Christopher R. Hain SPoRT Data Assimilation Workshop May 5, 2009

1. Developing Synergistic Data Assimilation Approaches for Passive Microwave and Thermal Infrared Soil Moisture Retrievals Christopher R. Hain SPoRT Data Assimilation Workshop May 5, 2009

2. Soil Moisture and Data Assimilation • Soil moisture is one of the most important variables within a land surface model and a correct representation is advantageous for accurate predictions of sensible and latent heat fluxes. • Satellite-based retrievals of soil moisture using both passive microwave (PM) and thermal-infrared (TIR) sensors can begin to alleviate this lack of ground-based observations. • Various studies have shown that the assimilation of such retrievals with the use of data assimilation techniques such as the ensemble Kalman filter (EnKF) can provide a more accurate representation of soil moisture in land surface models (Reichle et al. 2002a,b; Crow et al. 2005; Drusch 2007; Reichle et al. 2007).

3. Atmosphere Land Exchange Inversion Model (ALEXI) • The differential in surface radiometric temperature is computed from GOES Sounder Band 8 (11.0 micron) during the time period from 1.5 hours after sunrise to 1 hour before local noon. • The time-differential Trad drives the change in surface temperature and subsequent boundary layer growth.

4. Atmosphere Land Exchange Inversion Model (ALEXI) • ALEXI provides daily estimates of surface fluxes (LE; H; G; Rnet) over cloud-free pixels at spatial resolutions greater to or equal to the native resolution of the TIR data. • ALEXI provides a valuable signal of surface soil moisture conditions over bare soil, root-zone soil moisture conditions over dense vegetative canopies, and a combination of surface and root-zone signal over mixed cover pixels. • Hain et al. (2009a,b) showed that retrievals of a soil moisture proxy using ALEXI surface flux estimates agreed well with averaged 0-75 cm soil moisture observations over the Oklahoma Mesonet. The average error between ALEXI and the observations was on the order of 15 to 20%, or about 0.04 m3 m-3.

5. AMSR-E PM Soil Moisture Retrievals • PM sensors such as AMSR-E provide retrievals of surface soil moisture in the first few centimeters depending on the wavelength of the sensor. • This study will make use of the VUA retrieval products (Owe et al. 2001; 2007) which uses one dual polarized channel (either 6.925 or 10.65 GZz) for the retrieval of both surface soil moisture and vegetation water content, while LST is derived separately from the vertically polarized 36.5 GHz channel. • This differs from the official NSIDC AMSR-E retrieval products because of the use of a higher frequency band for retrieval of LST, and the parameterization of vegetation water content, leaving only the retrieval of soil moisture to be optimized (Rudiger et al. 2009).

6. Soil Moisture Retrieval Specifications • PM and TIR retrievals of soil moisture provides a unique opportunity to synergistically exploit the strengths of each methodology in a data assimilation framework.

7. Land Information System (LIS) Soil Moisture Estimates • The model-based soil moisture product will taken from a 11-year long (1997-2008) Land Information System (LIS) simulation. • The LIS will provide soil moisture estimates at four layers of the soil profile (0-5 cm, 5-40 cm, 40-100 cm, and 100-200 cm).

8. Intercomparison of LIS NOAH, ALEXI and AMSR-E • Several studies have performed quantitative comparisons between modeled surface soil moisture and PM retrievals of surface soil moisture. • This study attempts to perform a quantitative comparison between modeled soil moisture and PM soil moisture retrievals consisted with the “sensing depth signal” from ALEXI. • Root-zone soil moisture from AMSR-E is estimated using the surface soil moisture retrievals and an exponential filter as shown by Albergel et al. (2008)

9. Intercomparison of LIS NOAH, ALEXI and AMSR-E • Soil moisture datasets from various sources typically exhibit very different mean values and variability. • These differences must be removed by transforming each dataset through statistical methods such as anomaly or CDF matching before any data assimilation is performed. • In this study, 14-day composites of ALEXI and AMSR-E soil moisture are “scaled” into a representative value based on the statistical properties of the model-based LIS soil moisture.

10. Yearly Spatial Anomaly Correlation

11. Yearly Spatial Anomaly Correlation

12. Time Series Anomaly Correlation (2003-2008) • ALEXI performs better than AMSR-E over most of the eastern US, which is consistent with high vegetation cover. • AMSR-E and ALEXI both perform well over the central US and western US. • This analysis highlights the potential of dual assimilation of each retrieval to provide added skill over a vast majority of the United States.

13. Estimation of Retrieval Error with Triple Collocation • The goal of intercomparison studies before implementation of data assimilation is to allow the user to make a more educated quantification of the error structure for each soil moisture dataset. • An estimated value of RMSE can be computed using a triple collocation error estimation technique (Janssen et al. 2007; Scipal et al. 2009) once the soil moisture datasets have been “scaled” to a consistent distribution/climatology and assuming each soil moisture dataset has uncorrelated errors.

14. Western Texas Lat: 34 N Lon: -100 W Northern Alabama Lat: 34.5 N Lon: -87.5 W Eastern Montana Lat: 47 N Lon: -107.5 W

15. Triple Collocation RMSE (% error; dynamic range)

16. Implications for EnKF Data Assimilation • It has been shown the soil moisture retrievals both from ALEXI and AMSR-E provide significant skill over a large portion of the United States. • It should be noted that the LIS simulation is exploiting a very dense precipitation monitoring network, which further validates the relationships observed with each soil moisture retrieval, which rely on no antecedent precipitation information. • This study also highlights the potential with respect to the use of a data assimilation system which can implement multiple soil moisture retrievals. • Therefore, it can be hypothesized that the neither retrieval will exceed the “added skill” attained by the potential dual assimilation technique in a respective single assimilation.

17. Implications for EnKF Data Assimilation • Based on error estimation using the triple collocation technique, there was no observed large systematic differences in RMSE between each of the three soil moisture datasets. • Data assimilation work has begun testing the impact of each soil moisture retrieval in a single assimilation framework, along with future work with a dual assimilation framework. • Quantification of assimilation impact will be assessed using ground-based soil moisture observations from the SCAN network and the Oklahoma Mesonet over the study period of 2003-2008. • Additionally, data-denial experiments are being formulated in attempt to quantify skill of assimilation when using “degraded” or poor observations of parameters such as precipitation.