Estimation via Data Assimilation Using. Mississippi State University GeoResources Institute

Transcription

1 High Resolution Soil Moisture Estimation via Data Assimilation Using NASA Land Information System Mississippi State University GeoResources Institute

2 LIS Evaluation Team & Collaborators RPC Team Valentine Anantharaj, Georgy Mostovoy, Nicholas Younan, and Anish Turlapathy Christa Peters-Lidard (NASA GSFC HSB) Paul Houser and Yan Luo (GMU CREW) Bailing Li and Sujay Kumar (NASA GSFC) Collaborators and Consultants USDA NRCS MSU DREC and USDA (Stoneville, MS) NASA Review (4/18/08) 2

3 Identified Needs of USDA NRCS Routine analysis soil moisture over the continental needs water soils sun weather climate vegetation terrain observe, model, assimilate NASA Review (4/18/08) 3

4 Soil Moisture Data Sources in this RPC Experiment In-situ observations USDA Soil Climate Analysis Network (SCAN) Remotely sensed and estimated NASA and JAXA Aqua Advanced Scanning Microwave Radiometer EOS (AMSR-E) Numerical Models The Noah model in the NASA Land Information System NASA Review (4/18/08) 4

5 USDA NRCS SCAN NASA Review (4/18/08) 5

6 Anticipated Societal Benefits 1. provides critical information to support drought monitoring and mitigation 2. provides essential information for predicting droughts based on weather and climate predictions 3. supports irrigation water management 4. supports fire risk assessment 5. supports water supply forecasting and NWS flood forecasting 6. supplies a critical missing component to assist with snow, climate and associated hydrometeorological data analysis 7. supports climate change assessment 8. enables water quality monitoring 9. supports a wide variety of natural resource management & research activities such as NASA remote sensing activities of soil moisture and ARS watershed studies. NASA Review (4/18/08) 6

7 An Integrated Framework for Land Data Assimilation il System Inputs Physics Outputs Applications Topography, Soils Land Cover and Vegetation (MODIS, AMSR, TRMM, SRTM) Meteorology Modeled & Observed (TRMM, GOES, Station) Observed Land States (Snow, ET, Soil Moisture, Water, Carbon, etc.) Land Surface Models (LSM) Physical Process Models Noah, CLM, VIC, SiB2, Mosaic, Catchment, etc. Data Assimilation Modules (EnKF, EKF) Rule-based Energy Fluxes: Le & H Biogeochemistry: Carbon, Nitrogen, etc. Water Fluxes: Runoff Surface States: Moisture, Carbon, Ts Water Supply & Demand, Agriculture, Hydro- Electric Power, Ecological Forecasting, Water Quality Improved Short Term & Long Term Predictions (Peters-Lidard, Houser, Kumar, Tian, Geiger) NASA Review (4/18/08) 7

8 LIS Evaluations: Purpose and Activities NASA Review (4/18/08) 8

9 Purpose of RPC Evaluations Primary: Evaluate LIS capabilities and NASA data to enhance and extend USDA-NRCS SCAN Approach: Evaluate LIS performance Assimilate SCAN and AMSR-E observations and evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE) Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km 2 to 1x1 km 2 Quantify uncertainties at all scales NASA Review (4/18/08) 9

10 Team Activity MsState: Project Management, RPC Integration, Control Run, MODIS-VF, [SSURGO] NASA GSFC: LIS Support, AMSR-E data assimilation, science expertise GMU CREW: SCAN data assimilation, science expertise NASA Review (4/18/08) 10

11 Five Data Assimilation Experiments System : LIS-Noah LSM (v.2.7.1) and 1D-EnKF Synthetic Run: observations are derived from model simulations plus random errors AMSR-E Run: observations are derived from AMSR-E retrievals Scaled AMSR-E Run: observations are derived from Scaled AMSR-E retrievals SCAN Run: observations are derived from 12 SCAN site measurements AMSR-E LIS 5.0 EnKF Run (Part II) NASA Review (4/18/08) 11

