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Analog forecasting of ceiling and visibilityusing fuzzy logic and data mining
Bjarne Hansen
Meteorological Research Branch
Meteorological Service of Canada
Dorval, Quebec
Eastern Canada Aviation Weather Workshop
Montréal, Quebec, 16-18 September 2003
Introduction
Fuzzy Logic
Case-Based Reasoning
Weather Prediction
Ceiling and Visibility Prediction
Prediction System: WIND
Results
Conclusion
Future
Outline
By building expert systems that combine forecaster expertise,AI, large amounts of data (climatological and current),and currently available computing power, we can increase forecast quality and increase forecasting efficiency.
* Presentation at: http://iweb.cmc.ec.gc.ca/~armabha/metai
*
Basic computer science
Basic meteorology
Use of fuzzy logic has
increased exponentially
over the past 30 years,
based on the number of uses of
the word “fuzzy” in titles of
articles in engineering and
mathematical journals. 1
In meteorological systems,
use of fuzzy logic began
about ten years ago. 2
Fuzzy Logic
1. Lofti Zadeh, 2001: Statistics on the impact of fuzzy logic, http://www.cs.berkeley.edu/~zadeh/stimfl.html
2. Applications of fuzzy logic for nowcasting, http://chebucto.ca/Science/AIMET/applications
Fuzzy Logic Definition
1. Free On-line Dictionary of Computing, http://foldoc.doc.ic.ac.uk/foldoc
“Fuzzy logic a superset of Boolean logic dealing with the concept of
partial truth – truth values between ‘completely true’ and ‘completely false’.
It was introduced by Dr. Lotfi Zadeh of UCB in the 1960’s as
a means to model the uncertainty of natural language.” 1
0.00
0.25
0.50
0.75
1.00
-20 -10 0 10 20
difference (°C)
m
very
slightly
quite
Fuzzy set to describe
the degree to which
two numbers are
similar, for example,
degree of similarity
of temperatures.Simila
r
Not similar
Non-fuzzy (classical) setloses information aboutdegree of similarity.
1. Munakata, T. and Jani, Y., 1994: Fuzzy Systems: An Overview, Communications of the ACM, Vol. 37, No. 3, pp.69-76.2. Hansen et al. 1999, http://chebucto.ca/Science/AIMET/fuzzy_environment
Fuzzy logic is used in expert systems in many domains:
transportation, automobiles, consumer electronics, robotics,
pattern recognition, classification, telecommunications,
agriculture, medicine, management, and education. 1
Fuzzy logic often models continuous, real-world systems. There are
hundreds of fuzzy logic based systems that deal with environmental data.
agriculture, climatology, ecology, fisheries, geography, geology,
hydrology, meteorology, mining, natural resources, oceanography,
petroleum industry, risk analysis, and seismology. 2
Fuzzy Logic Applications
Meteorological view: CBR = analog forecasting
AI view: CBR = retrieval + analogy + adaptation + learning 1
CBR is a way to avoid the “knowledge acquisition problem.”
CBR is very effective in situations “where the acquisition
of the case-base and the determination of the features is
straightforward compared with the task of developing the
reasoning mechanism.” 2
CBR and analog forecasting recommended when models are
inadequate, e.g., ceiling and visibility, which are strongly
determined by local effects below scale of current computer models.
Case-Based Reasoning
1. Leake, D. B., 1996: CBR in context. The present and future; in Leake, D. B. (editor), Case-Based Reasoning: Experiences, Lessons & Future Directions, American Association for Artificial Intelligence, Menlo Park California, USA, 3-30.2. Cunningham, P., and Bonzano, A., 1999: Knowledge engineering issues in developing a case-based reasoning application, Knowledge-Based Systems, 12, 371-379.
k-Nearest Neighbor(s) Technique: k-nn
Definition: “For a particular point in question, in a population
of points, the k nearest points.” 1
Intuition: The closer the neighbors, the more useful they are for prediction.
“It is reasonable to assume that observations which are close together
(according to some appropriate metric) will have the same classification.
