JMA_METEOROLOGICAL_MONTHLY【WT_MM】
言語：
英語
プロダクト種別：
データ
親プロダクト：
JAPANESE WEATHER DATA(日本の天気データ)
truestar inc. point every month
Summary
This dataset is a collection of meteorological information processed by truestar based on past meteorological data released by the Japan Meteorological Agency. It provides various meteorological data such as temperature and precipitation on a monthly basis.
※Update: 3rd business day of every month
Sample Data
Please select the table you wish to view from the data dictionary in the snowflake marketplace.
Click here for sample data from Japanese Weather Data
Features/Usages
We provide weather open data on temperature, precipitation, wind and sunshine hours at JMA stations from January 1, 2015 to the previous month. We have cleansed the original data by removing text data in numerical columns and added latitude/longitude, and prefecture information for the stations, so that the data can be analyzed immediately.
Data Fields
Field Name |
|---|
BLOCK_CODE |
OBSERVATORY_NAME |
OBSERVATORY_TYPE |
OBSERVATION_DATA |
PREF_CODE |
PREF_NAME |
JMA_AREA_CODE |
JMA_AREA_NAME |
LATITUDE |
LONGITUDE |
DATE |
AIR_PRESSURE |
AIR_PRESSURE_SEA_LEVEL |
RAINFALL |
RAINFALL_MAX_DAILY |
RAINFALL_MAX_1H |
RAINFALL_MAX_10MIN |
AIR_TEMPERATURE |
AIR_TEMPERATURE_DAILY_MAX_AVERAGE |
AIR_TEMPERATURE_DAILY_MIN_AVERAGE |
AIR_TEMPERATURE_MONTHLY_MAX |
AIR_TEMPERATURE_MONTHLY_MIN |
HUMIDITY |
HUMIDITY_MIN |
WIND_SPEED |
WIND_SPEED_MAX |
WIND_SPEED_MAX_DIRECTION |
WIND_SPEED_INSTANTANEOUS_MAX |
WIND_SPEED_INSTANTANEOUS_MAX_DIRECTION |
SUNLIGHT_HOURS |
GLOBAL_SOLAR_RADIATION |
SNOWFALL |
SNOWFALL_MAX_DAILY |
SNOWFALL_DEEPEST |
CLOUDAGE |
SNOWFALL_DAYS |
FOG_DAYS |
THUNDER_DAYS |
POINT_OBSERVATORY |
References
Prepared by truestar based on the following data:
JMA (Japan Meteorological Agency) meteorological observation data
https://www.data.jma.go.jp/obd/stats/etrn/
Special Notes
Points to note when visualizing with Tableau
This dataset includes point data of weather stations and observatories called “POINT_OBSERVATORY”, but because of large number of records, visualization including mapping may take some time to draw.
Countermeasure 1: On Tableau, point data can be generated quickly from latitude and longitude information, which is a numerical type. Point data can be created using the “makepoint([LATITUDE], [LONGITUDE])” function in the Tableau calculation field.
Countermeasure 2: By extracting data from Snowflake in Tableau, it can be drawn quickly. In this case, the extraction file needs to be updated as necessary.
About each statistical value
For each data value, statistic = 0 includes those less than 0.5.
Also, the statistical values that are null are either not observable or observable but unobservable values. Null does not equal 0.
Discrepancies between daily and monthly aggregate values
In some data, the daily monthly aggregate values and monthly aggregate values may not match because there are missing data in the daily data due to changes in observation locations or equipment.
The table below shows precipitation data for Niimi Observatory in 2016 , but since there was a change in observation location, etc. between August 23-24 and the before and after data are not homogeneous, the monthly value in August is the sum of the 25th through the 31st.
Update History
2022/1/28: Rename JMA_METEOROLOGICAL_DATA_MONTHLY to JMA_METEOROLOGICAL_MONTHLY