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dlebauer merged 3 commits into from
Aug 27, 2018
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updated met data tutorial #31

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updated met data
dlebauer Jan 27, 2018
3df8442
update stream_ids
dlebauer Aug 27, 2018
2864da4
Updated streams and added some additional information on querying geo…
dlebauer Aug 27, 2018
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65 changes: 54 additions & 11 deletions sensors/01-meteorological-data.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -22,9 +22,6 @@ Geodashboard:

[Sample raw meteorological (1/s) data on Clowder](https://terraref.ncsa.illinois.edu/clowder/files/588ba5474f0c06726acfcace?dataset=587fc7444f0cd67174e7a92d&space=57e42cd44f0cff4b58dd3eea)




## Meteorological data formats

### Dimensions:
Expand Down Expand Up @@ -71,9 +68,46 @@ The key is to process each type of met data (site, reanalysis, forecast, climate

### The Geostreams Database


!(schema)[https://cloud.githubusercontent.com/assets/9286213/16991300/b2f2b09a-4e60-11e6-96b7-8b63c3d1f995.jpg]

#### Querying the API

key query terms:

* /sensors
* sensor_name=''
* /streams
*
* /datapoints
* &since=YYYY-MM-DD
* &until=YYYY-MM-DD
* stream_id=

How to find the stream_id from a particular station or sensor:

1. find streams on "UA-MAC AZMET Weather Station" https://terraref.ncsa.illinois.edu/clowder/api/geostreams/sensors?sensor_name=UA-MAC AZMET Weather Station
2. take sensor id from that response and find the streams: https://terraref.ncsa.illinois.edu/clowder/api/geostreams/sensors/438/streams
3. take stream id from that and use in the datapoints query: https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?stream_id=46431&since=2017-01-02&until=2017-01-31


#### Locations with met data

These are some of the locations with met data:

| stream id | name |
|------------|------------------------------------------|
| 3211 | UA-MAC AZMET Weather Station - weather |
| 3212 | UA-MAC AZMET Weather Station - irrigation |
| 46431 | UA-MAC AZMET Weather Station - weather (5 min) |
| 3208 | EnvironmentLogger sensor_weather_station |
| 3207 | EnvironmentLogger sensor_par |
| 748 | EnvironmentLogger sensor_spectrum |
| 3210 | EnvironmentLogger sensor_co2 |
| 4806 | UIUC Energy Farm SE |
| 4807 | UIUC Energy Farm CEN |
| 4805 | UIUC Energy Farm NE |


Here is the json representation of a single five-minute observation:

```
Expand Down Expand Up @@ -108,18 +142,27 @@ Here is the json representation of a single five-minute observation:

The data represent 5 minute summaries aggregated from 1/s observations.

First, this is what the API looks like
#### Using Curl

First, this is what the API looks like as a URL. Try pasting it into your browser

https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?stream_id=46431&since=2017-01-02&until=2017-01-31

This is how you can automatically download the met data to a local file:

```{sh eval=FALSE}
curl -O spectra.json -X GET https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?stream_id=46431&since=2017-01-02&until=2017-01-31
```

And this is how you can access the data in R:

https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?key=Pb3AUSqnUw&stream_id=4807&since=2017-01-02&until=2017-01-31

```{r met-geostream}
library(dplyr)
library(ggplot2)
library(jsonlite)

url = ""
mac_weather.list <- jsonlite::fromJSON('https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?key=Pb3AUSqnUw&stream_id=4807&since=2017-01-02&until=2017-01-31')
#mac_weather.list <- jsonlite::fromJSON('https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?key=Pb3AUSqnUw&stream_id=3208&since=2017-01-02&until=2017-12-31', flatten = FALSE)
mac_weather.list <- jsonlite::fromJSON('https://terraref.ncsa.illinois.edu/clowder/api/geostreams/datapoints?stream_id=46431&since=2017-01-02&until=2017-01-31', flatten = FALSE)

# change time to human-readable
mac_weather <- mac_weather.list$properties %>%
Expand All @@ -136,14 +179,14 @@ mac_weather <- mac_weather.list$properties %>%
```{r weather}
theme_set(ggthemes::theme_few())
ggplot(data = mac_weather) +
geom_line(aes(x = time, y = wind_speed), size = 0.1)
geom_point(aes(x = time, y = wind_speed), size = 0.1)
```

#### Rainfall

```{r precipitation}
ggplot(data = mac_weather) +
geom_line(aes(x = time, y = precipitation_rate), size = 0.1)
geom_point(aes(x = time, y = precipitation_rate), size = 0.1)
```

#### Your turn!
Expand Down