Validation and Applications of Global Soil Moisture ... · 3rd International Soil Moisture...
Transcript of Validation and Applications of Global Soil Moisture ... · 3rd International Soil Moisture...
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1 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation and Applications of Global Soil Moisture Retrievals from SMAP
for NOAA operations
Xiwu Zhan1, Li Fang1,2, Jicheng Liu1,2, Jifu Yin1,2, Chris Hain1,2, Weizhong Zheng3, Jun Wen4, Kun Yang5, Michael Ek3
1NOAA-NESDIS-STAR, 2UMD-ESSIC-CICS, 3NOAA-NWS-NCEP, 4CAREERI-CAS, 5Tsinghua University
3rd International Soil Moisture Validation and Application Workshop, New York, NY, September 21-22, 2016
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2 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
OUTLINE
NOAA Weather and Climate Operations
NESDIS SMOPS
NASA JPL NRT SMAP TB data
Validation of SMAP SM from SMOPS
Applications of SMAP SM in Operations
Summary
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3 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Regional Hurricane GFDL, WRF-
NMM + Noah
Climate Forecast System (CFS)
North American Ensemble Forecast System
GEFS, Canadian Global Model
Dispersion HYSPLIT
Air Quality
CMAQ
Regional NAM
NMMB Noah land model
3D
-VA
R
DA
Regional Bays •Great Lakes (POM)
•N Gulf of Mexico (FVCOM) •Columbia R. (SELFE) •Chesapeake (ROMS)
•Tampa (ROMS) •Delaware (ROMS)
Space Weather
ENLIL
North American Land Surface Data Assimilation
System Noah Land Surface Model
Global Spectral Noah LSM 3
D-E
n-V
ar
DA
Global Forecast System (GFS)
3D
-VA
R
DA
3D
-VA
R
DA
Rapid Refresh
3D
-VA
R
DA
Waves WaveWatch III
Ocean (RTOFS) HYCOM
Ecosystem EwE
Global Ensemble Forecast System (GEFS)
21 GFS Members
ESTOFS ADCIRC
SURGE SLOSH
P-SURGE SLOSH
WRF ARW
3D
-VA
R
DA
High-Res RR (HRRR)
NEMS Aerosol Global Component (NGAC)
GFS & GOCART
GFS, MOM4, GLDAS/LIS/Noah, Sea Ice
WRF (ARW + NMM) NMMB 7members each
Short-Range Ensemble Forecast 21members
WRF-ARW & NMMB
High Res Windows
WRF ARW
RUC LSM
From Michael Ek 3
Land Prediction in Weather & Climate Models:
NOAA’s Operational Numerical Guidance Suite
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4 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Caveats on NCEP Data Need for
SMAP Products
SM impact intra-seasonal, inter-seasonal, to inter-annual climate variabilities.
SM are needed in model initializations.
SM observational data are also important for LSM validation as well as other land remote sensing data, e.g.: soil Temperature, snow, albedo, green vegetation fraction.