Furthermore, it is also reasonable to say that one might wish to weight the
evidence of a neighbor close to an unclassified observation more heavily than
the weight of another neighbor which is at a greater distance from the
unclassified observation.” 2
k-nn is a basic CBR method. Commonly used to explain an observation
when there is no other more effective method. 2
1. Dudani, S. A., 1976: The distance-weighted k-nearest neighbor rule, IEEE Transactions on Systems, Man, and Cybernetics, Volume SMC-6, Number 4, April 1976, 325-327.2. Aha, D. W. (1998) Feature weighting for lazy learning algorithms. In Liu, H. and Motoda, H. (Eds.), Feature Extraction, Construction, and Selection: A Data Mining Perspective. Norwell MA: Kluwer,
Fuzzy k-Nearest Neighbor(s) Technique: fuzzy k-nn
Definition: “Nearest neighbor technique in which the
basic measurement technique is fuzzy. 1
Two improvements to k-nn technique by using fuzzy k-nn approach: 1
“Improve performance of retrieval in terms of accuracy because of
avoidance of unrealistic absolute classification.”
“Increase the interpretability of results of retrieval because the
overall degree of membership of a case in a class that provides a
level of assurance to accompany the classification.”
1. Keller, J. M., Gray, M. R., and Givens Jr., J. A., 1985: A fuzzy k-nearest neighbor algorithm, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 4, 258-263.
Two basic methods to predict weather: 1
• Dynamical - based upon equations of the atmosphere, uses finite element
techniques, and is commonly referred to as computer modeling.
• Empirical - based upon the occurrence of analogs, or similar weather situations.
1. Lorenz, E. N., 1969: Three approaches to atmospheric predictability, Bulletin of the American Meteorological Society, 50, 345-349.
Weather Prediction
In practice, hybrid methods used: Models + Observations
Statistical methods infer estimated expected distributions under specified conditions.
Theoretical distributions are fit to sparse data, e.g. normal distributions, MLR.
Resampling methods are a recently feasible option, thanks to advances in computer
speed and storage, when data sets are large, and when condition specification is
deferred to the last minute (run time, time-zero), e.g., k-nn, WIND.
Statistical
Analog / Resampling
2. Rudner, Lawrence M. & Shafer, Mary Morello, 1992: Resampling a marriage of computers and statistics. Practical Assessment, Research & Evaluation, 3(5). http://EdResearch.org/pare/getvn.asp?v=3&n=5
Ceiling and Visibility PredictionCeiling height and visibility prediction demands precision:
Ceiling height, when low, accurate to within 100 feet.
Visibility, when low, accurate to within 1/4 mile.
Time of change of flying category should be
accurate to within one hour.
1. RAP/NCAR, Ceiling and visibility, Background, http://www.rap.ucar.edu/asr2002/j-c_v/j-ceiling-visibiltiy.htm
Safety concern
“Adverse ceiling and visibility conditions can produce major
negative impacts on aviation - as a contributing factor in
over 35% of all weather-related accidents in the U.S. civil aviation
sector and as a major cause of flight delays nationwide.” 1
Motivation for ceiling and visibility prediction research
Economics and Efficiency
Every 1% increase in TAF accuracy would result in $1M per year of value to
the air traffic system in Canada – estimating conservatively, and assuming
increase relative to recently measured levels of TAF accuracy. 1
The commonest cause for TAFs needing to be amended is the occurrence
of unforecast categories of cloud ceiling and visibility. 2
The National Weather Service (NWS) estimates that a 30 minute lead-time
for identifying cloud ceiling or visibility events could reduce the number of
weather-related delays by 20 to 35 percent and that this could save between
$500 million to $875 million annually. 3
“The economic benefit of a uniform, hypothetical increase in TAF accuracy
of 1% is approximately $1.2 million [Australian] per year for Qantas Intl.
flights into Sydney.” 4
1. Assessment of Aerodrome Forecast (TAF) Accuracy Improvement, NAV CANADA, May 2002, pg. 22.2. Henry Stanski, 1999: Personal communication.3. Jim Valdez, NWS Reinventing Goals for 2000, http://govinfo.library.unt.edu/npr/library/announc/npr5.htm4. Leigh, R. J., 1995: Economic benefits of Terminal Aerodrome Forecasts (TAFs) for Sydney Airport, Australia, Meteorological Applications, 2, 239-247.