12 Data Assimilation using LIS (Part I) NASA Review (4/18/08) 12

13 Soil Moisture Data Assimilation and Evaluation AMSR-E on NASA AQUA Satellite OR Soil Climate Analysis Network Soil Moisture Data EnKF DA Noah Land Surface Model of NASA Land Information System Soil Climate Analysis Network No DA Soil Moisture Data Soil Moisture Data Soil Moisture Data Evaluation NASA Study Review (4/18/08) 13

14 Data assimilation scheme-- Ensemble Kalman Filter (EnKF) Propagation Step Analysis Step Propagation Step x α t x - state vector f - land surface model y - observations x f i,t+1 x f i,t x i,t-1 t n-1 t n t n+1 Time D t Propagation Step Propagation Step ( x, u, w ) f x i, t = f i, t 11 α, P = t f = N 1 1 D t D T t f f f [ x1, t x t, x 2, t x t,..., x N, t x t ] K a i, t Analysis Step f ( y Hx v ) x + t f = x i, t + K t t i, t f T ( HP H + ) 1 f T = P H R t t i NASA Review (4/18/08) 14

15 LIS-based Noah LSM Noah Land Surface Model (NOAH) Model is driven by NLDAS forcing (observationcorrected meteorological forcing input) Top layer in upper 10cm NASA Land Information System Inputs Physics Topography, Land Surface Models Soils (Noah,Mosaic,CLM,VIC,SiB,CLSM,..) Land Cover, Vegetation Properties Meteorology Snow Soil Moisture Temperature Data Assimilation Modules (DI,EKF,EnKF) Outputs Soil Moisture & Temperature Evaporation Runoff Snowpack Properties Application s Weather/ Climate Water Resources Homeland Security Military Ops 15 Natural Hazards

16 AMSR-E soil moisture retrievals and CDF matching Advanced Microwave Scanning Radiometer (AMSR) Official AMSR-E Soil moisture dataset available since June 18, 2002 Upper about 1cm, global, ~twice daily at 06Z, 18Z AMSR-E Brightness Temperature Radiative Transfer Model NASA Review (4/18/08) Soil Moisture 16

17 Surface Soil Moisture [v/v%] 4yr ( ) Climatology Noah AMSRE Noah - AMSRE Noah is wetter NASA Review (4/18/08) 17

18 Bias correction-cdf matching CDF matching CDF Soil moisture CDF matching at 31.73N, W AMSR AMSR scaled NOAH model Soil Moisture [v/v] NASA Review (4/18/08) CDF Soil moisture CDF matching at 42.02N, 93.73W AMSR AMSR scaled NOAH model Soil Moisture [v/v]

19 Bias correction-cdf matching Before CDF Matching After CDF Matching NOAH AMSR-E SCAN NOAH Scaled AMSR-E SCAN Ames, IA NASA Review (4/18/08) 19

20 Experiment Domain - Mississippi Delta Region AMSR-E data assimilation runs 1/8th Degree (~15 km) LIS domain SCAN data assimilation runs 12 SCAN sites and 1-km LIS domain MS *Synthetic data assimilation runs *Synthetic data assimilation runs NASA Review (4/18/08) 20

21 Assimilation of real soil moisture data( SCAN, AMSR-E) 1/2 hr forecast+obs 1/2 hr forecast+obs 00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z 1/2 hour time step, 3 hourly output, and 20 ensemble members Data assimilation frequency is twice daily at 06Z and 18Z, with 184 assimilation events over a fixed time period, from 1 Jun thru 31 August DA will not be turned on until observation is available we take the ensemble mean as first guess for next time step initial conditions NASA Review (4/18/08) 21

22 The performance of EnKF: Synthetic Runs 0.125lat/lon 0.125lat/lon 1 km NASA Review (4/18/08) 1 km 22

23 The performance of EnKF: Synthetic Runs 0.125lat/lon 0.125lat/lon 1 km 1 km NASA Review (4/18/08) 23

24 The performance of EnKF: Synthetic Runs Mean( ) Variance ( ) 0.125lat/lon 0.125lat/lon Open Loop EnKF OBS Truth Open Loop EnKF Mean( ) Variance ( ) 1 km NASA Review (4/18/08) 1 km 24

