Status of AMSR2 Level-2 Products(Algorithm Ver. 2.00)
Japan Aerospace Exploration Agency
Earth Observation Research Center
March 25, 20151

Contents
1. Total Precipitable Water (TPW)
2. Cloud Liquid Water (CLW)
3. Precipitation (PRC)
4. Sea Surface Temperature (SST)
5. Sea Surface Wind speed (SSW)
6. Sea Ice Concentration (SIC)
7. Soil Moisture Content (SMC)
8. Snow Depth (SND)
These geophysical products in version 2.0 have the same caveats as
those in version 1.0. Please check the following file:
http://suzaku.eorc.jaxa.jp/GCOM_W/materials/product/AMSR2_L2.pdf.

9. Summary of Accuracies21. Total Precipitable Water• Principal Investigator– Masahiro Kazumori (NumericalMeteorological Agency)

Prediction

Division,Japan• Update Highlights– Smoothing between the highest T850 table column and othercolumn in the look up table (T850, transmittance to mean emission
temperature).
– Update the look up table (Precipitable Water Index (PWI) to Total
Precipitable Water (TPW)) based on relationship between GPS
TPW and AMSR2 PWI.

• Improvement Highlights– Reductions of discontinuity in retrieved TPW field.
– Improvements in low and high TPW range against radio sonde(RAOB) and GPS TPW.

• Validation Method– Comparison of AMSR2 TPW with RAOB temperature and humidity
profiles and GPS TPW over the global ocean, and evaluation ofroot-mean-square-error (RMSE) of instantaneous values in 15 km
spatial resolution.
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1. Total Precipitable Water• Validation Data and Matchup Condition– Matchup AMSR2 and RAOB data included in JMA GTS with timedifference within 1 hour and distance less than 30 km.
– Matchup AMSR2 and GPS TPW with time difference within 5
minutes and distance less than 30 km.

• Validation Period– From July 24, 2012 to September 30, 2014.

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1. Total Precipitable Water• Conclusion– RMSE compared with RAOB data is 2.6 kg/m2.
– RMSE compared with GPS TPW is 1.5 kg/m2 .
– Retrieved accuracy is improved compared to current version.
– Both validation results showed less RMSE than standard
accuracy of 3.5 kg/m2, especially, RMSE compared with GPS
TPW, which enable to matchup with AMSR2 in shorter timedifferences, achieved target accuracy of 2.0kg/m2.

• Future Works– Comparison with other AMSR2 total precipitable water products
provided by other agencies, such as RSS, NOAA/NESDIS, etc.
– Applying this algorithm to other microwave imagers, such as
GMI, SSMIS, etc.

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2. Cloud Liquid Water• Principal Investigator– Masahiro Kazumori (Numerical
Meteorological Agency)

Prediction

Division,Japan• Update Highlights– Smoothing between the highest T850 table column and othercolumn in the look up table (T850, transmittance to mean emissiontemperature).
– Update the look up table (PWI to TPW) based on relationshipbetween GPS TPW and AMSR2 PWI, and improve Cloud Liquid
Water (CLW) accuracy.
– Empirical correction of water vapor effect on CLW.

• Improvement Highlights– Error reduction in other geophysical parameter dependence.

• Validation Method– Evaluation of CLW errors as variation of microwave radiometer
observation over the clear sky areas. AMSR2 CLW is comparedwith clear sky information (cloud flag information) obtained fromoptical imagers on board the other satellites using probability
density functions (PDFs).
92. Cloud Liquid Water• Validation Data and Matchup Condition– Detection of clear sky areas by using MODIS cloud flag data,and calculation of monthly biases and noises (standarddeviation) from the probability density functions (PDFs).
– Evaluation of CLW total error, including geophysical parameter
dependency, as linear sum of the biggest noise (worst standarddeviation) and bias (worst bias) over clear sky areas.
– Those errors were also evaluated for various environmental
conditions such as under different SST, sea surface wind speed,
and TPW observed by AMSR2.

• Validation Period– From August 1, 2012 to September 30, 2014.

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2. Cloud Liquid Water• Conclusion– Comparison with MODIS CLW in PDF over clear sky areas shows
the worst standard deviation of 0.023 kg/m2 and worst bias of 0.015
kg/m2 .
– Linear sum of the worst bias and noise, which is defined as total
accuracy, is 0.038 kg/m2. Temporal variation of monthly bias andnoise also shows lower values in new version than those of currentversion.
– Evaluation of error dependency to other geophysical parameters
shows improvements in new version, and especially, dependencies
to TPW and SST are reduced.
– Validation results achieved standard accuracy of 0.05kg/m2.

• Future Works– Comparison with direct observation data, such as ARM sites data inislands.
– Comparison with MODIS CLW product– Applying this algorithm to other microwave imagers, such as GMI,SSMIS.
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3. Precipitation• Principal Investigator– Kazumasa Aonashi (Meteorological Research Institute, Japan
Meteorological Agency)• Update Highlights– Modification of correction coefficients for AMSR2 L1 brightnesstemperature.
– Introduction of orographic rainfall correction scheme (Yamamotoand Shige, 2014)– Bug fix in handling of missing values in merging 89GHz A/B.

