Also poised to make a huge leap are the number and quality of platforms and sensors that feed data to the models. Even with today’s global satellite observations, the data collection process has gaping holes. Most existing weather satellites measure only visible and infrared radiation. And none can measure even a single variable all over the globe at one time. Instead, data stream in continuously to the assimilation system software, with information from different locations coming from different satellites.
Some of the satellite sensors, including those that detect visible and infrared radiation, can’t see below the clouds and therefore can’t make measurements when cloud cover is present. Newer sensors, like those that use microwave radar, can penetrate cloud cover. And modelers increasingly try to incorporate other types of satellite measurements, such as data on aerosol and ozone concentrations or on the character of the ocean’s surface.
They’re also exploring detailed and direct measurements of the wind. These data could greatly improve accuracy in forecasting conditions in which nonlinearity is likely to have a huge effect, such as in predictions of hurricanes and intense winter storms. Today direct measurements of the wind are infrequent.
The WMO projects that the amount of weather-related data provided by satellite systems will increase by a factor greater than 10 000 during the next decade, thanks to the launch of new satellites in the next few years. This remote-sensing revolution will vastly improve our ability to characterize the atmosphere and the earth’s surface.
To help do that job, six satellites were launched simultaneously in April as a joint venture between the United States and Taiwan. The United States calls the project COSMIC (for Constellation Observing System for Meteorology, Ionosphere, and Climate). In Taiwan the moniker is Formosat-3. COSMIC/Formosat-3 is designed to track temperature in the upper atmosphere up to 55 km and to give detailed 3-D information related to distribution of temperature and water vapor in the troposphere—crucial for predicting precipitation. The satellites use a technique called radio occultation. They intercept signals from Global Positioning System satellites after they pass through the atmosphere close to the horizon, calculate the delay in the expected arrival of the signal, and relate this delay to the bending of the path of the signal and its dependence on the atmospheric conditions.
NASA added two more satellites, CloudSat and CALIPSO, to the weather-data effort in April. CloudSat uses microwave radar to map the vertical structure of clouds. The strength of the radar signal that returns to the satellite is related to the amount of water in a cloud. CALIPSO (for Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) is a joint venture with the French space agency, CNES. CALIPSO uses light detection and ranging, or lidar, a technology that sends out laser pulses in the infrared and visible spectra and senses the backscattered radiation. CALIPSO’s lidar studies aerosols—tiny particles in the atmosphere, including water vapor, dust, and pollutants.
CloudSat and CALIPSO are helping scientists understand how clouds form, evolve, and affect weather and the climate. Researchers expect the information to let them improve forecasts of events involving heavy rainfall, an area where progress has been much slower than for other types of forecasts. Improving the prediction of heavy rainfall by one day in the next 10 years would be a huge breakthrough. That is, four-day forecasts in 2016 would be as accurate as three-day forecasts are today.
Next year, the Paris-based European Space Agency, in a project led by researchers in the United Kingdom, plans to launch Aeolus, the first satellite designed specifically to collect data on wind. Aeolus also will use lidar, in this case to produce wind profiles at different altitudes. Aeolus is expected to track the length of time it takes the laser signal to bounce back from the aerosols and use that information to determine the altitude of the particles. It also will track the change in the frequency of the light, called the Doppler shift, to determine the wind speed and direction.
These are all research satellites, intended to improve the science of weather prediction. New and better operational satellites, those used for current forecasting, are also contributing to accuracy. The United Kingdom began replacing its two Earth-observing geostationary satellites in 2002; Japan replaced its single geostationary weather satellite last year. The two U.S. geostationary satellites are due to be replaced starting in 2012. The United States also plans to launch a set of three polar orbiting satellites in 2008 to replace its current civilian and defense polar satellites; the European Space Agency plans to launch Europe’s first polar orbiting meteorological satellite this summer. And in the same way that the burden of ensemble modeling will someday be shared around the world, 44 countries are developing plans to share their collected data as part of the Global Earth Observation System of Systems, or GEOSS.
Although the new satellites can help fill important gaps in our global observing system, the information flowing from them is likely to overwhelm our ability to process it. We must fine-tune data assimilation software to select those items most important to a forecast and then use the system to determine the initial conditions for the model.
