Software

Daily Global Temperatures 1948-2023

I’ve spent much of the day adding a viewer to my DIYDIY Do It Yourself Global temperature application. The new viewer layers a line series of daily temperatures for each year from 1948 to 2023. The lines series are coloured grey apart from, 2023 in red, and 2016 in blue. I picked 2016 because that’s the warmest year in the series. I’ve also added a yellow band for the daily standard deviation and a black line for the long-term average. For much of this year 2023 has been trailing 2016, but in the first four days of July 2023 there’s been a very sharp spike in daily global temperatures, such that, and quite remarkably, the latest available global mean on the 4th of July was the highest in the entire 75 year series at 11.42°C. Not a lot of people know that.

I think it’s a great application, and the results from it are fascinating to see and I’m quite proud to have developed it. It’s a simple concept and can work on any Windows PC using NCEPNCEP The United States National Centers for Environmental Prediction (NCEP) delivers national and global weather, water, climate and space weather guidance, forecasts, warnings and analyses to its Partners and External User Communities. reanalysis data.

3 Comments / July 6, 2023

Global Temperatures, News, Software

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A real-time Summer Index

Fine weather over central & SE England identified by Summer Index

I’ve been toying with the idea of a Real-time summer index [SISI Summer Index] for many years. Of course the idea of a summer index is very subjective – everyone’s perfect summer day is different. I use a very simple algorithm of total cloud amount, present weather, relative humidity, surface visibility, mean wind speed and of course temperature to calculate it with. Results look promising. There’s no reason why the idea couldn’t be extended to use NWPNWP Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. data for forecasting summer index for many days ahead. The values are using my preferences for an ideal temperature of 25°C. This is not ideal because what I would like to have used was anomalies. A fixed temperature also means the SI will oscillate through the day as things warm up, so it’s far from perfect. A perfect day to me really requires an okta of cumulus or cirrus cloud rather than complete blue skies. The perfect day also requires some wind, no more than five knots, perfect visibility and low humidities. I factor all of them into the algorithm, but thats still very much work in progress. The 25°C ideal temperature may be a little too high for me, but at that rules out SI levels much above 75 across the Mediterranean in the summer.

Hourly temperature analysis

I’ve developed a single purpose application to analyse hourly temperatures for any station during a month. There’s more work to be done on it as it could be extended to look at other climate variables.

Notice the cold thread with the easterlies at the start of the month and the much warmer second half thread. Average temperatures sometimes hide the true story.

Compare those results with those from maritime climate of southern Ireland.

April 2023 – A new twist to scatter correlation graphs

I’ve been producing scatter correlation graphs for UKUK The United Kingdom of Great Britain and Northern Ireland, commonly known as the United Kingdom (UK) or Britain, is a country in Europe, off the north-western coast of the continental mainland. It comprises England, Scotland, Wales and Northern Ireland. gridded data for a number of years now, but recently I tried the same idea with climate data drawn from SYNOPSYNOP SYNOP (surface synoptic observations) is a numerical code (called FM-12 by WMO) used for reporting weather observations made by manned and automated weather stations. SYNOP reports are typically mad hourly and consist of groups of numbers (and slashes where data is not available) describing general weather information, such as the temperature, barometric pressure and visibility at a weather station. data for many stations for a single time period. I’ve now developed a viewer that displays a scatter chart of monthly rainfall POAPOA Percentage Of Average along the X axis, with temperature anomalies along the Y axis. To differentiate between each country I’ve coloured coded each point with one of five different colours. As you can see for April 2023 it works quite well, and you can quickly see that Scotland (blue) had a cold but reasonable dry month, whereas England (yellow) were much wetter.

The above scatter graph shows the correlation between rainfall POA (Y axis) and sunshine POA (X axis), and again it’s easy to see at a glance how much more sunnier and drier Scotland were when compared to England. I’m sure it will be only a matter of time before someone else uses this idea of mine.

May 2, 2023

April, Software

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