Weather Data
Few Fields Deal With Such
Quantities of Information
• Climatological Data
• Observational Data
• Model Output
Data Collection
• Weather is observed throughout the world and the
data is distributed in real time.
• Many types of data and networks, including:
–
–
–
–
–
–
–
–
Surface observations from many sources
Radiosondes and radar profilers
Fixed and drifting buoys
Ship observations
Aircraft observations
Satellite soundings
Cloud and water vapor track winds
Radar and satellite imagery
Data Collection
• Satellite data is now the dominant data
source (perhaps 90%)—we are talking
hundreds of terabytes per day
• Huge increases in the numbers of surface
stations and aircraft reports.
Data Quality Control
• Automated algorithms and manual intervention to
detect, correct, and remove errors in observed
data.
Pacific Analysis
At 4 PM
18 November
2003
Bad Observation
Observation and Data Collection
Radiosonde
ASOS: Automated Surface
Observing System:
Backbone Observing System in the U.S.
Observing
Networks at
the Surface
3000-4000
observations
per hour over
WA and OR
ACARS: Aircraft Observations
Generally on wide-body aircraft
Aircraft Communications Addressing and Reporting System
Weather Satellites Give Us Much
More than Pretty Pictures
• We start with imagery in several wavelengths:
– Visible
– Infrared
– Water vapor (wavelengths where we see the water
vapor distribution)
• Plus the ability to get winds from tracking
clouds/water vapor, vertical soundings, and
winds based on ocean waves
Better than Star
Trek!
Each
wavelength
gives us
information
Cloud and
Water Vapor
Track Winds
Based on
Geostationary
Weather
Satellites
QuickScat Satellite
Bounces microwaves off the ocean surface
Capillary waves dependent on wind speed and directon
Camano
Island
Weather
Radar
Numerical Weather Prediction
Objective Analysis/Data
Assimilation
• Numerical weather models
are generally solved on a
three-dimensional grid
• Observations are scattered in
three dimensions
• Need to interpolate
observations to grid points
and to insure that the various
fields are consistent and
physically plausible (e.g.,
most of the atmosphere in
hydrostatic and gradient wind
balance).
Objective Analysis/Data Assimilation
• Often starts with a “first guess”, usually the
gridded forecast from an earlier run (frequently a
run starting 6 hr earlier)
• This first guess is then modified by the
observations.
• Adjustments are made to insure proper physical
balance.
• Objective Analysis/Data Assimilation produces
what is known as the model initialization, the
starting point of the numerical simulation.
Model Integration: Numerical
Weather Prediction
• The initialization is used as the starting
point for the atmospheric simulation.
• Numerical models consist of the basic
dynamical equations (“primitive equations”)
and physical parameterizations.
“Primitive” Equations
•
•
•
•
•
3 Equations of Motion: Newton’s Second Law
First Law of Thermodynamics
Conservation of mass
Perfect Gas Law
Conservation of water
With sufficient data for initialization and a
mean to integrate these equations, numerical
weather prediction is possible.
Example: Newton’s Second Law: F = ma
Simplified form of the primitive
equations
Numerical Weather Prediction
• A numerical model includes the primitive
equations, physics parameterization, and a way to
solve the equations (usually using finite
differences on a grid)
• Makes use of powerful computers
• Keep in mind that a model with a certain
horizontal grid spacing is barely simulating
phenomenon with a scale four times the grid
spacing. So a 12-km model barely is getting 50
km scale features correct.
P
Forecast Skill Improvement
NCEP operational S1 scores at 36 and 72 hr
over North America (500 hPa)
National Weather Service
75
S1 score
65
"useless forecast"
55
36 hr forecast
72 hr forecast
45
Forecast
Error 35
10-20 years
Better
"perfect forecast"
25
15
1950
1960
1970
Year
1980
Year
1990
2000
NGM,
80 km,
1995
2001: Eta Model, 22 km
2007-2008
12-km
UW MM5
Real-time
12-km WRF-ARW
and WRF-NMM
are similar
December 3, 2007
0000 UTC Initial
12-h forecast
3-hr precip.
2007-2008
4-km MM5
Real-time
Surface Temperature-12km
Temperature-1.3 km
Many Models
• The National Weather Service runs several
models.
• So do other weather services around the
world.
• So do regional groups like the UW.
• HUGE amounts of output! (each run can
easily produce 100s of GB.
More Models Yet
• In a real sense, the way we have
been forecasting is essentially
flawed.
• 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
• Thus, there is fundamental uncertainty in
weather forecasts that can not be ignored.
• We should be using probabilities for all our
forecasts or at least providing the range of
possibilities.
• There is an approach to handling this issue
that is being explored by the forecasting
community…ensemble forecasts.
Ensemble Prediction
• Instead of making one forecast…make
many…each with a slightly different
initialization
• Possible to do now with the vastly greater
computation resources that are available.
The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
SLP and winds
1: cent
Verification
- Reveals high uncertainty in storm track and intensity
- Indicates low probability of Puget Sound wind event
2: eta
5: ngps
8: eta*
11: ngps*
3: ukmo
6: cmcg
9: ukmo*
12: cmcg*
4: tcwb
7: avn
10: tcwb*
13: avn*
Ensemble-Based Probabilistic Products
Probability Density Functions
Everywhere
PROBCAST: www.probcast.com
The National Weather Service Data Interaction
Forecaster at the Seattle National Weather Service Office
AWIPS
Problems in Communication
Icons are not effective in providing probabilities
And a “slight” chance of freezing
drizzle reminds one of a trip to
Antarctica
Commercial
sector
is no better
A great deal of research and
development is required to
develop effective approaches for
communicating probabilistic
forecasts which will not
overwhelm people and allow
them to get value out of them.
Traditional Approaches of Weather
Information Dissemination/Display Are
Incapable of Delivering the Specificity and
Volume of Data
Typical TV weathercasters have only 2-4 minutes!
Many of us worried about this
problem in the 90’s but now the
solution is literally at hand
There are now HUNDREDS of
weather apps for smartphones…and
the best are yet to come!
The End