Corporate Targets

The following Key Performance Targets (KPTs) have been set for the Chief Executive of the Met Office and announced in Parliament for the financial year (FY) 2008/9. The targets are designed to drive further improvements to the Agency's performance and are as follows.

Forecast Accuracy

To achieve at least three out of four of the following forecast accuracy measures. However, any failed element will be required to meet the same level as the FY 2007/8 outturn for the overall KPT to be met.

1. Improve the forecasting skill using the combined Numerical Weather Prediction Index to at least 125.8
2. More skilfully predict whether precipitation will occur at selected locations to achieve a skill score of at least 0.438
3. Predict maximum temperature at selected locations to within 2 degrees accuracy 86.2% of the time
4. Predict minimum temperature at selected locations to within 2 degrees accuracy 84.4% of the time.

Business Profitability

To achieve a business profitability target of £7.0 million.

Return on Capital Employed

In line with Treasury requirements to achieve a Return on Capital Employed of at least 3.5%.

Support to wider government controls

To deliver the outputs of the Customer Supplier Agreements (CSA) for Public Weather Services, Defence and Defra within the tolerances agreed with the customers and defined in the CSAs.

Weather forecast verification – how accurate are Met Office forecasts?

The Met Office has an open and transparent policy in the verification of its forecasts.

Verification is needed to measure how we are doing against the Key Performance Targets (KPT) for forecast accuracy, as set by government. These KPTs are part of our ongoing long-term commitment to improving the accuracy of our forecasts to the public. We will do this by reaching accuracy targets set for:

* next-day rainfall forecasts for 11 places within the UK (the nearest major towns to these places are shown on the map opposite);
* next-day temperature forecasts (max and min) for 11 places within the UK (the nearest major towns to these places are shown on the map opposite);
* the forecasting skill of our computer models.

Computer model forecast accuracy

All forecasts, of whatever type, are ultimately based on the predictions from our suite of sophisticated atmospheric and oceanic models, run on our powerful supercomputer.

This form of forecasting is known as Numerical Weather Prediction (NWP). The accuracy of the Met Office computer model forecasts is measured by the NWP Index.

The NWP Index takes into account computer forecasts for several weather elements, including mean sea level pressure, for regions covering the entire globe from one to five days ahead. The higher the NWP Index, the more accurate the computer forecasts are.

Rainfall

Accurate forecasts of rainfall are essential for partner organisations such as the Environment Agency for flood warnings and the Highways Agency for road safety

Unlike temperature, rainfall may or may not happen. Rain can be highly variable, especially when falling as showers; one side of town may get rain while the other remains dry. So for the purpose of verification, forecasts of rain are expressed in terms of the “chance” (probability) of a place being wet or dry.

The Met Office uses a verification method that:

• gives a score (called the Brier Skill Score) that takes account of the probability applied to the forecast;
• takes account of how difficult or easy the forecast was by measuring it against a climatological reference.

A positive score indicates that the forecasts are, on average, better than climatology. A completely accurate forecast would have a score of 1.

High scores are achieved when:

• a high probability of rain is forecast and rain subsequently occurs
• a low probability of rain is forecast and it subsequently remains dry

Conversely low scores are achieved when:

• a high probability of rain is forecast and it subsequently remains dry
• a low probability of rain is forecast and rain subsequently occurs

Minimum temperature

Accurate forecasts of temperature are vital for utility companies, for road authorities and for health forecasting

This graph shows how accurate our forecasts of minimum temperatures are for the next day ahead. It shows the percentage of forecasts that are within 2 °C of the actual temperature recorded. The accuracy of minimum temperature forecasts is lower than that for maximum temperature. This is because minimum temperature is more sensitive to variations in cloud cover, wind speed and wind direction and so is harder to predict.

Maximum temperature

Accurate forecasts of temperature are vital for utility companies, for road authorities and for health forecasting

This graph shows how accurate our forecasts of maximum temperatures are for the next day ahead. It shows the percentage of forecasts that are within 2 °C of the actual temperature recorded.

How our forecasts have improved

Through continual investment in research, supercomputing and observations, Met Office scientists have steadily improved the accuracy of our forecasts. All of the forecasts we produce are stored and their accuracy assessed, so that we can learn from what went wrong with inaccurate forecasts and make sure that they keep getting better. Our targets for forecast accuracy are set each year by the government.

There are a number of ways of measuring the accuracy of a forecast. One method used at the Met Office is called the NWP index. This combines the accuracy of a number of different elements into a single measure of overall accuracy. An example of how our computer-generated sea-level pressure forecasts have improved over the years is shown below.

Forecasting further ahead

Our three-day forecasts are now as accurate as our one-day forecasts were 20 years ago. This improvement in forecast accuracy stems from investment in research, faster supercomputers and greater coverage by observations.

In addition to improving the accuracy of the one- to five-day forecast, research has enabled us to make forecasts that were previously impossible. We can now forecast further into the future allowing regular seasonal forecasts to be produced, and predictions of climate change are continually improving. New research is expected to provide further significant improvements in our ability to forecast heavy thunderstorms a few hours in advance.

Creating forecasts for months ahead

From forecasting whether the coming season will be warmer or drier than normal to predicting what the world will be like in 100 years, our scientists use the same process used to produce weather forecasts hours or days ahead.

These long-range forecasts incorporate more of a global view to look out a month or more into the future. They factor in details such as the current average state of the atmosphere and the ocean at distances often thousands of miles away from the specific location of interest. Long-range forecasts estimate only the average weather, not specific weather events. Therefore, in seasonal forecasts, language such as 'warmer than normal' and 'wetter than normal' is common.

Seasonal forecasting

Seasonal forecasts provide information on how weather, averaged over the next few months, is expected to vary from normal.

Forecasts for other regions

We make seasonal forecasts for all parts of the world, focusing on specific regions where there is particular vulnerability to seasonal anomalies such as droughts, and where relatively good predictability has been identified. These include forecasts for rainfall in the north-east corner of Brazil during their wet season (February-May), in East Africa during the October-December wet season and in tropical West Africa, including the Sahel region, for July to September.

Seasonal forecasting

What is a seasonal forecast?

Seasonal forecasts provide information on how weather, averaged over the next few months, is expected to vary from normal, e.g. "Are UK rainfall totals this winter likely to be above or below the long-term average?". The UK/Europe forecasts relate to the conventional seasons — winter, spring, summer and autumn. For other parts of the world the period of the forecast may vary, e.g. the North Atlantic tropical storms forecast refers to the June to November season. Seasonal forecasts are indications of an overall picture, as it is impossible to forecast individual events so far ahead; the short-range forecasts are where the details begin to appear.

Because of uncertainty in forecasting at long range, seasonal forecasts are generally expressed in terms of probabilities. For example, our forecast for mean UK temperatures for the winter of 2007/8 gave probabilities for a relatively warm winter, an average winter and a relatively cold winter, as 50%, 30% and 20% respectively.

Long-range forecasts are part of the advice provided to the public on prospects over a range of time scales, and can help government agencies and companies with their long-term strategic planning. The forecasts have global coverage and are used in areas like Africa to help plan for year-to-year variability in rainy seasons.

How are seasonal forecasts possible?

Slowly varying aspects of the Earth's climate, in particular fluctuations in the surface temperature of the global oceans, can influence patterns in the weather. These influences are not easily noticed in day-to-day weather events but become evident in long-term weather averages.