Researchers are discovering that parts of the global weather model are more susceptible to the butterfly effect than others, and they expect to use that knowledge to avoid forecasting errors. It turns out that the extent to which an initial data error multiplies as a model runs depends on where in the world, or where in a particular weather pattern, an erroneous approximation occurs.
Decades of experience in running weather models has taught us that, for example, using incomplete data from the region just off the coast of Asia, particularly in the winter, can cause dramatic mistakes, like the floods in Seattle described earlier. By contrast, data gathered from high-pressure systems sitting over an area of fair weather adds little to forecast accuracy. Summer weather in the Midwestern United States, for example, can be clear and stable for weeks.
So researchers are considering doing something that would have been considered heretical until recently: making selective use of satellite observations. They would gather and assimilate huge volumes of satellite data in certain times and places, like the coast of Asia in winter, where uncertainty in the initial conditions tends to degrade the forecast dramatically. Conversely, they’d thin the satellite data flow in areas like the Midwest in summer, where the potential for error growth is smaller.
Meteorologists have begun to further attack uncertainty by taking additional observations in sensitive regions using weather balloons, remotely piloted aircraft, commercial aircraft, and stratospheric balloons, which fly higher and longer than standard weather balloons. The U.S. National Oceanographic and Atmospheric Administration and the U.S. Air Force Reserve Hurricane Hunters have collaborated on such a targeting strategy to reduce errors in the prediction of hurricane landfall, flying aircraft near and even through hurricanes and releasing half-kilogram instrument packages, called dropsondes, that transmit temperature, wind, humidity, and pressure data as they fall through the atmosphere.
Taiwan recently began using such dropsondes for typhoon surveillance. And for the current hurricane season in the North Atlantic, a collaboration of CNES and the U.S. National Center for Atmospheric Research, in Boulder, Colo., deployed a fleet of dropsonde-carrying stratospheric balloons from Africa [see diagram, ” ”].
Thanks to all these advances—in data collection and assimilation, in computer processing power, and in our understanding of how weather evolves—we will have the capability within a decade to dramatically change weather prediction and perhaps even make accurate weather forecasts twice as far in advance as we do today for many disastrous weather events. Historically, global weather prediction accuracy has improved by one day every 10 to 15 years, but researchers are optimistic, based on early experimental results, that the changes under way will likely accelerate that rate of improvement dramatically and we will be able to make accurate hurricane position forecasts five to six days in advance.
What will that mean? Fast forward to August 2016. Hurricane Karl is due to make landfall somewhere on the east coast of the United States sometime on the 29th of the month. On the 23rd, forecasters pinpoint the region most likely to be affected by the hurricane’s path—Fort Lauderdale, Fla., on the Atlantic Coast. Emergency officials begin planning an evacuation. Buses are ordered. Emergency shelters outside the potential path of the hurricane are identified and stocked with food, water, cots, and blankets. People in the potential landfall area contact friends and relatives for emergency housing and do what they can to protect their valuables.
On the 25th, forecasters narrow down Karl’s landfall to within 50 km. They estimate the intensity, size, and structure of the storm and predict the height of the waves and the storm surge. Karl is a narrow and intense storm, so the governor gives an order to evacuate 100 km of coastline, as opposed to the 300 km typically evacuated today, just to be safe. Aware of several years of accurate forecasts preceding this one, residents are quick to pack their cars or make reservations on the evacuation buses. Emergency workers fly in to build barriers around low-lying areas and protect valuable public property.
One day before the predicted hurricane strike, Fort Lauderdale and its coastal neighbors are ghost towns, except for a few TV crews setting up remote cameras and getting ready to leave.
On 29 August, Karl hits as predicted. Thanks to the efforts to protect key low-lying areas, damage is reduced by hundreds of millions of dollars as people have more time to move belongings to higher ground and get boats, automobiles, and expensive industrial equipment out of the path of the storm. And, much more important, thanks to the orderly evacuation, no one is killed.
About the Author
Robert Gall is director of the Developmental Testbed Center at the U.S. National Center for Atmospheric Research (NCAR), in Boulder, Colo.
David Parsons is a senior scientist at NCAR and co-lead for North American activities under The World Meteorological Organization’s Observing System Research and Predictability Experiment (Thorpex).