Motivation for AI-based ceiling and visibility prediction research
Scientific and Engineering
Ceiling and visibility are “not resolvable” with current computer models
(aka NWP, numerical weather prediction models).
“Unfortunately, cloud cover is the most difficult of meteorological variables for
numerical models to predict. [MOS] output for predictions of ceiling and visibility
is heavily dependent on the most recent station observations rather than the
output of the numerical model. Consequently, the quality of ceiling and visibility
forecasts has not increased as it has for other forecast variables. For 3- and 6-
hour forecasts, several studies have shown that local forecasters could not do
better and often did worse than persistence. MOS forecasts were not clearly
better than those of the local forecaster for time frames of 9 hours or less.” 1
Persistence forecasting is a difficult technique to beat for very short-range
forecasting. 2 [Because of high ratio of VFR : IFR]
1. The COMET Outreach Program, http://www.comet.ucar.edu/outreach/9915808.htm
2. Dallavalle, J. P., and Dagostaro, V. J., 1995: The accuracy of ceiling and visibility forecasts produced by the National Weather Service, Preprints of the 6th Conference on Aviation Weather Systems, American Meteorological Society, 213-218
Prediction System: WIND
WIND: “Weather Is Not Discrete”
Consists of three parts: Data – weather observations and model-based guidance. Fuzzy similarity-measuring algorithm – small C program. Prediction composition – fairly trivial, predictions are based on selected percentiles of cumulative summaries of k nearest neighbors, k-nn.
Data Past airport weather observations, 32 years of hourly observations. Recent and current observations. NWP-based guidance.
Data: Past and current observations
Category
temporal
cloud ceilingand visibility
wind
precipitation
spread andtemperature
pressure
Attribute
date
hour
cloud amount(s)cloud ceiling heightvisibility
wind directionwind speed
precipitation typeprecipitation intensity
dew point temperaturedry bulb temperature
pressure trend
Units
Julian date of year (wraps around)
hours offset from sunrise/sunset
tenths of cloud cover (for each layer)height in metres of 6/10ths cloud coverhorizontal visibility in metres
degrees from true northknots
nil, rain, snow, etc.nil, light, moderate, heavy
degrees Celsiusdegrees Celsius
kiloPascal × hour -1
Data: Past and current observations
E.g., over 300,000 consecutive hourly obs for Halifax Airport, quality-controlled.
YY/MM/DD/HH Ceiling Vis Wind Wind Dry Dew MSL Station Cloud
Directn Speed Bulb Point Press Press Amount
30's m km 10's deg km/hr deg C deg C kPa kPa tenths Weather
64/ 1/ 2/ 0 15 24.1 14 16 -4.4 -5.6 101.07 99.31 10
64/ 1/ 2/ 1 13 6.1 14 26 -2.2 -2.8 100.72 98.96 10 ZR-
64/ 1/ 2/ 2 2 8.0 11 26 -1.1 -2.2 100.39 98.66 10 ZR-F
64/ 1/ 2/ 3 2 6.4 11 24 0.0 -0.6 100.09 98.36 10 ZR-F
64/ 1/ 2/ 4 2 4.8 11 32 1.1 0.6 99.63 97.90 10 R-F
64/ 1/ 2/ 5 2 3.2 14 48 2.8 2.2 99.20 97.50 10 R-F
64/ 1/ 2/ 6 3 1.2 16 40 3.9 3.9 98.92 97.22 10 R-F
64/ 1/ 2/ 7 2 2.0 20 40 4.4 4.4 98.78 97.08 10 F
64/ 1/ 2/ 8 2 4.8 20 35 3.9 3.3 98.70 97.01 10 F
64/ 1/ 2/ 9 4 4.0 20 29 3.3 2.8 98.65 96.96 10 R-F
64/ 1/ 2/10 6 8.0 20 35 2.8 2.2 98.60 96.91 10 F
64/ 1/ 2/11 8 8.0 20 32 2.8 2.2 98.45 96.77 10 F
64/ 1/ 2/12 9 9.7 23 29 2.2 1.7 98.43 96.75 10 F
64/ 1/ 2/13 9 11.3 23 32 1.7 1.1 98.37 96.69 10
...
