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mP. cP. mT. cT. Review questions:. Describe the movement of water through the hydrologic cycle. . Most water evaporates from the oceans. Some of the water is transported considerable distances, condenses to form clouds, and eventually falls to earth’s surface as precipitation.

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Review questions:

Describe the movement of water through the hydrologic cycle.

  • Most water evaporates from the oceans.
  • Some of the water is transported considerable distances, condenses
  • to form clouds, and eventually falls to earth’s surface as
  • precipitation.
  • Once precipitation has fallen on land, some reenters the atmosphere
  • by evaporation and transpiration, some soaks in and some runs off.
  • The water that soaks in or runs off eventually makes is way back
  • to the ocean to begin the cycle again.

Review questions:

How does stable air differ from unstable air?

  • Stable air resists vertical motion whereas unstable air
  • reinforces vertical motion.

Review questions:

Why are high clouds always thin in comparison to

low or middle clouds?

  • High clouds are always thin because of the low
  • temperatures and thus the small quantity of water vapor
  • available at the altitudes where they form

Review questions:

Explain why air pressure decreases with an increase

in altitude.

  • Air pressure is exerted by the weight of air above.
  • Air pressure decreases with an increase in altitude because, as one moves away from the Earth’s surface, there is less air above to exert a downward force.

Chapter 12 Weather Analysis and Forecasting

-The Weather Business

-Weather Analysis

-Weather Forecasting

-Long Range Forecasts

-Forecasting Accuracy

-Tools in Weather Forecasting





The Weather Business

In the United States, the governmental agency responsible for

gathering and disseminating weather related information is the

National Weather Service (NWS).

The most important services provided by the NWS are forecasts

and warnings of hazardous weather information including:

thunderstorms, flooding, hurricanes, tornadoes, winter weather

and extreme heat.

Weather forecasting is the process, (science, art) by which, from

current weather patterns, various methods are used to determine

the state of the atmosphere.


The National Weather Service is responsible for issuing forecasts and

warnings of hazardous weather including thunderstorms, flooding,



Stream Gaging

By using automated equipment in the gaging station, river stage can be continuously monitored and reported to an accuracy of 1/8 of an inch. Linking battery-powered stage recorders with satellite radios enables transmission of stage data to computers in USGS and NWS facilities even when extreme high waters and strong winds disrupt normal telephone and power services. In this way, USGS and NWS hydrologists know the river stage at remote sites and how fast the water is rising or falling.


Typical NWS product

To produce the image shown above is very complicated, labor intensive undertaking.


The Weather Business

To produce even a short range forecast involves numerous steps, including collecting weather data, compiling it on a global scale, and

transmitting it.

The NWS is not responsible for the plethora of animated

weather images we see on TV. It is not part of the mission of the

NWS to provide these data. Private enterprises, using “raw data”

from the NWS, undertake this task.

It is a unique joint venture between the NWS and the private weather sector. The NWS is the official voice, and therefore

is ultimately responsible in the United States for issuing warnings.


Weather Analysis

Before issuing a forecasting, the forecaster must have an

accurate picture of the atmospheric conditions; this project

is called the weather analysis.

Because the atmosphere is always changing, time is of the

essence when analyzing the weather. Clearly, high speed

computers have revolutionized the weather analysis process.

First introduced in 1985, this system had a peak

performance of 1.9 gigaflops. At the time, the

CRAY-2 system had the world's largest central

memory with the possibility of 2048 megaBytes,

which is still considered large today.


Gathering Data

A critical mass of information is needed to generate a useful

weather chart for even a short range forecast.

The World Meteorological Organizationwhich

includes more than 130 nations, was formed to address this problem.

It is their responsibility for gathering the needed data and for

producing numerous weather maps and upper-level charts that

describe the current state of the atmosphere.


Information is collected by the WMO

then transmitted to three World

Meteorological Centers. From there

the world centers send data to the

meteorological center in each office.

