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294 lines
11 KiB
Markdown
294 lines
11 KiB
Markdown
---
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slug: /en/getting-started/example-datasets/tw-weather
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sidebar_label: Taiwan Historical Weather Datasets
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sidebar_position: 1
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description: 131 million rows of weather observation data for the last 128 yrs
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---
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# Taiwan Historical Weather Datasets
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This dataset contains historical meteorological observations measurements for the last 128 years. Each row is a measurement for a point in date time and weather station.
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The origin of this dataset is available [here](https://github.com/Raingel/historical_weather) and the list of weather station numbers can be found [here](https://github.com/Raingel/weather_station_list).
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> The sources of meteorological datasets include the meteorological stations that are established by the Central Weather Administration (station code is beginning with C0, C1, and 4) and the agricultural meteorological stations belonging to the Council of Agriculture (station code other than those mentioned above):
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- StationId
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- MeasuredDate, the observation time
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- StnPres, the station air pressure
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- SeaPres, the sea level pressure
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- Td, the dew point temperature
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- RH, the relative humidity
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- Other elements where available
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## Downloading the data
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- A [pre-processed version](#pre-processed-data) of the data for the ClickHouse, which has been cleaned, re-structured, and enriched. This dataset covers the years from 1896 to 2023.
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- [Download the original raw data](#original-raw-data) and convert to the format required by ClickHouse. Users wanting to add their own columns may wish to explore or complete their approaches.
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### Pre-processed data
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The dataset has also been re-structured from a measurement per line to a row per weather station id and measured date, i.e.
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```csv
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StationId,MeasuredDate,StnPres,Tx,RH,WS,WD,WSGust,WDGust,Precp,GloblRad,TxSoil0cm,TxSoil5cm,TxSoil20cm,TxSoil50cm,TxSoil100cm,SeaPres,Td,PrecpHour,SunShine,TxSoil10cm,EvapA,Visb,UVI,Cloud Amount,TxSoil30cm,TxSoil200cm,TxSoil300cm,TxSoil500cm,VaporPressure
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C0X100,2016-01-01 01:00:00,1022.1,16.1,72,1.1,8.0,,,,,,,,,,,,,,,,,,,,,,,
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C0X100,2016-01-01 02:00:00,1021.6,16.0,73,1.2,358.0,,,,,,,,,,,,,,,,,,,,,,,
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C0X100,2016-01-01 03:00:00,1021.3,15.8,74,1.5,353.0,,,,,,,,,,,,,,,,,,,,,,,
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C0X100,2016-01-01 04:00:00,1021.2,15.8,74,1.7,8.0,,,,,,,,,,,,,,,,,,,,,,,
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```
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It is easy to query and ensure that the resulting table has less sparse and some elements are null because they're not available to be measured in this weather station.
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This dataset is available in the following Google CloudStorage location. Either download the dataset to your local filesystem (and insert them with the ClickHouse client) or insert them directly into the ClickHouse (see [Inserting from URL](#inserting-from-url)).
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To download:
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```bash
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wget https://storage.googleapis.com/taiwan-weather-observaiton-datasets/preprocessed_weather_daily_1896_2023.tar.gz
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# Option: Validate the checksum
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md5sum preprocessed_weather_daily_1896_2023.tar.gz
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# Checksum should be equal to: 11b484f5bd9ddafec5cfb131eb2dd008
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tar -xzvf preprocessed_weather_daily_1896_2023.tar.gz
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daily_weather_preprocessed_1896_2023.csv
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# Option: Validate the checksum
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md5sum daily_weather_preprocessed_1896_2023.csv
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# Checksum should be equal to: 1132248c78195c43d93f843753881754
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```
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### Original raw data
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The following details are about the steps to download the original raw data to transform and convert as you want.
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#### Download
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To download the original raw data:
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```bash
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mkdir tw_raw_weather_data && cd tw_raw_weather_data
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wget https://storage.googleapis.com/taiwan-weather-observaiton-datasets/raw_data_weather_daily_1896_2023.tar.gz
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# Option: Validate the checksum
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md5sum raw_data_weather_daily_1896_2023.tar.gz
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# Checksum should be equal to: b66b9f137217454d655e3004d7d1b51a
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tar -xzvf raw_data_weather_daily_1896_2023.tar.gz
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466920_1928.csv
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466920_1929.csv
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466920_1930.csv
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466920_1931.csv
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...