Data: Computer model based guidance
a(t0)
b(t0)
a(t0-p)
b(t0-p)
guidance
b(t0+p)
Timezero
Recentpast Future
...
... ...
... ...... ... ...
Present Case
Past Cases
TraversingCase BaseSimilarity measurement
Prediction System – Data Structure and Case Retrieval
Compose present case: recent obs + NWP
Collect most similar past cases
m c (x 1 - x 2 )
0.00
0.25
0.50
0.75
1.00
-c 0 c
x 1 - x 2
very
slightlynear
Fuzzy similarity-measuring function
Three types of fuzzy operations designed to measure
degree of similarity between three types of attributes.
1. Continuous. (e.g., wind direction, temperature, etc.)
Design tight fit for critical elements, such as wind direction, relatively loose fit for others, such as temperature. An expert forecaster suggests values that
correspond to varying degrees of similarity.
Attribute slightly near near very near
date of the year 60 days 30 days 10 days
hour of the day 2 hours 1 hours 0.5 hours
wind direction 40 degrees 20 degrees 10 degrees
dew point temperature 4 degrees 2 degrees 1 degree
dry bulb temperature 8 degrees 4 degrees 2 degree
pressure trend 0.20 kPa hr -1 0.10 kPa hr -1 0.05 kPa hr -1
Expertly configured similarity-measuring function
Expert specifies thresholds for various degrees of near
0 8 16 24 320
8
16
24
32
m(x1,x2)
x1
x2
m(x1,x2)
0.75-1
0.5-0.75
0.25-0.5
0-0.25
Fuzzy similarity-measuring function
2. Magnitude. (e.g., wind speed)
FuzzyDecisionSurface
Calm to lights wind speedsrequire special interpretation.0
04 8
4
8
Nil 1.00Drizzle 0.02 1.00
Showers 0.03 0.50 1.00Rain 0.01 0.50 0.75 1.00
… … … … … …Nil Drizzle Showers Rain …
Fuzzy similarity-measuring function
3. Nominal. (e.g., precipitation)
Fuzzy Relationships
Different types of weather havedifferent perceiveddegrees of similarity.
Algorithm: Collect Most Similar Analogs, Make Prediction
.
. . .
Analogensemble
Climate archive
Archive search is like contracting hyperellipsoid centered on present case.
Axes measure differences weather elements between compared cases.
“Distances” determined by fuzzy similarity-measuring functions, expertly tuned, all applied together simultaneously.
C&V evolution
Forecast ceiling and visibilitybased on outcomes ofmost similar analogs.
Spread in analogs helps toinform about appropriateforecast confidence.
Prediction
WIND makes 11 series of deterministic forecasts based on
percentiles of C&V in analogs (0, 10, 20, ..., 100): 0%ile is the
lowest C&V, 50%ile is the median, 100%ile is the highest.
Using MSC / Nav Canada performance measures, experiments
showed that the series in the 20 to 40 range verified fairly well.
Be aware of systematic tradeoffs between Frequency of Hits,
False Alarm Ratio, and Probability of Detection, e.g.,
IFR POD and FAR
VFR POD and FAR
Prediction
Forecast: ceiling and visibility based on 30%ile of analogs
Prediction
Probabilistic forecast: 10 %ile to 50%ile cig. and vis. from analogs
Results
Forecasts are competitive with
persistence in 0-to-6 hour range,
and better than persistence in
the 0-to-24 hour range
based on FOH, FAR, and POD
of alternate and VFR forecasts. WIND runs in real-time for climatologically different sites.Data-mining/forecast processtakes about one second.
First impressions and
forecaster feedback:
Probabilistic forecasts of cig. & vis. informative, high “glance value”.
A “heads-up” message about the current “forecasting issue”
would be helpful, e.g., if wind > 8 knots and temp > -40oC
then Chance(ice fog) = Low.
Forecaster Feedback
1. WIND forecast blizzard conditions to improve to VFR after
one hour.
Analog ensemble used to base predictions on was too large,
as blizzards are a relatively rare event. Made a few changes to
the code and then WIND forecast blizzard conditions more
intelligently.
2. WIND often provides very good timing of significant category
changes.
Owe some credit to model guidance in many cases as, if wind
shifts and precipitation are well forecast by the model, WIND
benefits directly, and forecasts ceiling and visibility accordingly.