Washington, DC

Geneva, Switzerland

Moscow, Russia

Melbourne, Australia


Weather Maps: Pictures of the Atmosphere

Once weather data has been collected, the analyst displays it on

a number of different synoptic weather maps which are used

to produce weather forecasts.

Synoptic means coincident in time. (syn- same, optic – to look at)

(Of or relating to data obtained nearly simultaneously over a large area of the atmosphere)

Over 200 surface maps and charts are produced by the NWS

and its forecast centers.

As we already know, different levels in the atmosphere are used

to study different processes important to weather forecasting….


Surface Maps

Surface maps show the location of major pressure centers, fronts, weather conditions at several locations.


850-millibar Maps



850-mb maps are useful to analyze temperature advection and

observe surface features.


700-millibar Maps

700-mb maps are useful to track the movement of air-mass thunderstorms.


500-millibar Maps





500-mb maps are useful to predict the movement of upper level cyclones. Prior to high-speed computing 500-millibar maps were heavily relied on.


300-millibar Maps

300 (200mb) maps show jet stream location. Recall the jet stream

associated with areas of upper level divergence and convergence.


The Challenge of Forecasting (pg 324)

Imagine a system on a rotating sphere that is 8000 miles wide,

consisting of different materials, different gases that have different

properties (one of the most important of which is water which is

exists in different concentrations), heated by a nuclear reactor

93 million miles away.Then just to make life interesting, this sphere

is oriented such that as it revolves around the reactor it is heated

differently at different locations at different times of the year:

Then someone is asked to watch the mixture of gases, a fluid only

20 miles deep, that covers an area of 250 million square miles,

and to predict the state of that fluid at one point on a sphere 2 days

from now.

This is the problem forecaster have to face. (Bob Ryan WRC-TV)


Synoptic Weather Forecasting

  • Until the late 1950’s synoptic weather forecasting was the
  • primary basis for weather prediction. Forecasters relied
  • heavily on synoptic weather maps to forecast.
  • Other methods of weather forecasting include:
  • Persistence Forecasting: yesterday’s weather today
  • Trend Forecasting: similar to persistence with slight changes in previous day’s weather
  • Analog Approach: current weather conditions are matched with records of similar past weather events (Model Output Statistics).

The persistence method assumes that the conditions at the time of the forecast will

not change. For example, if it is sunny and 87 degrees today, the persistence method

predicts that it will be sunny and 87 degrees tomorrow. If two inches of rain fell

today, the persistence method would predict two inches of rain for tomorrow.


The trends method involves determining the speed and direction of movement for fronts, high and low pressure centers, and areas of clouds and precipitation.


Numerical Weather Prediction

Numerical weather prediction relies on the fact that the gases of

the atmosphere obey a number of known physical principles.

(PV=nRT for example)

Numerical weather prediction employs a number of highly

refined computer models to attempt to mimic the behavior of the


Using numerical models, the National Weather Service produces

a number of prognostic charts whichpredict the state of the

atmosphere in the future (temperature, winds, moisture, clouds,



History of Numerical Weather Prediction

  • 1775 equations of fluid mechanics were formulated
  • (Leonhard Euler)
  • 1827 terms for molecular viscosity were added
  • (Cluade-Louis Navier), (George Stokes) in 1845. The
  • Navier-Stokes equations.
  • 1888 rough equations developed by Stokes and
  • Navier were refined by Helmhotz.
  • 1922 Lewis Fry Richardson published a book describing the
  • first experimental NWP. (took 6weeks for a 6 hr forecast)
  • Surface pressure was way off. Not deemed feasible; no
  • work done for almost 2 decades.

History of Numerical Weather Prediction

  • 1945 Physicists (John von Neuman) at Princeton and RCA’s Princeton Laboratories (Zworykin) proposed to initiate NWP using the electronic computers. Simulate general circulation. Very hard time agreeing how to approach the problem.
  • Major Players (Carl-Gustav Rossby, Arnt Eliassen, Jule Charney, and George Platzman)…decided they needed to simplify the full primitive equations to focus on the long waves of general circulation.
e lectronic n umerical i ntegrator a nd c omputer
Electronic Numerical Integrator And Computer

The ENIAC machine occupied a room thirty by fifty feet. The controls are at the left, and a small part of the output device is seen at the right.