25 EnKF Assimilation of AMSR-E SM Retrievals EnKF Assimilation of Scaled AMSR-E SM Retrievals NASA Review (4/18/08) 25

26 Impact of AMSR-E assimilation Open Loop AMSR-E EnKF AMSR-E SCAN NASA Review (4/18/08) Mean( ) Variance ( ) 26

27 Impact of Scaled AMSR-E assimilation Open Loop Scaled AMSR-E EnKF Scaled AMSR-E SCAN NASA Review (4/18/08) Mean( ) Variance ( ) 27

28 Impact of SCAN assimilation Open Loop SCAN EnKF SCAN NASA Review (4/18/08) Mean( ) Variance ( ) 28

29 Data assimilation verification against SCAN observations Verification: SCAN point-scale measurements (Assumed truth!). Performance varied among assimilations of various real observations with the best one resulting from the SCAN EnKF: Smallest RMSE for 12 out of 12 sites Highest correlations for 10 out of 12 sites Compared to Open Loop, EnKF improved Soil moisture estimation in SCAN assimilation, but performance was degraded in AMSR-E assimilation RMSE(v/v%) Correlation ms_2025 ms_2032 ms_2034 ms_2035 ms_2046 ms_2086 ms_2087 ar_2030 SCAN Sites ar_2083 ar_2084 ar_2085 ar_2091 Open Loop AMSR-E EnKF Scaled AMSR-E EnKF SCAN EnKF SCAN Sites Open Loop AMSR-E EnKF Scaled AMSR-E EnKF SCAN EnKF NASA Review (4/18/08) 29

30 Data assimilation verification against SCAN observations (2) 25 When soil moisture model simulation performs better than satellite retrieval comparing with field measurements, using CDF-matching to calibrate the retrievals may make the retrieval accuracy better and consequently cause their assimilation improved relative to non- calibrated case. RMSE(v/v%) RMSE(v/v%) ms_ ms_2032 ms_2034 ms_2035 ms_2046 ms_2086 ms_2087 ar_2030 SCAN Sites ar_2083 ar_2084 NOAH AMSR-E Scaled AMSR-E ar_2085 ar_ ms_2025 ms_2032 ms_2034 ms_2035 ms_2046 ms_2086 ms_2087 ar_2030 ar_2083 ar_2084 ar_2085 ar_2091 SCAN Sites Open Loop AMSR-E EnKF Scaled AMSR-E EnKF SCAN EnKF NASA Review (4/18/08) 30

31 SCAN DA: Summary (1/2) The synthetic DA runs demonstrates a reasonable skill of the EnKF framework Increased resolution has less of an impact on the soil moisture assimilation, suggesting the importance of model physics and data assimilation algorithm. Assimilations of various real soil moisture observations have been evaluated against in-situ observations at 12 SCAN sites over the Mississippi Delta Region during June-August 2005 Soil moisture results from SCAN assimilation are encouraging: the SCAN assimilation has overall positive impact on soil moisture estimation, reflected not only in RMS errors but also in the correlations. The improved simulation skill by assimilating the SCAN data using EnKF is consistently better than assimilating the AMSR-E data into the data assimilation system. Although assimilation product of rescaled AMSR-E via CDF matching agrees better with ground data than that of unscaled AMSR-E, both perform worse than the no assimilation il case (open loop Noah simulations) NASA Review (4/18/08) 31

32 SCAN DA: Summary (2/2) In spite of the success in the SCAN assimilation, the quality of SCAN data in some sites (e.g. VANCE) should be further investigated. Very close sites, similar Noah simulations, but different SCAN measurement results, lead to different SCAN assimilation results Open Loop SCAN EnKF SCAN NASA Review (4/18/08) Open Loop SCAN EnKF SCAN 32

33 Data Assimilation using LIS (Part II) NASA Review (4/18/08) 33

34 Completed Activities Evaluation of Precipitation Forcing Data Evaluation of Noah Model Physics Spin-up Experiment Data Assimilation Control Run AMSR-E Assimilation Module in LIS AMSR-E Data Assimilation Findings and Recommendations NASA Review (4/18/08)

35 Evaluation of Precipitation Forcing Data In order to evaluate which precipitation forcing should be used for the RPC project, Stage IV and NLDAS forcing data were compared to measured rainfall amounts at five SCAN sites.