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# Option: Validate the checksum
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cat *.csv | md5sum
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# Checksum should be equal to: b26db404bf84d4063fac42e576464ce1
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```
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#### Retrieve the Taiwan weather stations
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```bash
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wget -O weather_sta_list.csv https://github.com/Raingel/weather_station_list/raw/main/data/weather_sta_list.csv
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# Option: Convert the UTF-8-BOM to UTF-8 encoding
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sed -i '1s/^\xEF\xBB\xBF//' weather_sta_list.csv
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```
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## Create table schema
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Create the MergeTree table in ClickHouse (from the ClickHouse client).
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```bash
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CREATE TABLE tw_weather_data (
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StationId String null,
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MeasuredDate DateTime64,
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StnPres Float64 null,
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SeaPres Float64 null,
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Tx Float64 null,
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Td Float64 null,
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RH Float64 null,
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WS Float64 null,
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WD Float64 null,
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WSGust Float64 null,
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WDGust Float64 null,
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Precp Float64 null,
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PrecpHour Float64 null,
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SunShine Float64 null,
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GloblRad Float64 null,
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TxSoil0cm Float64 null,
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TxSoil5cm Float64 null,
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TxSoil10cm Float64 null,
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TxSoil20cm Float64 null,
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TxSoil50cm Float64 null,
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TxSoil100cm Float64 null,
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TxSoil30cm Float64 null,
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TxSoil200cm Float64 null,
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TxSoil300cm Float64 null,
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TxSoil500cm Float64 null,
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VaporPressure Float64 null,
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UVI Float64 null,
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"Cloud Amount" Float64 null,
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EvapA Float64 null,
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Visb Float64 null
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)
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ENGINE = MergeTree
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ORDER BY (MeasuredDate);
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```
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## Inserting into ClickHouse
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### Inserting from local file
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Data can be inserted from a local file as follows (from the ClickHouse client):
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```sql
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INSERT INTO tw_weather_data FROM INFILE '/path/to/daily_weather_preprocessed_1896_2023.csv'
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```
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where `/path/to` represents the specific user path to the local file on the disk.
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And the sample response output is as follows after inserting data into the ClickHouse:
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```response
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Query id: 90e4b524-6e14-4855-817c-7e6f98fbeabb
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Ok.
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131985329 rows in set. Elapsed: 71.770 sec. Processed 131.99 million rows, 10.06 GB (1.84 million rows/s., 140.14 MB/s.)
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Peak memory usage: 583.23 MiB.
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```
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### Inserting from URL
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```sql
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INSERT INTO tw_weather_data SELECT *
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FROM url('https://storage.googleapis.com/taiwan-weather-observaiton-datasets/daily_weather_preprocessed_1896_2023.csv', 'CSVWithNames')
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```
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To know how to speed this up, please see our blog post on [tuning large data loads](https://clickhouse.com/blog/supercharge-your-clickhouse-data-loads-part2).
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## Check data rows and sizes
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1. Let's see how many rows are inserted:
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```sql
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SELECT formatReadableQuantity(count())
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FROM tw_weather_data;
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```
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```response
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┌─formatReadableQuantity(count())─┐
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│ 131.99 million │
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└─────────────────────────────────┘
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```
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2. Let's see how much disk space are used for this table:
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```sql
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SELECT
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formatReadableSize(sum(bytes)) AS disk_size,
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formatReadableSize(sum(data_uncompressed_bytes)) AS uncompressed_size
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FROM system.parts
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WHERE (`table` = 'tw_weather_data') AND active
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```
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```response
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┌─disk_size─┬─uncompressed_size─┐
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│ 2.13 GiB │ 32.94 GiB │
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└───────────┴───────────────────┘
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```
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## Sample queries
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### Q1: Retrieve the highest dew point temperature for each weather station in the specific year
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```sql
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SELECT
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StationId,
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max(Td) AS max_td
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FROM tw_weather_data
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WHERE (year(MeasuredDate) = 2023) AND (Td IS NOT NULL)
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GROUP BY StationId
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┌─StationId─┬─max_td─┐
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│ 466940 │ 1 │
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│ 467300 │ 1 │
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│ 467540 │ 1 │
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│ 467490 │ 1 │
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│ 467080 │ 1 │
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│ 466910 │ 1 │
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│ 467660 │ 1 │
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│ 467270 │ 1 │
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│ 467350 │ 1 │
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│ 467571 │ 1 │
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│ 466920 │ 1 │
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│ 467650 │ 1 │
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│ 467550 │ 1 │
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│ 467480 │ 1 │
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│ 467610 │ 1 │
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│ 467050 │ 1 │
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│ 467590 │ 1 │
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│ 466990 │ 1 │
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│ 467060 │ 1 │
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│ 466950 │ 1 │
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│ 467620 │ 1 │
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│ 467990 │ 1 │
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│ 466930 │ 1 │
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│ 467110 │ 1 │
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│ 466881 │ 1 │
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│ 467410 │ 1 │
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│ 467441 │ 1 │
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│ 467420 │ 1 │
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│ 467530 │ 1 │
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│ 466900 │ 1 │
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└───────────┴────────┘
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30 rows in set. Elapsed: 0.045 sec. Processed 6.41 million rows, 187.33 MB (143.92 million rows/s., 4.21 GB/s.)