History of Numerical Weather Prediction

  • 1948-1949 Charney and von Neuman presented results from a simple barotropic model at NWP conferences.
  • (machine code, domain size?, many calculations done with slide rule and mechanical calculators)
  • 1950 Electronic Numerical Integrator and Computer ENIAC forecasts were made (three case studies). Results were promising. Swedish computer “BESK” became operational in 1953
  • 1954 With Swedish Air Force support 24, 48 and
  • later 72hr forecasts of 50 kPa Heights (not surface
  • conditions)

History of Numerical Weather Prediction

  • 1953 IBM announced specifications for a new computer.
  • 1954 US Weather Bureau, USAF Weather Service formed
  • the Joint Numerical Weather Prediction Unit (JNWPU).
  • Operational weather forecasts using a quasi-geostrophic
  • baroclinic model developed by Charney and Eliassen).
  • Three layer 40, 70, and 90 kPa.
  • 1955 IBM 701 was introduced; Panofsky proposed a
  • scheme for automated analysis of weather data.

History of Numerical Weather Prediction

  • 1955-57 Loss of personnel limited the growth of the
  • JNWPU. Weather forecasts were of poor quality and not
  • appreciated by experienced forecasters. During the
  • first two years the forecasts were largely ignored.
  • 1958 National Meteorological Center (NMC)
  • formed to run NWS operational weather models.
  • 1958 USAF GWC (Omaha, NE) NAVY FNWC
  • (Monterey, CA) formed.

History of Numerical Weather Prediction

  • At first numerical forecasts were initializied with hand-analyzed data
  • Mid 1950’s an objective analysis method was adopted (fitting new observations to previous forecast fields)
  • 1955,1956 first attempt at a baroclinic (multi-layer) model failed because it was poorly calibrated and awkward to use
  • Revert back to one layer barotropic domain…computer power now underutilized!!
  • To better use power of the computer the domain of the barotropic model was expanded

History of Numerical Weather Prediction

  • The models (because they lacked sufficient topography) did not produce realistic results in the westward movement of longwaves in the general circulation pattern. Removal and reinsertion (after fudging) of the longest waves was the work around…..challenging times in NWP
  • 1963 faster computers lead to six layer primitive equation models. Forecast scores improved dramatically.
  • Since the mid 60’s improvements in forecast quality has been closely tied to computer power.
  • -more grid points, more vertical layers, larger domain
  • -topography, landscape, snow/ice coverage
  • -radiation, precipitation, clouds, turbulence

Current Numerical Weather Prediction Models

  • There are four computer models that are run by the National Centers for Environmental Prediction (NCEP) which are used for forecasting the day-to-day weather in the United States:
  • Nested Grid model (NGM) 1990
  • Eta Model early 1990’s
  • Global Forecast System (GFS)
  • Rapid Update Cycle (RUC)
  • Each model is a self-contained set of computer programs which include means of analyzing data and computing the evolution of the atmosphere's winds, temperature, pressure, and moisture based on the analyses.
  • These individual models are all quite different from each other in one essential aspect or another, either in their formulation or their implementation.

Current NCEP Numerical Weather Prediction Models

  • GFS (formerly the MRF)
  • Became operational in 1985.
  • It is a Global Spectral Model.
  • A spectrum of waves in the atmosphere is represented by
  • sine and cosine functions rather than specific grid point
  • values.
  • - Momentum Equation
  • - Thermodynamic Equation
  • - Continuity Equation
  • - Moisture Equation (transport, sources, sinks)
  • - Hydrostatic Equation

Eta Model

The number and distribution of vertical layers in the operational Eta Model has changed throughout the years

Implemented in 1993 (0-48hrs) 80km resolution

Oct 1995 Eta -48km w/EDAS

Feb 1998 Eta –32km (0000, 1200 UT) 48 hr forecast 3D var

Mar 1995 Eta –29km (meso Eta) (0300 and 1500 UT)

Feb 1998 Eta –32km (0300 + 1800UT) 45 levels

2001 Eta –12km 60 layers


Vertical Coordinates

A model’s vertical structure is as important in defining the model’s behavior as the horizontal configuration and model type. Proper depiction of the vertical structure requires selection of an appropriate vertical coordinate and sufficient vertical resolution.