36 Comparison of NLDAS (red) and Stage IV (green) yearly statistics relative to measured rainfall at five SCAN sites SCAN site ID Beasley Lake Bias (NLD/STG4) (mm/sec) e e-06 RMSE (NLD/STG4) (mm/sec) 1.53 e e-07 FAR (NLD/STG4) POD (NLD/STG4) TotalRain (NLD/STG4) (mm) 1,441 1,403 TotalRain (observation) (mm) 1,640 Perthshire 1.55 e e e e ,523 1,553 1,475 Scott 5.81 e e e e ,597 1,635 1,415 Silver City 5.54 e e06 e e e e ,296 1,043 1,151 North Issaquena 4.59 e e e e ,331 1,134 1,342 NLDAS, in general, has a lower bias and root mean squared error (RMSE) than Stage IV forcing. While NLDAS shows slightly higher false alarm rates (FAR) than Stage IV, it has better probability of detection (POD) rates. The annual rainfall amounts estimated from the two analyses are in reasonable agreement with the in situ measured rainfall amounts at each site. Stage IV shows no advantage over NLDAS.

37 Evaluation of Noah Model Physics The underlining mathematic principle of the Noah model implies that the simulated soil moisture fields from Noah are point values and so can be readily compared to in situ soil moisture measurements. This evaluation focuses on using point values of the measured soil moisture field to examine the simulated soil moisture profiles by the Noah LSM. 37 NASA Review (4/18/08)

38 SCAN Evaluation of Noah Model Physics Subsurface measurements taken at five depths: 5.1, 10.2, 20.3, 50.8, 101.6cm below the surface Hourly Measured fields: precipitation, soil moisture, soil temperature, air temperature, wind, relative humidity, etc. 38

39 Study Area Evaluation of Noah Model Physics Study area: ~ Lat ~ Lon Five scan sites Perthshire Scott Beasley Lake Silver City North Issaquena 39

40 Model Setup Evaluation of Noah Model Physics Land Surface Model: Noah 1 km horizontal grid resolution 20 uniform layers (10 cm for each layer) outputs are interpolated to the exact depths of obs for comparisons Model run period: 03/2004~03/ /2006 Base forcing: NLDAS Supplemental forcing: SCAN site measured precipitation STATSGO soil texture classes UMD land cover and vegetation type Initial soil moisture condition: 0.3 Platform: Land Information System (LIS) 40

41 Monthly Precipitation in the Region Evaluation of Noah Model Physics Monthly precipitation amount averaged on five sites 41

42 Noah Control Run vs. SCAN Observations Simulated soil moisture content vs. measured at the 5 cm depth (averaged over all five sites) Evaluation of Noah Model Physics

43 Noah Control Run vs. SCAN Observations Simulated soil moisture vs. measured at the 102 cm depth (averaged over all five sites) Evaluation of Noah Model Physics

44 What is the problem? free drainage boundary condition Evaluation of Noah Model Physics The 1-D Richards equation: θ = t θ ( D + K ( θ )) + R E z z Lower boundary(2 m below the surface): free drainage (gravity driven) θ Darcy s Law: q = ( D( θ ) + K( θ )) z q z = θ z 2 m = K( θ) = z = 2 m 0 Constant drainage No upward movement of moisture by capillary force 44

45 What is a more appropriate boundary condition? constant water content Evaluation of Noah Model Physics Prescribed water content: constant in this study θ = θ ( t) z= 2m b By linear extrapolation θ b =

