1. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Aerosol horizontal structure – variability Jens Redemann (BAER Institute) + J-31 team (Preliminary results – do not cite)

2. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Scientific motivation for studying aerosol spatial variability  Spatial variability of aerosol properties in the vicinity of clouds → cloud haloes → aerosol indirect effect  Spatial variability in satellite radiances is used in cloud-screening → contributions of aerosol variability to radiance variability? → separation of aerosol and cloud signal?  Comparison of aerosol spatial variability as seen by satellite and suborbital sensors gives insight into satellite retrieval performance (e.g., surface vs. aerosol variance)  Feasibility of new satellite sensor designs

3. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Example 1: Spatial variability of AOD in the vicinity of clouds – cloud haloes or cloud contamination?

4. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Spatial variability of AOD in the vicinity of clouds – cloud haloes or cloud contamination? -1- …but, what about cloud haloes?

5. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Low-level flight segments (<100m) to study spatial variability in AOD and in situ properties, ACE-Asia 2001

6. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Example: Water vapor, in situ aerosol extinction and AOD during low-level flight leg in ACE-Asia

7. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Auto-correlation:

8. J31 Flight 8, 15 July 2004 MODIS AOD, COT & J31 Track: L. Gumley, A. Chu, C. Kittaka, B. Pierce 0.0 0.2 0.4 0.6 0.8 1.0 Aerosol Optical Depth (550 nm) 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Cloud Optical Thickness (670 nm)

9. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005

12. Approach for aerosol spatial variability work Identify all J-31 low-level legs Identify all J-31 low-level legs Compute metrics for spatial variability, e.g., auto- correlation, structure functions in suborbital AOD Compute metrics for spatial variability, e.g., auto- correlation, structure functions in suborbital AOD Compare spatial variabilities in ICARTT with other field campaigns [first US East Coast then different geographical regions – different proximities to aerosol source regions] Compare spatial variabilities in ICARTT with other field campaigns [first US East Coast then different geographical regions – different proximities to aerosol source regions] Find subsets of J-31 data that have satellite coincidences with MISR and MODIS data and compare spatial variabilities Find subsets of J-31 data that have satellite coincidences with MISR and MODIS data and compare spatial variabilities Fine-comb data for cloud haloes Fine-comb data for cloud haloes

13. Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005