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README for the Model Output through the JAXA's P-Tree System
Prepared by Earth Observation Research Center (EORC),
Japan Aerospace Exploration Agency (JAXA).
Oct. 31, 2018: Aerosol Model product (Ver. beta), and Sea Surface Temperature
Model product (Ver. 20180705) are released.
Dec. 7, 2022: Modify descriptions to apply to Himawari-9.
Mar. 31, 2023: Ensemble ocean analysis product "LORA" (Ver.1.0) is released.
May 17, 2023: Add information of grid file for "LORA"
May 26, 2025: 1km version of Sea Surface Temperature Model product
(JCOPE-T 1ks, Ver. 202501.1) is released.
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In this directory, the Geophysical Parameter Data estimated from
the geostationary Himawari Standard Data and Model Paramteters are
available in near-real-time.
You can also download the past period data of the Geophysical Parameter Data
since Mar.20, 2015.
Please note that past period data of the JAXA Geophysical Parameter Data
will be uploaded to the ftp site upon completion of its processing.
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# Model Outputs
## Model Aerosol Property by MRI/JMA
Latest version: Beta Version
Area: Global
Temporal resolution: 1-hour (Level 4)
Spatial resolution: Longitude 0.375 deg., Latitude 0.37147 to 0.37461 deg. (Gaussian)
(Pixel number: 960, Line number: 480)
NOTE: This product is the forecast (every one hour) of aerosol properties by
the MRI/JMA global aerosol model called Model of Aerosol Species IN the Global
AtmospheRe (MASINGAR). This product is assimilated by Himawari L3 aerosol
optical depth at 00, 03, 06, and 09UTC. The opposite side of the Himawari
observation area is assimilated at 12 and 18UTC using MODIS/Terra+Aqua L3
Value-added Aerosol Optical Depth - NRT dataset due to lack of aerosol
retrievals by Himawari.(As for the image on the top page, there are cases
where preliminary forecast is displayed that was derived by assimilating
observation data before the previous day.)
Please refer to the reference below for the assimilation method etc.
The aerosol data assimilation system based on MASINGER was developed
by Meteorological Research Institute and Kyushu University.
The products are produced at Meteorological Research Institute, and provided
by JAXA P-Tree System, Japan Aerospace Exploration Agency (JAXA).
Acknowledgements:
MODIS/Terra+Aqua L3 Value-added Aerosol Optical Depth - NRT datasets
were acquired from the Level-1 and Atmosphere Archive & Distribution System
(LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space
Flight Center in Greenbelt, Maryland (https://ladsweb.nascom.nasa.gov/).
## Model Sea Surface Temperature in 3km (JCOPE-T DA) by JAXA/JAMSTEC
Latest version: v20180705
Area: Around Japan (117E-150E, 17N-50N)
Temporal resolution: 1-hour (Level 4)
Spatial resolution: About 3km (1/36 deg.) (Pixel number: 1190, Line number: 1190)
NOTE: This research is JAXA-JAMSTEC joint research and a part of the Japan Coastal
Ocean Predictability Experiment (JCOPE).
This product is constructed by data assimilation using high resolution
regional ocean model "JCOPE-T" developed by JAMSTEC and observation data
including the 4 types satellite SST data provided by JAXA.
When we do the data assimilation, we do the bias correction of satellite
SST data using GCOM-W/AMSR2 SST data as refer to reference value,
because the bias in observation data is undesirable for data assimilation.
Near Real-Time data (analysis and forecast) and Best Estimate data are
included in this product.
Update frequency and period are follow.
Near Real-Time data: Every day except Saturdays.
- Analysis (ANAL): 5-days (replaced every update)
- Forecast (FCST): 16-days (replaced every update)
Best Estimate data: Every week
(update in the beginning of the week; Sunday or Monday)
- This is delay mode data which is provided about two weeks late.
- 7-days data are added every update.
This work has been done under the joint research between JAXA and JAMSTEC,
and as a part of the Japan Coastal Ocean Predictability Experiment (JCOPE).
## Model Sea Surface Temperature in 1km (JCOPE-T 1ks) by JAXA/JAMSTEC
Latest version: v202501.1
Area: Around Japan (117E-150E, 17N-50N)
Temporal resolution: 1-hour (Level 4)
Spatial resolution: About 1km (1/12 deg.) (Pixel number: 3894, Line number: 3894)
NOTE: JCOPE-T 1ks (1 km scale) (Wang et al., 2025) is a high-resolution ocean weather
forecasting system developed through refinement of JCOPE-T DA (Data Assimilation)
(Miyazawa et al., 2021). While JCOPE-T DA assimilates satellite sea surface height,
satellite sea surface temperature, and in-situ temperature and salinity using
multiscale 3DVAR in daily basis, JCOPE-T 1ks ajdusts temperature and salinity fields
to be consistent with those of JCOPE-T DA. Note that no adjustment is applied in areas
shallower than 200 m.
