Soil Moisture Data Assimilation in Process Based Models

Transcription

1 Soil Moisture Data Assimilation in Process Based Models Wade Crow, Rolf Reichle, John Bolten, and Iva Mladenova Emerging Technologies and Methods in Earth Observation for Agricultural Monitoring February 13-15, 2018 Supported by the NASA SMAP mission and the NASA Applied Sciences Program (Water Resources)

2 Background/Motivation Soil moisture data assimilation: Updating dynamic and continuous model state predictions (ds/dt) using sporadic soil moisture observations (θ). In its most common form: S = Profile soil moisture and temperature states within a land surface model. θ = Surface soil moisture retrievals from a satellite-based radar or radiometer. Motivation: 1) Provides a spatially and temporally continuous soil moisture analysis. 2) Random errors in analysis those found in underlying model/observations. 3) Provides a mathematical basis for updating unobserved states. Example system: The NASA Soil Moisture Active/Passive (SMAP) Level 4 (L4) Surface and Root-zone Soil Moisture Product.

3 Background/Motivation Soil moisture data assimilation: Updating dynamic and continuous model state predictions (ds/dt) using sporadic soil moisture observations (θ). In its most common form: S = Profile soil moisture and temperature states within a land surface model. θ = Surface soil moisture retrievals from a satellite-based radar or radiometer. Motivation: 1) Provides a spatially and temporally continuous soil moisture analysis. 2) Random errors in analysis those found in underlying model/observations. 3) Provides a mathematical basis for updating unobserved states. Example system: The NASA Soil Moisture Active/Passive (SMAP) Level 4 (L4) Surface and Root-zone Soil Moisture Product

4 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model

5 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model SMAP Satellite Compare

6 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model Update all moisture and temperate states in model, not just those directly impacting SMAP measurements Update SMAP Satellite Compare

7 SMAP L4 Surface and Root-Zone Soil Moisture Example Benchmark rainfall (available > 1 week later) Near real-time rainfall

8 SMAP L4 Surface and Root-Zone Soil Moisture Example Benchmark rainfall (available > 1 week later) Near real-time rainfall Brightness temperature differences (SMAP minus model)

9 SMAP L4 Surface and Root-Zone Soil Moisture Example Dynamic corrections to model states in response to observations

10 SMAP L4 Validation at SMAP Ground Core Sites Blue = Model, Black = SMAP L4 SMAP core validation sites

11 General Operational Land Data Assimilation Systems: SMAP Level 4 surface and root-zone soil moisture analysis NASA GMAO/NASA SMAP [SMAP, Global, 9-km, hourly, 2-3 day latency] H14/SM-DAS-2 root-zone soil moisture ECMWF/EUMETSAT [ASCAT, Global, 25-km, daily, <12 hour latency] Targeted Agriculture Systems: FEWS- NET (see previous talk) NASA GSFC/USDA ARS/USDA FAS root-zone product [SMAP/SMOS, Global, 0.25-degree, daily, 2-3 day latency] Targeted user: USDA FAS International Production Assessment Division Funded by NASA Applied Sciences grant (J. Bolten and I. Mladenova, NASA GSFC, W. Crow, USDA ARS, C. Reynolds USDA FAS) Current ARL level = 5 (SMAP), Project target ARL = 8 (Reached an ARL of ~7 with older satellite systems)

12 General Operational Land Data Assimilation Systems: SMAP Level 4 surface and root-zone soil moisture analysis NASA GMAO/NASA SMAP [SMAP, Global, 9-km, hourly, 2-3 day latency] H14/SM-DAS-2 root-zone soil moisture ECMWF/EUMETSAT [ASCAT, Global, 25-km, daily, <12 hour latency] Targeted Agriculture Systems: FEWS- NET (see previous talk) NASA GSFC/USDA ARS/USDA FAS root-zone product [SMAP/SMOS, Global, 0.25-degree, daily, 2-3 day latency] Targeted user: USDA FAS International Production Assessment Division Funded by NASA Applied Sciences grant (J. Bolten and I. Mladenova, NASA GSFC, W. Crow, USDA ARS, C. Reynolds USDA FAS) Current ARL level = 5 (SMAP), Project target ARL = 8 (Reached an ARL of ~7 with older satellite systems)

