A History of Modern Weather Forecasting

The Beginning: Weather Sayings "Red Sky at night, sailor's delight. Red sky in the morning, sailor take warning." "Mare's tails and mackerel scales make tall ships take in their sails." "Clear moon, frost soon.". "Halo around the sun or moon, rain or snow soon." "Rainbow in the morning gives you fair warning." "When the stars begin to huddle, the earth will soon become a puddle."

By the late 1700s, reasonable weather instruments became available

More and more people took observations….and even some early networks were started

The First Weather Forecaster?

The problem: no way to rapidly communicate weather observations This changed around 1845 with the invention of the telegraph

First Real-Time Weather Maps

Weather Prediction Began The key approach…simple extrapolation

“Ol Probs” Professor Cleveland Abbe, who issued the first public “Weather Synopsis and Probabilities” on February 19, 1871 Cleveland Abbe (“Ol’ Probabilities”), who led the establishment of a weather forecasting division within the U.S. Army Signal Corps, Produced the first known communication of a weather probability to users and the public.

On May 7, 1869, Abbe proposed to the Cincinnati Chamber of Commerce "to inaugurate such a system, by publishing in the daily papers, a weather bulletin, which shall give the probable state of the weather and river for Cincinnati and vicinity one or two days in advance”. Cleveland Abbe released the first public weather forecast on September 1, Following the signing by President Ulysses S. Grant of an authorization to establish a system of weather observations and warnings of approaching storms, on February 19, 1871, Abbe issued the first “official” public Weather Synopsis and Probabilities based on observations taken at 7:35 a.m.

An early example of a report: "Synopsis for past twenty-four hours; the barometric pressure had diminished in the southern and Gulf states this morning; it has remained nearly stationary on the Lakes. A decided diminution has appeared unannounced in Missouri accompanied with a rapid rise in the thermometer which is felt as far east as Cincinnati; the barometer in Missouri is about four- tenths of an inch lower than on Erie and on the Gulf. Fresh north and west winds are prevailing in the north; southerly winds in the south. Probabilities [emphasis added]; it is probable that the low pressure in Missouri will make itself felt decidedly tomorrow with northerly winds and clouds on the Lakes, and brisk southerly winds on the Gulf."

The Next Major Advance The Norwegian Cyclone Model, around 1920

Norwegian Cyclone Model Provided a coherent consistent picture of airflows, clouds, and precipitation of cyclones and fronts Provided a model for frontal and cyclone evolution, aiding future prediction.

1940s: Upper Air Charts Became Available Gave a 3D picture of what was happening Upper flow steered storms, and thus provided a tool for forecasting cyclone movement.

Upper Level Chart

Summary I Prior to approximately 1955, forecasting was basically a subjective art, and not very skillful. The technology of forecasting was basically subjective extrapolation of weather systems, in the latter years using the upper level flow (the jet stream). Local weather details—which really weren’t understood-- were added subjectively.

The Development of NWP Vilhelm Bjerknes in his landmark paper of 1904 suggested that NWP was possible. –A closed set of equations existed that could predict the future atmosphere (primitive equations) –But NWP wasn’t practical then because there was no reasonable way to do the computations and sufficient data for initialization did not exist.

L. F. Richardson: An Insightful But Unsuccessful Attempt In 1922 Richardson published a book Weather Prediction by Numerical Process that described an approach to solving the primitive equations: solving the equations on a grid using finite differences.

L. F. Richardson He attempted to make a numerical forecast using a mechanical calculator Unfortunately, the results were not good, probably because of problems with his initial conditions. He imagined a giant theater filled with human calculators… So NWP had to wait until a way of doing the computations quickly was developed and more data…especially aloft… became available.

NWP Becomes Possible By the mid to late 1940’s there was an extensive upper air network, plus many more surface observations. Thus, a reasonable 3-D description of the atmosphere was possible. Also during this period digital programmable computers were becoming available…the first..the ENIAC

The Eniac

The Last Piece of the Puzzle Meteorologists realized that useful numerical weather predictions were possible using a simplified equation set that were easier to solve. The Barotropic Vorticity Equation (conservation of absolute vorticity) was suggested as a first step

First NWP The first successful numerical prediction of weather was made in April 1950, using the ENIAC computer at Maryland's Aberdeen Proving Ground The prediction was for 500 mb height, covered North America, used a two- dimensional grid with 270 points about 700 km apart. The results showed that even primitive NWP was superior to human subjective prediction. The NWP era had begun.

