2 – slicing Fit to referenced atmosphere (GDAS, ECMWF)  Effective cloud emissivity (product of cloud emissivity and cloud fraction)  Comprehensive quality control All-sky Dual Regression Retrieval Algorithm (in collaboration with University of Wisconsin – Madison) Ultimate Goal: to make retrieval algorithm for clear and cloudy sky conditions.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Dual Regression Algorithm Technique: Step II: get “Cloud” retrievalStep III: compare with sondeStep I: get “Clear” retrieval

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Comparison with Dropsondes, JAIVEx case Apr. 27

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Retrieved Cloud Altitude, Apr. 27, 2007: Laps 1 to 5

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Retrieved Cloud Altitude, Apr. 29, 2007: Laps 1 to 7 MetOp AVHRR channel 1 (left,  m) and channel 4 (right,  m). Sunglint seriously contaminates the eastern part of the channel 1 image while SST variations and low level cloud influence the IR channel 4.

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center  NOAA Cal/Val data set for the “Focus Day” Oct 19, 2007 is used: 236 granules of IASI radiances, scans in each (total ~650,000 IFOVs) Corresponding ECMWF atmospheric states (T(p), 6 gases, surface)  The eff. cloud emissivity  *  = Cld_Frc  CLD  is obtained with CO 2 slicing method  Empirical model of the eff. cloud emissivity is created on this basis as a function of P CLD  Retrieval of the  *  PC-scores is incorporated into DR algorithm The effective cloud emissivity retrieval:

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Effective cloud Emissivity    ): TRUE vs RETR ( 9 P CLD classes) P CLD 1 P CLD 2P CLD 3 P CLD 4 P CLD 5P CLD 6 P CLD 7 P CLD 8 P CLD 9

National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center Summary:  Fast and accurate regression algorithm to retrieve atmospheric thermodynamic state, cloud and surface characteristics for GOES-R ABI is under development and intensive validation  The algorithm can process both, clear and cloudy, observation conditions and shows consistent retrievals of cloud parameters: cloud top altitude, pressure, and cloud fraction  GOES-R ABI has a potential for mesoscale atmospheric soundings in combination with JPSS observations, although can not fully replace having hyperspectral sounder on a geostationary satellite.