The slow fluctuations of sea-surface temperature (SST) can be predicted, to some extent, at least up to six months ahead. The links between SST and weather can be represented in computer models of the atmosphere and ocean. Computer models developed at the Met Office, like those used in making both daily forecasts and long-term climate change predictions, form the basis of our seasonal prediction systems.

The strongest links between SST patterns and seasonal weather conditions are found in tropical regions, and it is here that seasonal forecasting is most successful. The best known links are those associated with sustained large-scale warming (or cooling) of SST in the tropical Pacific known as El Niño (or La Niña) events. These events can disrupt the normal pattern of weather around the globe, bringing, for example, large changes in seasonal rainfall that lead to droughts in some regions and floods in others.

Although the strongest links between SST and seasonal weather are found in the tropics, there is good evidence that similar, if weaker, links are present in other parts of the globe. The computer model forecasts can thus provide the best available guidance on likely seasonal conditions in many parts of the world, including Europe.

Because the link between weather and SST is best detected in long-term weather averages, and because the uncertainty in forecasts generally rises as the forecast range increases, seasonal forecasts look rather different in format compared to the familiar daily forecasts. The two key differences are:

• forecasts are for conditions averaged over three-month periods
• forecasts are stated in terms of probability

How are the forecasts produced?

The same computer models of the atmosphere that are used to make the daily weather forecasts are used, with some differences:

• they are run forward in time up to many months ahead, rather than just for a few days
• active oceanic, as well as atmospheric, components are included
• they are run many times, with slight variations to represent uncertainties in the forecast process

We occasionally use statistical forecasting methods on the seasonal timescale — in winter and summer for UK and Europe. This is done where physical relationships between weather and the state of the oceans have been found, but where models do not yet show sufficient skill to pick up these particular relationships. This gives rise to a mixed statistical and physical model forecast process.

We also use this mixture of methods for forecasting the mean global mean surface temperature for a year ahead. However, on even longer time scales, such as a century ahead, only physical models are used, as no more skilful statistical approach has been found.

Creating forecasts for days ahead

To produce weather forecasts for one to 15 days ahead, we use a variety of techniques. Our North Atlantic and European numerical forecast model provides more detailed information over this region in forecasts out to two days ahead. In addition, our short-range ensemble prediction system, MOGREPS, provides information on the degree of uncertainty associated with the forecasts.

Forecasting the oceans

For those working at sea or living near the coast, forecasts of wave height, ocean currents or storm surges up to days ahead are just as vital as forecasts of the weather.

Forecasting days ahead

When forecasting more than two days ahead we need to use our global forecast model, because the weather happening many thousands of miles away today will affect the weather over the UK in a few days time. In medium-range forecasts, two to 15 days ahead, the use of an ensemble is essential as the uncertainty in the large-scale weather patterns becomes greater.

Nowcasting

Nowcasting is a technique for very short-range forecasting that maps the current weather, then uses an estimate of its speed and direction of movement to forecast the weather a short period ahead — assuming the weather will move without significant changes. Since it takes time to gather and map the weather observations, a short forecast is needed to even know what the weather is 'now'.

How nowcasting works

Rainfall and associated severe weather, such as hail and lightning, are the most widespread and most advanced applications of nowcasting. In the UK, rainfall nowcasts can be useful up to three or four hours ahead in widespread rain bands in winter, but only one to two hours ahead for summer thunderstorms.

To extend the period of predictability nowcasts can be combined with output from numerical weather prediction models.

The Met Office uses nowcasting for many weather variables including wind, temperature, snow and fog. Because it is a forecasting technique that can be applied quickly, either by human forecasters or by modest-sized computers, it is possible to update the forecasts frequently — every time there are new observations available. In the Met Office most nowcasts are updated every hour.

As computer models improve, the lead times will become shorter and, ultimately, these simple techniques may be used for instant forecasting, such as the immediate path of a tornado.

Short Term Ensemble Prediction System (STEPS)

Fig 1. Historic example of radar

The Met Office has STEPS (Short Term Ensemble Prediction System), a state-of-the-art rainfall nowcasting system, developed in collaboration with the Australian Bureau of Meteorology.

In STEPS, the rainfall distribution is separated into different sizes of rainfall feature, so the large rainfall events (which are the more predictable) can be nowcast for longer, while the small events are only nowcast for a very short time.

Beyond this predictability limit, information is used from the NWP model for larger rainfall features, and the smaller features are filled in realistically using a random statistical method.

The 'ensemble' in the title refers to the fact that many forecasts are produced, with the rainfall areas moving at slightly different speeds, and with the small rainfall features represented by slightly different random statistics. Using this approach enables a realistic range of uncertainty to be estimated for flood forecasting.

History of nowcasting

Nowcasting is a very old technique. When Admiral FitzRoy first produced forecasts at the Met Office in the 1860s, he did it by collecting reports of storms from around the coast, and then sharing these reports with coastal ports that may be downwind, so that they knew there was bad weather coming. This was a simple form of nowcasting.

The term 'nowcasting' was actually coined in the 1980s by Met Office scientist Professor Keith Browning, to describe the process of extrapolating a sequence of radar images to produce a very short-range rainfall forecast.

Mesoscale modelling

Forecasting with high-resolution models

A system called numerical weather prediction (NWP) forms the basis of modern weather forecasting. The system uses a mathematical model of the atmosphere which has been derived from the laws of physics. This model provides a set of equations to solve in order to predict the future weather. These equations are solved by averaging over 'chunks' of the atmosphere (grid boxes) and short periods of time (time steps) to then give us numerical equations which are put into the supercomputer.

Model resolution

The size of a chunk is called the 'resolution' of the model, similar to the resolution of an image from a digital camera. More than one grid box is needed to represent weather features in the much the same way as a digital image needs more than one pixel to represent something like a face. The number of equations which have to be solved depends on the total number of grid boxes. As the equations have to be solved long before the weather happens, the size of grid boxes (given the forecast area) is limited by how quickly they can be solved.

The current global forecast model has a horizontal resolution of about 40 km over the UK, meaning 160 million equations have to be solved just to step the atmosphere 15 minutes in time. This resolution is very good for information about the general weather conditions over the UK, and the Met Office's current computer power means a 5-day forecast can be produced in a few hours. At shorter range (1-2 days) a higher-resolution model (about 12.5 km) is used, because it provides more regional detail.

Convective clouds

Much of the UK's most damaging weather involves clouds which 'bubble up' from near the surface when a layer of cool air lies above a layer of relatively hot, moist air. These are called convective clouds, and the most energetic are often thunderstorms which may produce torrential rain, snow, damaging hail, flash flooding and strong winds, including tornados.

The resolution of current models is unable to represent individual convective clouds. A large thunderstorm may be about 10 km across, with a very strong core less than 1 km across, so the current models completely average over even the largest thunderstorm. While the forecast might state that a particular region is at risk from thunderstorms and torrential rain, it is currently impossible to know where individual thunderstorms might form.

Convective-scale NWP

As computer power increases it will be possible to reduce the grid box size in the models. In preparation, the Met Office is already experimenting with models which make a major jump in resolution. Instead of completely averaging over thunderstorms, models are being developed which actually allow thunderstorms to form. So far there have been good results for large storms from a resolution of around 1 km. This is known as 'convective-scale' NWP. This resolution is also very useful for other aspects of weather, such as major areas of fog and detailed features of the wind around ranges of hills.

