1. May 15, 2002MURI Hyperspectral Workshop1 Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate Center University of Alabama - Huntsville Research goals (year 1): Identifying cloud and surface characteristics in high spectral resolution data that best delineate clouds, aerosols, and surface characteristics from one another, and leads to a superior cloud product. Refine the Tracking Error Lower Limit (TELL) parameter to include instrument characteristics and observing requirements of the GIFTS/IOMI. Presentation centers on capabilities to satisfy these goals

2. May 15, 2002MURI Hyperspectral Workshop2 Cloud Detection Current geostationary cloud property retrieval technique at GHCC – detect clouds and retrieve cloud information, mask for atmospheric & surface parameter retrieval (http://wwwghcc.msfc.nasa.gov/goesprod) Cloud detection Bi-spectral THreshold (BTH) method (Jedlovec and Laws 2001) Used operationally at the GHCC (24h a day) GOES Imager or Sounder Single pixel resolution (4 or 10 km) 3.7 - 11 micrometer difference provides key cloud signature Three (3) tests applied to difference image 1.~2.8K spatial pixel deviation (edge detection) 2.~2.1K adjacent pixel (element direction) change (fills in clouds) 3.Historical (20 day) minimum difference image check for each time (detects low clouds/fog, and incorporates synoptic influences ) Performance documented against NESDIS products (Jedlovec and Laws 2001) link link

3. May 15, 2002MURI Hyperspectral Workshop3 Cloud and Aerosol Products Parameter retrieval Cloud height (CTP) – infrared look-up with model guess for GOES imager and opaque clouds oEasy to implement, uses model T(p) as a reference oHighly accurate for opaque clouds oCO2 slicing H2O intercept possible with Sounder (currently not implemented) Cloud phase – water or ice, mixed – reflective information at 3.7 micrometers (under development) Aerosol optical thickness (AOT) – visible channel approach to retrieve AOT in cloud-free regions (Zhang and Christopher, 2001) oDISORT model (Ricchiazzi et al. 1998) used to generate look up tables describing radiance, AOT, ,  oCorrelation with sun photometer data as high as 0.97 chart

4. May 15, 2002MURI Hyperspectral Workshop4 Cloud Product Comparisons GOES-8 CTP – 16:45 UTC 18 April 2002 NESDIS Sounder CTP GHCC BTH Imager CTP

5. May 15, 2002MURI Hyperspectral Workshop5 Cloud Product Comparisons GOES-8 vs MODIS - 18 April 2002 MODIS CTP (1635 UTC) GHCC BTH - GOES Imager CTP (1645 UTC)

6. May 15, 2002MURI Hyperspectral Workshop6 Cloud Research Focus Examine spectral signature of clouds, aerosols, and dust for unique features Use AIRS radiance data for selected periods Begin to adapt the Bi-spectral Threshold method for for high spectral measurements for the retrieval of cloud products

7. May 15, 2002MURI Hyperspectral Workshop7 Sources of wind tracking errors When clouds and wv features are non-conservative tracers of wind Changes in cloud shape (often result of too large of image separation) Improper height assignment Mis-identification of targets (dependent on tracking algorithm) Incorrect image displacements (navigation and registration inaccuracies) The effect of incorrect image displacements on the cloud-tracked wind is a function of image registration, image separation time, and image resolution. Tracking Error Lower Limit (Tell) is the theoretical lower limit error in wind tracking algorithms due to image resolution (  ), time separation (  ), and image stability or registration accuracy (  ) uncertainties. TELL = (    ) /  GOES infrared pixel resolution (  ) is 4km image-to image registration accuracy (  ) is typically about 2km (~0.5 pixel) For 15 minute images (  = 15), TELL = 2.22 ms -1 This means that GOES derived winds under these conditions will typically have a 2 ms -1 error component due to these image uncertainties alone! Satellite-derived Wind Errors

8. May 15, 2002MURI Hyperspectral Workshop8 Science Requirement: Accurate mesoscale winds for diagnostic and modeling studies (<2.0 ms -1 ) use small time intervals high resolution imagery accurate image-to-image registration Imaging Requirement: Resolution trades/constraints: as image separation (  ) is decreased (point 1 to 2), the registration accuracy (R) must improved to maintain quality of wind data if image resolution (  ) is improved, registration accuracy can be relaxed (point 2 to 3) for an equivalent image separation interval (  ) Imaging Requirements for Cloud-drift Winds 0 5 10 15 20 25 30 35 40 45 50 55 60 Image Interval, Resolution, and Registration Accuracy Constraints 1 2 3 TELL =(R *  )/  GOES-R  = 15 min R = 0.125  = 4km TELL = 0.55 TELL Surface of 0.55  - Image Separation Time (min) R - Image Registration Accuracy (m)  - Image Resolution (km)

9. May 15, 2002MURI Hyperspectral Workshop9 Wind Tracking Error Emphasis Refine Tracking Error Lower Limit (TELL) for GIFTS Instrument characteristics Observing scenarios

10. May 15, 2002MURI Hyperspectral Workshop10 Summary / Deliverables Focus of research Examine spectral signature of clouds, aerosols, and dust for unique features Use AIRS radiance data for selected periods Begin to adapt the Bi-spectral Threshold method for for high spectral measurements for the retrieval of cloud products Refine Tracking Error Lower Limit (TELL) for GIFTS Instrument characteristics Observing scenarios Deliverables Key spectral signatures and wavelengths for the detection of clouds and aerosols Insight on how these characteristics can be included in a cloud product algorithm Estimates of the lower limit on satellite derived wind errors from GIFTS

11. May 15, 2002MURI Hyperspectral Workshop11 Backup Charts Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate Center University of Alabama - Huntsville

12. May 15, 2002MURI Hyperspectral Workshop12 15 points (locations on the image to right) used each hour to validate cloud detection schemes subjective determination of clouds (man in the loop) visible, multiple channel IR any pixel cloudy in 32x32km area, then all cloudy Statistical performance at hourly intervals - 2 times below Results are for: CLC = ground truth clear – retrieval scheme correct CLI = ground truth clear – retrieval scheme incorrect CDC = ground truth cloudy – retrieval scheme correct CDI = ground truth cloudy – retrieval scheme incorrect NESDIS = NESDIS operational algorithm (Hayden et al. 1996) BSC = Bi-spectral Spatial Coherence method (Guillory et al. 1998) used operationally at GHCC BTH = Bi-spectral Threshold algorithm – under development Daytime: 1845 Statistics -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Ratio CLC CLI CLR CLDCDC CDI BTH CLC CLI CDC CDI CLC CLI CDC CDI BSC NESDIS Night: 0645 Statistics -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Ratio CLC CLI CLR CLD CDC CDI BTH CLC CLI CDC CDI CLC CLI CDC CDI BSCNESDIS Cloud Detection Validation Case Study: September 11 – October 8, 2001 back

13. May 15, 2002MURI Hyperspectral Workshop13 MODIS IR C31 – 16:35 UTC 18 April 2002

14. May 15, 2002MURI Hyperspectral Workshop14