CReSS Daily Simulation (around Japan 2km mesh)


The citation for this dataset is:
Kazuhisa Tsuboki. (2020). CReSS Daily Simulation (around Japan 2km mesh) [Data set]. Data Integration and Analysis System (DIAS). https://doi.org/10.20783/DIAS.597
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IDENTIFICATION INFORMATION

Name CReSS Daily Simulation (around Japan 2km mesh)
Abbreviation CReSS_JPN20DK
DOI doi:10.20783/DIAS.597
Metadata Identifier CReSS_JPN20DK20210607145630-DIAS20210525095249-en

CONTACT

CONTACT on DATASET

Name Masaya Kato
Organization Institute for Space-Earth Environmental Research, Nagoya Univ.
Address Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
TEL 052-789-3495
E-mail kato@rain.isee.nagoya-u.ac.jp

CONTACT on PROJECT

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-mail dias-office@diasjp.net

DOCUMENT AUTHOR

Name Takahiro INOUE
Organization Remote Sensing Technology Center of Japan

DATASET CREATOR

Name Kazuhisa Tsuboki
Organization Institute for Space-Earth Environmental Research, Nagoya Univ.
E-mail tsuboki@nagoya-u.jp
Name Masaya Kato
Organization Institute for Space-Earth Environmental Research, Nagoya Univ.
E-mail kato@rain.isee.nagoya-u.ac.jp

DATE OF THIS DOCUMENT

2021-06-07

DATE OF DATASET

  • publication : 2020/08/20

DATASET OVERVIEW

Abstract

The Laboratory of Meteorology, Institute for Space-Earth Environmental Research, Nagoya University, has been performing daily high-resolution weather simulations using the cloud resolving storm model CReSS (Cloud Resolving Storm Simulator). To demonstrate how well the current model can simulate real-world weather, the results of the simulations are mainly intended for experts. This dataset is the original data from that simulation, shown on the Web page "CReSS Daily Simulation".

The area of calcuration is about 2300km by 2800km, surrounding Japan, with about 2km grid size. The map projection method is the Lambert Conformal Conic.

The actual simulations are performed prior to the actual time, but due to the limitations of the Meteorological Service Act, only results prior to the current time are made available to the public.

If you wish to use this data to conduct weather forecasting services in Japan, you must follow the appropriate procedures in accordance with the Meteorological Service Act. Please contact the Japan Meteorological Agency for more information on the law.

The simulations are calculated once a day for up to 36 hours ahead.

This dataset contains the following 17 three dimensional variables and 30 two dimensional variables, the former being output every three hours and the latter every hour.

3D Variables:

u : x components of velocity [m/s]

v : y components of velocity [m/s]

w : z components of velocity [m/s]

p : pressure [Pa]

pt : potential temperature [K]

qv : water vapor mixing ratio [kg/kg]

qc : cloud water mixing ratio [kg/kg]

qr : rain water mixing ratio [kg/kg]

qi : cloud ice mixing ratio [kg/kg]

qs : snow mixing ratio [kg/kg]

qg : graupel mixing ratio [kg/kg]

nci : cloud ice concentrations [1/kg]

ncs : snow concentrations [1/kg]

ncg : graupel concentrations [1/kg]

qt : tracer mixing ratio

tke : turbulent kinetic energy [J/kg]

zph : z physical coordinates [m]

2D variables:

us : x components of velocity at an altitude of 10m [m/s]

vs : y components of velocity at an altitude of 10m [m/s]

ps : pressure at an altitude of 1.5m [Pa]

pts : potential temperature at an altitude of 1.5m [K]

qvs : water vapor mixing ratio at an altitude of 1.5m [kg/kg]

tgs : soil and sea surface temperature [K]

hs : sensible heat over surface [W/m^2]

le : latent heat over surface [W/m^2]

rgd : global solar radiation [W/m^2]

rsd : net downward short wave radiation [W/m^2]

rld : downward long wave radiation [W/m^2]

rlu : upward long wave radiation [W/m^2]

cdl : cloud cover in lower layer

cdm : cloud cover in middle layer

cdh : cloud cover in upper layer

cdave : averaged cloud cover

usflx : surface momentum flux for x components of velocity [N/m^2]

vsflx : surface momentum flux for y components of velocity [N/m^2]

ptsflx : surface heat flux [(kg K)/(m^2 s)]

qvsflx : surface moisture flux [kg/(m^2 s)]

pcr : cloud water fall rate [m/s]

pca : accumulated cloud water fall [m]

prr : rain fall rate [m/s]

pra : accumulated rain fall [m]

pir : cloud ice fall rate [m/s]

pia : accumulated cloud ice fall [m]

psr : snow fall rate [m/s]

psa : accumulated snow fall [m]

pgr : graupel fall rate [m/s]

pga : accumulated graupel fall [m]