The animation on the right is an example forecast from an NWP model running at 1.5 km resolution, showing a representation of cloud (grey) and rainfall (colours).

Convective-scale NWP in action

We have tested our experimental system using a wide variety of recent events over the UK. For example, the major flash flood in High Wycombe on 3 August 2004.

Creating forecasts for hours ahead

At short time ranges a higher level of detail can be forecast with more reliability. The forecasting of the weather in the 0- to 6-hour time frame is often referred to as nowcasting

Traditionally, numerical computer models have been poor at forecasting thunderstorms and other small-scale details. Therefore, the human forecaster has had an advantage over computer NWP models when it comes to forecasting small- (meso-) scale features. The forecaster is able to compare a model field against actual observations and respond quickly and amend a forecast, should the situation warrant it. Rainfall radar observations are very useful in this time frame, and post-processing is used to make very short-range predictions.

More than six hours ahead, numerical model forecasts gain an advantage over other forecasting techniques. Ongoing research at the Met Office, to develop the next generation high-resolution numerical weather prediction system over the UK, should eventually allow numerical model forecasts to become the dominant nowcasting tool as the model's ability to forecast thunderstorms and small-scale features dramatically improves.

National Severe Weather Warning Service

The Met Office has a responsibility to provide warnings of severe weather under the National Severe Weather Warning Service. Nowcasting is used to decide when a Flash warning, which indicates a high confidence of severe weather occurring in the next few hours, needs to be issued. Many forecasts are provided to our customers in this time range.

How we forecast the behaviour of our oceans

For those working at sea or living near the coast, forecasts of wave height, ocean currents or storm surges are just as vital as forecasts of the weather.

We routinely run a number of ocean forecast computer models that help organisations, such as ferry operators and oil companies, to plan their operations at sea, and allow accurate warnings of coastal flooding to be issued. These include:

• ocean circulation analysis and forecast models for the deep ocean
• ocean circulation forecast models for the seas of the continental shelf that surrounds the UK (shelf seas)
• wave forecast models
• a high-resolution sea-surface temperature analysis
• coupled ecosystem forecast models that describe changes in the chemical and biological make-up of both deep ocean and UK shelf-seas

To further develop these capabilities we have established a National Centre for Ocean Forecasting (NCOF) in association with Proudman Oceanographic Laboratory, Plymouth Marine Laboratory, National Oceanography Centre, Southampton and the NERC Environmental Systems Science Centre. The aim of NCOF is to establish ocean forecasting as part of the national infrastructure, based on world-class research and development.

For more details and sample data please visit the NCOF web site

Deep ocean

Although far removed from our coastal communities, the deep ocean exerts an important influence on the weather we all experience. As industry explores the deep ocean to meet our energy and other resource needs, predicting temperature and current through the entire water column will ensure safe and effective operations. Understanding the processes of change of the deep ocean is vital to improving our understanding of climate change.

The Met Office has run the global Forecasting Ocean Assimilation Model (FOAM) daily since 1997, forecasting three-dimensional ocean currents, temperature, salinity and sea-ice concentration, thickness and velocity.

FOAM incorporates observational data into ocean and sea-ice models and produces an accurate representation of the present ocean state. In order to forecast how ocean temperatures, currents and the sea-ice field will evolve, atmospheric forecasts of surface pressure, wind and solar heating are used to drive the model.

Each daily run uses observations from the previous 10 days, including:

• temperature and salinity profiles including data from Argo floats and moored instruments
• sea-surface height data measured from earth orbiting satellites
• ship, buoy and satellite sea-surface temperature observations
• sea-ice concentration fields received from the Canadian Meteorological Centre

Mobile circulation patterns, such as ocean eddies, fronts and jets, are the ocean equivalent of the storm systems we recognise from weather charts. Model resolution is the key to ensuring that these features are properly captured. In order to do this, a number of high-resolution configurations, are nested within the global FOAM model.

Shelf seas

Tides and storm surges have a major influence on the character of the regional seas around the UK, and predicting these correctly is of critical importance to mariners.

Following the catastrophic storm surge that flooded part of the south-east of England in 1953, the Met Office established a storm tides forecast service and now make daily runs of a storm surge model developed for the Environment Agency by the Proudman Oceanographic Laboratory (POL).

The storm surge model is specifically designed to calculate changes in water level due to tide and surge. To forecast other key characteristics of our coastal seas (e.g. currents at the surface and through the water column, sea temperature, water quality) a more detailed model — the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) — is used.

Both models are driven by tides (gravitational effects of the sun and moon) and forecasts of atmospheric parameters, including surface pressure, wind and solar heating. Close to the coastline the sea-bed becomes more complex, so a number of high-resolution configurations are used to represent the sea’s response to this complexity as accurately as possible.

Waves

On stormy days around our coasts breaking waves provide us with a constant reminder of the power of the oceans. High seas are dangerous for mariners and bring the risk of coastal flooding.

Over the past two decades, we have run and maintained wave models to provide predictions of wave conditions, globally and around the UK.

Waves develop as a result of the wind blowing over the sea surface, leading to high 'wind-waves' developing during the strongest storms. Away from storm centres, wind-wave energy becomes more uniform and moves without further external influence — at this stage the waves are termed as swell. Wave energy is lost from the ocean when the waves break (e.g. white-capping in windy conditions) and due to friction as waves move through shallow water.

The Met Office model represents all these processes, including shallow-water physics and is driven by forecasts of surface winds.

Outputs from the models are used for a variety purposes, from predicting offshore vessel motion characteristics to forecasts of coastal waves and overtopping.

Sea-surface temperature

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system produces a high-resolution (1/20°, approx. 5 km) daily analysis of the current sea-surface temperature (SST) for the global ocean. OSTIA uses satellite data provided by the Global High Resolution Sea Surface Temperature Pilot Project (GHRSST-PP), together with in-situ observations, to determine the sea-surface temperature.

More details and sample data can be found on the OSTIA pages on the NCOF web site

Ocean ecosystems

Marine ecosystem modelling is of fundamental importance in understanding issues of water quality, environmental health, and maintaining sustainable ecologies. The science of forecasting marine ecosystems has many challenges, but the Met Office has committed to developing this field by becoming one of the first centres to run an operational forecast model.

Our models simulate the carbon-cycle and predict the biological and chemical make-up of both the open ocean and shelf seas. For example, in the shelf-seas around the UK we use models which separate the ecosystem into functional groups, including:

• one bacteria, four phytoplankton and three zooplankton functional groups
• a fully resolved diurnal cycle
• variable carbon to chlorophyll ratios
• independent nutrient pools for carbon, nitrogen, phosphorous and silicate

Open-ocean ecosystem forecasts are currently not operational, but are planned to become part of the operational deep ocean modelling system (from 2009) through the inclusion of the Hadley Centre Ocean Carbon Cycle (HadOCC) model in the Forecasting Ocean Assimilation Model (FOAM). HadOCC is a Nutrient, Phytoplankton, Zooplankton, Detritus (NPZD) ecosystem model with the addition of two components representing carbon in the ocean: dissolved inorganic carbon and alkalinity.