Evolving NWS Early 50s: one-level barotropic model Late 50s: Two-level baroclinic QG model (just like Holton!) 1960s: Primitive equation models of increasing resolution and number of levels. Resolution increases (distance between grid points decrease): 1958: 380 km, 1985: 80 km, 1995: 40 km, 2000: 22 km, 2002: 12 km

NWP Improvements in the Later 20th Century Better resolution Rapidly increasing data for initialization from weather satellites, radars, more surface observations, and other sources. Better models: better numerics and physics

Forecast Skill Improvement Forecast Error Year Better National Weather Service

The Advent Of Statistical Post- Processing In the 1960s and 1970s, the NWS developed and began using statistical post-processing of model output…known to most as Model Output Statistics…MOS. The idea: models have systematic biases..why not remove them based on past performance? Also, might be able to statistically add the effects of local features not resolved by the model.

MOS Based on linear regression: Y=a 0 + a 1 X 1 + a 2 X 2 + a 3 X 3 + … MOS is available for many parameters and time and greatly improves the quality of most model predictions.

The computers models become capable of simulating/forecasting local weather. As the grid spacing decreased to 15 km and below… it became apparent that many of the local weather features could often be simulated and forecast by the models.

Revolutions in Remote Sensing Greatly Enhanced Weather Prediction from the 1950s Through Today

Satellite and Weather Radars Give Us a More Comprehensive View of the Atmosphere

Camano Island Weather Radar

A More Fundamental Issue The work of Lorenz (1963, 965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts. Not unlike a pinball game….

A More Fundamental Problem Similarly, uncertainty in our model physics also produces uncertainty in the forecasts. Lorenz is a series of experiments demonstrated how small errors in initial conditions can grow so that all deterministic forecast skill is lost at about two weeks. Talked about the butterfly effect…

Probabilistic NWP One approach to probabilistic predicton, ensemble prediction, was proposed by Leith (1974), who suggested that prediction centers run a collection (ensemble) of forecasts, each starting from a different initial state. The variations in the resulting forecasts could be used to estimate the uncertainty of the prediction. But even the ensemble approach was not tractable at this time due to limited computer resources.

The Thanksgiving Forecast h forecast (valid Thu 10AM) 13: avn* 11: ngps* 12: cmcg* 10: tcwb* 9: ukmo* 8: eta* Verification 1: cent 7: avn 5: ngps 6: cmcg 4: tcwb 3: ukmo 2: eta - Reveals high uncertainty in storm track and intensity - Indicates low probability of Puget Sound wind event SLP and winds

Ensemble Prediction Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur. Can also predict forecast skill! It appears that when forecasts are similar, forecast skill is higher. When forecasts differ greatly, forecast skill is less.

Ensemble Prediction To create a collection of ensembles one can used slightly different initializations or different physics. By the early 1990s, faster computers allowed the initiation of global ensemble prediction at NCEP and ECMWF (European Centre for Medium Range Weather Forecasts).

Ensemble Prediction During the past decade the size and sophistication of the NCEP and ECMWF ensemble systems have grown considerably, with the medium-range, global ensemble system becoming an integral tool for many forecasters. Also during this period, NCEP has constructed a higher resolution, short- range ensemble system (SREF) that uses breeding to create initial condition variations.

Ensemble-Based Probabilistic Products

The Evolving Forecasting Problem Prior to ~1955, humans did everything-- subjectively forecast at all scales. Between 1955 and 1980 numerical weather prediction essentially took over synoptic forecasting. Humans were left with deciding between models or modifying (often unsuccessfully) the computer guidance). Humans retained the role of translating synoptic guidance to the mesoscale/microscale. Model Output Statistics (MOS) became competitive with human forecasters during the 1980s.

Summary II: Evolving Forecasting By around 1990, large scale forecasts had gotten very good for scales >~ 1000 km for 0 to 48h Starting to consistently get the big storms But humans still crucial for local forecasting, interpreting imagery, and providing guidance on the reliability of forecasts. During the past ten years high resolution (or mesoscale) NWP has begun to make inroads in taking over mesoscale prediction. The ability to produce probabilistic forecasts using ensembles is improving quickly and should become a central element in NWP during the next decade.

Forecaster at the Seattle National Weather Service Office The National Weather Service