GOES-R AWG & Risk Reduction Review Meeting, University of Maryland, 20-24 July, 2009

1. GOES-R AWG & Risk Reduction Review Meeting, University of Maryland, 20-24 July, 2009 Developing an Algorithm for Validation of GOES-R Retrieved Land Surface Temperature Using SURFRAD Observed LST as Ground Truth and Taking into Account Systematic Diurnal-Seasonal Cycles in the Differences Konstantin Y. Vinnikov (University of Maryland), Yunyue Yu, Ming Chen, Dan Tarpley, Kevin Gallo, and Mitchell D. Goldberg (NOAA/NESDIS) AWG LAND TEAM Testing Three- Observational Validation Approach: GOES-8 vs. SURFRAD vs. GOES-10. DATA: 2001 Assessment of Opportunity to Use CRN Observed LST as Ground Truth to Validate GOES-R Observed LST Data Testing Two-Observational Validation Approach: GOES-10 vs. SURFRAD. DATA: 2001, 2004, 2005 CLR Sky. Bondville, IL. 40.05oN, 88.37oW RAW VALIDATION DIFFERENCES OF GOES-10 AND SURFRAD OBSERVRD LST CLR Sky. Desert Rock, NV. 36.63ºN, 116.02ºW • Three-Observational Validation Strategy • Select land based stations with observation of LST (SURFRAD and CRN). • Use three instantaneous LST observations at surface station yS (t) and two satellites GOES-W yW(t), & GOES-E yE(t). • Compute three differences between each pair of independently observed LST for each time: yW(t)-yS (t), yE(t)-yS(t), & yE(t)-yW(t). • Monitor these mean differences and their standard deviations in the moving time window. The optimal width of the window and the acceptable thresholds for these parameters should be determined. • As soon as at least one full year of observed data is available, it should be used to obtain estimates of time dependent expected values YS(t), YW(t), & YE(t). • Compute residuals rS(t)=yS(t)-YS(t), rW(t)=yW(t)-YW(t), & rE(t)=yE(t)-YE(t) and differences of the residuals: rW(t)-rS(t), rE(t)-rS(t), & rE(t)-rW(t). • Monitor these mean residual differences and their standard deviations in the moving time window. DIURNAL-SEASONAL CYCLES ADJUSTED LST VALIDATION DIFFERENCES CLR Sky. Fort Peck, MT. 48.31ºN, 105.10ºW Approximation of diurnal-annual cycles in Land Surface Temperature y(t)=Y(t)+y’(t)+ε(t). (1) y(t) – observed LST, Y(t) – time (t) dependent expected value (diurnal and annual cycles), y’(t) – weather/ climate related signal, ε(t) – random error of observation. The mathematical model for approximation of Y(t) is the same as in [Vinnikov & Grody, 2003; Vinnikov et al., 2004; Vinnikov et al., 2006], but without trend component: CLR Sky. Sioux Fall, SD. 43.81ºN, 96.95ºW THREE YEAR (2001, 2004 & 2005) STATISTICS OF CLEAR SKY LAND SURFACE TEMPERATURE SIMULTANEOUSELY OBSERVED AT GOES-10 SATELLITE AND AT SIX SURFRAD STATIONS. Version 2 of Clear Sky Detection Algorithm by Ming Chen et al. (2009) Nobs– is Number of observations; SD(TG-TS) – is Standard Deviation of LST validation difference; SD(TG’-TS’) – is Standard Deviation of diurnal/seasonal cycles adjusted LST validation difference; SD(<TG-TS>) – is Standard Deviation of diurnal/seasonal component LST validation difference; Two harmonics of annual and diurnal periods (N=K=2) are used to describe the annual (T=1 year) and diurnal (H=1 day) variation in the expected value of the land surface temperature, Y(t). Estimates of Standard Deviations (σ) and Standard Errors (δ) of LST Observations at SURFRAD Stations, and Satellites GOES-8 & GOES-10 • CONCLUSIONS: • Estimated Standard Errors of LST observation at SURFRAD stations are found to be in the range of 0.6-1.3ºC. Standard Errors of GOES-E and GOES-W LST observation at location of these stations are found to be almost in the same range of 0.7-1.3ºC. All these random errors of observation are relatively small compared to Standard Deviation of LST signal which is in the range of 3.0-5.1ºC. • Systematic (bias) differences between SURFRAD and GOES-E & W observed LST have noticeable diurnal-seasonal cycles that are evaluated using observations of GOES-8 & 10 GOES satellites, the predecessors to GOES-R. This systematic diurnal-seasonal cycles are found to be equivalent to random error with Standard Deviation in range of 0.6-1.0ºC. • Raw validation differences, LSTSURFRAD – LSTGOES, have standard deviations in the range 1.3-1.9ºC. The adjustment of these differences for systematic diurnal-seasonal cycles decreases their standard deviations to 1.0-1.7ºC. The adjusted validation differences can be analyzed as zero mean normally distributed random errors. • Analysis of multi-year parallel observation of LST at four collocated Climate Reference Network (CRN) and SURFRAD stations revealed significant irregularities and instabilities in CRN LST records. Currently unavailable, five-minute averages of LST observed at CRN stations should be included in the list of archived variables. They could be better alternative to currently archived one hourly averages. More work on quality control, validation and improvement of CRN LST observation itself is needed before it can be used as ground truth for validation of satellite observed LST. • Testing of three-observational approach should be continued using more data. More observation of GOES-8 / SURFRAD observation is needed to improve statistics of diurnal-seasonal LST variations that is needed to increase GOES-R retrieved LST validation accuracy. Theory of errors: y1(t)=Y1(t)+y’(t)+ ε1(t), SURFRAD y2(t)=Y2(t)+y’(t)+ ε2(t), GOES-W y3(t)=Y3(t)+y’(t)+ ε3(t). GOES-E VAR[y1(t)-Y1(t)-y2(t)+Y2(t)]=δ12+δ22, VAR[y2(t)-Y2(t)-y3(t)+Y3(t)]=δ22+δ32, VAR[y3(t)-Y3(t)-y1(t)+Y1(t)]=δ32+δ12. LST Variance: σ2=E(y’2); Variances of Observation Errors: δ12 =E(ε12), δ22=E(ε22), δ32=E(ε32). Empirical estimates of Standard Errors in SURFRAD and GOES satellites observed LST. Data: Hourly observations for 2001: Version 1 of Clear Sky Detection Algorithm - Manual screening by Rama Varma Raja. M. K. et al. (2008) Communicating author: Kostya Vinnikov, kostya@atmos.umd.edu, 301-405-5382