Global Soil Wetness Project
Phase 3 Atmospheric BoundaryConditions (Experiment 1)
1. IDENTIFICATION INFORMATION
Name

Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment1)

EditionVersion 1

Abbreviation

GSWP3.E1-ABC

DOI

doi:10.20783/DIAS.501 [https://doi.org/10.20783/DIAS.501]

Metadata
Identifier

GSWP3_EXP1_Forcing20230727092724-DIAS20221121113753-en

2. CONTACT
2.1 CONTACT on DATASET
Name

Hyungjun Kim

Organization

Institute of Industrial Science, The University of Tokyo

Address

Be607, 4-6-1 Komaba, Tokyo, Tokyo, 153-8505, JapanTEL+81-3-5452-6382

FAX+81-3-5452-6383

E-mailhjkim@iis.u-tokyo.ac.jp

2.2 CONTACT on PROJECT
2.2.1 Data Integration and Analysis System
Name

DIAS Office

Organization

Japan Agency for Marine-Earth Science and Technology

Address

3173-25, Showa-Cho, Kanazawa-ku, Yokohama-shi, Kanagawa, 236-0001, Japan

E-maildias-office@diasjp.net

3. DOCUMENT AUTHOR
Name

Hyungjun Kim

Organization

Institute of Industrial Science, The University of Tokyo

E-mailhjkim@iis.u-tokyo.ac.jp

4. DATASET CREATOR
1NameHyungjun Kim

Organization

Institute of Industrial Science, The University of Tokyo

E-mailhjkim@iis.u-tokyo.ac.jp

5. DATE OF THIS DOCUMENT
2023-07-27

6. DATE OF DATASET
�creation : 2017-06-01

7. DATASET OVERVIEW
7.1 Abstract
retrospective atmospheric boundary conditions (9 variables: Rainfall, Snowfall, 2m Air Temperature,
2m Specific Humidity, Surface Pressure, Downward Shortwave Radiation, Downward Longwave Radiation,
10m Wind Speed, and Cloud Cover Fraction) for 1901-2010 in 3-hourly resolution are generated. 20thCentury Reanalysis (20CR) [compo2011] [Compo el al., 2011] on global 2° resolution is dynamicallydownscaled into T248 (~0.5°) grid using a spectral nudging technique [Yoshimura and Kanamitsu
2008] in a Global Spectral Model (GSM) [Figure 2]. This successfully keeps the low frequency
signal of original reanalysis, providing additional high frequency signals, which are lacking inprevious products [e.g., Weedon et al., 2011]. It is essential to investigate phenomena at higher
spatiotemporal scales such as extreme events. In order to relieve known artifacts (e.g., ripple
patterns and persistent overcast in high latitude region), additional techniques, such as single
ensemble correction [Yoshimura and Kanamitsu, 2013] and vertically weighted damping [Hong and Chang,2012], are applied. Model biases in the downscaled 20CR are corrected using observational data (e.g.,GPCC for precipitation, SRB for short/long wave radiation, and CRU for air temperature and daily
temperature range). In addition to previously introduced bias correction algorithms [e.g., Weedon
et al., 2011], variability in higher temporal (&amp;amp;amp;amp;lt;month) resolution is carefullycorrected [Kim et al., in preparation]. Also, wind-induced precipitation undercatch correction isapplied considering different types of gauges and their global distribution [Hirabayashi et al.,2008]. Through the above mentioned data generation strategy, GSWP3 has further reliability andconsistency over the century long target timespan with higher spatiotemporal resolutions.

7.2 Topic Category(ISO19139)
�climatologyMeteorologyAtmosphere

7.3 Temporal Extent
Begin Date

1901-01-01

End Date

2010-12-31

Temporal
Characteristics

3hourly

7.4 Geographic Bounding Box
Northlatitudebound 902Westlongitudebound -180

Eastboundlongitude180

Southlatitudebound -907.5 GridDimension Name

Dimension
Size Resolution Unit
(slice number ofthe dimension)

time321416

180 (minute)row3600.5 (deg)column720

0.5 (deg)

7.6 Geographic Description
7.7 Keywords
7.7.1 Keywords on Dataset
Keyword Type

Keyword

KeywordNamediscipline

GSWP3, Forcing Data, Surface Climate, Surface Meteorology others

thesaurus

7.7.2 Keywords on Project
7.7.2.1 Data Integration and Analysis System
Keyword Type

Keyword

KeywordName

thesaurus

theme

DIAS &amp;gt; Data Integration and Analysis System

No_Dictionary

7.8 Online Resource�Please contact data creator to obtain the authentication information : http://www.dias.nii.ac.jp/gswp3/input.html

7.9 Data Environmental Information
7.10 Distribution Information
nameversion

netCDF4E1V1specification

3

8. DATA PROCESSING
9. DATA REMARKS
10. DATA POLICY
10.1 Data Policy by the Data Provider
Limited Access until Project Accomplishment (CC-BY 4.0 afterward; planned)

10.2 Data Policy by the Project
10.2.1 Data Integration and Analysis System
If data provider does not have data policy, DIAS Terms of Service (https://diasjp.net/en/terms/)
and DIAS Privacy Policy (https://diasjp.net/en/privacy/) apply.
If there is a conflict between DIAS Terms of Service and data provider's policy, the data provider'spolicy shall prevail.

11. LICENSE
12. DATA SOURCE ACKNOWLEDGEMENT
12.1 Acknowledge the Data Provider
12.2 Acknowledge the Project
12.2.1 Data Integration and Analysis System
If you plan to use this dataset for a conference presentation, paper, journal article, or reportetc., please include acknowledgments referred to following examples. If the data provider describes
examples of acknowledgments, include them as well.
" In this study, [Name of Dataset] provided by [Name of Data Provider] was utilized. This dataset
was also collected and provided under the Data Integration and Analysis System (DIAS), which wasdeveloped and operated by a project supported by the Ministry of Education, Culture, Sports, Scienceand Technology. "13. REFERENCES

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