Two Common Vertical Coordinates:

Sigma Vertical Coordinate 


Eta (or Step) Vertical Coordinate 


In its simplest form , the sigma coordinate is defined by

 = p/ps

p is the pressure on a forecast level within the model and ps is the pressure at the earth’s surface, not mean sea level pressure.

The lowest coordinate surface ( = 1) follows a smoothed version of the actual terrain.


Sigma Vertical Coordinate ( )

To address the problem of discontinuous forecast surfaces, Phillips (1957) developed a terrain-following coordinate called the sigma ( ) coordinate, illustrated below. The sigma coordinate or variants are used in the NGM, AVN/MRF, ECMWF, NOGAPS, and UKMET models and appear in some meso-scale models, such as AFWA MM5, COAMPS, and RAMS.


Advantages of the Sigma Vertical Coordinate

  • Advantages:
  • Since the sigma coordinate is related to pressure, it produces relatively simple formulations for handling the lower boundary without overly complicating the equations of motion. The simplified formulations are easier to program.
  • The terrain-following nature of the sigma coordinate lends itself to increasing vertical resolution near the ground consistently over the full model domain. The model can better define boundary-layer processes and features that contribute to sensible weather elements (diurnal heating, low-level winds/turbulence/moisture)

1) Model wind forecasts depend on accurate calculation of the pressure gradient force (PGF). When sigma surfaces slope, the PGF must be expanded from its simple pressure coordiante form account for the slope. This can introduce errors because the lapse rate must be expanded approximated at points that lie between the pressure surfaces. With steep slopes the errors can be significant. 2) Because the actual and often abrupt steepness of mountain slopes is smoothed in sigma coordinate models, they often misrepresent the true surface elevation. This can cause forecasts for locations immediately adjacent to mountain ranges to severely misrepresent the surface pressure and thus the temperature and moisture (PGF as well).

Limitations of the Sigma Vertical Coordinate

eta or step vertical coordinate
Eta (or Step) Vertical Coordinate ( )
  • The eta coordinate ( ) was created in the early 1980s in an effort to reduce the errors incurred in calculating the pressure gradient force using sigma coordinate models.
  • The eta coordinate is actually another form of the sigma coordinate, but uses mean sea level pressure instead of surface pressure as a bottom reference level.
  •  = (pr(zs) – pt/(pr(z = 0)-pt)
  • where
  • pt is the pressure at the top of the model (25, 1hPa)
  • pr(z = 0) is the standard atmosphere MSL pressure (1013hPa)
  • pr(zs) is the standard atmosphere pressure at the model terrain zs

Eta (or Step) Vertical Coordinate ( )

The eta coordinate system allows the bottom atmospheric layer to be represented within each grid box as a flat “step”, rather than a sloping sigma terrain. For this reason it is sometimes called the step mountain coordinate system.


Advantages of the Eta Vertical Coordinate

Eta models do not need to perform the vertical interpolations that are necessary to calculate the PGF in sigma. This reduces the error in PGF calculation and improves the forecast of wind and temperature and moisture changes in areas of steeply sloping terrain.

Although the numerical formulation near the surface is more complex, the low-level convergence in areas of steep terrain are far more representative of real atmospheric conditions than sigma models. The improved forecasts of low-level convergence result in better precipitation forecasts in these areas that out way the extra computer time needed.


Limitations of the Eta Vertical Coordinate

The step nature of the eta coordinate makes it difficult to retain detailed vertical structure in the boundary layer over the entire model domain (elevated terrain). Because the depth of the layers typically increases with height from sea level, the eta coordinate system can have difficulty representing boundary layer processes over elevated terrain.