13 General Operational Land Data Assimilation Systems: SMAP Level 4 surface and root-zone soil moisture analysis NASA GMAO/NASA SMAP [SMAP, Global, 9-km, hourly, 2-3 day latency] H14/SM-DAS-2 root-zone soil moisture ECMWF/EUMETSAT [ASCAT, Global, 25-km, daily, <12 hour latency] Targeted Agriculture Systems: FEWS- NET (see previous talk) NASA GSFC/USDA ARS/USDA FAS root-zone product [SMAP/SMOS, Global, 0.25-degree, daily, 2-3 day latency] Targeted user: USDA FAS International Production Assessment Division Funded by NASA Applied Sciences grant (J. Bolten and I. Mladenova, NASA GSFC, W. Crow, USDA ARS, C. Reynolds, USDA FAS) Current ARL level = 5 (SMAP), Project target ARL = 8 (Reached an ARL of ~7 with older satellite systems)

14 Impact on Vegetation Condition Forecasting Net impact of assimilation on the lagged correlation between the root-zone soil moisture anomaly (for this month) and the NDVI anomaly (for next month) Sigma Levels NASA GSFC/USDA ARS/USDA FAS rootzone product Red Areas = Areas where the assimilation of satellite soil moisture significantly improves our ability to forecast NDVI anomalies

15 Challenge #1: Spatial Resolution Limited by the coarse spatial resolution of soil moisture retrievals (SMAP is still ~30 km). SAR is an attractive solution but has been under utilized to date. Upcoming missions (e.g., NISAR and SAOCOM) will close this gap by providing ~weekly, high-resolution (~ m), L-band surface soil moisture retrievals. Challenge #2: Statistical Representation of Errors All data assimilation systems are based on assumptions regarding the error covariance structure of model and assimilated observations. Recent application of triple collocation (TC) strategies to estimate required error covariance statistics. A better statistical description of error equates directly into an improved analysis.

16 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model Update all moisture and temperate states in model, not just those directly impacting SMAP measurements Update SMAP Satellite Compare

17 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model Update all moisture and temperate states in model, not just those directly impacting SMAP measurements Update SMAP Satellite Compare BAD GOOD

18 SMAP L4 Surface and Root-Zone Soil Moisture Example Land Surface Model Update all moisture and temperate states in model, not just those directly impacting SMAP measurements Update SMAP Satellite Compare GOOD BAD

19 Challenge #1: Spatial Resolution Limited by the coarse spatial resolution of soil moisture retrievals (SMAP is still ~30 km). SAR is an attractive solution but existing systems generally do not provide sufficient temporal sampling. Upcoming missions (e.g., NISAR and SAOCOM) will close this gap by providing ~weekly, high-resolution (~ m), L-band surface soil moisture retrievals. Challenge #2: Statistical Representation of Errors All data assimilation systems are based on assumptions regarding the error covariance structure of model and assimilated observations. Recent application of triple collocation (TC) strategies to estimate required error covariance statistics. A better statistical description of error equates directly into an improved analysis.

20 Challenge #3: Application to Crop Growth Models Talked a lot about soil moisture states in a hydrologic model, but what about plant/grain states in a crop growth model? Some work (but not a lot only 3-5 papers) on constraining crop growth model yield predictions via soil moisture data assimilation. Significant new technical issues have emerged during the sequential updating of plant/grain states in more complex crop growth models (e.g., DSSAT). Better results using less complex crop models and/or the assimilation of vegetation indices. Soil moisture assimilation into crop growth models = ARL of ~1 or 2 List of challenges is (unfortunately) not exhaustive happy to discuss more later!

21 Thank you

22 SMAP L4 Surface and Root-Zone Soil Moisture Example Blue = Model, Red = Ground Obs., Black = SMAP L4