JMA_METEOROLOGICAL_DAILY【WT_MD】

■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.