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NASA Soil Moisture Active Passive (SMAP) Mission Formulation  Dara Entekhabi  (MIT) Eni Njoku  (JPL Caltech/NASA) Peggy O'Neill  (GSFC/NASA) Kent Kellogg  (JPL Caltech/NASA) Jared Entin  (NASA HQ) IGARSS’11 Session WE1.T03.1 Paper #3178
Talk Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Project Milestones and Upcoming Activities ,[object Object],[object Object],[object Object],[object Object],2007 US National Research Council Report: “ Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond ”   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Tier 1: 2010–2013 Launch Soil Moisture Active Passive (SMAP) ICESAT II DESDynI CLARREO Tier 2: 2013–2016 Launch SWOT HYSPIRI ASCENDS GEO-CAFE ACE Tier 3: 2016–2020 Launch LIST PATH GRACE-II SCLP GACM 3D-WINDS
May 10  Dry soil, clear, mild winds. (LE≈H)  May 18  90 mm Rain May 20  Moist soil, clear, mild winds. (LE>H)  Pathways of Soil Moisture Influence on Weather and Climate CASES’97 Field Experiment,  BAMS , 81(4), 2000. Dry Soil Moist Soil 5°C Dry  Surface Moist  Surface Deep Mixing up to 1.5 km Altitude Shallow Mixing to 1.0 km
Source: Cahill et al.,  J. Appl. Met ., 38 Key Determinants of Land Evaporation Latent heat flux (evaporation)  links  the  water ,  energy , and  carbon  cycles at the surface.  Closure relationship, yet virtually unknown. Lack of knowledge of soil moisture control on surface fluxes causes uncertainty in  weather  and  climate  models.
NOAH CLM What Do We Do Today? Dirmeyer et al., J. Hydromet., 7,  1177-1198, 2006 Atmospheric model representations of this function are essentially “guesses”, given scarcity of soil moisture and evaporation data.
(*)  % classification accuracy (binary Freeze/Thaw)  (**) [cm 3  cm -3 ] volumetric water content, 1-sigma Science Requirements  (1) North of 45N latitude Requirement Hydro-Meteorology Hydro-Climatology Carbon Cycle Baseline Mission Soil Moisture Freeze/Thaw Resolution 4–15 km 50–100 km 1–10 km 10 km 3 km Refresh Rate 2–3 days 3–4 days 2–3 days (1)   3 days 2 days (1)   Accuracy 4–6% ** 4–6%** 80–70%* 4%** 80%* DS Objective Application Science Requirement Weather Forecast Initialization of Numerical Weather Prediction (NWP) Hydrometeorology Climate Prediction Boundary and Initial Conditions for Seasonal Climate Prediction Models Hydroclimatology Testing Land Surface Models in General Circulation Models Drought and Agriculture Monitoring Seasonal Precipitation Prediction Hydroclimatology Regional Drought Monitoring Crop Outlook Flood Forecast Improvements River Forecast Model Initialization Hydrometeorology Flash Flood Guidance (FFG) NWP Initialization for Precipitation Forecast Human Health Seasonal Heat Stress Outlook Hydroclimatology Near-Term Air Temperature and Heat Stress Forecast  Hydrometeorology Disease Vector Seasonal Outlook Hydroclimatology Disease Vector Near-Term Forecast (NWP) Hydrometeorology Boreal Carbon Freeze/Thaw Date Freeze/Thaw State
Sources: Global Forecast/Analysis System Bulletins http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html The ECMWF Forecasting System Since 1979 http://ecmwf.int/products/forecasts/guide/The_general_circulation_model.html Trends in Short-Term Weather (0-14 Days) NWP Resolution Hydrometeorology Applications: NWP  SMAP
Operational Flood and Drought Applications Current : Empirical Soil Moisture Indices Based on Rainfall and Air Temperature  ( By Counties >40 km and Climate Divisions >55 km ) Future : SMAP Soil Moisture Direct Observations of Soil Moisture at 10 km
SMAP Mission Concept ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],National Aeronautics and  Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California
