R Programming tips to read and plot IGRA2 Radiosonde data

By Andy May

R is an extremely powerful programming language for processing, analyzing and displaying data from large datasets. As discussed in the first post of this series on analyzing IGRA2 radiosonde data with R, the language has improved considerably in recent years. Surprisingly it is free and can be downloaded here. This post will cover some necessary R programming techniques for those interested in reading and plotting IGRA2 data. The IGRA2 measurements have had minimal processing and are as close to raw data as possible, unlike RICH or ROABCORE data, thus it is a useful check on climate model output. The IGRA2 data can be downloaded here or from its ftp site: ‘ftp.ncei.noaa.gov/pub/data/igra.’ It is well formed but requires some manipulation to make it useable. Once the data are prepared, some further tricky programming is needed to plot it. I’ll briefly introduce the key programming techniques here. The full suite of complete R programs that I used to analyze the IGRA2 data (May, 2025) can be downloaded here (warning the file is 658 MB and processing it will require > 32 GB of RAM). For a simple and brief list of the programs and what they do, download this pdf.

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R Programming – Improvements in the Language

By Andy May

This is an introductory post to a series on using R to read IGRA2 radiosonde data, process it, and produce both plots and maps of the data. I started using R over 10 years ago mainly because it was a free and very powerful language for statistical analysis (download the current 64-bit Windows version here). At the time, it was a clunky programming language and difficult to use, but that has recently changed. While working on new R programs to analyze the radiosonde data I saw the many substantial improvements to the language added since around 2020. It is now a very impressive language and much easier to use and to read. Before we get into the radiosonde analysis, I’d like to cover the recent improvements in the language. Future posts in this series will provide more details about the R language and my analysis of IGRA2.

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The Story behind my Paper on the ITCZ and the Hadley Circulation

By Andy May

It all began eight years ago when I read and reviewed Ronan and Michael Connolly’s first three papers on their ideas about the “molar density intersection” which is located just below the tropopause. I was quite fond of Michael Connolly, who sadly and suddenly passed away in August 2025, we all miss him.

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What is a “climate crisis?”

By Andy May

In a new paper by Gianluca Alimonti and Luigi Mariani, they argue that the public needs a proper definition of precisely what a climate crisis is to make rational decisions about how to address potential climate change threats (Alimonti & Mariani, 2025). They propose a set of measurable “Response Indicators” (RINDs) based on the IPCC AR6 Climate Impact drivers (IPCC, 2021, pp. 1851-1856).

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An Orwellian firing at the American Journal of Economics and Sociology

By Andy May

Well, it is official, Marty Rowland PhD has been fired from his position as Special Issue Editor at the American Journal of Economics and Sociology (AJES). The reason he was given for being fired was his publication of our paper, Carbon Dioxide and a Warming Climate are not problems. The paper has been cited 23 times according to google scholar. It was first published online May 29, 2024, and is already in the top 1% of all 29 million papers followed by Wiley’s Altmetric tracker. It is the #2 paper published in the 83-year history of the AJES.

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Climate Oscillations 12: The Causes & Significance

By Andy May

In this post we will examine the idea that ocean and atmospheric oscillations are random internal variability, except for volcanic eruptions and human emissions, at climatic time scales. This is a claim made by the IPCC when they renamed the Atlantic Multidecadal Oscillation (AMO) to the Atlantic Multidecadal Variability (AMV) and the PDO to PDV, and so on. AR6 (IPCC, 2021) explicitly states that the AMO (or AMV) and PDO (or PDV) are “unpredictable on time scales longer than a few years” (IPCC, 2021, p. 197). Their main reason for stating this and concluding that these oscillations are not influenced by external “forcings,” other than a small influence from humans and volcanic eruptions, is that they cannot model these oscillations, with the possible exceptions of the NAM and SAM (IPCC, 2021, pp. 113-115). This is, of course, a circular argument since the IPCC models have never been validated by predicting future climate accurately, and they also make some fundamental assumptions that simply aren’t true.

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Climate Oscillations 11: Oceanic Niño Index (ONI)

By Andy May

The Oceanic Niño Index or ONI is NOAA’s primarily indicator for monitoring the sea surface temperature (SST) anomaly in the critical Niño 3.4 region. It is a 3-month running mean of ERSST.v5 SST anomalies in the Niño 3.4 region, defined as 5°N-5°S and 120°W-170°W. Figure 1 shows the ONI as computed from the NOAA ERSST dataset. ERSST is a two-degree gridded dataset, so the region averaged for figure 1 is 6°N-6°S and 120°W-170°W.

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Climate Oscillations 10: Aleutian Low – Beaufort Sea Anticyclone (ALBSA)

By Andy May

The Aleutian Low – Beaufort Sea Anticyclone climate index or ALBSA is designed to compare the Aleutian Low Pressure and the Beaufort Sea High Pressure Centers. The intent is to relate air circulation patterns in the North Pacific and Arctic to climate and the timing of spring sea ice and snow melt.

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Climate Oscillations 9: Arctic & North Atlantic Oscillations

By Andy May

The Arctic Oscillation (AO) is closely related to the NAO (the North Atlantic Oscillation discussed below) but they are not the same. The NAO is usually measured using the SLP (sea level air pressure) difference between the Azores or the Iberian Peninsula and Iceland and is a North Atlantic regional phenomenon, whereas the Arctic Oscillation is the SLP difference between the northern mid-latitudes and the Arctic, and is evident in all longitudes (Thompson & Wallace, 2001). The AO accounts for more of the variance in Northern Hemisphere surface air temperature than the NAO and is tightly connected to the stratospheric polar vortex (Higgins, et al., 2000) and (Thompson & Wallace, 1998). We will discuss these oscillations together in this post.

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Climate Oscillations 8: The NPI and PDO

By Andy May

The North Pacific Index (NPI) and the Pacific Decadal Oscillation (PDO)

The North Pacific Index (NPI) is computed from the area-weighted sea level air pressure (SLP) over the region 30°N-65°N and 160°E-140°W. It measures interannual to multidecadal variations in Pacific atmospheric circulation. As explained in Trenberth and Hurrel, the winter Aleutian low pressure system moves on a decadal time scale and changes the climate and sea surface temperature (SST) along western North America and in the Northern Central Pacific. These changes are closely related to the PDO (Pacific Decadal Oscillation), which describes the same multidecadal weather and SST pattern in the same region but is calculated with SSTs using a different statistical method. Other oscillations that describe this pattern or something similar are the Interdecadal Pacific Oscillation (IPO) and the North Pacific Oscillation (NPO).

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