Yet more cleverness: getting ambient temperature data from cellphones

Following up on an earlier post about some smarty-pantses that figured out how to use cell phone towers to extract estimates of local rainfall, many of these same smarty-pantses have now figured out how to use those same cell phones to provide information on local temperatures.  Here's the new paper, just out in Geophysical Research Letters [HT: Noah Diffenbaugh]:

Crowdsourcing urban air temperatures from smartphone battery temperatures
A. Overeem, J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, and R. Uijlenhoet

Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. A straightforward heat transfer model is employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real-time temperature monitoring in densely populated areas.

They validate their technique in a few big cities around the world, and it looks pretty neat.  As shown in their Fig 2, which shows temperatures for London over a 4-month period and is reproduced below, raw changes in battery temperature are highly correlated with variation in ambient temperature (compare the orange line and black line, reported correlation r=0.82), and their heat transfer model is able to get the levels close to right (compare the blue dots with the black line).

What we really want to know, of course, is whether this can also work in places where the weather-observing infrastructure is currently really poor (e.g. most of Africa), and thus were techniques like this could be extra useful.  It seems like there are a couple hurdles.  First, you need a lot of people with smartphones.  According to this article, smartphone penetration in Africa is currently around 20%, but Samsung (who might know) puts it at less than 10%.  Nevertheless, smartphone adoption appears to be growing rapidly (you can find them in just about any tiny rural market in western Kenya, for instance), and so this might not be such a limitation in a few years.  And something the authors worry about in colder and richer climes -- that their battery temperature readings are biased because people are in heated or air-conditioned buildings a lot -- is much less of a worry in places where people are outside more and don't keep their houses at a perfect 70F.

Second, to get temperature levels right, it appears that the authors have to calibrate battery temperatures in a given area to data on observed temperatures in that area -- which is obviously not helpful if you don't have observed data to start with.  But if all you care about is temperature deviations -- e.g. if you're running a panel model that is linear in average temperature -- then it seems like the raw battery temperature data give you this pretty well (see figure).  Then if you really need levels -- say you want to estimate how a crop responds to temperatures above a given threshold -- you could do something like David did in his 2011 paper on African maize and add these deviations back to somebody else's estimate of the climatology (David used WorldClim).

Given this, the authors' optimism on future applications seems fitting:


"In the end, such a smartphone application could substantially increase the number of air temperature observations worldwide. This could become beneficial for precision agriculture, e.g., for optimization of irrigation and food production, for human health (urban heat island), and for energy demand planning."

But hopefully the expansion of this technique into rural areas won't have to wait for observed data with which to calibrate their heat transfer model.  If that London plot above is representative, it seems like just getting the raw battery data could be really helpful.

From cell towers cometh rainfall estimates?

Getting trustworthy data on rainfall and temperature over space and time is really critical to science across a range of disciplines. Sadly, the world's basic weather monitoring network has been in serious decline over the last few decades. Here is a depressing plot from a recent paper by Lorenz and Kuntsman showing the decline in the number of reporting weather stations around the world:

As the number of stations declines, our estimates of how much rain fell in a given area at a given time gets worse and worse, and this can create really bad problems for applied social scientists.  Rainfall and temperature typically show up as independent variables in social scientists' statistical analyses.  If these variables are measured with error and this error is "classical", i.e. uncorrelated with the "true" underlying thing being measured, then your estimates of the effect of these variables on the outcome of interest get biased towards zero.  That is, you (perhaps erroneously) conclude that rainfall or temperature had no effect -- or a much smaller effect -- on crop yields or infant mortality or conflict than what actually occurred.

There are some work-arounds to having limited gauge data.  One is to use "reanalysis" data, in which some kind climate scientists sitting in a basement somewhere take the available observations and use them as inputs to a climate model, spitting out a physically consistent record of weather over time.  Another option is to use data from satellites, but satellites appear to measure rainfall better than temperature, and the available satellite output is often at a fairly coarse scale (e.g. 200km grids).  A third is to use interpolations of the gridded data, which imputes missing observations using a weighted average of nearby observations.  This works well when there are nearby observations, but the station density is so low in places like Africa that interpolation might introduce substantial error.

Enter a fourth option.  Cell phones (and cell phone infrastructure) can apparently be used to do more than update your Facebook status.  From a new paper in PNAS this week:


Country-wide rainfall maps from cellular communication networks
Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km2), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.

This seems potentially really cool, particularly for places like Africa where the cell network is expanding incredibly fast.  Here's a plot of how they did in the Netherlands, comparing their cell-tower derived estimates (y-axis) to what you get off radar (x-axis).

I'm not sure why they didn't compare against rainfall gauge estimates (have the Dutch stopped operating their rainfall stations too?), but nevertheless this doesn't look too bad.  Really small average difference between the two measurements, and a high correlation.  It's pretty much impossible to generate a similar plot over a region of interest in, say, Africa, comparing what you get from interpolated station data or satellite data to what the "true" observation is on the ground, but I would be surprised if it looks this good.

The authors do note that cell phone towers in the tropics tend to operate at a lower frequency, which "can increase errors in rainfall estimates due to the increased nonlinearity of the relationship between rainfall intensity and specific attenuation at lower frequencies".  But given the low baseline number of weather stations in much of the tropics, and the fact that the number of stations is getting even smaller, it seems like this sort of tool might have a lot of potential.

Hopefully, as the authors say, "this research report will contribute to persuade cellular communication companies worldwide to provide received signal level (RSL) data from their radio link networks free of charge for both research purposes and other applications of societal relevance."  It would be really neat if they could show how this works in some place with slightly sparser cell coverage.  Oh, and it would be nice if they could get us temperature too.