Operational use of land remote sensing data including SM will start from North America Land Data Assimilation System (NLDAS) that is current operational at NCEP Center of Operations
SMAP soil moisture is processed through NESDIS SMOPS
From Weizhong Zheng & Michael Ek
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5 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Soil Moisture Operational Product System SMOPS
WindSat SMOS ASCAT
Blended
NESDIS SMOPS Ingests NASA SMAP NRT Data to retrieve global soil moisture with NOAA ancillary Data and blends them with other satellite observations for NOAA uses
AMSR2
SMAP SM
NASA SMAP NRT L1B-TB
NOAA Ancillary Data
GFS/NAM NLDAS/GLDAS
DoD AFWA USDA
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6 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Date Total_In Total_Orbits Percent_In Minimum Median Average Maximum Oct 1 11 29 38 1:33 3:56 4:17 6:09
2 14 27 52 1:20 3:57 4:02 5:51 3 13 29 45 1:23 3:30 3:50 5:54 4 14 28 50 1:58 3:56 4:04 5:39
5 12 30 40 1:28 3:59 9:30 87:25 6 14 27 52 1:32 4:13 4:01 5:50 7 13 28 46 1:33 4:33 4:12 5:22 8 13 27 48 1:35 4:15 4:10 6:05 9 11 29 38 1:58 4:07 4:15 5:29
10 11 28 39 1:20 4:31 4:11 5:48 11 11 27 41 1:23 4:02 4:07 5:32 12 14 29 48 1:46 4:35 4:16 5:39 13 13 26 50 1:28 4:12 3:54 5:43 14 11 28 39 1:10 3:57 4:01 5:39 15 13 28 46 1:28 4:07 3:58 6:00
16 12 27 44 1:30 3:51 3:58 6:01 17 12 29 41 1:28 3:59 4:08 6:20 18 13 28 46 1:16 3:55 4:04 5:48 19 13 26 50 1:18 3:48 3:55 5:53 20 13 27 48 1:41 4:08 4:04 5:34 21 11 27 41 1:58 3:53 4:13 8:26
Latencies of SMAP L1B-TB Data from JPL NRT Data Server
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7 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Latencies of SMAP L1B-TB Data from NSIDC Data Server
Date Total_In Total_Orbits Percent_In Minimum Median Average Maximum Oct 1 1 30 3 4:29 5:37 7:49 15:41
2 5 28 18 4:29 5:20 5:47 12:31 3 6 30 20 4:43 5:17 5:38 10:40 4 7 29 24 4:36 5:13 5:30 6:37
5 6 32 19 4:34 5:06 10:33 87:43 6 5 33 15 4:36 5:22 40:23 351:13 7 2 29 7 4:45 323:45 252:09 344:13 8 3 29 10 4:05 5:05 5:25 6:57 9 6 30 20 4:08 5:16 5:31 7:00
10 6 29 21 4:29 5:17 5:33 6:28 11 5 29 17 4:31 5:19 5:28 6:29 12 7 29 24 4:54 5:23 5:35 6:39 13 5 30 17 4:34 5:09 5:28 6:40 14 4 29 14 4:52 6:02 12:21 26:03 15 2 29 7 4:29 5:43 8:08 16:13
16 5 29 17 4:26 5:14 5:27 6:36 17 6 30 20 4:22 5:09 5:28 7:12 18 5 29 17 4:29 5:17 5:33 6:51 19 3 29 10 4:32 5:22 5:36 7:18 20 2 29 7 4:55 11:16 15:22 29:14 21 5 30 17 4:59 5:30 6:32 11:25
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8 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
SMAP L1B_TB: JPL NRT vs NSIDC
More than 99% of footprint TBs have difference smaller than 0.2 degree.
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9 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Soil Moisture Retrieval Algorithm
Single Channel Algorithm (SCA) : (Jackson, 1993)
TBh = Ts [1 –(1-er) exp (-2 /cos)] = b * VWC, VWC = f(NDVI) eh = f(ev, h, Q)
es = f(ε) -- Fresnel Equation ε = f(SM) -- Mixing model Ts = Ts
LSM
NDVI = VIIRS near real time TsLSM = GFS Tskin
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10 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
SMAP Soil Moisture Retrieved from SMOPS
NESDIS SMOPS uses NASA SMAP NRT L1B_TB and NOAA ancillary data (GDAS Tskn and VIIRS NDVI) to retrieve soil moisture
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11 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu Naqu
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12 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Maqu SM Network of CAS
In the east TP, about 25 sites,
sensors at 5, 10, 20, 40, 80cm depths,
covers about 100km×60km area.
Obs period: 2008-now
SM
From Jun Wen of CAS
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13 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Naqu SM Network of CAS
56 stations
4 observations at each station:
0-5, 10, 20, 40 cm
3 spatial scales:
1.0, 0.3, 0.1 degree
3 field campaigns since 2010
Soil profile samples are collected
at each station; soil carbon content
(SOC) and soil texture are
measured.
