1st NCEO-GSSTI Data Assimilation and Earth Observation Training Course

1st NCEO-GSSTI Data Assimilation and Earth Observation Training Course

by Javier Amezcua

25 November 2019

I spent the last week in Accra, the capital of Ghana. It was incredibly hot and stuffy for this time of the year (minimum 26C, maximum 31C), which is natural if we consider this city is only 5N of the Equator. The food was delicious (I stuffed myself with joloff rice and fried fish) and I enjoyed sunsets when colonies of bats flew over the city.

In this trip I was accompanied by Ewan Pinnington and Tristan Quaife from University of Reading, and Jose Gomez-Dans from University College London. We were in a mission for the UK National Centre of Earth Observation (NCEO), to which the four of us belong. Our mission was to deliver a training course in data assimilation and Earth observation for the young Ghana Space Science and Technology Institute (GSSTI). This institute is located in the northern outskirts of Accra in the campus of the School of Nuclear and Allied Sciences. The participants of the course included people from GSSTI, the Ghana Statistical Institute, the Ghana Meteorological service, and a member of the United Nations Food and Agriculture Organisation (FAO).

This course is part of the continuous collaboration between scientists of the UK and Ghana under the Official Development Assistance (ODA). This program exhorts developed countries to dedicate a percentage of their gross domestic product (GDP) as aid to help foster prosperity in developing countries. This scheme was started by the Organisation for Economic Co-operation and Development (OECD). A country can participate directly with monetary aid, but also through knowledge and expertise. Our training course belongs to the latter category.

In the course I went through the fundamental aspects of data assimilation: definining the estimation and forecasting problem, revising some basic concepts of probability and statistics, and emphasizing the role of Bayes’ Theorem as a central aspect of data assimilation. I then explained some of the basic families of data assimilation methods: variational and Kalman-based. We did some computer experiments with a toy model in order to illustrate some ideas.

My colleague Jose Gomez-Dans then presented something more specific to fulfill the needs of our audience. In particular, people were quite interested in using satellite observations to infer the conditions of crops in the north of Ghana, and then use land-surface models to predict the yield at the end of the season. These models contain information about the biology of the crops, human activities, and they are forced by meteorological products. He helped the participants run some experiments remotely from some computers from UCL with observations from the Sentinel Mission of the European Space Agency (ESA).

We had a great week and it the course was very well received by the participants. It was rewarding to see science transcending the ever tighter borders, institutions opening doors instead of closing them, and people collaborating instead of fighting. We hope to continue our collaboration with GSSTI, and we are planning on coming back in 2021.

Investigating the uncertainty of weather radar data

Investigating the uncertainty of weather radar data

This blog describes the work of Masters student Vasiliki Kouroupaki, carried out in collaboration with the UK Met Office.

UK weather radar network

In numerical weather prediction, nowcast, hindcast and forecast models can be improved through data assimilation. Data assimilation is the technique which combines observations with output from a previous short-range forecast (background) to produce an optimal estimate of the state of the atmosphere (analysis).

Radar reflectivity observations are assimilated by the Met Office in order to provide up-to-date information about rainfall in the initial conditions for UK weather forecasts. In assimilation, observations are assigned weights according to their error statistics. Depending on the kind of observation, there are different factors or processes which can result in errors. In order to have an optimal analysis these errors must be correctly specified. However, due to the fact that the true errors are not known their statistics need to be estimated. In this work, the uncertainties of radar reflectivity observations assimilated into the Met Office UKV model are examined using a diagnostic technique. Data come from the operational UKV model with hourly cycling 4D-VAR or from trial experiments four times per day. The diagnostic is based on combinations of observation-minus- background, observation-minus-analysis and background-minus-analysis differences. The results show that observation error variances are higher for Winter (1 Dec 2017-18 Jan 2018) than for Summer (16 Jul-16 Aug 2018) and that they increase for higher reflectivity values. Further investigations classified the data by beam elevation and by radar ID. These showed that for values of beam elevation between 0.5- 1.0 and 3.0-4.0 degrees the error variance had greater values. Also, error statistics for different radars were positively correlated with the mean reflectivity observed by each radar.

Further investigation of observation error statistics in the assimilation could improve the initial conditions and thereby operational forecasts for convective rainfall events.

Operational meteorology for the public good, or for profit?

Operational meteorology for the public good, or for profit?

Most countries have a national weather service, funded by the government. A key role is to provide a public weather service, publishing forecasts to help make everyday business and personal decisions, as well as providing weather warnings for hazardous events. Large sums of money are invested in research and development of forecasting systems; supercomputing resources and in observing networks. For example in 2018-19 the UK Met Office invested £55 million in satellite programmes. International cooperation between weather services means that weather data obtained by one country are usually distributed to others through the World Meteorological Organisation (WMO) Global Transmission Service (GTS) in near real time, and for the common good. Is this all about to change?

In the future there is likely to be an increasing need for smart, local forecasts for the safety of autonomous vehicles (e.g. to allow the vehicle to respond to rain, snow, ice etc). Such vehicles also provide an observing platform able to take local measurements of weather that could be used to improve forecasts.  But who owns the data (the driver, the car owner, the car manufacturer…) and can it be distributed for the common good? Can the data be trusted? What about privacy concerns?

IBM weather infographic

Across the observation sector, access to space is getting less expensive. For example, depending on the specifications, a nanosatellite can be built and placed in orbit for 0.5 million euros.  Furthermore, industry is beginning to run its own numerical weather prediction models (e.g., IBM weather).  This means that there are a growing number of companies investing in earth observation and numerical weather prediction,  and wanting financial returns on their investments.

Do we need a new paradigm for weather prediction?