Acknowledging: Paul R. Houser (CREW & GMU)

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1 Recent Advances in Land Surace Data Assimilation Acknowledging: Paul R. Houser (CREW & GMU) Yan Luo, Xiwu Zhan, Je Walker, Brian Cosgrove, Jared Entin, Jiarui Dong, Alok Sahoo, Gabrielle de Lannoy Water Cycle Research Making a Dierence Paul R. Houser,4 June 28, Page 1

2 ET and Precip Background: Land Surace Modeling Iniltration Land Surace Prediction: Accurate land model prediction is essential to enable data assimilation methods to propagate or extend scarce observations in time and space. Based on water and energy balance. Input - Output = Storage Change P + Gin (Q + ET + Gout) = ΔS Rn - G = Le + H Subsurace Flow Source Area Overland Flow Total Flow e a r a r c r d E r sur e * (T s ) ABL Canopy MPr r plant r soil Soil r a H Dominant land surace horizontal processes: Groundwater movement Horizontal temperature/water diusion/advection Runo Assume 1-D Physics at mesoscales (greater than 1m) Gravity and gradient driven water & energy movement Horizontal processes very weak Observed horizontal correlations related to orcing Perturbation in state will not change neighbor Ramiications o 1-D assumption: 1-D (vertical) assimilation very cheap No account or horizontal correlation in observation/model No advection o observation inormation horizontally Land observations mostly at surace Surace skin temperature, soil moisture Snow cover Want to retrieve ull root-zone proile; longer memory states Nonlinear Processes Freeze/Thaw, Iniltration, Interception, Snow Cover, Lea Fall Diicult to linearize, derive adjoint, etc. Paul R. Houser, 4 June 28, Page 2

3 Land Surace Observation O-line LDAS Forcing Precipitation Wind Humidity Radiation Air Temperature Validation Fluxes Evapotranspiration Sensible Heat Flux Radiation Runo Drainage Calibration Parameters Soil Properties Vegetation Properties Elevation & Topography Subgrid Variation Catchment Delineation River Connectivity Soil Moisture Snow, Ice, Rainall Radiation orcing Vegetation Snow States Assimilation Soil Moisture Temperature Snow Carbon Nitrogen Biomass Paul R. Houser, 4 June 28, Page 3

4 Land Surace Data Assimilation Summary Data Assimilation merges observations & model predictions to provide a superior state estimate. Remotely-sensed hydrologic state t or storage observations (temperature, t snow, soil moisture) ) are integrated t into a hydrologic model to improve prediction, produce research-quality data sets, and to enhance understanding. Soil Moisture Assimilation Snow Cover Assimilation Theory Development Insertion o Data into the Model Data Model Integration x t = dynamics + physics +Δx Skin Temperature Assimilation Snow Water Assimilation Assimilation with Bias Correction SSM/I Snow Observation Observation No Assimilation Assimilation Also: Runo, Evapotranspiration, groundwater (gravity), and Carbon Assimilation Paul R. Houser, 4 June 28, Page 4

5 Soil Moisture Observation Error and Resolution Sensitivity: Mo oisture RMS Er rror (% v/v) surace (assimilation) root zone (assimilation) proile (assimilation) surace (no assimilation) root zone (no assimilation) il proile (no assimilation) NOTE: Assimilation o near-surace soil moisture can degrade proile soil moisture i errors are not known perectly Mo oisture RMS Er rror (% v/v) surace (assimilation) root zone (assimilation) proile (assimilation) surace (no assimilation) root zone (no assimilation) proile (no assimilation) Observation Error (% v/v) Temporal Resolution (days) Soil Moisture Error (% v/v) 2 1 obs bias (v/v) obs rms (v/v) Moisture RMS Error (% v/v) surace (assimilation) root zone (assimilation) il proile (assimilation) surace (no assimilation) root zone (no assimilation) proile (no assimilation) Spatial Resolution (minutes o arc) Spatial Resolution (minutes o arc) Paul R. Houser, 4 June 28, Page 5

6 Soil Moisture Data Assimilation State and bias iltering: which requency is optimal? State and bias iltering extracts more ino rom observations less requent updating required Obs Ens orecast EnBKF Obs Only state estimation CLM2. + OPE3 data State + bias estimation Every day (a) Every 2 days (b) Every 4 days (c) Every week (d) Every 2 weeks (e) Every month () Every 2 months (g) De Lannoy, G.J.M., Houser, P.R., Pauwels, V.R.N., Verhoest, N.E.C. (26). State and bias estimation or soil moisture proiles by an ensemble Kalman ilter: eect o assimilation depth and requency. Water Resources Research, 43(6), W641, doi:1.129/26wr51. Paul R. Houser, 4 June 28, Page 6

7 Soil Moisture Data Assimilation Adaptive iltering: retrieval o o-diagonal error covar elements Training period Application period Paul R. Houser, 4 June 28, Page 7

8 Soil Moisture Data Assimilation Adaptive iltering: result when assimilating only 1 proile Assimilation il i only at arrow location EnKF: only eect at arrow location, other locations are ens mean integration without update (zero oblock diagonal P - ) ADEnKF: eect o DA at 1 location is spread over total ield De Lannoy, G.J.M., Houser, P.R., Verhoest, N.E.C., Pauwels, V.R.N. (28). Adaptive soil moisture proile iltering or horizontal inormation propagation in the independent column-based CLM2., Journal o Hydrometeorology, under review. Paul R. Houser, 4 June 28, Page 8