Met Office Unified Model

The Met Office Unified Model (UM) is the numerical modelling system developed and used at the Met Office. It is unique, because it has been designed to allow different configurations of the same model to be used to produce all our weather forecasts and climate predictions. The system has been in continual development since 1990, taking advantage of steadily increasing supercomputer power, improved understanding of atmospheric processes, and an increasing range of observational data sources.

The UM is highly versatile, capable of modelling a wide range of time and space scales including kilometre-scale mesoscale nowcasts, limited-area weather forecasts, global weather forecasts (including the stratosphere), seasonal foreasts, global and regional climate predictions as well as being run as part of an ensemble prediction system.

In addition, the UM can be coupled to other models which represent different aspects of the Earth's environment that influence the weather and climate, such as the ocean and ocean waves, sea-ice, land surface, atmospheric chemistry and carbon cycle. This allows the Met Office Unified Model to be used for Earth System Modelling applications.

The Met Office Unified Model is run in many different configurations at the Met Office:

• a high-resolution model over the UK, to help predict the weather a few hours ahead
• a model over the North Atlantic and Europe (NAE) to predict the weather one to two days ahead, run as part of a short-range limited area ensemble MOGREPS
• a global model to predict the weather several days ahead, run as part of a medium-range global ensemble
• a seasonal forecasting model (GloSea) coupled with an ocean model to predict the likely trends over the coming six months
• a decadal prediction system (DePreSys)
• a regional climate model (PRECIS)
• a global climate model to predict up to a century ahead (HadGEM)

The operational NWP system

The operational suite does all the individual tasks that are required to produce a forecast.

• Observation processing — extracting all the observations that have been received, quality controlling them and finally reformatting them into a form ready for use by the model
• Reconfiguration (for some runs) — incorporating data fields from external files, such as the sea-surface temperature, or to update a climatological field
• Data assimilation scheme — adjusting the model background field, a forecast from a previous model run, towards the new data received from the observations
• Main forecast run — the length varies according to the particular run of the model

Using the forecast model data

The forecast data are written into files known as 'fieldsfiles'. Using these, various plotted charts and maps are produced, which forecasters then use to produce the weather forecast.

As it is important that the charts are available as early as possible the fieldsfiles only cover a 24-hour period. This enables charts for 24 hours ahead — T (current forecast time) +24(hours) to be plotted and made available even though the forecast is continuing.

Producing forecasts

Running a numerical weather prediction model is only part of the process in producing a weather forecast. Before a forecast is issued, the output from the model is studied by a forecaster.

For short ranges (hours ahead), the forecaster is able to compare a model field against actual observations. This means they can identify any possible errors, make appropriate allowances and possibly add extra detail to the model forecast — things like summer showers are often too small for the computer to pick up. The forecaster is also able to respond quickly and amend a forecast should the situation warrant it.

For medium-range forecasts (days and weeks ahead), the forecaster is able to compare the results from our model with those from other centres such as ECMWF (European Centre for Medium-Range Weather Forecasts), NC EP (National Centers for Environmental Prediction) and DWD (Deutscher Wetterdienst).

If all models are producing approximately the same solution confidence in the forecast would be high. Confidence is also decided by the consistency between model runs. If the model is consistent then confidence may be high but if it suddenly changes then confidence falls rapidly. In these situations the solutions of other models may be crucial. Sometimes, alternative forecasts may be issued with probabilities assigned.

This human-machine partnership is very important in producing accurate weather forecasts.

The Met Office disseminates various products from the operational numerical weather prediction models in GRIB and GRID formats.

The Met Office also has the capability to rapidly relocate regional models to any area of interest worldwide. These Crisis Area Models (CAMs) are run in support of allied military operations and disaster relief.

The atmosphere — surface and sub-surface processes

Each land point is assigned characteristics according to the soil type and the vegetation type. These are important in the calculation of the heat, moisture and momentum fluxes at each grid point. If land is covered by snow then the properties, such as albedo, will be drastically altered.

The soil temperature is calculated in four separate levels. The temperature of the soil will change according to the radiation balance at the soil surface. Snow cover will act as an insulator to the soil.

Each land point has a value of the soil moisture content in four layers of different thickness which is altered according to how much evaporation is occurring and the amount of precipitation at that point. The vegetation plays an active role in the hydrology at the surface. When precipitation falls some is intercepted by and held in the canopy of the vegetation. The remainder is known as 'canopy throughfall' and falls to the soil's surface. This water is absorbed by the soil unless the intensity is too great or the soil is already saturated in which case surface run-off, into rivers and lakes, occurs. Soil water is primarily lost though evaporation through plants, in which case the term transpiration should be used. The amount of transpiration that can occur is limited by the soil moisture, as the soil dries it becomes progressively more difficult for plants to extract water.

Over the sea the roughness length, a representation of surface drag, is increased with increasing wind speed to represent the interaction with waves.

Climate change and aerosols

Atmospheric aerosols are microscopic particles suspended in the Earth's atmosphere, which generally act to cool the climate by reflecting sunlight back to Space and also by affecting clouds. The net impact of human activities, including greenhouse gases and aerosols, has been to warm the world's climate.

Atmospheric aerosols

The Earth's atmosphere is made up of a number of components. These include gases such as nitrogen, oxygen and water vapour, and also atmospheric aerosols. Atmospheric aerosols are microscopic particles that are emitted from human and natural sources. The aerosols become suspended in the atmosphere. Human sources of aerosols include industrial aerosols from emissions of gases such as sulphur and nitrogen oxides, as well as direct emissions of smoke and soot from fossil-fuel and biomass burning.

How aerosols affect the climate

Once in the atmosphere, aerosols have two effects upon climate, both of which lead to a cooling of the Earth:

• They can directly reflect sunlight back away from the Earth (left hand panel of Fig 1)
• They can interact with clouds in complex ways leading to changes in cloud reflectivity, cloud lifetime, cloud height and cloud precipitation (Fig 1).

Fig 1. Interaction of aerosols with sunlight and clouds (from IPCC, 2007).

The overall impact of aerosols on climate

Human activities have increased concentrations of atmospheric aerosols, which have led to an associated cooling of climate. This cooling acts to counterbalance some of the warming due to increased concentrations of greenhouse gases which are also caused by human activities.

Just how much of a cooling effect these aerosols have on the climate is still uncertain owing to the complexity of the problem (Fig 1); but the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC, 2007) went some way to assessing this. It concluded that, although these microscopic particles do act to cool the climate, they do not offset the global warming effect of greenhouse gases (Fig 2).

Fig 2. The warming associated with greenhouse gases, the cooling associated with aerosols, and their effect on climate. Based on IPCC (2007) and Collins et al. (2007).

The overall impact of spray can aerosols

A common misconception about aerosols is that they come from spray canisters, used for products such as deodorant, and that they damage the ozone layer. In the past the gases used as propellants in spray cans were damaging to the ozone layer, but not the aerosol particles themselves. Under the Montreal Protocol, these propellants have been replaced by non-ozone depleting substitutes. However, these gas replacements are greenhouse gases and add a small component to the global warming problem (from IPCC, 2007).

The atmosphere — radiative transfer

Radiative transfer describes the transfer of electromagnetic energy in the atmosphere.

In order to make weather predictions and conduct climate simulations our forecast models need to represent the processes of radiative transfer and their correct distribution across the globe, and with height in the atmosphere.