Limitations of the Eta Vertical Coordinate

Eta models do not accurately depict gradually sloping terrain. Since all terrain is represented in discrete steps, gradual slopes that extend over large distances can be concentrated within as few as one step.


Eta Model

This model is the primary weather prediction model for forecasting for the United States. The model is under active development and is therefore frequently being improved.

The model uses a limited-area domain, centered over North America and including much of the surrounding oceans. It has the finest horizontal and vertical resolution of all the models currently in use at NCEP, and thus has the potential of forecasting the weather with the greatest accuracy. However, it is also the newest model, and is therefore not as sophisticated in some aspects as the other models.


Numerical Weather Prediction

  • Weather prediction models initiate with observations of the current
  • state of the atmosphere and then use equations to compute new
  • values at 5-10 minute intervals, called time steps. Predicted values
  • serve as the next set of initial conditions for the next series
  • of computations.
  • The weather models are not perfect representations of the
  • atmosphere. This is so for three basic:
  • inadequate representation of physical processes
  • errors in initial observations
  • inadequate model resolution

Propagation of Errors

Giving that many time steps are needed to produce a forecast, the

problem of “propagation of errors” is a constraint on weather


Small errors may make

little difference in the

early stages of prediction,

can amplify dramatically

over time.

Yt = (a x Yt+1) – Y2t


Tools in Weather Forecasting

  • ASOS Automated Surface Observations
  • AWIPS Advanced Weather Interactive Processing Systems
  • NEXRAD Next Generation Radar (Doppler)
  • Satellites


Geostationary Satellites


Geostationary satellites were placed in orbit over the equator; they remain fixed over a point. To keep the satellite in place over Earth the satellite must orbit at a farther distance than

polar orbiters (35,000 km). Some resolution is lost but very powerful satellites.



Polar Satellites

Polar orbiting satellites orbit the globe

at low altitudes (a few hundred km)

which allows them to complete one

pass in 100 minutes. With such a

quick orbit the satellite can capture

two sweeps of the globe in 24 hours.


The TIROS Program (Television Infrared Observation Satellite) was NASA's first experimental step to determine if satellites could be useful in the study of the Earth.






The images above show the stark contrast between the first image beamed down from

TIROS-1 on April 1, 1960 and the full-color full-Earth images that GOES-8 produces every

three hours. But, if it hadn't been for TIROS and the TIROS experiment, there would

be no GOES images today.



NOAA's National Environmental Satellite,Data and Information Service

NESDIS operates the satellites and manages the processing and distribution

of millions of bits of data and images these satellites produce daily. The

prime customer for the satellite data is NOAA's National Weather Service,

which uses satellite data to create forecasts for television, radio, and weather

advisory services.

NOAA's operational environmental satellite system is composed of:

geostationary operational environmental satellites (GOES) for short-range

warning and "nowcasting," and polar-orbiting environmental satellites (POES)

for longer term forecasting. Both kinds of satellites are necessary for

providing a complete global weather monitoring system. The satellites carry

search and rescue instruments, and have helped save the lives of about

10,000 people to date. The satellites are also used to support aviation safety

(volcanicash detection),

and maritime/shipping safety (ice monitoring and prediction).



Around the world...around the clock...NOAA proudly stands watch. As an integral part of worldwide search and rescue, NOAA operates the Search & Rescue Satellite Aided Tracking (SARSAT) System to locate those in distress almost anywhere in the world at anytime and in most conditions.The SARSAT system uses NOAA satellites in low-earth and geostationary orbits to detect and locate aviators, mariners, and land-based users in distress. The satellites relay distress signals from emergency beacons to a network of ground stations and ultimately to the U.S. Mission Control Center (USMCC) in Suitland, Maryland. The USMCC processes the distress signal and alerts the appropriate search and rescue authorities to who is in distress and, more importantly, where they are located. Truly, SARSAT takes the "search" out of search and rescue.