1. Vegetation and O2-O2 cloud research products for TEMPO Joanna Joiner, contributions from Eun-Su Yang, Sasha Vasilkov, Yasuko Yoshida, Nick Krotkov, Dave Haffner, Wenhan Qin

2. Vegetation (and ocean) products Chlorophyll fluorescence (land and ocean, red and far- red bands) Various spectral indices, but not the most commonly used (not traditional NDVI nor PRI) Directional Area Scattering Function (DASF, Knyazikhin et al., 2013) – only works where there is no soil contamination, fit gives guidance on where it works 6/02/16Joiner - TEMPO science team meeting 2

3. Solar-induced fluorescence (SIF) from chlorophyll There is a great interest in measurements throughout the data of SIF in both land and ocean made with the same instrument TEMPO and Sentinel 4 will be the first instruments to make such SIF measurements over land; GOCI is already measuring ocean SIF (does not require high spectral resolution) TEMPO will have unique capability to measure both red and far-red SIF hourly! 6/02/16Joiner - TEMPO science team meeting 3

4. Improving the monitoring of crop producitivity using spaceborne solar-induced fluorescence (SIF) Provides framework to link SIF and crop yield, accounting for respiration losses Apply to US crop productivity 2007- 2012 with GOME-2 SIF SIF-based approach provides best measure of crop productivity (compared with traditional approaches) SIF provides ability to infer impacts of environmental stresses on respiration and carbon-use efficiency (CUE), with good sensitivity to high temperatures (negative impact on CUE). Guan et al., Glob. Change Biol., 2015

5. Drought monitoring Studies on the 2010 Russian drought (Yoshida et al., 2015), 2011 Texas drought, and 2012 great plains drought (Sun et al., 2015, Wang et al., 2016) show declines in structure (chlorophyll amount, LAI, etc.) or fraction of absorbed PAR additional decline due to physiology (as indicated by fluorescence normalized by absorbed PAR)

6. SIF and how we measure it 6/02/16Joiner - TEMPO science team meeting 6 TEMPO Sent 4

7. New developments in SIF (red SIF retrieval) GOME-2 Dec 2009 GOME-2 July 2009 6/02/16Joiner - TEMPO science team meeting 7 Ground-based studies show that red SIF should be complementary to far- red SIF, more sensitive to physiological effects Red SIF is really difficult to measure (extremely small signals, impacted by instrumental artifacts) Follows from some work Sasha Vasilkov did looking at the oxygen gamma band for GEO-CAPE. We use this band to help reduce noise in red SIF retrievals that use the O2 B-band Drought-induced red SIF anomalies look similar to far-red anomalies Joiner et al., Atmos. Meas. Tech., 2016.

8. Spatial downscaling of GOME-2 SIF using MODIS information Improved estimates of Gross Primary Production (GPP)? with better linkages to plant functional types Duveiller and Cescatti, Rem. Sens. Env., 2016 Coarse resolution GOME-2 SIF Fine resolution downscaled SIF

9. Recent developments (ocean SIF) GOME-2 MODIS Useful for red tide detection, studies on physiology of phytoplankton Uses whole range of SIF emissions from 660-712 nm to achieve high precision and accuracy (appears not very sensitive to instrumental and other artifacts) Algorithms exist and can be delivered 6/02/16Joiner - TEMPO science team meeting 9

10. Cloud O2-O2 algorithm development at NASA GSFC Developed for use in NASA end-to-end OMI NO2 algorithm, can serve as backup cloud algorithm for TEMPO Latest temperature-dependent absorption cross-sections of O2-O2 collision pairs New spectral fitting similar to Marchenko et al. NO2, uses independently retrieved O2, NO2, and H2O slant columns Temperature profiles and surface pressures for computation of air densities are taken from the GMI model. The algorithm uses VLIDORT-driven AMF and radiance tables to derive effective cloud fraction and pressure Computationally fast (a few minutes per orbit on a single CPU) 6/02/16Joiner - TEMPO science team meeting 10

11. Cloud O2-O2 algorithm: Mixed Lambertian equivalent Reflectivity (MLER) model Consistent with existing trace-gas and cloud algorithms (plug and play) Incorporates new geometry-dependent surface LER derived from MODIS BRDF data for land and the Cox-Munk & a model of water-leaving radiance for ocean Effective cloud fraction (ECF) computed at 466 nm (with minimum interference from RRS and ozone absorption) The optical centroid pressure (OCP) is derived from a match of the measured SCD to that calculated with MLER at 477 nm 6/02/16Joiner - TEMPO science team meeting 11

12. Good agreement with the operational OMI O2-O2 cloud algorithm Excellent agreement of Effective Cloud Fractions (ECFs) Good agreement of cloud Optical Centroid Pressures (OCPs) for ECF > 0.2 Land Ocean 0.2<ECF<0.40.6<ECF<0.8

13. Effects of surface BRDF on cloud retrievals OMI orb 12414 LER differences at 466 nm (geometry-dependent LER minus climatological LER) result in OCP differences ECF differences0.05<ECF<0.25