Assimilation of Geostationary Satellite Land Surface Skin Temperature Observations into the GEOS-5 Global Atmospheric Modeling and Assimilation System Clara Draper 1 , 2 , Rolf Reichle 2 , Gabrielle De Lannoy 1 , 2 , and Qing Liu 3 1. GESTAR, Universities Space Research Association. 2. Global Modeling and Assimilation Office, NASA Goddard Space Flight Center. 3. Science Systems and Applications, Inc. 6th WMO DA Symposium - October 9, 2013 1 / 16
Project outline ◮ Aim: assimilate geostationary T skin observations into the land surface of the GEOS-5 GCM/Atmospheric DA system ◮ Enhance assimilation of surface-sensitive atmospheric radiances ◮ Improve land surface flux forecasts ◮ Coupled GMAO’s EnKF-based land data assimilation system to the GEOS-5 GCM/ADAS 2 / 16
T surf in GEOS-5 Catchment land surface model ◮ Catchment T surf is the average temperature of the canopy and soil surface (represented by an arbitrarily thin layer with minimal heat capacity) LH + SH R N dT surf 1 T surf = shc ( R N − LH − SH − G ) dt R N = (1 − α ) R S ↓ + ... G ǫ ( R L ↓ − σ s ( T surf ) 4 ) Diffusive LH = r L ( e sat ( T surf ) − e air ) heat flux SH = r H ( T surf − T air ) b’ween soil layers Surface specific heat capacity (shc): 200 J/K (70,000 J/K for broad-leaf e’green) 3 / 16
Geostationary T skin data ◮ Near-real time geostationary T skin data set from NASA Langley Research Center (LaRC) ◮ TIR clear sky observation of the effective radiative temperature of the land surface ◮ GOES-E, GOES-W, Meteosat-9, MTSAT-2, FengYun-2E ◮ Comparable accuracy to MODIS (vs. in situ T skin ) ◮ Currently 3-hourly (clear sky) at 0.25 ◦ resolution GOES-E T skin vs. SGP ARM IRT MODIS T skin vs. SGP ARM IRT Scarino, B., Minnis, P., Palikonda, R.,Reichle, R., Morstad, D., Yost, C., Shan, B., and Liu, Q. (2013), Retrieving surface skin temperature for NWP applications from global geostationary satellite data, Rem. Sens. 4 / 16
Correcting forecast-observation biases in coupled LA-DAS 5 / 16
Forecast-observation biases Catchment − GOES−W Tskin August 2012, 18:00 UTC [K] Catchment − GOES−E Tskin August 2012, 18:00 UTC [K] 10 10 40 ° 40 ° 0 0 −10 −10 −120 ° − 80 ° −120 ° − 80 ° ◮ Large forecast-observation biases (ubiquitous in land DA) ◮ Unknown if bias is in forecasts and/or observations (likely both) ◮ Common in land DA to assign f’cast-obs bias to observations ◮ At least ensures that the f’cast and obs are not biased relative to each other ◮ Usual methods require long data record to estimate forecast and observed climatological statistics ◮ For assimilation into an atmospheric system (frequent model updates!), do not have a long data record 6 / 16
Observation bias and state estimation ◮ Similar to forecast bias correction of Dee and Todling [2000] ◮ State forecast and update: x − k , i = f ( x + k − 1 , i , q k , i ) x + k , i = x − k − b + k − H k x − y o k , i + K k (˜ k , i ) ( K is unchanged by inclusion of bias estimate) ◮ Bias forecast and update: b − k = b + k − 1 b + k = b − y o k − b − k − < H k x − k + L k (˜ k > ) ◮ Simplify by replacing L with empirical Λ, designed to update bias more aggressively when observations are available less frequently 7 / 16
The estimated bias Estimated f’cast-obs bias (K) after one month (30 June 2012) 06 UTC 12 UTC 18 UTC 00 UTC 8 / 16