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```
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### Q2: Raw data fetching with the specific duration time range, fields and weather station
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```sql
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SELECT
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StnPres,
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SeaPres,
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Tx,
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Td,
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RH,
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WS,
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WD,
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WSGust,
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WDGust,
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Precp,
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PrecpHour
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FROM tw_weather_data
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WHERE (StationId = 'C0UB10') AND (MeasuredDate >= '2023-12-23') AND (MeasuredDate < '2023-12-24')
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ORDER BY MeasuredDate ASC
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LIMIT 10
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```
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```response
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┌─StnPres─┬─SeaPres─┬───Tx─┬───Td─┬─RH─┬──WS─┬──WD─┬─WSGust─┬─WDGust─┬─Precp─┬─PrecpHour─┐
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│ 1029.5 │ ᴺᵁᴸᴸ │ 11.8 │ ᴺᵁᴸᴸ │ 78 │ 2.7 │ 271 │ 5.5 │ 275 │ -99.8 │ -99.8 │
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│ 1029.8 │ ᴺᵁᴸᴸ │ 12.3 │ ᴺᵁᴸᴸ │ 78 │ 2.7 │ 289 │ 5.5 │ 308 │ -99.8 │ -99.8 │
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│ 1028.6 │ ᴺᵁᴸᴸ │ 12.3 │ ᴺᵁᴸᴸ │ 79 │ 2.3 │ 251 │ 6.1 │ 289 │ -99.8 │ -99.8 │
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│ 1028.2 │ ᴺᵁᴸᴸ │ 13 │ ᴺᵁᴸᴸ │ 75 │ 4.3 │ 312 │ 7.5 │ 316 │ -99.8 │ -99.8 │
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│ 1027.8 │ ᴺᵁᴸᴸ │ 11.1 │ ᴺᵁᴸᴸ │ 89 │ 7.1 │ 310 │ 11.6 │ 322 │ -99.8 │ -99.8 │
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│ 1027.8 │ ᴺᵁᴸᴸ │ 11.6 │ ᴺᵁᴸᴸ │ 90 │ 3.1 │ 269 │ 10.7 │ 295 │ -99.8 │ -99.8 │
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│ 1027.9 │ ᴺᵁᴸᴸ │ 12.3 │ ᴺᵁᴸᴸ │ 89 │ 4.7 │ 296 │ 8.1 │ 310 │ -99.8 │ -99.8 │
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│ 1028.2 │ ᴺᵁᴸᴸ │ 12.2 │ ᴺᵁᴸᴸ │ 94 │ 2.5 │ 246 │ 7.1 │ 283 │ -99.8 │ -99.8 │
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│ 1028.4 │ ᴺᵁᴸᴸ │ 12.5 │ ᴺᵁᴸᴸ │ 94 │ 3.1 │ 265 │ 4.8 │ 297 │ -99.8 │ -99.8 │
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│ 1028.3 │ ᴺᵁᴸᴸ │ 13.6 │ ᴺᵁᴸᴸ │ 91 │ 1.2 │ 273 │ 4.4 │ 256 │ -99.8 │ -99.8 │
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└─────────┴─────────┴──────┴──────┴────┴─────┴─────┴────────┴────────┴───────┴───────────┘
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10 rows in set. Elapsed: 0.009 sec. Processed 91.70 thousand rows, 2.33 MB (9.67 million rows/s., 245.31 MB/s.)
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```
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## Credits
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We would like to acknowledge the efforts of the Central Weather Administration and Agricultural Meteorological Observation Network (Station) of the Council of Agriculture for preparing, cleaning, and distributing this dataset. We appreciate your efforts.
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Ou, J.-H., Kuo, C.-H., Wu, Y.-F., Lin, G.-C., Lee, M.-H., Chen, R.-K., Chou, H.-P., Wu, H.-Y., Chu, S.-C., Lai, Q.-J., Tsai, Y.-C., Lin, C.-C., Kuo, C.-C., Liao, C.-T., Chen, Y.-N., Chu, Y.-W., Chen, C.-Y., 2023. Application-oriented deep learning model for early warning of rice blast in Taiwan. Ecological Informatics 73, 101950. https://doi.org/10.1016/j.ecoinf.2022.101950 [13/12/2022]
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