• Improvement Highlights– Heavy rainfall caused by orographic effect over mountainous areas
along the coast, which is under estimated by microwave imagers,
are well captured.

• Validation Method– Simultaneous observation by TRMM Precipitation Radar (PR) andAMSR2 are averaged in 50 km spatial resolution, and calculate
relative error (ratio of RMSE against average rain rate).
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3. Precipitation• Validation Data and Matchup Condition– Comparison of AMSR2 PRC and PR estimated surface rain rate
data when orbit crossing time difference of the GCOM-W andTRMM satellites is within 10 minutes.
– Comparison of those data with average of 10GHz footprint size
(about 約50km).

• Validation Period– From July 24, 2012 to October 7, 2014.

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3. Precipitation• Conclusion– Relative errors compared by TRMM/PR matchup data are 48%
for over the ocean and 84% for over land.
– New version shows smaller error compared to current version.
– Validation result achieves standard accuracy of 50% for over theocean and 120% for over land.

• Future Works– Collaboration with the GPM/GSMaP team for algorithm
improvements and validation.
– Overestimation and false-positive of orographic rainfall remained
and should be improved in future algorithm updates.
– Transition from TRMM/PR-based to GPM/DPR-based database.

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4. Sea Surface Temperature• Principal Investigator– AkiraShibata(Meteorological
Meteorological Agency)

SatelliteCenter,Japan• Update Highlights– Addition of RFI removal method.
– Refining 6GHz Vertical polarization brightness temperaturecorrection table.
– Refining sea surface wind speed correction method.
– Addition of 10GHz observed SST (research product, missing values
stores SST less than 9 ºC) to the second layer in the SST product.

• Improvement Highlights– Improvements in RFI error removal.
– Improvements in brightness temperature error correction.
– Improvements in sea surface wind speed error removal.

• Validation Method– Comparison of buoy SST and AMSR2 SST using iQuam buoy
dataset, which is compiled and quality controlled by NOAA, andobtain monthly RMSE.

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4. Sea Surface Temperature• Validation Data and Matchup Condition– Matchup AMSR2 and buoy data included in NOAA’s iQuam
dataset with time difference within 2 hour and distance less than30 km.

• Validation Period– From August 1, 2012 to July 31, 2014.

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4. Sea Surface Temperature• Conclusion– Comparison with quality controlled iQuam buoy SST data shows
RMSE of 0.58 ºC.
– New version shows smaller error compared to current version.
– This value also satisfied release accuracy of 0.8 ºC.

• Future Works– Correction of long-term trend in 6GHz V-polarization and 10GHz
V-polarization brightness temperature.
– Intercomparison with GPM/GMI.

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4. Sea Surface Temperature• 10GHz Sea Surface Temperature (Research Product)– Standard SST algorithm uses 6GHz channel for SST retrieval.
– 10GHz channel also has sensitivity to SST higher than 10-12 ºC,
and has finer spatial resolution (about 30km) than 6GHz channel(about 50km). It is defined as one of AMSR2 research products
in March 2015.
– From Ver.2.0 product, 10GHz observed SST is included in theAMSR2 SST product file in addition to standard 6GHz SST, inorder to provide complementary information to users.
– In Ver.2.0 product, 10GHz SST that is less than 9 ºC is set tomissing value since 10GHz channel has poor sensitivity to lowtemperature range. Retrieval error of 10GHz SST becomesbigger where 10GHz SST is less than 10 ºC.
– Validation of 10GHz SST uses same method and data to thoseof 6GHz SST. RMSE of 10GHz SST to iQuam buoy SST is 0.61ºC, and 10GHz SST more than 10 ºC shows almost equal
performance to that of 6GHz SST.
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5. Sea Surface Wind Speed• Principal Investigator– Akira Shibata (MeteorologicalMeteorological Agency)

SatelliteCenter,Japan• Update Highlights– Improvements in wind direction correction.
– Improvements in wind speed conversion table.

• Improvement Highlights– Improvements in positive biases of AMSR2 in weak wind speed
range.
– Improvements in positive biases of AMSR2 in strong wind speedrange.

• Validation Method– Comparison with buoy wind speed data available via internet,
and obtain monthly RMSE between AMSR2 and buoy windspeed.
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5. Sea Surface Wind Speed• Validation Data and Matchup Condition– Quality control of buoy data, such as moving speed check, time
continuity check, and comparison with numerical models.
– Matchup AMSR2 and buoy data included in NOAA’s iQuam
dataset with time difference within 2 hour and distance less than
30 km.

• Validation Period– From July 23, 2012 to July 31, 2014.

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5. Sea Surface Wind Speed• Conclusion– Comparison with quality controlled buoy SST data shows RMSEof 1.1 m/s.
– New version shows smaller error compared to current version.

• Future Works– Evaluation of error in 36GHz brightness temperature.
– Intercomparison with GPM/GMI.

