PrepperPrepper Open Data Bank

JMA_METEOROLOGICAL_MONTHLY【WT_MM】

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 of every month

Sample 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

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