Cloud and Aerosol Products From GIFTS/IOMI

1. 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 MURI Hyperspectral Workshop

2. 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 • ~2.8K spatial pixel deviation (edge detection) • ~2.1K adjacent pixel (element direction) change (fills in clouds) • 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 MURI Hyperspectral Workshop

3. Cloud and Aerosol Products • Parameter retrieval • Cloud height (CTP) – infrared look-up with model guess for GOES imager and opaque clouds • Easy to implement, uses model T(p) as a reference • Highly accurate for opaque clouds • CO2 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) • DISORT model (Ricchiazzi et al. 1998) used to generate look up tables describing radiance, AOT, ,  • Correlation with sun photometer data as high as 0.97 chart MURI Hyperspectral Workshop

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

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

6. 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 MURI Hyperspectral Workshop

7. Satellite-derived Wind Errors • 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! MURI Hyperspectral Workshop

8. Imaging Requirements for Cloud-drift Winds 16.0 8.0 4.0 2.0 2000 1.0 1000 500 250 0.5 125 63 Image Interval, Resolution, and Registration Accuracy Constraints TELL =(R*)/ • 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 () TELL Surface of 0.55 • GOES-R •  = 15 min • R = 0.125 • = 4km TELL = 0.55 60 55 50 45 40  - Image Separation Time (min) 35 30 1 25 20 15 10 2 3 5 0  - Image Resolution (km) R - Image Registration Accuracy (m) MURI Hyperspectral Workshop

9. Wind Tracking Error Emphasis • Refine Tracking Error Lower Limit (TELL) for GIFTS • Instrument characteristics • Observing scenarios MURI Hyperspectral Workshop

10. 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 MURI Hyperspectral Workshop

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

12. Cloud Detection Validation Case Study: September 11 – October 8, 2001 • 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 Night: 0645 Statistics Daytime: 1845 Statistics 1 1 0.9 0.9 BTH BSC BTH BSC NESDIS NESDIS 0.8 0.8 0.7 0.7 CLD CDC CDC 0.6 0.6 CLC CLR CLC CDC CLD CDC 0.5 0.5 CLC CLC CLR 0.4 0.4 CDC CLC 0.3 Ratio Ratio 0.3 CLC 0.2 0.2 CDC 0.1 0.1 0 0 CLI CLI CLI CDI CLI CDI CDI -0.1 -0.1 CDI -0.2 -0.2 CDI -0.3 -0.3 CLI CLI -0.4 -0.4 CDI -0.5 -0.5 MURI Hyperspectral Workshop back

13. MODIS IR C31 – 16:35 UTC 18 April 2002 MURI Hyperspectral Workshop

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