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Extraordinary spring temperatures in Central Asia have coincided with drought and extreme floods in large regions of Australia higher than Texas and fires in South Korea.
Researchers from the World Weather Assignment group (WWA), which performs a rapid analysis of meteorological events, said that high temperatures in Central Asia were 10 ° C above the pre-industrial level at around 30 ° C.
Climate change has made this “crazy” heat wave around 4C more intense and about three times more likely. They pointed out that these figures were probably conservative estimates.
To map temperature data, we turned to NASA Giovanni application.
Its user -friendly interface facilitates the selection of climatic data for a very simple specific area and period of time. It has a wide range of atmospheric variables (and others) with a different temporal and spatial resolution.
This may not provide absolutely all the metrics you need, but it is a good one -shop store for fast reversal data graphics. The search result is viewed in the browser, before you can download the underlying data.
The temperature anomalies resulting from the heat wave were fired in QGIS, the geospatial software of the choice FT, for the style. A divergent blue-red color palette has been used, a standard choice for temperature anomalies.
Since we have visualized the data of temperature anomalies several times and in different ways, I wanted to watch additional data.
The United Kingdom has so far had an exceptionally dry, warm and sunny spring and the Met Office has recorded the sunniest walk from England since the start of the measures in 1910 (third more than the United Kingdom in the United Kingdom).
Although more time than a climate graph, this file inspired a graphic on the English sun.
The Met Office publishes Sunshine data on their websiteoffering breakdowns by region. In addition to typical temperature parameters, this page contains the sun, precipitation, rainy days and air frost days, such as monthly, seasonal and annual series.
I have downloaded the Sunshine data and used the R programming language to reformat it, before creating a barcode route from the monthly Sunshine. The months are through the X axis, while the number of monthly sun hours are along the Y axis (opposite for mobile), and each tile represents a year of sun for this given month. (Nerd Note: I used Geom_tile from the GGPLOT2 library to create this layout layout).
In relation to this, the EMPER research group has published monthly electricity data and an interesting visual to support it. This chart Electricity production from Solar in the United Kingdom also shows how sunny it has been.
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