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6. Sea Ice Concentration• Principal Investigators– Josefino Comiso (NASA/GSFC)– Kohei Cho (Tokai Univ.)• Update Highlights– The algorithm was updated as it retrieves SIC values directly
using AMSR2 brightness temperatures. (cf. The old algorithm
retrieved SIC values using AMSR-E brightness temperatures, so
it required the conversion table from AMSR2 to AMSR-Ebrightness temperatures based on the intercalibration results.)– Tie points were updated.
– Parameters for the atmosphere filter to detect clear and cloudy
areas were changed.
– The SST base map as an ancillary data was updated.

• Improvement Highlight– According to the above update highlights, detection of quasi-sea
ices was reduced.
316. Sea Ice Concentration• Conclusion– The SIC product of version 2 achieved the release and standard
accuracies (10%). Additionally, its accuracy improved slightly
than that of version 1.

• Future works– Improvement of the tie points and the atmosphere filter isrequired to reduce detection of quasi sea ices more adequately.
– Improvement of the quality of the validation data created by theMODIS reflectance data is required; because the MODIS's
footprint size is much different from the AMSR2's, we will have tosimulate the MODIS reflectance data observed by AMSR2'sfootprint by introducing the technique of weighted mean in theL1R product.

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7. Soil Moisture Content•

Principal Investigators– Toshio Koike (Univ. of Tokyo)•Update Highlights– Vegetation database was updated.
– The difference between vertical and horizontal polarized microwave
signals are lost in a strong precipitation area to become the identicalresponse to the dry area. To prevent wrong retrievals in such strong
precipitation areas, a precipitation flag was additionally defined.

•Improvement Highlight– According to the precipitation flag, wrong retrievals as the strong
precipitation areas are recognized as the dry areas were reduced.

•Validation method– The SMC values observed in validation sites at Mongolia, Australia andThailand were compared with those retrieved by AMSR2 data.
Additionally, the SMC values observed in Little River, US, one of theUSDA/SCAN sites was also compared with those retrieved by AMSR2
data.
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7. Soil Moisture Content• Conclusion– Validation results：• V1: MAE 3.846 (Ascending + Descending)• V2: MAE 3.732 (Ascending + Descending)– The SMC product of version 2 achieved not only therelease and standard accuracies (MAE 10) but also
the goal accuracy (MAE 5). Additionally, its actual
accuracy improved slightly than that of vertion 1.

• Future work
– Improvement of overestimation of SMC values indesert areas.

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8. Snow depth•Principal Investigator– Richard Kelly, University of Waterloo•

Update Highlights– Forest transmissivity data was implemented, and the AMSR2 brightness
temperature was corrected using the forest correction factors based on
this data.
– Snow depth detection flag and land surface temperature estimation
were updated.
– Water fraction data was updated based on the WWF GIS dataset.
– Snow depth detection was improved over the Tibetan Plateau.

•Improvement Highlights– Improvement of the dense forest region by updated forest improvement.
– Improvement over the coast region and Siberia region by theimprovement of the water fraction data using the updated L1R AMSR2data.
– Improvement of snow depth distribution over the Tibetan Plateau.
438. Snow depth•Validation method– AMSR2's estimated snow depth is compared with the in situ snow depth
at the WMO GSOD 1,007 stations, and the momentary absolute
average error (MAE) is calculated within 30 km spatial resolution. Andthen, the estimation accuracy is evaluated by MAE.

•Validation data and comparison requirements– Area within 40 km around the WMO GSOD station, which has water
fraction less than 10 %, was checked. In consequence, the WMO
GSOD 1,007 stations were selected. And then, the AMSR2's estimatedsnow depth, which is within 7 km in distance and 1 day in time, wascompared with the in situ snow depth at the WMO GSOD 1,007 stations.
– Estimated accuracy is evaluated in the mid-winter season from October
to February.
– Estimation range of algorithm: 5 - 100 cm•Validation period– July 23, 2012 – September 30, 201444

8. Snow depth•Conclusion– Validation result is as follows:– Comparison with the in situ snow depth data at the WMO GSOD
1,007 stations (July 23, 2012 – September 30, 2014)– Whole period– Mid-Winter (Oct.-Feb.)

MAE 17.7 cm
MAE 15.9 cm– Release and Standard accuracy (±20cm) has been satisfied at the WMO GSOD
1,007 stations.
– Snow depth distribution over the Tibetan Plateau was improved.
– Estimation accuracy was improved over the dense forest region by the correctionof the AMSR2 brightness temperature by the forest correction factors based on
the forest transmissivity.
– Estimation accuracy over the coast region and Siberia region were improved by
the improvement of the water fraction data using the updated L1R AMSR2 data.

•Future Works– Developer has tried the improvement of an influential forest for achievement ofgoal accuracy (±10cm) this time. However, the SND algorithm still has aproblem for the evaluation of snow grain growth.
– Developer has perfect awareness of this problem. He already began to improving
the estimation process for snow grain growth.
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