Rescuing early satellite data to improve long-term estimates of past weather.

By: Jade Westfoot 

This post is contributed by Jade Westfoot, a year-12 school student who did work experience in the department recently. During her week with us, Jade worked with Drs. Jon Mittaz and Tom Hall on rescuing historic satellite data to make it more usable for historical weather analysis. Jade is passionate about science communication and is interested in both looking up at the sky and back down at Earth, and she is aiming to study a mixture of space science and Earth science!

Today, satellites are a fundamental part of our everyday lives, with a multitude of roles such as navigation, communication, space science and Earth observation. Earth observation is increasingly important in the race to understand our planet to combat and adapt to the climate crisis.

Nearly 1000 Earth observation satellites are available to help with this. Most orbit in sun synchronous or polar orbits, meaning that they fly over locations at a fixed time each day on an orbit that takes them pretty much over the poles. 1000 satellites seems like a lot, but many of them simply image the land beneath them, which is useful, but to understand the state of the atmosphere (in terms of temperature, humidity etc at different heights), more specific sensors are needed. For example, the European Centre for Medium-range Weather Forecasting (ECMWF) currently collects data from roughly 100 useful sensors to inform weather forecasts. However, we haven’t always had this wealth of information: as recently as the 1990s, ECMWF was using fewer than 15 satellites!

As well as forecasting, ECMWF (located in Reading) is an important centre for estimating the weather conditions of the past, which is immensely useful for environment and climate science, as well as engineering and planning. This ‘retrospective weather forecast’ is known in the field as “re-analysis”. The reduction in satellite information back in time is a challenge for re-analysis going back many decades. Mostly, satellite data have been introduced from the late 1970s onwards, but there are more measurements from earlier satellite missions that can be rescued and may be useful.

A good example is the Nimbus programme, NASA’s second programme of experimental Earth observation satellites, with 7 satellites successfully launched between 1964 and 1978. Over the lifetime of the programme the instruments changed, but during the 1960s one of the instruments for atmospheric sounding (used on Nimbus 3) was the MRIR sensor. MRIR was able to take measurements in 5 bands: 1 in the visible spectrum to detect reflected sunlight, and 4 in the infrared spectrum measuring radiation from Earth. Each infrared channel effectively measured different layers of the atmosphere by measuring the signal at different frequencies. For example, the 6.7μm channel was sensitive to radiation emitted by atmospheric water vapour, so by measuring it the MRIR data can be used to estimate the amount of water at a certain height in the atmosphere.

At the time, the Nimbus data was used to refine the accuracy of weather forecasting, and now it is hoped that accessing the data will help ECMWF improve re-analyses to understand long-term weather changes.

How do we know if the early data are valid?

Unfortunately, the age of the data brings with it some problems. Some of the data are missing, and the data that have survived are generally more noisy than modern instruments.

This doesn’t mean it’s useless though! We can infer things from each of the channels individually (such as the presence of clouds). To show their potential, we can combine the MRIR data into a false colour image, which can then be compared to photographs of the Earth. Where do we get photos of the Earth from space in the 1960s? Well, it just happens that some of these satellites were in operation at the same time as NASA’s Apollo missions, during which astronauts took many photos of the whole Earth.

Figure 1: Comparison between the Apollo photo and each of the MRIR channels

For example, looking at the figure you can see that Indonesia and Papua New Guinea are covered by clouds which share similar patterns between the photo and observation. This can be seen on both channel 1 (which measures water absorption) and channel 2, which tries to measure surface temperature but here is blocked by clouds.

The photo and MRIR data don’t perfectly match, which is expected: A photograph is an instantaneous capture of the whole Earth, taken from 400,000 km away, whereas the false colour image is generated from data taken by the satellites as they scan strips of the scene beneath them during their approximately 110 minute orbits. This means that the whole Earth is not captured at one time in the satellite view, so clouds can move and develop in the time it takes to build up the MRIR pictures. However, because of the distances, the MRIR measurements have a higher resolution (45 km) than the Apollo photo.

Comparing satellite data to the Apollo photo boosts our confidence in the data collected, as the similarity between the two independent observations generally confirms the MRIR data have been correctly ‘rescued’.

How will rescued data be used?

Simulations and re-analyses of the climate during the 1960s, including ECMWF’s, don’t take advantage of much old satellite data like that provided by Nimbus. Instead, they rely on in-situ data (measurements taken by ground stations or weather balloons). In situ data are highly informative, but are not available everywhere, particularly in the southern hemisphere. Satellites capture information about the whole planet.

Including the Nimbus data will mean future re-analyses can extend the timescale over which satellite data are used, to more than 50 years, making the re-analyses even more relevant for looking at weather changes over many decades. The more data from different sources we can put into a re-analysis, the more accurate it should become. Having accurate information about past weather will continue to be incredibly important in order to respond to the changing climate.

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