46 Comparison of Soil Moisture Profiles The effect of boundary conditions Evaluation of Noah Model Physics Soil moisture profile at Silver City at 10Z Sept.29, 2005 Free drainage: The near vertical soil moisture profile at the lower end The depletion of soil moisture in the middle soil profile Constant water content 0.41 at the bottom Much wetter profile through entire domain 46

47 Evaluation of Noah Model Physics Free Drainage vs. Constant Water Content Soil moisture at the 5 cm depth (averaged over five sites)

48 Evaluation of Noah Model Physics Free Drainage vs. Constant Water Content Soil moisture at the 102 cm depth (averaged over five sites)

49 Evaluation of Noah Model Physics Free Drainage vs. Constant Water Content Bias averaged over five sites and five depths

50 Evaluation of Noah Model Physics Free Drainage vs. Constant Water Content RMSE averaged over five sites and five depths

51 Evaluation of Noah Model Physics Free Drainage vs. Constant Water Content Daily averaged water storage in the upper 1-m soil column

52 Effect of Boundary Condition on a Perfect Initial Condition Five-depth averaged bias at Silver City Evaluation of Noah Model Physics Both runs were initiated with measured soil moisture content 52

53 Effect of Different Precip Forcing on Averaged Soil Moisture Profiles Free drainage Constant water content Evaluation of Noah Model Physics 53

54 Ancillary results Evaluation of Noah Model Physics The free drainage condition used by many land surface models including Noah is not applicable in this region and possibly many other areas as well; instead, a constant head boundary condition may be more appropriate based on the observations and the hydrogeological conditions in the region. The constant water content condition improves Noah s soil moisture simulation in Mississippi with unbiased estimation of soil moisture. 54

55 Results (continued) Evaluation of Noah Model Physics This approach and results of this evaluation demonstrate that an in-depth examination of the modeled soil moisture fields against observations at all levels can reveal deficiencies in model physics and result in more accurate soil moisture profile predictions. The SCAN measurements (in depth and continuous observations) are crucial in identifying the problem with the free drainage and finding an alternative. 55

56 Spin-up Experiment Spin-up is a common approach used in the land surface modeling community to generate initial conditions needed for numerical simulations. A spin-up experiment is said to reach a converging solution if the simulated profiles remain unchanged at the end of the model run as the run period increases. Three Noah runs were conducted using a six months, eight months and one year spin up period, respectively, with all runs ending at 00Z, May 1,

57 Spin-up Experiment Vertical water content profiles of three spin-up runs and the observations at Silver City, Mississippi at 23Z April 30, In each spin-up run, initial water content was set at 0.3 and the entire model was forced with NLDAS precipitation data. The three runs yield very similar soil moisture profiles at the end of each run, with the profiles from 10 months and one year spin-ups overlapping each other. It is clear that 10 months are long enough to spin up the soil moisture in this Mississippi region. 57

58 Ancillary Conclusions #2 Spin-up Experiment The converged solution from any spin-up process is not warranted to match the observations. As shown in the previous slide, the differences between the simulations and field measurements at the SCAN site can be significant. The discrepancy can be caused by many sources. But based on the previous analyses, the model physics, i.e., the free drainage boundary condition is mostly responsible for the drier soil moisture profile, especially at the lower profile. This experiment further demonstrates that, without correct model physics, the generated soil moisture state from any spin-up experiment is not warranted to be consistent with in situ measurements, no matter how long the model is spun up. 58

59 Data Assimilation Control Run The control run (CR) is designed to establish baseline simulated soil moisture fields so that any improvements made by the data assimilation can be illustrated. Even though the free drainage boundary condition is found to be inappropriate in this region, we still choose to use it for the data assimilation. The primary reason is that the free drainage condition is used in the official version of the Noah land surface model which is used by the majority of the community. 59

60 Simulation domain and location of SCAN sites used for data assimilation Data Assimilation Control Run Larger domain than used for evaluating model physics so more SCAN sites can be included for evaluating data assimilation 300 km by 300 km with latitude and longitude ranging g from to , and to , respectively Horizontal grid resolution is 0.01 degree Official standard four layers of soil Forced with NLDAS forcing data UMD 1 km land cover data set used to provide vegetation type STATSGO soil texture data set used for deriving the soil hydraulic parameters Baseline run period is from 2002 to 2006 Initial soil moisture set to 0.3 Soil moisture content output at 3-hour intervals. 60