High SOC contents are found in
top layer (0-20cm), which will
dramatically affect the thermal
properties and dielectricity of soil.
From Kun Yang of CAS
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14 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu CST_05
14
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15 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu NST_06
15
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16 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu NST_08
16
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17 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu All Ave
17
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18 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Maqu ubRMSE r
Site NASA NOAA NFPR NASA NOAA NFPR
CST_05 0.080 0.065 0.060 0.634 0.665 0.708
NST_01 0.070 0.065 0.067 0.660 0.624 0.652
NST_03 0.080 0.070 0.066 0.644 0.581 0.628
NST_06 0.067 0.059 0.062 0.705 0.596 0.608
NST_07 0.071 0.065 0.066 0.653 0.545 0.559
NST_08 0.073 0.059 0.056 0.572 0.717 0.750
NST_09 0.058 0.062 0.059 0.769 0.537 0.604
Ave 0.065 0.065 0.055 0.742 0.716 0.734
SMAP SM retrievals from gridded TBs in SMOPS is compatible with NASA baseline products
SMAP SM retrievals from footprint TBs in SMOPS could be slightly better than those from gridded TBs
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19 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Validation of SMAP SM from SMOPS
Naqu Large Scale Averages (1d x 1d)
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20 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
OUTLINE
NOAA Weather and Climate Operations
NESDIS SMOPS
NASA JPL NRT SMAP TB data
Validation of SMAP SM from SMOPS
Applications of SMAP SM in Operations
Summary
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21 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Soil Moisture Operational Product System SMOPS
WindSat SMOS ASCAT
Blended
GFS/NAM NLDAS/GLDAS
DoD AFWA USDA
NESDIS SMOPS Ingests NASA SMAP NRT Data to retrieve global soil moisture with NOAA ancillary Data and blends them with other satellite observations for NOAA uses
AMSR2
SMAP SM
NASA SMAP NRT L1B-TB
NOAA Ancillary Data
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22 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Assimilating SMAP SM from 8/1 – 8/10/2015 Reduces the warm biases of NCEP GFS seven (7) day forecasts of day time 2 meter air temperature
SMAP SM Impact on GFS Forecasts
From Weizhong Zheng & Michael Ek
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23 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
SMAP SM Impact on GFS Forecasts
Impact of assimilating SMAP SM from 8/1 – 8/10/2015 on NCEP GFS four (4) day forecasts of 2 meter specific humidity varies significantly spatially
From Weizhong Zheng & Michael Ek
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24 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Blending Sat SM for Drought Monitoring
Procedure of constructing the Blended Drought Index (BDI)
using the RMSEs estimated from the Triple Collocation Error
Model implemented for each grid in each calendar month.
RMSEmin is the minimum RMSE for the grid. And RMSESMOPS,
RMSENLSM and RMSEESI are the monthly RMSE values for soil
moisture data sets from SMOPS, NLSM and ESI cases,
respectively.
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25 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Blending Sat SM for Drought Monitoring
Correlation coefficients (R) between PDSI standard
anomalies (against 1985-2014 averages) and drought
estimations for (a) ASCAT, (b) SMOS, (c) WindSat, (d)
SMOPS, (e) NLSM and (f) ESI cases. Grey color indicates insignificant correlation.
The Blended Drought Index (BDI) has better correlation than all other data sources
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26 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
SUMMARY
NOAA NESDIS SMOPS is capable of processing SMAP NRT L1-TB to generate soil moisture data products for NWS operation and research uses
Preliminary validation results indicated that the quality of the NOAA SMAP SM data product with better latency is compatible with NASA baseline retrievals
Retrieving soil moisture for SMAP footprints may produce better data products than grid retrievals
Making use of SMAP data in NOAA weather forecasts and drought monitoring could have positive impacts
Further algorithm refinement is still going on
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27 3rd International Soil Moisture Workshop, New York, NY, September 21-22, 2016
Thanks for your attention!