9 AMSR-E & Model Soil Moisture Evaluation Averaged soil moisture plot rom 17 sites (SMEX3-Georgia) over AMSR-E 1/4 degree grid. Noah (1 cm and 5 cm layer SM), CLM (4.5 cm layer, layer 1+ layer 2), SCAN (just one station, 5 cm), AMSR-E (2 cm layer), SMEX3 (6 cm layer). 35 Soil Moistur re (% vol/vol) Noah_grid3_Soil Moisture CLM_grid3_Soil il Moisture12 SCAN_GA37_Soil Moisture Amsre_Soil Moisture smex3_sm_6cm_ daily 1 Noah_grid3_5cm_ Soil Moisture 5 1-Jun 8-Jun 15-Jun 22-Jun 29-Jun 6-Jul 13-Jul 2-Jul 27-Jul Paul R. Houser, 4 June 28, Page 9

10 Assimilation o AMSR-E Land Products into the NOAH LSM Paul Houser, Yan Luo, Xiwu Zhan, Alok Sahoo, Kristi Arsenault, Brian Cosgrove Noah Model (no assimilation) Unscaled AMSR-E Soil Moisture Unscaled AMSR-E SM Assimilation GOAL: Implement Kl Kalman Filter to assimilate ilt land satellite data products into the Noah land surace model installed in the Land Inormation System (LIS) PROGRESS: Three data assimilation algorithms (DI, EKF, EnKF) have been implemented in LIS and has been tested with various soil moisture observations.4 CDF Matching 1. Quandary: Oicial i AMSR-E soil moisture product has very low variability,.8 wich produces an assimilated product.6 with low variability.2. FUTURE: Soil Moisture [v/v] Expand validation o assimilation results. Optimize ensemble perturbation procedures Scaled AMSR-E Soil Moisture Scaled AMSR-E SM Assimilation Finalize AMSR-E scaling philosophy Explore brightness temperature assimilation (CRTM) Expand to snow cover assimilation amsr amsr_c model CDF Matching: Scales AMSR-E to model climatology, erasing any real variability in AMSR-E Paul R. Houser, 4 June 28, Page 1

11 Bias Correction Method: Dee and Silva (1998) & Dee and Todling (2) Estimating Bias: b b t a a = μ bt 1 = b L[ y o ( Hx Hb )] L = P bias H T ( HP bias H T + HP H T + R ) 1 Correcting Bias: ~ y x o a o a = y + Hb = x + K[ ~ y K = P H T o ( HP Hx H T ] + R) 1 a. Full Scheme b. Approximate Scheme bias P = γ *P L = α * K < μ, γ, α 1 tunable bias correction parameters Paul R. Houser, 4 June 28, Page 11

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14 EnKF data assimilation with model climatology ke but to correct observation error AMSR_E Noah Forecast Corrected AMSR_E Observation bias Paul R. Houser, 4 June 28, Page 14

15 (% vol/vol) Soil Moisture 3 5 In-situ Precip/Irrigation (a) Aternoon SM Time Series Plot LSMEM SM In-situ SM AMSR-E SM 1-Jan 31-Jan 2-Mar 1-Apr 1-May 31-May 3-Jun 3-Jul 29-Aug 28-Sep 28-Oct 27-Nov 27-Dec Date (b) Corr. =.54 RMSE = 6.8 % v/v ) Daily Precipitation an nd Irrigation (mm (c) Corr. =.81 (a) Time series plot o the in-situ, AMSR-E and LSMEM SM at 13 hr along with daily average precipitation and irrigation or 23. Scatter plot o the corresponding (b) insitu and AMSR-E soil moisture, (c) () in-situ and LSMEM soil moisture data. RMSE = 3.5 % v/v Paul R. Houser, 4 June 28, Page 15

16 Soil Moisture (% vol/vol) SM Time Series Plot openloop_spinup In-Situ LSMEM enk_spinup Daily top layer soil moisture time series plots rom Noah (1 cm layer) open loop and EnKF simulations, in-situ measurements (5 cm layer) and LSMEM retrieval (~ 1 cm). 5 1-Jan 5-Feb 12-Mar 16-Apr 21-May 25-Jun 3-Jul 3-Sep 8-Oct 12-Nov 17-Dec Date Noah Open Loop Soil Moisture (% vol/vo ol) (a) Open Loop N: 248 Bias: 11.1 RMSD: R: LSMEM Observed Soil Moisture (% vol/vol) Noah EnKF Soil Moisture (% vol/vol) (b) EnKF N: 248 Bias: 2.26 RMSD: 2.97 R: Paul LSMEM R. Observed Houser, Soil 4 June Moisture 28, (% vol/vol) Page 16

17 SMMR Snow Retrieval Error & Assimilation Impact Error due to Error due to Error due to signal saturation snowpack liquid water body contamination Dong et al., 25, 26 Paul R. Houser, 4 June 28, Page 17

18 Land Surace Data Assimilation: Summary Progress: Soil moisture, skin temperature, t and snow assimilation il have been demonstrated. t d Evapotranspiration, runo, groundwater (gravity), and carbon assimilation are underway Lessons Learned: We need to pay attention to the consequences o assimilation, not just the optimum assimilation technique. i.e. does the model do silly things as a result o assimilation, as in snow assimilation example. Land model physics can be biased, leading to incorrect luxes, given correct states. t Most land observations are only available at the surace, meaning that biased dierences in surace observations and predictions can be improperly propagated to depth. Assimilation does not always make everything in the model better. In the case o skin temperature assimilation into an uncoupled model, biased air temperatures caused unreasonable near surace gradients to occur using assimilation that lead to questionable surace luxes. Near Future Directions: Methods to address simultaneous model and observation bias. New observations (SMOS, Aquarius, SMAP, etc.). Coupled Assimilation (to avoid uncoupled biases). Mass/Energy conserving data assimilation techniques? Paul R. Houser, 4 June 28, Page 18