Measuring Rainfall by Chris Skinner (@cloudskinner)
Before I embarked upon my PhD research I had not paid much attention to how we recorded rainfall. My previous experience, probably like many people, came from my Primary School that had a small weather station in the grounds, that consisted of a weather vane for measuring wind direction, and a raingauge for recording the rainfall. It was nothing more than a small bucket, which collected the rainfall and you recorded the level from the side each day. If it was up to 4mm, you would record it as 4mm of rain having fallen in the last day.
That was it, as far as my knowledge went, and as far as I assumed it went in regards to recording rainfall for the weather forecasts. I wasn’t wrong, the Met Office here in the UK do still make extensive use of raingauges to observe rainfall. I will let Ralph James explain them to you –
However, as I soon learnt, raingauges only measure rainfall at one stationary point. The little bucket I used at my Primary School could tell me how much rain fell at the school, but it could not tell me how much rain fell at my house, or how extensive that rainfall was. To fill in the gaps, meteorologists use weather radars. Over to Biz Kyte –
Brilliant! There we have it then, measuring rainfall, easy peasy. You just need a network of thousands of raingauges, enough radar stations to cover your country and enough highly qualified engineers and scientists to operate and maintain it all.
You won’t be surprised to hear that these conditions do not extend to many areas of the world. Sub-Saharan Africa for example has not had the resources and/or the political will to establish the infrastructure required for timely, accurate rainfall observations, and this has implications when trying to forecast floods, crop yields, droughts or water resources. Obviously, being able to observe rainfall in realtime in this region would be greatly beneficial, but the installation and maintenance of raingauge and radar networks is just not currently feasible.
One way is to turn to satellite observations. Satellite platforms carrying Passive Microwave (PM) sensors are the most accurate for this role, with the instruments measuring the amount of microwave backscatter from the Earth’s surface. As droplets of water scatter the microwave signal in a distinctive way it is possible to directly observe where it is raining and its relative intensity. But (there’s always a but), PM sensors have to be placed in Low Earth Orbits (LEO) to operate, and therefore travel over the planet’s surface, recording snap shots of the rainfall as it goes. To add to problem, sandy ground scatters microwaves in a similar way to water, making observations by PM satellites more difficult in arid regions, such as much of sub-Saharan Africa.
Another way, such as that adopted by the TAMSAT team at the University of Reading, is to use Thermal InfraRed (TIR) instruments mounted on geostationary platforms. These satellites orbit at a distance that allows them to orbit at the same speed as the Earth’s rotation, meaning they always observe the same area of the planet’s surface - this is known as a geo-stationary orbit. TAMSAT use a relationship called Cold Cloud Duration (CCD), where it is assumed that if a cloud is cold enough, it will be raining, and the amount of time a cloud is below that temperature will let the team calculate the rate of rainfall. It is an indirect relationship, so it does not directly record the rainfall, but it does provide an estimate that is accurate enough, and timely enough, to be useful in forecasting seasonal crop yields or droughts.
Again, there is a but. TAMSAT produces ten-day observations, useful for the above applications, but not very useful for flood forecasting, for example, that requires realtime observations at atleast a daily timestep. It is possible to use the CCD method for this, but the observations are highly uncertain so require some complex statistics to be properly used. This has led meteorologists to get creative.
Telecommunications are taking off in sub-Saharan Africa, with mobile phones spreading fast. Professor Hagit Messer, of the University of Tel Aviv, suggested that interference of signals sent between antennas by rainfall could be used to measure the rainfall rate between the antennas. Over a whole network of telecommunication antennae the spatial spread of rainfall and its intensity could be built up, evolving over time. This form of rainfall observation could be used to dramatically improve the spatial and temporal coverage over sub-Saharan Africa, with little need for additional investment.
And again, there is a but. Whether it is observation by radar, satellite or telecommunication networks, the instruments can only record where it is raining, when it is raining, and the relative intensity of the rainfall. That relative intensity needs calibrating, bringing scientists full circle back to the humble raingauge. There are raingauges in sub-Saharan Africa, but not a lot of them. The study area I researched had one gauge per 7,000km^2, enough to cover the whole of the UK with just 27 raingauges, and of course these weren’t evenly spread, concentrated along rivers and in towns, leaving large areas relatively uncovered. They can also be poorly maintained and not all raingauges record all of the time.
There are some good stories about raingauges in Africa. A couple I have heard from the TAMSAT team are of one gauge that recorded no rainfall at night, even during the wet season. When investigated it was found the locals looking after the gauge were storing inside so it wouldn’t be stolen. Another gauge was consistently recording a light drizzle – this was caused by people hanging wet clothes on it to dry. We have similar issues in the UK, with one organisation who should know better placing a gauge on their roof next to an air conditioning vent that blew rainfall away from it.
One project that I am excited about is TAHMO. The project team have the highly ambitious objective of dramatically increasing the raingauge coverage (as well as coverage of other meteorological instruments), for sub-Saharan Africa by mass producing a cheap, self-contained weather station and distributing them to schools. One of the most significant outputs to date was the creation of a low cost acoustic disdrometer, that uses the vibrations of falling raindrops to measure rainfall rates and reports the readings automatically using mobile phone technology. For me, this is the great hope of rainfall observation for poorly gauged regions and really hope they can pull it off. For now, I’ll leave you with Rolf Hut discussing TAHMO, acoustic disdrometers and tinkering.