Update frequency and period are follow.
Near Real-Time data: Every day except Saturdays.
- Analysis (ANAL): 3-days (replaced every update)
- Forecast (FCST): 9-days (replaced every update)
This work has been done under the joint research between JAXA and JAMSTEC,
and as a part of the Japan Coastal Ocean Predictability Experiment (JCOPE).
## Ensemble ocean analysis product "LORA" by JAXA/RIKEN
Latest version: v1.0
Area: Western North Pacific (108E-180,12N-50N), Maritime Continent (95E-136E,18S-30N)
Temporal resolution: 1-day (Level 4)
Spatial resolution: About 10km (0.1 degree), 50 sigma-layers
Data:
- Daily averaged ensemble mean and spread (one 2D-variable, five 3D-variables):
Sea surface height, temperature, salinity, and zonal, meridional, and vertical velocities
- Daily averaged all sea surface ensemble (five 2D-128 ensemble variables):
Sea surface height, temperature, salinity, and zonal and meridional velocities
- Ensemble mean of each term in the daily averaged mixed layer temperature and salinity
budget equations and of the daily averaged related variables (forty-one 2D-variables,
see MLT_MLS_namelist_en.pdf for more details)
Note: An ensemble ocean analysis product, LORA, is created by a regional ocean data assimilation
system, sbPOM-LETKF, which is developed by RIKEN. sbPOM-LETKF assimilates the following
satellite and in-situ observations at a 1-day interval:
- Satellite-based sea surface temperature (Himawari-8/AHI and GCOM-W/AMSR2)
provided by JAXA
- Satellite-based sea surface salinity (SMAP and SMOS, respectively)
provided by NASA and ESA
- Satellite-based sea surface height provided by CMEMS
- in-situ temperature and salinity (GTSPP and AQC Argo, respectively)
provided by NOAA and JAMSTEC
This product has been created under a JAXA-RIKEN collaborative research project.
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# TOP FTP Directory
/pub/
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# Structure of FTP Directories
### Level 4 (model parameters)
### Model Aerosol Property (MRI/JMA)
/pub/model
+---/ARP
+---/MS
+---/[VER]
+---/[YYYYMM]
+---/[DD]
where VER: algorithm version;
YYYY: 4-digit year observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline; and
hh: 2-digit hour of timeline.
### Model Sea Surface Temperature 3km (JAXA/JAMSTEC)
/pub/model
+---/SST
+---/JCPT_DA
+---/[VER]
+---/FCST
+---/ANAL
+---/BEST
+---/[YYYYMM]
+---/[DD]
where VER: algorithm version;
YYYY: 4-digit year observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline; and
hh: 2-digit hour of timeline.
FCST: Near Real-Time data (Forecast)
ANAL: Near Real-Time data (Analysis)
BEST: Best Estimate data
### Model Sea Surface Temperature 1km (JAXA/JAMSTEC)
/pub/model
+---/SST
+---/JCPT_1ks
+---/[VER]
+---/FCST
+---/[YYYYMM]
+---/[DD]
+---/ANAL
+---/[YYYYMM]
+---/[DD]
where VER: algorithm version;
YYYY: 4-digit year observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline; and
hh: 2-digit hour of timeline.
FCST: Near Real-Time data (Forecast)
ANAL: Near Real-Time data (Analysis)
# Ensemble ocean analysis product "LORA" (JAXA/RIKEN)
/pub/model
+---/OCN
+---/LORA
+---/[VER]
+---/[AREA]
+---/[CAT]
+---/[YYYYMMDD]
where VER: algorithm version;
AREA: Analysis area;
縲縲縲 MC: Maritime Continent
縲縲縲 WNP: North West Pacific
CAT: category
縲 縲縲 ens: Daily averaged all sea surface ensemble
縲縲縲 mean: Ensemble mean and daily average of mixed layer related variabless
縲縲縲 sprd: Daily averaged ensemble mean and spread
YYYY: 4-digit year observation start time (timeline);
MM: 2-digit month of timeline; and
DD: 2-digit day of timeline.
NOTE: Grid information of output data for each area is stored as grid.nc under [AREA] directory.
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# File Naming Convention
## Level 4 (model parameters)
### Model Aerosol Property (MRI/JMA)
Hnn_YYYYMMDD_hhmm_MSARPVER_ANL.xxxxx_yyyyy.nc
where nn: 2-digit number of Himawari satellite;
08: Himawari-8
09: Himawari-9
YYYY: 4-digit year of observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline;
hh: 2-digit hour of timeline;
mm: 2-gidit minutes of timeline;
VER: algorithm version;
xxxxx: pixel number; and
yyyyy: line number.
