Can observations of the ocean help predict the weather?

Can observations of the ocean help predict the weather?

by Dr Amos Lawless

It has long been recognized that there are strong interactions between the atmosphere and the ocean. For example, the sea surface temperature affects what happens in the lower boundary of the atmosphere, while heat, momentum and moisture fluxes from the atmosphere help determine the ocean state. Such two-way interactions are made use of in forecasting on seasonal or climate time scales, with computational simulations of the coupled atmosphere-ocean system being routinely used. More recently operational forecasting centres have started to move towards representing the coupled system on shorter time scales, with the idea that even for a weather forecast of a few hours or days ahead, knowledge of the ocean can provide useful information.

A big challenge in performing coupled atmosphere-ocean simulations on short time scales is to determine the current state of both the atmosphere and ocean from which to make a forecast. In standard atmospheric or oceanic prediction the current state is determined by combining observations (for example, from satellites) with computational simulations, using techniques known as data assimilation. Data assimilation aims to produce the optimal combination of the available information, taking into account the statistics of the errors in the data and the physics of the problem. This is a well-established science in forecasting for the atmosphere or ocean separately, but determining the coupled atmospheric and oceanic states together is more difficult. In particular, the atmosphere and ocean evolve on very different space and time scales, which is not very well handled by current methods of data assimilation. Furthermore, it is important that the estimated atmospheric and oceanic states are consistent with each other, otherwise unrealistic features may appear in the forecast at the air-sea boundary (a phenomenon known as initialization shock).

However, testing new methods of data assimilation on simulations of the full atmosphere-ocean system is non-trivial, since each simulation uses a lot of computational resources. In recent projects sponsored by the European Space Agency and the Natural Environment Research Council we have developed an idealised system on which to develop new ideas. Our system consists of just one single column of the atmosphere (based on the system used at the European Centre for Medium-range Weather Forecasts, ECMWF) coupled to a single column of the ocean, as illustrated in Figure 1.  Using this system we have been able to compare current data assimilation methods with new, intermediate methods currently being developed at ECMWF and the Met Office, as well as with more advanced methods that are not yet technically possible to implement in the operational systems. Results indicate that even with the intermediate methods it is possible to gain useful information about the atmospheric state from observations of the ocean. However, there is potentially more benefit to be gained in moving towards advanced data assimilation methods over the coming years. We can certainly expect that in years to come observations of the ocean will provide valuable information for our daily weather forecasts.

Figure 1




















Smith, P.J., Fowler, A.M. and Lawless, A.S. (2015), Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model. Tellus A, 67, 27025,

Fowler, A.M. and Lawless, A.S. (2016), An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error. Monthly Weather Review, 144, 4007-4030,

First recording of surface flooding in London using CCTV cameras

On Friday 2nd of June 2017 Met Office issued a yellow warning of heavy rain with possible hail and lightning over London. Also Environmental Agency issued a number of flood alerts for London for the same period of time. This allowed us to test our newly setup system for recording open data CCTV images from London Transport Cameras (aka JamCams).

Following the flood alerts we setup to record all Transport for London (TFL) cameras which where within the main flood alert areas, these were 4 areas in London.

Figure 1. Areas selected for recording TFL CCTV camera images on 2nd of June 2017 corresponding to flood alerts from Environmental Agency.

This resulted in downloading images from just over 110 CCTV cameras accross from  the marked areas in Figure 1. Dowload started on many cameras at 2:30pm on 2nd of June 2017 and continued for 24h with an image downloaded every 5min.

Many of these images showed heavy rain as it passed over London on the afternoon of the 2nd June 2017; some cameras even captured images of lightning which was seen over North London but we didn’t capture any images of flooding in the four coloured areas in Figure 1.

Figure 2. Image of heavy rain on A23 Brixton Rd/Vassell Rd as seen by one of the CCTV cameras in London on 2nd July 2017 at 5:19pm

Figure 3. Image of lightning on captured on London CCTV camera at A12 East Cross Route on 2nd of June 2017 at 4:17pm

However, following the flooding allert on London for Transport site allowed us to capture surface flooding that happened on the North Circular road between 4-7pm resulting in traffic jams in the area.

Figure 4. Map of the surface flooding on the North Circular on 2nd of June 2017

The surface flooding was very localised and only one camera captured it, the one just below the blue circle in the Figure 4. We recorded both still and video images from this camera. In the video below you can see the surface flooding affecting the slip road going North.

We are currently setting up similar systems to download live traffic CCTV images from Leeds, Bristol, Exeter, Newcastle, Glasgow, and Tewkesbury.

Data Assimilation in the Snow

by Sarah Dance

Snowbird mountains

I’ve just got back from attending the Society of Industrial and Applied Mathematics (SIAM) Conference on Dynamical Systems in the beautiful mountains of Snowbird, Utah,USA.  I was invited to attend the meeting to give part of a Mini-Tutorial on Data Assimilation(available here) with Elaine Spiller and Eric Kostelich.

Even though my undergraduate degree and PhD were in Applied Mathematics, I don’t tend to go to many Mathematics conferences. I often meet with fellow data assimilation practitioners at Meteorology conferences instead.  So it was great to see people proving data assimilation related theorems, applying data assimilation in different applications like neuroscience and cancer treatment, and of course to get some new ideas from dynamical systems approaches that have potential to be applied in different ways.  I particularly enjoyed Mary Silber’s talk on using Landsat data to understand vegetation pattern formation in the drylands of Africa

A slow march through the desert

Gowda/Silber’s work on African drylands. This image shows shrublands in Somalia from high above. Two images – from 1952 (purple) and 2006 (green) – are overlaid here for comparison. The colors highlight the large communities of shrubs and grasses which grow in bands along this sloping landscape. Over the fifty years shown here, all the vegetation has moved uphill – the green bands of modern plant growth are further up the hillside than the purple bands from 1952.