Antarctic winds

Antarctica has impressive surface winds. They are unusual because they are related in a simple way to topography. The map shows a 1979-2014 climatology of surface winds derived from ECMWF’s ERA-interim.  In the interior of the continent, the combination of a strong temperature inversion (radiative cooling of the ice cap under clear skies) and sloping terrain generates an “inversion wind”[1]. The cold bottom layer simply slips downhill. Large-scale motion is affected by the earth’s rotation, deflecting winds to the left. Near the coast, steeper gradients generate extreme “katabatic” winds, especially when channelled into straits or valleys.

The graph below shows the distribution of \mathrm{cos} \theta over the Antarctic continent, where \theta is the angle between wind direction and local surface slope vector (both vector fields taken at the resolution of the ERA data =0.75^o). As expected, winds run overwhelmingly downslope, but with a large deflection from 180^o due to coriolis effect.


[1] The Inversion Wind Pattern over West Antarctica, Parish & Bromwich, 1986

Hidden history of the bond market

Sometimes time-series data derive from an underlying system which can exist in multiple distinct states. Hidden Markov Models (HMMs) are a popular approach in this situation. In a particular state, the data fluctuate with variance characteristic of that state. An N x N transition probability matrix describes the additional dynamics associated with flipping between the N states. The value of N and nature of the unobserved states may or may not be obvious depending on the problem.

Yields on long-term British government debt for the period 1727-2013 are available from the UK debt management office (annual mean yields on perpetual bonds or “consols”). Bond yields change in response to market perceptions of risk. Historically, volatile periods tend to be associated with large attritional wars, such as Seven Years War (1754-1763), Napoleonic wars (1803-1815), World War One (1914-1918) etc.

It is tempting to fit a HMM to the (differenced) gilt yield data. N is found by minimising the Bayesian Information Criterion (BIC). It turns out that N=3 is optimal for the differenced time-series (code and graph below).

The modern era of high inflation requires a third underlying state “I” which lasted from 1968-1999.


The R code below returns N=3.

library(RHmm) <- “”
gilt <- read.csv(
returns <- diff(gilt$yield)
bics <- sapply(2:10, function(nn) HMMFit(returns, dis=”NORMAL”, nStates=nn)$BIC)