Radiative transfer — an overview

The Earth, atmosphere and oceans are driven by the energy reaching the Earth from the sun. This incoming solar radiation is balanced by thermal emission to outer space.

However, the distribution of the incoming solar radiation on the planet is not uniform because the Earth is a sphere and it’s axis of rotation is tilted. It can also be affected by clouds, aerosols and the albedo of the surface (the extent to which it diffusely reflects light from the sun) — all of which vary significantly across the globe.

The thermal emission from the Earth atmosphere system to space is also not evenly distributed with surface properties — clouds and aerosols have an effect.

The amount of energy arriving at the top of the atmosphere from the sun varies according to wavelength (or colour) of the light. The peak in intensity is in the visible part of the spectrum where our eyes have evolved to be most sensitive. This range of energy with wavelength is called a spectrum of energy. Different surfaces, cloud types, gases in the atmosphere and chemical species of aerosols affect different parts of the solar spectrum in different ways.

The thermal emission from the planet back to space varies across the spectrum of wavelength — the peak in the emission is at longer wavelengths than the incoming solar spectrum and is not visible to the human eye.

This graphic shows some of the key interactions between solar radiation (black lines) and the outgoing thermal radiation (red lines).

The atmosphere — orography

How do hills and mountains affect the atmosphere?

Undulations on the surface of the Earth of any size and shape, from small hills to major mountain ranges that span continents, are known collectively as the orography of the Earth.

Orography affects the weather in a variety of ways, both on a local and global scale, including:

• enhancement of precipitation and increased wind speeds over mountain summits
• large-scale effects on the global circulation via a drag force exerted on the flow

Understanding and representing the effects of orography is crucial for weather forecasting and climate prediction since the phenomena usually occur on scales too small to be resolved by the computer models.

We are increasing our understanding through field and laboratory experiments and analytical and computer modelling studies.

The atmosphere — convection and convective precipitation

A cloud model is used to represent cumulus and cumulonimbus convection, in which an updraught and precipitation-induced downdraught are considered. A test is made for convective instability and if the potential temperature of any level is higher than that of the level above convection is initiated. Convection will continue as long as the air within the cloud continues to be buoyant. Dilution of the cloud is represented by entrainment of environmental air.

Before the cloud detrains completely at the level where the parcel of air ceases to be buoyant, the remaining mass, heat, water vapour and cloud water/ice are completely mixed into the environment at the cloud top.

A single cloud model is used to represent a number of convective plumes within the grid square, and precipitation is diagnosed within that square if:

• cloud liquid/ice content exceeds a critical amount, and
• the cloud depth exceeds a critical value

Typically this value is set to 1.5 km over the sea and four kilometres over land. However, if the cloud-top temperature is less than -10 °C, the critical depth is reduced to one kilometre over land or sea. As with large-scale precipitation, the convection scheme allows for evaporation and melting of precipitation. Cloud physics research is leading to scientific development in this area of our modelling.

The atmosphere — clouds

Clouds significantly affect how nature balances the energy of the atmosphere around the Earth. This energy distribution helps determine how weather systems move and develop around the globe. Therefore, for accurate predictions of both weather and climate, clouds must be well represented in our computer models.

To understand clouds and how precipitation (rain, snow and hail) forms within them we use both observed evidence and theories.

A lot of evidence is gained from the specialised aircraft FAAM (Facility for Airborne Atmospheric Measurement) BAe 146. This plane has instruments for measuring the number, size and chemical composition of aerosol particles, cloud droplets and ice crystals within clouds.

Large-scale cloud and precipitation parameterization

Clouds interact strongly with solar and infrared radiation and may produce precipitation, hence their representation in models is required. Latent heat is also released or required when water changes phase, and this heating or cooling plays a critical role in the way air moves in the atmosphere.

Clouds form in air that contains more water vapour than the saturation vapour content. The saturation vapour content increases dramatically as the temperature is increased, hence most clouds form when air is cooled. This is usually achieved when air is lifted (when air cools due to adiabatic expansion) although thermal radiative cooling and mixing of air may also produce cloud.

Water in the model is assumed to be in the form of vapour, liquid cloud droplets, ice crystals or aggregates, or raindrops. The purpose of the large-scale cloud and precipitation schemes are:

• to transfer water between these categories as a result of the most significant cloud physics processes that occur in the atmosphere
• to let ice and rain fall downwards towards the surface
• to calculate the fractional coverage of cloud in a gridbox

The cloud physics processes that are represented include:

• the condensation of water vapour to cloud droplets and the evaporation of these droplets
• the deposition of water vapour to ice crystals or aggregates and the evaporation of these particles
• the riming of supercooled cloud droplets by ice particles
• melting of ice particles to produce raindrops
• evaporation of raindrops
• accretion ('sweep-out') of cloud droplets by raindrops
• the collision/coalescence mechanism to form raindrops from cloud droplets
• the fall downwards of ice particles and raindrops

The quantitative calculation of these transfers is performed using standard cloud physics theory assuming raindrop and ice particle size distributions and fall speed/diameter relationships, etc.

Cloud fractions for liquid clouds are calculated assuming a distribution of water vapour across a gridbox — the regions where the water vapour exceeds the saturation value will be condensed to liquid cloud droplets, the other regions will remain free of liquid cloud. Cloud fractions for ice cloud are calculated assuming a relationship between the cloud fraction and the amount of ice present in the gridbox.

The atmosphere — modelling cirrus clouds

Cirrus are high-level clouds, which are mainly composed of ice crystals, creating a wispy-like appearance. They appear at altitudes usually greater than six kilometres, and are spatially and temporally well distributed around the globe. At any one time cirrus can cover up to 30% of the Earth's surface.

While lower clouds — such as cumulus — more directly affect our weather, by blocking the sun and bringing conditions such as rain and snow, cirrus chiefly affect climate and global warming. Cirrus can reflect solar radiation back into space, thereby cooling the atmosphere. They can also trap the Earth's outgoing radiation, creating a warming effect. These two opposing processes depend on the cloud's properties, such as the cloud's horizontal or vertical position, ice water content and ice particle microphysical properties.

Understanding cirrus clouds

The measurement of cirrus properties is crucial in improving and validating models, and consequently our understanding of the impact of cirrus on the climate.

We use airborne data from the FAAM (Facility for Airborne Atmospheric Measurement) BAe 146 aircraft to obtain remote sensing and in-situ measurements of cirrus.

The following are used for cirrus studies:

• Visible, infrared and far-infrared spectrometers and a microwave radiometer for radiative measurements
• A full suite of cloud physics instrumentation for in situ measurements of aerosols, liquid water and ice particles
• In situ aircraft and chemistry instrumentation for temperature, pressure, humidity, ozone and CO, and dropsondes to supplement the data gathered during aircraft profiles

Cirrus properties can vary greatly, so it is important to study the clouds using many different wavelength regions, each of which is sensitive to particular parameters. For example, visible and infrared techniques can be used to detect the presence of ice clouds and to determine the cloud-top altitude and optical depth, whereas longer wavelengths can probe into the clouds and are more sensitive to larger particle sizes and ice-water content.

The atmosphere — boundary layer

The boundary layer, also called the atmospheric or planetary boundary layer, is the part of the atmosphere which is directly influenced by the surface.

The characteristics of the boundary layer depend on the underlying surface and the time of day. They vary in two main ways.