Data Products SMAP is Taking Aggressive Hardware & Softwate Measures to Detect & Partially Mitigate RFI  Product Description Resolution Latency L1A_TB Radiometer Data in Time-Order - 12 hrs Instrument Data L1A_S0 Radar Data in Time-Order - 12 hrs L1B_TB Radiometer  T B  in Time-Order 36x47 km  12 hrs L1B_S0_LoRes Low Resolution Radar  σ o  in Time-Order 5x30 km 12 hrs L1C_S0_HiRes High Resolution Radar  σ o  in Half-Orbits 1-3 km 12 hrs L1C_TB Radiometer T B  in Half-Orbits 36 km 12 hrs L2_SM_A Soil Moisture (Radar) 3 km 24 hrs Science Data (Half-Orbit) L2_SM_P Soil Moisture (Radiometer) 36 km 24 hrs L2_SM_A/P Soil Moisture (Radar+Radiometer) 9 km 24 hrs L3_F/T_A Freeze/Thaw State  3 km 50 hrs Science Data (Daily Composite) L3_SM_A Soil Moisture (Radar) 3 km 50 hrs L3_SM_P Soil Moisture (Radiometer)  36 km 50 hrs L3_SM_A/P Soil Moisture (Radar+Radiometer) 9 km 50 hrs L4_SM Soil Moisture (Surface and Root Zone ) 9 km 7 days Science  Value-Added L4_C Carbon Net Ecosystem Exchange (NEE) 9 km 14 days
L-band Active/Passive Assessment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],National Aeronautics and  Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California
L2_SM_AP  Radar-Radiometer Algorithm  Heterogeneity in Vegetation and Roughness Conditions Estimated by Sensitivities  Γ   in Radar HV Cross-Pol: T B ( M j  )  is Used to Retrieve Soil Moisture at 9 km  T B -Disaggregation Algorithm is: National Aeronautics and  Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Temporal Changes in  T B   and  σ pp   are Related. Relationship Parameter  β  is Estimated at Radiometer-Scale Using Successive Overpasses. Based on PALS Observations From:  SGP99, SMEX02, CLASIC and SMAPVEX08
SGP99, SMEX02, CLASIC and SMAPVEX08 WE2.T03.2 Paper #: 3398 Title: Evaluation of the SMAPCombined Radar-Radiometer Soil Moisture Algorithm Authors: N. Das, D. Entekhabi, S. Chan, S. Kim, E. Njoku, R. Dunbar, J.C. Shi Active-Passive Algorithm Performance Minimum Performance Algorithm RMSE:  0.055  [cm 3  cm -3 ] Active-Passive Algorithm RMSE:  0.033  [cm 3  cm -3 ]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SMAP Applications Activities WE1.T03.2 Paper #2906 Title: The Soil Moisture Active Passive (SMAP) Applications Aactivity Authors: M. Brown, S. Moran, V. Escobar, D. Entekhabi, P. O'Neill, E. Njoku
SMAP Algorithm Testbed TB (K) L2_SM_AP  Combined Soil Moisture Product (9 km) L2_SM_P  Radiometer  Soil Moisture Product (36 km) L3_SM_A  Radar Soil Moisture Product (3 km) L1C_TB  Radiometer Brightness Temperature Product (36km) Simulated products generated with prototype algorithms on the SDS Testbed National Aeronautics and  Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California WE2.T03.1 Paper #2069 Title: Utilization of ancillary data sets for SMAP Algorithm Development and Product Generation  Authors: P. O'Neill, E. Podest, E. Njoku L1C_S0_Hi-Res  Radar Backscatter Product (1-3 km)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SMAP Working Groups http:// smap.jpl.nasa.gov /science/wgroups/
Back-Up Slides
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Mission Science Objective Primary Controls on Land Evaporation and Biosphere Primary Productivity Freeze/ Thaw Radiation Soil  Moisture

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3178_IGARSS11.ppt

  • 1. NASA Soil Moisture Active Passive (SMAP) Mission Formulation Dara Entekhabi (MIT) Eni Njoku (JPL Caltech/NASA) Peggy O'Neill (GSFC/NASA) Kent Kellogg (JPL Caltech/NASA) Jared Entin (NASA HQ) IGARSS’11 Session WE1.T03.1 Paper #3178
  • 2.
  • 3.
  • 4. May 10 Dry soil, clear, mild winds. (LE≈H) May 18 90 mm Rain May 20 Moist soil, clear, mild winds. (LE>H) Pathways of Soil Moisture Influence on Weather and Climate CASES’97 Field Experiment, BAMS , 81(4), 2000. Dry Soil Moist Soil 5°C Dry Surface Moist Surface Deep Mixing up to 1.5 km Altitude Shallow Mixing to 1.0 km
  • 5. Source: Cahill et al., J. Appl. Met ., 38 Key Determinants of Land Evaporation Latent heat flux (evaporation) links the water , energy , and carbon cycles at the surface. Closure relationship, yet virtually unknown. Lack of knowledge of soil moisture control on surface fluxes causes uncertainty in weather and climate models.
  • 6. NOAH CLM What Do We Do Today? Dirmeyer et al., J. Hydromet., 7, 1177-1198, 2006 Atmospheric model representations of this function are essentially “guesses”, given scarcity of soil moisture and evaporation data.