Example: H08_20180727_0000_MSARPbet_ANL.00960_00480.nc
### Model Sea Surface Temperature 3km (JAXA/JAMSTEC)
JCPT_DA_JPN03_SST_YYYYMMDD_hhmm.nc
where YYYY: 4-digit year of observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline;
hh: 2-digit hour of timeline;
mm: 2-gidit minutes of timeline;
Example: JCPT_DA_JPN03_SST_20181024_1200.nc
### Model Sea Surface Temperature 1km (JAXA/JAMSTEC)
JCPT_1k_JPN01_SST_YYYYMMDD_hhmm.nc
where YYYY: 4-digit year of observation start time (timeline);
MM: 2-digit month of timeline;
DD: 2-digit day of timeline;
hh: 2-digit hour of timeline;
mm: 2-gidit minutes of timeline;
Example: JCPT_1k_JPN01_SST_202503024_1200.nc
## Ensemble ocean analysis product "LORA" (JAXA/RIKEN)
prmYYYYMMDD.nc
where prm: parameters;
el: sea surface height
縲縲縲 s: salimity
縲 縲縲t: temprature
縲縲縲 u: zonal velocity
縲縲縲v: meridional velocity
w: vertical velocity
縲縲縲 ** See MLT_MLS_namelist_en.pd for mixed layer related parameters
YYYY: 4-digit year of observation start time (timeline);
MM: 2-digit month of timeline; and
DD: 2-digit day of timeline.
Example: el20230330.nc
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# Format
All data is in NetCDF4 format.
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# References
## About Model Aerosol Property (MRI/JMA):
(assimilation method)
Yumimoto, K., T. Y. Tanaka, N. Oshima, and T. Maki, 2017: JRAero: the Japanese Reanalysis
for Aerosol v1.0, Geosci. Model Dev., 10, 3225-3253
https://doi.org/10.5194/gmd-10-3225-2017.
(assimilation with Himawari-8 Aerosol optical properties)
Yumimoto, K., T. Y. Tanaka, M. Yoshida, M. Kikuchi, T. M. Nagao, H. Murakami, and T. Maki,
2018: Assimilation and forecasting experiment for heavy Siberian wildfire smoke in May 2016
with Himawari-8 aerosol optical thickness. J. Meteor. Soc. Japan, 96B
http://jmsj.metsoc.jp/GA/JMSJ2018-035.html.
(quotation for MODIS assimilation)
MODIS/Terra+Aqua Near Real Time value-added Aerosol Optical Depth
Product, 6.1NRT, L2 Swath 1 km and 5 km, C6, NASA Level-1 and Atmosphere
Archive & Distribution System (LAADS) Distributed Active Archive Center
(DAAC), Goddard Space Flight Center, Greenbelt, MD.
http://dx.doi.org/10.5067/MODIS/MCDAODHD.NRT.061
## About Model Sea Surface Temperature (JAXA/JAMSTEC):
(Ocean model)
Varlamov, S. M., X. Guo, T. Miyama, K. Ichikawa, T. Waseda, and Y. Miyazawa, 2015:
M2 baroclinic tide variability modulated by the ocean circulation south of Japan,
J. Geophys. Res. Oceans, 120, 3681-3710. DOI:10.1002/2015JC010739.
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015JC010739.
(Data assimilation method)
Miyazawa, Y., S. M. Varlamov, T. Miyama, Y. Kurihara, H. Murakami, M. Kachi, 2021:
A Nowcast/Forecast System for Japan窶冱 Coasts Using Daily Assimilation of Remote Sensing
and In Situ Data. Remote Sens., 13, 2431.
https://doi.org/10.3390/rs13132431
Miyazawa, Y., S. M. Varlamov, T. Miyama, X. Guo, T. Hihara, K. Kiyomatsu, M. Kachi,
Y. Kurihara, H. Murakami, 201: Assimilation of high-resolution sea surface temperature data
into an operational nowcast/forecast system around Japan using a multi-scale
three-dimensional variational scheme, Ocean Dyn., 67, 713-728. DOI: 10.1007/s10236-017-1056-1.
https://link.springer.com/article/10.1007/s10236-017-1056-1.
## About Ensemble Ocean Analysis Product "LORA" (JAXA/RIKEN)
(Data assimilation)
Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, M. Kachi, and T. Miyoshi, 2022:
An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0,
Geosci. Model Dev., 15, 8395-8410, DOI:10.5194/gmd-15-8395-2022.
https://gmd.copernicus.org/articles/15/8395/2022/
Ohishi, S., T. Miyoshi, and M. Kachi, 2022: An ensemble Kalman filter-based ocean data
assimilation system improved by adaptive observation error inflation (AOEI),
Geosci. Model Dev., 15, 9057-9073, DOI:10.5194/gmd-15-9057-2022.
https://gmd.copernicus.org/articles/15/9057/2022/
(Validation)
Ohishi, S., T. Miyoshi, and M. Kachi, 2023: LORA: A local ensemble transform Kalman filter-based
ocean research analysis, Ocn. Dyn., DOI: 10.1007/s10236-023-01541-3.
https://doi.org/10.1007/s10236-023-01541-3
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