• Unstable boundary layers — the surface transfers heat to the atmosphere, usually during the day over land
• Stable boundary layers — the atmosphere transfers heat to the surface, usually during the night over land

Understanding the state of the boundary layer is crucial in forecasting weather conditions at, or near, the surface, such as the formation and dissipation of fog, or extremes of temperature.

Using mathematical equations in our computer models

Equations of motion

Newton's second Law of Motion — the law of acceleration — states the rate of change of momentum of a body is proportional to the resultant force acting on the body and is in the same direction. Basically it explains how an object will change velocity if it is pushed or pulled upon by other forces.

The main forces in the atmosphere are:

• the Coriolis force
• gravity
• pressure differences

In simple terms, as air begins flowing from high to low pressure, the Earth rotates under it, making the wind follow a curved path. In the Northern Hemisphere, the wind turns to the right of its direction of motion. In the Southern Hemisphere, it turns to the left. The Coriolis force is zero at the equator.

In the horizontal the pressure difference and Coriolis force are the main causes of acceleration. In the vertical the two main forces are gravity and the pressure gradient, due to the variation of pressure with height. In fact, the gravitational force is almost exactly balanced by the pressure gradient force, a condition known as hydrostatic equilibrium. Hydrostatic equilibrium explains why the Earth's atmosphere does not collapse to a very thin layer on the ground.

Many computer models assume hydrostatic equilibrium, but our model does not. This means it can take account of strong vertical wind motion, making it suitable for running at very high resolution.

The vertical component of the Coriolis force is also included in our model. Although it is comparable in magnitude with the horizontal components, it is negligible when compared against the gravitational and vertical pressure gradient forces separately. For this reason it is often ignored, but it can be significant in regions of strong vertical motion.

Thermodynamic equation

The First Law of Thermodynamics requires that the amount of heat added to a system is exactly balanced by the work done in increasing its volume and the increase in internal energy. It is an expression of the principle of the conservation of energy.

Temperature at a point in the atmosphere can change, either due to cooler or warmer air being blown to that point, as a consequence of local expansion or contraction, or from other local effects such as evaporation or condensation which are important when dealing with clouds.

Continuity equation

Continuity equation is the basic principle of Conservation of Mass, which essentially states that matter is neither created or destroyed, although it may be rearranged. For example, rising air in one location means downward motion somewhere else; southerly flow here requires northerly flow elsewhere. These weather principles ultimately come from 'continuity'.

Equation of state

An equation of state describes the state of matter under a given set of physical conditions:

• pressure
• density
• temperature for a perfect gas

The atmosphere obeys this equation quite well. Together with gravity, the equation of state is the key link between dynamics and thermodynamics, and enables 'heat' to drive motion.

Water vapour equation

A water vapour equation describes the way in which the amount of water vapour in a particular parcel of air changes as a result of transportation and condensation/evaporation (where clouds are involved).

Atmospheric processes — an overview

The atmosphere is a layer of gases surrounding our planet, kept in place by its own weight under gravity. The outer fringes extend to about 10,000 km, but the bulk of the atmosphere is compressed into the first 16 km (more like 9 km at the poles). Most of our weather takes place in this lower layer of the atmosphere, known as the troposphere. The layer above the troposphere is known as the stratosphere.

The atmosphere is a fluid and its motions are governed by the effects of pressure, rotation, gravity, friction and microphysical processes such as condensation, evaporation and precipitation. To forecast the weather, we need to first understand the processes which affect the atmosphere and then quantify and simulate them in a numerical forecast model.

Atmospheric processes occur on a very wide range of spatial scales — from the smallest gust of wind to weather patterns as large as continents. In numerical models, the processes are partitioned into two categories; those that are large enough to be represented by the model directly, and those that are too small and whose effects must be represented indirectly by further equations and approximations known as parametrizations.

Observations — monitoring and quality control

Monitoring

We monitor meteorological observations received from a variety of sources worldwide. This is primarily to maintain and improve the use of these observations in our computer assimilation system (4D-VAR), which is part of the numerical weather prediction (NWP) system.

The quantity and quality of observational data received and assimilated are checked daily, and any problems followed up. Observations are compared with short-period forecast (background) fields and observation-minus-background (o-b) statistics are used for monitoring over various time periods. On a monthly basis any poor quality data that are identified are either added to reject lists and excluded from the assimilation or corrected prior to use.

Automatic quality control

The current automatic quality control system is based on Bayesian probability theory, and a careful statistical analysis of observation and background errors.

Bad data

Each observed element is given an initial 'probability of gross error' (PGE). For example, we expect about 1.5% of SYNOP pressure observations to be 'bad' and assign them an initial PGE of 0.015. This PGE is increased if the element has failed one of the earlier consistency checks, e.g. the pressure is checked against the pressure three hours earlier and the reported pressure tendency.

Good data

Even 'good' observations have small errors (e.g. barometer accuracy is about 0.2 hPa — inaccuracies in knowledge of the station height can introduce larger errors). We take account of the fact that observations include small scale detail, not resolved by the computer model. Including this factor, the observation error for good SYNOP pressure observations is estimated as 1.0 hPa. We also estimate the root-mean-square error of the background (forecast) fields.

These estimates depend on various elements.

• They are larger in the vicinity of fast-moving vigorous depressions than in large anticyclones
• Climatological elements — latitudes having large numbers of observations will have generally lower values (average about 1.3 hPa) than data sparse latitude zones (typical errors of 2-3 hPa)
• The most important single check is that against the forecast background (T+3 hours or T+6 hours), updating the PGE. It includes an estimate of the probability that the whole observation is wrong, e.g. position reported incorrectly

Several observation-type specific checks are applied.

• Calm aircraft winds are rejected
• An asymmetric check for cloud track winds
• Radiosonde standard and significant levels are checked against the background, and then averaged over the model layers omitting levels with PGE > 0.999
• 'Buddy check' — compares each observation against neighbouring observations

Satellite sounding of the atmosphere — infrared

Satellite sounding instruments measure radiation at infrared and microwave wavelengths that have primarily been emitted by the atmosphere itself. The different wavelengths provide information on the temperature and composition (e.g. humidity, ozone amount) of the atmosphere over a range of altitudes. Infrared sounding instruments provide very high quality information in cloud-free areas, but can provide no information below cloud, which is where microwave instruments are essential.

The capability of these instruments is still advancing and work continues to improve the exploitation of the data they provide. Recently, information from a new generation of advanced infrared sounding instruments (spectrometers and interferometers) became available.

Infrared sounding radiometers

The High-resolution InfraRed Sounder (HIRS), a 20-channel radiometer, provides information on the temperature, water vapour and ozone structure of the atmosphere. Together with microwave instruments it forms part of the ATOVS sounding system which, following on from the previous TOVS sounding system, was the main source of satellite sounding data for more than 25 years, and remains valuable even today.

High spectral resolution sounders

A new generation of infrared sounding instruments have become available since 2003. These instruments, such as the Atmospheric Infrared Sounder (AIRS) on EOS-Aqua and the Infrared Atmospheric Sounding Interferometer (IASI) on Metop, boast very high spectral resolution and thousands of individual channels. These allow much finer scale structure in the vertical profiles of temperature and humidity to be successfully analysed (about one km, compared to three km for the previous generation of sounders).