61 AMSR-E Assimilation Module in LIS A general data assimilation module based on the ensemble Kalman filter is used for assimilating soil moisture into land surface models in LIS (Kumar et al., 2007). The filter has been tested with the Catchment model and AMSR-E soil moisture retrievals (Reichle et al., 2007), and a synthetic soil moisture assimilation using Noah. AMSR-E soil moisture is retrieved based on measured brightness temperature from the NASA polar-orbiting Aqua satellite. The level 3 AMSR-E soil moisture data set, which contains both ascending and descending retrievals, is used. Even though the level 3 are interpolated to the 25 km cell spacing, the actual foot print each retrieval represents is about 56 km. The daily AMSR-E data files are stored in HDF-EOS format and in the Equal-Area Scalable Earth Grid (EASE-Grid) projection. Since LIS uses equal distance cylindrical projection, a re-projection of the gridded AMSR-E, based on a nearest neighbor searching algorithm, is implemented in LIS to convert the equal area based EASE grid projection to the equal latitude/longitude projection. 61

62 CDF Matching Technique AMSR-E Assimilation Module in LIS One of the key issues in using AMSR-E soil moisture data is the apparent bias between AMSR-E retrieved soil moisture values and the modeled values. The difference can be attributed to retrieval errors, scale issues and the model bias. To reduce the bias, Reichle and Koster (2004) used the cumulative distribution function (CDF) matching technique which maps the CDF of observed soil moisture contents to that of the modeled ones and therefore forces the two sets of soil moisture to share the same mean value. For our AMSR-E assimilation, at each LIS grid point CDFs are derived for the soil moisture obtained from the 5 year baseline run and the 5 year AMSR-E retrievals, respectively. At each point, 500 bins are used for deriving these functions. When AMSR-E is assimilated into Noah, the retrieved soil moisture is converted to model compatible values based on the CDFs at the given location. 62

63 AMSR-E Data Assimilation To evaluate the assimilation results, the correlation coefficients of the daily mean anomalies of modeled soil moisture fields with that of the SCAN in situ measurements are calculated, as well as correlation coefficients for the baseline CR and the unconverted AMSR-E soil moisture. The five year soil moisture data for both the modeled and observed ed are treated as a complete continuous o time series. SCAN Sites CR Assimilation AMSR-E Years of SCAN Perthshire Silver City Scott Beasely Lake NIssaquena Tunica Vance Lonoke Farm Campus PB Marianna Earle DeWitt Average

64 AMSR-E DA Results AMSR-E Data Assimilation AMSR-E data in general have a lower correlation with SCAN than the modeled d soil moisture fields produced by the Noah baseline CR. This can be attributed to the NLDAS forcing data used in this study which, as shown earlier, compares very well with the gauged rainfall measurements. The lack of strong correlation for the AMSR-E retrievals with the SCAN data are likely related to the fact that AMSR-E retrievals are not sensitive to the daily changes of soil moisture either due to the retrieval algorithm or due to the larger scale they represent. In addition, there are only about two retrievals daily in the Mississippi region, which may lower dynamic ranges of the soil moisture at any pixel. On average the assimilation results did not improve over the Noah baseline simulation. However, at sites (for instance, Scott, Lonoke Farm, and Earle) where the correlation bt between AMSR-E AMSRE and SCAN is comparable to the correlation between the model baseline CR and SCAN, the assimilation did improve the modeled performance. It can be drawn from this study that the satellite observations need to have compatible quality y( (i.e., correlation in this study) with the model in order to see improvement through data assimilation. 64

65 Normalized Innovations AMSR-E Data Assimilation The underlining i filter used for the data assimilation il can be evaluated by examining i the mean and variance of the normalized innovation which is defined as the difference between actual observations and the predicted observations divided by the sum of the model and observations errors (standard deviation) at any given location. Mean (left) and variance (right) of five year innovation time series The spatial average of the innovation variance in the study area is around 1.2 which shows the filter is reasonably configured. 65

