Weather Reanalysis Models

By Andy May

My new paper (May, 2025) emphasizes that while many of the underlying observations used to build weather reanalysis datasets, such as ERA5 (European Centre for Medium-Range Weather Forecasts or ECMWF Reanalysis v5) (Soci et al., 2024) or MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) (Gelaro et al., 2017), are from radiosondes, weather reanalysis models are still models and have the same problems that other models have. Thus, they are not observations or measurements, like those in radiosonde data repositories such as IGRA2, and should not be treated as such. The reanalysis models assimilate surface measurements and satellite data in addition to radiosonde data and blend the measurements together into a global or regional grid using a general circulation atmospheric model. Weather reanalysis models produce reasonably consistent, physics-based periodic (usually every 6 to 12 hours) estimates of the global atmospheric state (Bloom et al., 1996), but they are not observations. Dr. Hans Hersbach of ECMWF (European Centre for Medium-Range Weather Forecasts) provides us with figure 1 below which is an illustration of the data assimilation process in ERA5.

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The Monthly ITCZ Central Latitude

By Andy May

The Intertropical Convergence Zone or ITCZ is where the trade winds from the Northern and Southern Hemisphere converge and where the column-integrated meridional (north-south) circulation and the “near-surface meridional mass flux” vanishes according to Adam et al., 2016. For a history of the discovery of the ITCZ see Nicholson, 2018. The ITCZ is not the solar equator, the latitude where the Sun is directly overhead at noon, but it is closely related to it, and they move in a coordinated fashion. The ITCZ is an oceanic phenomenon and doesn’t really exist over land in the same way as described here, except in coastal areas (Nicholson, 2018).

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R Programming tips to process IGRA2 Radiosonde data

By Andy May

In the last two posts (here and here) I showed how to read, plot, and map IGRA2 data. In this post I will discuss how to efficiently process large R data frames and lists to compute useful variables.

One of the more difficult things to do in R is to write readable code. This problem has been around for a long time, but in recent years a very useful tool has appeared, the “pipe” or %>%.

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R Programming tips on mapping IGRA2 Radiosonde data

By Andy May

Our last post was on reading and plotting IGRA2 weather balloon radiosonde data. This post shows how to map the data. The IGRA2 data can be downloaded from here (ftp site: ftp.ncei.noaa.gov/pub/data/igra). Once the data are prepared, some further tricky programming is needed to map it. This post will introduce the key concepts and code structure required. Writing complete programs will require understanding these concepts and working with Grok.

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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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