1. Land Surface Temperature Development and Validation for GOES-R 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 March 2013 1 Yunyue.Yu@noaa.gov Topics: Future Satellite Mission

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 2

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 3

4. March 2013 4 Deep Diving End Problems START User input: Sensor, stations, period Ground data reader and cloud filtering Geo-location matchup Time matchup LST calculation and analysis Read in TPW, Emissivity, etc. Satellite data reader and cloud filtering MODISSURFRAD Output graphics and statistics SEVIRI Others CRN Others Additional Cloud filtering Ancillary Preprocessed data package Visualization Flow chart of the LST validation system NO Yes

5. 4. Results 5 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. Validation Tool Widget

6. Routine Validation -- SURFRAD data results 6 Comparison results of GOES-8 LST (as proxy) using six SURFRAD ground station data, in 2001. Month Site 1Site 2Site 3Site 4Site 5Site 6 DayNightDayNightDayNightDayNightDayNightDayNight 148175564107 58481006710051 2433038 7357553441546448 300517194629379416312334 49418398081346261815713935 571222759127657583824516875 650268210282516550866418760 76491774082738404317374 84330129113413882137564510752 9115579510812449791101168518998 10103386295184399079745811561 1111434431291467064531165313188 12403867661247173701077411356 Total727314779100212236518238429407081609732 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. 7 T(x,y,t) T(x 0,y 0,t 0 ) ASTER pixelThe site pixelMODIS pixel  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(x 0,y 0 )  Establish relationship between the objective pixel and its sub-pixels (i.e., up-scaling model), e.g., T pixel = T(x,y) +  T (time dependent?) Site characterization analysis using ASTER data— an integrated approach for understanding site representativeness and for site- to-pixel model development Surface heterogeneity is shown in a 4km x 4km Google map (1km x 1km, in the center box) around the Bondville station area The Synthetic pixel/sub-pixel model Site MODIS Pixel- SURFRAD Synthetic Pixel- SURFRAD Nearest Aster Pixel – Synthetic pixel MeanStdDevMeanStdDevMeanStdDev Desert Rock, NV-0.441.842.091.690.040.44 Boulder,CO-0.492.081.492.90-0.090.67 Fort Peck, MT0.352.520.781.950.381.15 Bondville, IL-0.171.511.041.480.030.90 Penn State, PA-1.531.910.611.960.041.07 Site-to-Pixel Statistical Relationship for 5 SURFRAD sites ”Deep-Dive” Validation

8. EOGC 2009, May 25-29, 2009 8 Goodwin Creek, MS, observation pairs are about 510. View Zenith of GOES-8/-10: 42.68 0 /61.89 0 ”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.

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 9

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 10

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, 2012 DOI: 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., 2009 DOI: 10.1109/tgrs.2008.2006180 11