  • 7. (*) % classification accuracy (binary Freeze/Thaw) (**) [cm 3 cm -3 ] volumetric water content, 1-sigma Science Requirements (1) North of 45N latitude Requirement Hydro-Meteorology Hydro-Climatology Carbon Cycle Baseline Mission Soil Moisture Freeze/Thaw Resolution 4–15 km 50–100 km 1–10 km 10 km 3 km Refresh Rate 2–3 days 3–4 days 2–3 days (1) 3 days 2 days (1) Accuracy 4–6% ** 4–6%** 80–70%* 4%** 80%* DS Objective Application Science Requirement Weather Forecast Initialization of Numerical Weather Prediction (NWP) Hydrometeorology Climate Prediction Boundary and Initial Conditions for Seasonal Climate Prediction Models Hydroclimatology Testing Land Surface Models in General Circulation Models Drought and Agriculture Monitoring Seasonal Precipitation Prediction Hydroclimatology Regional Drought Monitoring Crop Outlook Flood Forecast Improvements River Forecast Model Initialization Hydrometeorology Flash Flood Guidance (FFG) NWP Initialization for Precipitation Forecast Human Health Seasonal Heat Stress Outlook Hydroclimatology Near-Term Air Temperature and Heat Stress Forecast Hydrometeorology Disease Vector Seasonal Outlook Hydroclimatology Disease Vector Near-Term Forecast (NWP) Hydrometeorology Boreal Carbon Freeze/Thaw Date Freeze/Thaw State
  • 8. Sources: Global Forecast/Analysis System Bulletins http://www.emc.ncep.noaa.gov/gmb/STATS/html/model_changes.html The ECMWF Forecasting System Since 1979 http://ecmwf.int/products/forecasts/guide/The_general_circulation_model.html Trends in Short-Term Weather (0-14 Days) NWP Resolution Hydrometeorology Applications: NWP SMAP
  • 9. Operational Flood and Drought Applications Current : Empirical Soil Moisture Indices Based on Rainfall and Air Temperature ( By Counties >40 km and Climate Divisions >55 km ) Future : SMAP Soil Moisture Direct Observations of Soil Moisture at 10 km
  • 10.
  • 11. Data Products SMAP is Taking Aggressive Hardware & Softwate Measures to Detect & Partially Mitigate RFI Product Description Resolution Latency L1A_TB Radiometer Data in Time-Order - 12 hrs Instrument Data L1A_S0 Radar Data in Time-Order - 12 hrs L1B_TB Radiometer T B in Time-Order 36x47 km 12 hrs L1B_S0_LoRes Low Resolution Radar σ o in Time-Order 5x30 km 12 hrs L1C_S0_HiRes High Resolution Radar σ o in Half-Orbits 1-3 km 12 hrs L1C_TB Radiometer T B in Half-Orbits 36 km 12 hrs L2_SM_A Soil Moisture (Radar) 3 km 24 hrs Science Data (Half-Orbit) L2_SM_P Soil Moisture (Radiometer) 36 km 24 hrs L2_SM_A/P Soil Moisture (Radar+Radiometer) 9 km 24 hrs L3_F/T_A Freeze/Thaw State 3 km 50 hrs Science Data (Daily Composite) L3_SM_A Soil Moisture (Radar) 3 km 50 hrs L3_SM_P Soil Moisture (Radiometer) 36 km 50 hrs L3_SM_A/P Soil Moisture (Radar+Radiometer) 9 km 50 hrs L4_SM Soil Moisture (Surface and Root Zone ) 9 km 7 days Science Value-Added L4_C Carbon Net Ecosystem Exchange (NEE) 9 km 14 days
  • 12.
  • 13. L2_SM_AP Radar-Radiometer Algorithm Heterogeneity in Vegetation and Roughness Conditions Estimated by Sensitivities Γ in Radar HV Cross-Pol: T B ( M j ) is Used to Retrieve Soil Moisture at 9 km T B -Disaggregation Algorithm is: National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Temporal Changes in T B and σ pp are Related. Relationship Parameter β is Estimated at Radiometer-Scale Using Successive Overpasses. Based on PALS Observations From: SGP99, SMEX02, CLASIC and SMAPVEX08
  • 14. SGP99, SMEX02, CLASIC and SMAPVEX08 WE2.T03.2 Paper #: 3398 Title: Evaluation of the SMAPCombined Radar-Radiometer Soil Moisture Algorithm Authors: N. Das, D. Entekhabi, S. Chan, S. Kim, E. Njoku, R. Dunbar, J.C. Shi Active-Passive Algorithm Performance Minimum Performance Algorithm RMSE: 0.055 [cm 3 cm -3 ] Active-Passive Algorithm RMSE: 0.033 [cm 3 cm -3 ]
  • 15.
  • 16. SMAP Algorithm Testbed TB (K) L2_SM_AP Combined Soil Moisture Product (9 km) L2_SM_P Radiometer Soil Moisture Product (36 km) L3_SM_A Radar Soil Moisture Product (3 km) L1C_TB Radiometer Brightness Temperature Product (36km) Simulated products generated with prototype algorithms on the SDS Testbed National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California WE2.T03.1 Paper #2069 Title: Utilization of ancillary data sets for SMAP Algorithm Development and Product Generation Authors: P. O'Neill, E. Podest, E. Njoku L1C_S0_Hi-Res Radar Backscatter Product (1-3 km)
  • 17.
  • 19.

Notas del editor

  1. Oct. 23, 2008