Infrared sounding — technical details

• The infrared region of the spectrum used for atmospheric remote sensing ranges from 16.6 to 3.3 microns in wavelength
• Carbon dioxide (CO2) spectral bands at 15 and 4.3 microns give us information on the temperature structure of the atmosphere
• Information on water vapour content can be gained from a large number of H2O lines between five and eight microns
• Parts of the infrared spectrum are sensitive to ozone (8.9-10.1 micron band), and other trace gases such as CH4 and N2O

Satellite sounding of the atmosphere — microwave

Satellite sounding instruments measure radiation at microwave and infrared wavelengths that have primarily been emitted by the atmosphere itself. The different wavelengths provide information on the temperature and composition (e.g. humidity, ozone amount) of the atmosphere, over a range of altitudes. Microwave sounding instruments provide good quality information, even in the presence of clouds, but not heavy rain. However, they can not provide the very high vertical resolution which the infrared sounders can deliver.

The technology is well-established, but work continues to improve the exploitation of the data provided, particularly the information on clouds and temperature and water vapour in the lowest few kilometres of the atmosphere and over land.

Microwave sounding radiometers

The Advanced Microwave Sounding Unit (AMSU), a 20-channel radiometer, provides information on the temperature and water vapour structure of the atmosphere. Together with the infrared instrument HIRS it forms part of the ATOVS sounding system.
AMSU is really three radiometers:

• AMSU-A1 which provides temperature information
• AMSU-A2 which provides information on clouds and water vapour
• AMSU-B which provides information on water vapour. On more recent satellites the AMSU-B has been replaced by the MHS instrument, but the capability remains very similar.

The microwave sounders are an important and well-established part of the observation network for weather forecasting.

Microwave sounding — technical details

The microwave region of the spectrum used for atmospheric remote sensing ranges from one to 200 GHz in frequency

An oxygen (O2) spectral band composed of many individual spectral lines between 50 and 60 GHz gives us information on the temperature structure of the atmosphere

Information on water vapour content can be gained from two water vapour (H2O) lines at 22.225 and 183.31 GHz.

Information on the Earth's surface (e.g. sea-surface winds and temperature) and rain is obtained at frequencies which are not close to absorption lines.

For lower frequencies the majority of clouds are essentially transparent but at frequencies above 20 GHz cloud effects have to be considered.

Information about cloud liquid water path can be obtained from the higher frequencies where cloud absorption and scattering becomes significant.

Satellite active sensing

Active remote sensing uses artificially-generated radiation emitted by a satellite or ground-based instrument, modified by the Earth's surface or atmosphere and received by the same instrument, or another receiver. Typical wavelengths used include microwaves (wavelengths of a few millimetres) to the optical and ultraviolet regions. The method contrasts with passive remote sensing techniques where an instrument measures the natural radiation signature from the atmosphere or surface.

Meteorological information from these 'active' measurements can be used in numerical weather prediction models. Quantities that can be derived include:

• near-surface wind speed and direction over the oceans
• vertical profiles of horizontal wind speed and aerosols
• profiles of pressure, temperature and humidity
• vertically integrated water vapour
• cloud depth
• rainfall rates

The Met Office uses:

• Wind scatterometers
• Radio occultation
• GPS ground-based sensing of integrated water vapour
• Doppler Wind Lidar (in the future)

Wind scatterometers

A scatterometer is a radar system that measures the level of transmitted microwave energy which has been backscattered from the surface at two or more look angles. Over the ocean, backscattering is due to in-phase reflections of the microwave radiation transmitted by the scatterometer.

This backscattering is due to the presence of trains of wind-generated waves of 5-20 cm wavelength. Because these small waves tend to lie at right angles to the wind direction, there is a larger backscatter in the up- or downwind direction than the crosswind direction.

Wave amplitude, and hence backscatter, also increases with increasing wind speed. Measuring the backscatter at two or more angles allows both wind speed and direction at the surface to be derived, which can then be assimilated into our models. Soil moisture amounts can also be derived from backscatter signatures over land.

Radio occultation

Radio occultation (RO) is based on measuring the path of radio waves passing through the atmosphere from one satellite to another. This path is bent by atmospheric density gradients. The variation of ray bending with height above the surface can be inverted to give the refractive index as a function of height. In regions where the atmosphere is dry, this information can be combined with the ideal gas law and the hydrostatic equation to give a temperature profile. At the Met Office, we assimilate RO refractive index directly, from which we can also extract information on humidity in the lower atmosphere.

Ground-based GPS

Signals transmitted by GPS satellites to ground-based receivers are slowed down due to the refractive index of the atmosphere. With a dry atmosphere, these path delays can be modelled with millimetric accuracy and so any residual delay is due to water vapour. These residual delays can be used to produce estimates of vertically integrated water vapour. Assimilation experiments have shown improvements in some short-range forecasts and data from the European network are used operationally in our regional and UK models.

Doppler Wind Lidar

The Doppler Wind Lidar (DWL) technique uses a lidar to detect the Doppler shift of the light backscattered from atmospheric molecules, cloud droplets or aerosols. The Doppler shift is directly related to the wind speed in the line-of-sight of the lidar beam. Since vertical wind speeds are relatively small, the instrument effectively measures the horizontal wind speed at the direction of the lidar beam — the 'horizontal line-of-sight' wind (HLOS). If two or more lidar beams are used at different azimuth angles, both wind speed and direction can be obtained.

The ESA Atmospheric Dynamics Mission (ADM) or 'Aeolus' is planned for launch in 2009. This mission is dedicated to the measurement of wind profiles using the DWL technique, but being a demonstrator mission, is limited to deriving the HLOS only. Such a measurement can only be easily interpreted by assimilation into NWP models. We are currently researching how HLOS winds can be assimilated, so this data source can be used soon after launch of ADM-Aeolus.

GRAS Satellite Application Facility

As a partner in the EUMETSAT GRAS Meteorology SAF we:

• developed data assimilation methods using the radio occultation instruments on Metop and other satellites
• deliver the Radio Occultation Processing Package (ROPP) to assist other numerical weather prediction centres to exploit radio occultation data in their models
• monitor the data flow and validate the quality of near-real time RO data delivered to users

Observations

Every day, hundreds of thousands of observations are made of the atmosphere around the world, measuring quantities such as pressure, wind, temperature and humidity. The observations are made in many ways — at single sites on land and sea; from the air by weather balloons and aircraft; from space by satellite, and by rainfall radar. To use these observations in an operational weather forecasting system we have to monitor their availability, quality control them, and process them into a form that can be used by the computer models and forecasters.

We develop and maintain software systems which can do all of these tasks for current operational data sources, and which can be readily extended for new observational systems in the future.

Types of observations

Surface data

These include:

• land-based synoptic observations, including cloud type and present weather
• marine surface data from ships and buoys
• wind data from measurements of ocean-backscatter observed from satellites

Surface pressure is usually reduced to a mean sea-level pressure for reporting and plotting purposes, also many stations report pressure at the station altitude and some high-level stations compute a height at 850 hPa.

These are all transformed into p* (the pressure at the model ground surface), in order to simplify the assimilation process. Wind data are taken at 10 m over land, but the height may vary for marine data depending on the observing system. Temperature and moisture are measured at a height of 1-2 m over land, but again the height may vary for marine data.