Offline assimilation results 9 / 16
Comparison to in situ observations ◮ Assimilated 1 year of GOES-E/W over North America into GEOS-5 land surface model, forced with MERRA atmospheric analyses Anom. Corr. (USCRN Tair,Catch Tsurf), 15 UTC, # 60 Anomaly RMSD (K) to SURFRAD Tskin # 6 1 2.5 Anom. Corr. (Assm tile, USCRN), mean: 0.82 0.9 TIR Tskin assimilation (mean 1.51K) 0.8 0.7 2 0.6 0.5 0.4 1.5 0.3 0.2 0.1 1 0 1 1.5 2 2.5 0 0.2 0.4 0.6 0.8 1 Openloop (mean: 1.61K) Anom. Corr. (Open tile, USCRN), mean: 0.80 10 / 16
Comparison to MODIS T skin : Aqua asc. ( ∼ 18UTC) Anomaly RMSD (K) over JJA 2012 a) Offline GEOS-5 (mean: 2.9 K) b) GOES-E/W (mean: 2.5 K) Improvement from assimilation of bias-corrected GOES c) GOES-E/W bias corrected to GEOS-5 (mean: 3.5 K) (mean: 0.15 K, 65% +ve) 11 / 16
Comparison to MODIS T skin : Aqua dsc. ( ∼ 06UTC) Anomaly RMSD (K) over JJA 2012 a) Offline GEOS-5 (mean: 1.6 K) b) GOES-E/W (mean: 1.3 K) Improvement from assimilation of bias-corrected GOES c) GOES-E/W bias corrected to GEOS-5 (mean: 1.8 K) (mean: 0.14 K, 78% +ve) 12 / 16
Assimilation into the GEOS-5 atmospheric system 13 / 16
b b b b b b b b b b b b b b b 03Z 09Z 15Z 21z PREDICTOR ATM B’GROUND A-IAU ATM FORCING L-IAU CORRECTOR 14 / 16
Summary/conclusions ◮ Assimilated TIR skin temperature observations into GEOS-5 land surface model ◮ Introduced a simple observation bias and state estimation scheme for use with atmospheric system (does not require long data record) ◮ Offline assimilation of GOES TIR T skin , with f’cast-obs bias correction, shows consistent small improvement in model short-term variability ◮ Is correction of short-term variability enough to enhance atmospheric assimilation / improve land surface fluxes ???? ◮ Results with GEOS-5 GCM/ADAS pending... 15 / 16
THANK YOU FOR LISTENING. 16 / 16
Catchment surface energy states and fluxes dW R N = (1 − α ) R S ↓ + ǫ ( R L ↓ − σ s ( T X C ) 4 ) dt = R N − LH − SH − G LH = RESIST L ( e sat ( T X C ) − e air ) LH + SH SH = RESIST H ( T X C − T air ) R N T SURF = w ( T SAT , T TRANS , T WILT T SAT T TRANS T WILT ) C C C C C C C ) = ∆( W X ) ∆( T X ∆Z(m) G shc ( sfc ) 0.10 shc=200 J/K, TP 1 0.19 or 70,000 J/K for b-l e’green TP 2 0.38 TP 3 Diffusive ghtcnt n + icehct n TP n = 0.76 TP 4 heat flux shc ( rck )+ shc ( wtr )+ shc ( ice ) 1.5 Water assumed 0 . 5 φ . If no ice: TP 5 10.0 TP n = ghtcnt / (2269050∆ Z n ) TP 6 zero heat flux boundary 17 / 16
Two-stage observation bias and state estimation: simplified ◮ Replace L with empirical Λ: λ 1 0 0 λ 2 0 ... 0 λ n − 1 0 0 λ n where λ j = 1 − e − ∆ t j /τ j ◮ ∆ t is time since last observation ◮ τ is time scale of bias memory (10 days for T skin assim.) 18 / 16
Enhance impact by relaxing ‘clear-sky’ cloud fraction for TIR T skin ? Impact of cloud cover on GOES−E − SURFRAD Tskin anomaly Impact of cloud cover on GOES−E − SURFRAD Tskin anomaly 600 600 All retrieved GOES−E data All retrieved GOES−E data # of 0.25 ° grid cells (May 2012−Apr 2013) # of 0.25 ° grid cells (May 2012−Apr 2013) Cloud fraction < 5% Cloud fraction < 20% 500 500 400 400 300 300 200 200 100 100 0 0 −5 0 5 −5 0 5 GOES−E − SURFRAD Tskin anomaly (K) GOES−E − SURFRAD Tskin anomaly (K) 19 / 16
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