Topic Category(ISO19139)

  • climatologyMeteorologyAtmosphere

Temporal Extent

Begin Date 20130526
End Date Under Continuation
Temporal Characteristics Hourly

Geographic Bounding Box

North bound latitude 46.463
West bound longitude 120.178
Eastbound longitude 149.518
South bound latitude 20.8597

Keywords

Keywords on Dataset

Keyword Type Keyword Keyword thesaurus Name
theme [Atmosphere ] > [Atmospheric Temperature ], [Atmosphere ] > [Precipitation ], [Atmosphere ] > [Atmospheric Phenomena ] > [Typhoons ], [Atmosphere ] > [Clouds ], [Atmosphere ] > [Atmospheric Radiation ], [Atmosphere ] > [Atmospheric Water Vapor ] GCMD_science
theme ATMOSPHERIC PROCESSES > Mesoscale meteorology, ATMOSPHERIC PROCESSES > Convective processes, ATMOSPHERIC PROCESSES > Precipitation, ATMOSPHERIC PROCESSES > Regional modeling AGU
theme Weather GEOSS
place Asia > Eastern Asia > Japan Country
theme Models GCMD_platform

Keywords on Project

Data Integration and Analysis System
Keyword Type Keyword Keyword thesaurus Name
theme DIAS > Data Integration and Analysis System No_Dictionary

Data Environmental Information

Each directory contains binary data files and corresponding Grads ctl files. The extension ".bin" is for the data file and ".ctl" is for the Grads ctl file. A file basename ending in "_dmp" is for 3D variables, one ending in "_mon" is for a horizontal 2D variables, "_geography" is for the geographical data. The "YYYYMMDDhhZ" part of the file name indicates the simulation start date and the data output date.

Distribution Information

name version specification
Fortran binary data N/A 4byte binary (big endian)

DATA POLICY

Data Policy by the Data Provider

This dataset is distributed under the CC BY-SA 4.0 International License.

http://creativecommons.org/licenses/by-sa/4.0/

The data author will not guarantee that the results of the simulations that produced this dataset are correct or represent the actual weather.

No responsibility is assumed by anyone, including the creator (and all individuals and organizations involved in the creation of this dataset, and the same applies below). No liability of any kind shall be assumed by anyone, including the creator.

No one, including the creator, shall be held liable or responsible for any detriment or damage or loss (whether financial, material, personal, emotional, social or otherwise) resulting from the use of or in connection with this dataset.

Data Policy by the Project

Data Integration and Analysis System

If data provider does not have data policy, DIAS Terms of Service (https://diasjp.net/en/policy/) and DIAS Privacy Policy (https://diasjp.net/en/privacypolicy/) apply.

If there is a conflict between DIAS Terms of Service and data provider's policy, the data provider's policy shall prevail.

DATA SOURCE ACKNOWLEDGEMENT

Acknowledge the Project

Data Integration and Analysis System

If you plan to use this dataset for a conference presentation, paper, journal article, or report etc., 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 was developed and operated by a project supported by the Ministry of Education, Culture, Sports, Science and Technology. "

REFERENCES

Tsuboki, K., and A. Sakakibara (2002), Large-scale parallel computing of Cloud Resolving Storm Simulator, in High Performance Computing, edited by H. P. Zima, K. Joe, M. Sato, Y. Seo, and M. Shimasaki, pp. 243-259, Springer, New York.

Tsuboki, K. (2008), High-resolution simulations of high-impact weather systems using the cloud-resolving model on the Earth Simulator, In High Resolution Numerical Modeling of the Atmosphere and Ocean, edited by K. Hamilton, and W. Ohfuchi, pp. 141-156, Springer, New York.


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