Radiosondes

A radiosonde is a unit used on weather balloons that measures various atmospheric parameters and transmits them to a fixed receiver. A special feature of the radiosonde processing is vertical averaging. The radiosonde system provides a standard set of measurements of wind speed and direction, temperature and dew-point temperature. The dew-point data provide information on moisture and are usually combined with the temperature data to provide the moisture information as relative humidity.

Radiosonde reports help us build a detailed picture of the atmosphere at that location. In some reports there may be more than 100 levels. The vertical averaging maps these reports on to the computer models. As well as providing the models with information which is consistent with its resolution, this also simplifies the subsequent processing as each report can then be treated in a similar manner regardless of how many levels it originally contained.

Aircraft observations

Observations of temperature and winds are available either from manual or automatic reporting systems. The altitude of the observations has to be converted from a flight level to a pressure before being assimilated in to the Met Office forecast models.

True representation of actual conditions is more of a problem with aircraft reports than some upper-air observation systems, particularly in the vertical. Planes flying in the direction of the wind may seek the core of the jet stream, which is restricted in height, and therefore may be sampling part of the atmosphere which is not fully resolved by the model (where typically the vertical resolution may be only 50 hPa). Automated reports from commercial airliners are provided through the AMDAR (Aircraft Meteorological DAta Relay) programme.

Recently, a new type of automated aircraft report has been developed in the USA, primarily for use by regional airlines, called TAMDAR (Tropospheric Airborne Meteorological DAta Report, or 'Tropospheric AMDAR'). It is envisaged that these two types of report will ultimately replace the manual reports known as AIREPs.

Weather satellites

We continually investigate ways to exploit satellite data, including atmospheric motion and wind reports and satellite sounding information on the temperature and composition of the atmosphere.

Using satellite imagery

The first meteorological satellite, TIROS, was launched in 1960 by the USA. Initially satellite images were treated purely as qualitative pictures, which were manually viewed and interpreted by meteorologists. Satellite imagery provides a picture of the current cloud conditions and is a familiar sight on TV weather forecasts. However, satellite imagery can also undergo various types of quantitative processing to obtain information on variables such as wind vectors, cloud height, cloud cover, surface temperature, sea-ice cover and rainfall.

Satellite imagery can also capture the development of transient features such as areas of fog, dust storms and plumes of volcanic ash. Slower changes in surface features, such as snow cover and vegetation, can also be inferred from the imagery.

How imaging works

Radiant energy from the earth is measured by a satellite radiometer and stored as digital values in two-dimensional arrays of pixels, which make up the image. The radiometer measures at different wavelengths which provides complementary information about the atmosphere and surface:

• Infrared radiation tells us about the temperature of emitting bodies, such as cloud tops or the surface in cloud-free regions. Infrared images are good for viewing high clouds and surfaces, at any time of the day or night. Water vapour (WV) images show radiation in the water-vapour absorption band and are good for viewing clouds and upper-level water vapour distributions in cloud-free areas
• Visible radiation shows low and high clouds, but only by reflected sunlight, so no images are produced at night. However, the pixel size of these images can be finer than for the infrared images, allowing more detail to be seen

How the Met Office uses satellite imagery

• By assimilation of satellite imagery products into NWP models to improve the forecast
• Through development of products for presentation to the forecasters in the form of enhanced imagery and movie loops

Satellites used by the Met Office

Satellite imagery used in meteorology is produced by instruments on board two types of satellite.

Polar orbiters are positioned about 900 km above the surface of the Earth, in a sun-synchronous orbit, which means they see the same part of the Earth at the same time each day. Polar orbiters make about 14 orbits a day and can view all parts of the atmosphere/surface at least twice a day. Although their frequency is limited, there is finer detail (typically around 1 km size pixels) since they are relatively close to the Earth's surface.

The main polar satellites used are the NOAA and Metop series, although several research satellites also provide data. Imagery from the polar orbiters is from the Advanced Very High Resolution Radiometer (AVHRR) sensor on the NOAA and METOP satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) data on the NASA Terra and Aqua research satellites.

Geostationary satellites are positioned about 36,000 km above the equator in a geostationary orbit, which means they are fixed in position above one part of the Earth. These satellites scan the same area continuously, so give more frequent images (15-30 minutes), but the detail is coarser (typically 3-10 km size pixels).

Geostationary satellites used by the Met Office are Meteosat, at longitudes of 0° E and 57° E, MTSAT at 140° E and GOES at 75° W and 135° W. The Meteosat Second Generation satellites — the first of which was launched in 2002 — provide greatly enhanced imagery over Europe and Africa. Work continues to fully exploit these data.

History of meteorology from space

1959	Launch of NASA's Vanguard II, which returned the first photograph from space of Earth's cloud cover
1960	NASA launched the Television Infrared Observation Satellite (TIROS) I, which proved that satellites can observe Earth's weather patterns. Subsequent TIROS satellites improved hurricane-tracking techniques and severe storm warnings, protecting lives and property in coastal areas around the world.
1964	Satellite cloud pictures are used operationally at Met Office HQ in Bracknell.
1966	US Environmental Sciences Services Administration I and II give the world's first global weather satellite system.
1975	The satellites SMS-A, the first spacecraft to observe Earth from geosynchronous orbit, and SMS-B started producing cloud-cover pictures every 30 minutes for weather forecasters.
1977	ESA's Meteosat 1 launched.
1978	Seasat demonstrated techniques for global monitoring of Earth's oceans.
	Nimbus 7 was launched, carrying a TOMS instrument that provided 14 years of data on Earth's ozone layer. Data from TOMS were part of the scientific basis for treaties banning the manufacture and use of ozone-depleting chemicals.
1981	Meteosat-2 launched, the first fully operational Meteosat launched. Although an ESA satellite, EUMETSAT took control of its operations in 1986.
1984	The Earth Radiation Budget Satellite began its study of how Earth absorbs and reflects the Sun's energy.
1988	Meteosat-3 launched, again an ESA satellite operated by EUMETSAT
1989	EUMETSAT's Meteosat-4 launched, marking the beginning of the EUMETSAT Meteosat Operational Programme (MOP)
1991	Respectively launched in 1991 and 1995, the ERS-1 and ERS-2 satellites for earth observation are an ESA success. Thanks to the quality, reliability and originality of the on-board instruments, many findings related to the Earth environment have been made and many applications derived from them. Meteosat-5, the second MOP satellite launched
1992	Data from the US-French TOPEX/Poseidon satellite began to detail the links between Earth's oceans and climate. By 1994, TOPEX data indicated that Earth's average global sea level had risen in the two previous years.
1993	Meteosat-6, the third and final MOP satellite
1997	Meteosat-7, the only satellite of the EUMETSAT Meteosat Transition Programme (MTP), launched to maintain operations until the first Meteosat Second Generation satellite (MSG-1) is launched in 2002
1999	QuikScat, a satellite mission to monitor ocean winds, was launched.
2001	Jason 1 satellite launched as a successor to the TOPEX/Poseidon ocean surface topography mission.
2002	ESA's Envisat launched, an advanced polar-orbiting Earth observation satellite, which will provide measurements of the atmosphere, ocean, land, and ice over a five-year period. Envisat data will support Earth science research and allow monitoring of the evolution of environmental and climatic changes.
	Meteosat-8, the first of the second generation MSG satellites launched.
2005	Polar ice mission Cryosat launch failure.
	Meteosat-9, the second of the second generation Meteosat satellites launched. This brings the extra functionality of the MSG series into the operational domain.