66 Preliminary Findings and Recommendations The LIS architecture is designed using advanced software engineering principles, allowing the interoperability of land surface models, meteorological inputs, land surface parameters and observational data, and data assimilation capabilities. The AMSR-E data assimilation experiments carried out in this project demonstrate the utility of the flexible, extensible LIS data assimilation framework to apply hydrological observations and modeling tools. A new module to re- project the gridded AMSR-E data was implemented in LIS to facilitate data assimilation using AMSR-E data and the Noah LSM. On average the assimilation of NASA AMSR-E soil moisture product did not improve over the Noah baseline simulation in the MS domain. However, at sites where the correlation between AMSR-E and SCAN is comparable to the correlation between the model baseline and SCAN, the assimilation il did improve the modeled d performance. 66

67 Preliminary Findings and Recommendations (continued) The approach and results of the Noah model physics evaluation demonstrate that an in-depth examination of the modeled d soil moisture fields against observations at all levels can reveal deficiencies in model physics and result in more accurate soil moisture profile predictions. The SCAN measurements (in depth and continuous observations) are crucial in identifying the problem with the free drainage and finding an alternative. A fundamental issue with the CDF matching technique is it does not correct the mean of the modeled soil moisture fields. When observations are transformed through the CDF matching process, they assume the mean of the modeled fields. If the model has a systematic bias, the assimilation with CDF matching will not correct it. Statistically and meteorologically, the mean behavior of the soil moisture fields is more important than others. Without a correct mean, the increased correlation from any data assimilation may not improve the soil moisture prediction. Therefore, data assimilation should be conducted in conjunction with examining model physics such as the one conducted on this project to achieve optimum soil moisture prediction. 67

68 Preliminary Findings and Recommendations (continued) The approach and results of the Noah model physics evaluation demonstrate that an in-depth examination of the modeled d soil moisture fields against observations at all levels can reveal deficiencies in model physics and result in more accurate soil moisture profile predictions. The SCAN measurements (in depth and continuous observations) are crucial in identifying the problem with the free drainage and finding an alternative. A fundamental issue with the CDF matching technique is it does not correct the mean of the modeled soil moisture fields. When observations are transformed through the CDF matching process, they assume the mean of the modeled fields. If the model has a systematic bias, the assimilation with CDF matching will not correct it. Statistically and meteorologically, the mean behavior of the soil moisture fields is more important than others. Without a correct mean, the increased correlation from any data assimilation may not improve the soil moisture prediction. Therefore, data assimilation should be conducted in conjunction with examining model physics such as the one conducted on this project to achieve optimum soil moisture prediction. 68

69 Recommendation for Future Work Conduct further verification of longer time period Study the impact of DA on soil moisture at deeper layers and the surface energy balance terms Examine the influence of assimilation frequency Evaluate complementary AMSR-E products derived using other algorithms (USDA and Princeton) Evaluate the feasibility of combining in-situ and remotely sensed data using the emerging implementation of the 3-D EnKF using LIS NASA Review (4/18/08) 69

70 Future Plans Extend assimilating SCAN soil moisture at top layer (~5cm) only to all layers(~5, 20, 51, 102cm). NASA Review (4/18/08) 70

71 Future Plans Extend 1D-EnKF to 3D-EnKF, which h can spread dinformation from observed to unobserved locations. 1D-EnKF: The state estimate is only updated at a grid point when observation is available (red dots), no observation (yellow dots) no update. 3D-EnKF: The state estimate is updated at any grid point (both red and yellow dots) with all observations within the local region (red circle). NASA Review (4/18/08) 71

72 NASA Review (4/18/08) 72

73 Accepted NASA Review (4/18/08) 73

74 Questions? NASA Review (4/18/08) 74

75 Contact Information Valentine Anantharaj edu> Tel: (662) NASA Review (4/18/08) 75