Land Surface Temperature Development and Validation for GOES-R Mission

1. Topics: Future Satellite Mission Land Surface Temperature Development and Validation for GOES-R Mission Yunyue Yu (STAR) Peng Yu, Yuling Liu, Kostya Vinnikov (UMD/CICS) Rob Hale (CSU/CIRA) Dan Tarpley (Short & Associates) GOES-R AWG Yunyue.Yu@noaa.gov

2. 2. Introduction Land Surface Temperature (LST) is one of baseline products for the GOES-R Mission LST algorithm has been developed and tested at STAR, and is implementing at Vender (Harris Corporation) Issues in the LST data • Satellite data issues: observation geometry, Instrument noise/stability • Ground data issues: emissivity variation, Instrument noise/stability • Others: temporal and spatial variability, cloud impacts Validation needs • discrimination among the above problems as much as possible • use real-time cal/val info from other products to identify problem cascades (instrument noise > cloud detection > LST) • need parallel cal/val system for ground observations

3. 3. Methodology/Expected Outcomes Validation approach of the LST data has been developed at STAR: • MODIS, SEVIRI data used as proxy • Utilize existing ground station data • Stations under GOES-R Imager coverage • Stations under MSG/SEVIRI coverage • Ground site characterization • Stringent cloud filtering • Multiple comparisons: satellite vs satellite, satellite vs ground station. • Direct and indirect comparisons • International cooperation Validation Outcomes: Routing validation, Deep-dive validation

4. START Flow chart of the LST validation system User input: Sensor, stations, period MODIS SURFRAD SEVIRI Satellite data reader and cloud filtering Read in TPW, Emissivity, etc. Ground data reader and cloud filtering CRN Others Others Geo-location matchup Ancillary Preprocessed data package Time matchup Yes Output graphics and statistics Problems LST calculation and analysis Visualization NO Additional Cloud filtering End Deep Diving

5. 4. Results Validation Tool Widget Routine Validation tool applied for comparing GOES-R LST (top-right, derived from MODIS as proxy), and the MODIS LST (top-left). Map of Difference (bottom-left) and histogram of the difference (bottom-right) are also displayed.

6. Routine Validation -- SURFRAD data results Comparison results of GOES-8 LST (as proxy) using six SURFRAD ground station data, in 2001. Numbers (Table, left) and scatter plots (right) of the match-up LSTs derived from GOES-8 Imager data vs. LSTs estimated from SURFRAD stations in year 2001. Data sets in plots are stratified for daytime (red) and night time (blue) atmospheric conditions

7. T(x,y,t) T(x0,y0,t0) ”Deep-Dive” Validation The Synthetic pixel/sub-pixel model Site characterization analysis using ASTER data— an integrated approach for understanding site representativeness and for site-to-pixel model development • Quantitatively characterize the sub-pixel heterogeneity and evaluate whether a ground site is adequately representative for the satellite pixel. The sub-pixels may be generated from pixels of a higher-resolution satellite. • For pixel that is relatively homogeneous, analyze statistical relationship of the ground-site sub-pixel with the surrounding sub-pixels: • {T(x,y) } ~ T(x0,y0) • Establish relationship between the objective pixel and its sub-pixels (i.e., up-scaling model), e.g., Tpixel = T(x,y) + DT (time dependent?) ASTER pixel MODIS pixel The site pixel Surface heterogeneity is shown in a 4km x 4km Google map (1km x 1km, in the center box) around the Bondville station area Site-to-Pixel Statistical Relationship for 5 SURFRAD sites

8. ”Deep-Dive” Validation Tools-- Directional effect study Due to the satellite LST directional properties (surface components, topography, shadowing etc.), the satellite LST can be significantly different from different view angles. Deep dive validation tools may be used for case studies and improved algorithms. Goodwin Creek, MS, observation pairs are about 510. View Zenith of GOES-8/-10: 42.680/61.890

9. 5.Possible Path to Operations The validation tools should be considered as non-operational, or semi-operational. Rather, it is for LST developers and users. A prototype development for the validation tool • Scientific approaches • Test data sets: satellite proxy and ground data Case studies for testing the improvement Technical Detail Documentation development Software design and architecture, coding standard

10. 6. Future Plans Improvement of site characterization model Provide LST improvement approach Case study of emissivity variation impact in LST and correction method Validation visualization tool improvement Additional Cloud filtering method Field data collection and processing

11. 7. Publication List Project Publications K. Vinnikov, Y. Yu, M. Goldberg, D. Tarpley, P. Romanov, I. Laszlo, M. Chen, “ Angular Anisotropy of Land Surface Temperature”, Geophysical Research Lett. VOL. 39, L23802, doi:10.1029/2012GL054059, 2012 H. Xu, Y. Yu, D. Tarpley, F-M. Göttsche and F-S. Olesen “Evaluation of GOES-R Land Surface Temperature Algorithm Using SEVIRI Satellite Retrievals with in-situ Measurements”. IEEE Geoscience and Remote Sensing, in revision, 2012 (Sept). Y. Liu, Y. Yu, D. Sun, D. Tarpley, L. Fang, “Effect of Different MODIS Emissivity Products on Land-Surface Temperature Retrieval From GOES Series”, IEEE Geoscience and Remote Sensing, in press, 2012 D. Sun, Y. Yu, H. Yang, Q. Liu, J. Shi, “Comparison between GOES East and West for Land Surface Temperature Retrieval from a Dual-Window Algorithm”, IEEE Geoscience and Remote Sensing Lett, in press, 2012 Yu, Y; Tarpley, D.;   Privette, J. L.;   Flynn, L. E.;   Xu, H.;   Chen, M.;   Vinnikov, K. Y.;   Sun, D.;   Tian, Y., Validation of GOES-R Satellite Land Surface Temperature Algorithm Using SURFRADGround Measurements and Statistical Estimates of Error Properties,IEEE Trans. Geosci. Remote Sens., vol. 50, No. 3, pp. 704-713, 2012DOI: 10.1109/TGRS.2011.2162338 Hale, Robert; Gallo, Kevin; Tarpley, Dan; Yu, Yunyue, Characterization of variability at in situ locations for calibration/validation of satellite-derived land surface temperature  data, REMOTE SENSING LETTERS Vol. 2, Issue: 1, 2011 DOI: 10.1080/01431161.2010.490569 Gallo, Kevin, Robert Hale, Dan Tarpley, Yunyue Yu,Evaluation of the Relationship between Air and Land Surface Temperature under Clear- and Cloudy-Sky Conditions, Journal of Applied Meteorology and Climatology, Volume: 50 Issue: 3 Pages: 767-775, 2011 . DOI: 10.1175/2010jamc2460.1 Vinnikov, K. Y.; Yu, Y.; Goldberg, M. D.; et al, Scales of temporal and spatial variability of midlatitude land surface temperature, Journal of Geophysical Research-Atmospheres, Volume: 116 , 2011 DOI: D02105 10.1029/2010jd014868 Yu, Y., D. Tarpley, J. L. Privette, M. K. Rama Varna Raja, K. Vinnikov, H. Xue, Developing algorithm for operational GOES-R land surface temperature product, IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 936-951., 2009DOI: 10.1109/tgrs.2008.2006180