Forecaster Feedback
3. WIND provides reasonable values for the 6-to-24 hour period which could help in writing TAFs. Forecasting ceiling and visibility in this time period is presently difficult for forecasters because nowcasting techniques, such as persistence and extrapolation, are unreliable.
4. WIND generated TAF for CYYT on May 29th and 06 & 12Z worked quite well. It was an increasing southeasterly flow bringing in low stratus and fog. I believe the WIND had it going very low at 18Z while in fact it was about 19Z. This morning's (30/06Z) TAF had the visibility a bit more variable than it really was. So again we see some success in the process with stuff moving in farther in the future. However once the stuff is there, it remains to be seen what the success rate will be.
For nowcasting, persistence is hard to beat.
Verification Method
Forecasts verified using standard performance measurement method, 1
according to the average accuracy of forecasts in the 0-to 6 hour and
the 0-to-24 hour projection period of significant flying categories.
Ceiling (m) Visibility (km) Flying category
< 200 or < 3.2 below alternate
200 and 3.2 alternate
330 and 4.8 VFR
Count three
sorts of events:
1. Stanski, H., Leganchuk, A., Hanssen, A., Wintjes, D.,Abramowski, O., and Shaykewich, J., 1999: NAV CANADA's TAF amendment response time verification , Eighth Conference on Aviation, Range, and Aerospace Meteorology, 10-15 January1999, Dallas, Texas, American Meteorological Society, 63-67.
OBSERVED
YES NO
FORECAST YES hit false alarm
NO miss (non-event)
Three performance measurements calculated:
Frequency of Hits (Reliability) FOH =
False Alarm Ratio FAR =
Probability of Detection, POD =
FOH and FAR for the 0-to-6 hours are routinely tracked.
However, other more comprehensive, cost-model based schemes
would give more meaningful results in terms of forecast value. 1
Statistics
hitshits + misses
false alarmshits + misses
hitshits + false alarms
1. Forecast Verification - Issues, Methods and FAQ, http://www.bom.gov.au/bmrc/wefor/staff/eee/verif/verif_web_page.html
Statistics: Caveats
Results refer to a fully automatic and therefore handicapped system:
WIND-2 runs without guidance-improving forecaster interaction.
Results could be significantly better if WIND had forecaster input.
Statistics are summaries of statistics at these airports:
CYEG, CYFB, CYHZ, CYOW, CYQB, CYUL, CYVR,
CYWG, CYXE, CYYC,CYYT, CYYZ, and CYZF.
Each airport's statistics are given equal weight. When the statistics
for individual airports are considered, other patterns appear.
Legends in the graphs refer to 20, 30, and 40%ile, three series of
forecasts produced by WIND-2, with ceiling and visibility (C&V) based
on the 20th, 30th, and 40th percentile of C&V among retrieved analogs.
The lower the percentile, the lower the forecast of C&V.
FOH VFR, 0-6 HR, ALL SITES
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
FO
H 20%ile
30%ile
40%ile
PER
FAR BLO ALT, 0-6 HR, ALL SITES
0
10
20
30
40
50
60
70
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
FA
R 20%ile
30%ile
40%ile
PER
FOH ALT, 0-6 HR, ALL SITES
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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H 20%ile
30%ile
40%ile
PER
POD BLO ALT, 0-6 HR, ALL SITES
0
10
20
30
40
50
60
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
PO
D 20%ile
30%ile
40%ile
PER
FOH VFR, 0-6 HR, ALL SITES
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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H 20%ile
30%ile
40%ile
PER
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JAN FEB MAR APR MAY JUN JUL AUG
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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30%ile
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POD BLO ALT, 0-6 HR, ALL SITES
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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30%ile
40%ile
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FOH VFR, 0-24 HR, ALL SITES
80
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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H 20%ile
30%ile
40%ile
PER
FAR BLO ALT, 0-24 HR, ALL SITES
0
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40
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
FA
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30%ile
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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H 20%ile
30%ile
40%ile
PER
POD BLO ALT, 0-24 HR, ALL SITES
0
10
20
30
40
50
60
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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D 20%ile
30%ile
40%ile
PER
FOH VFR, 0-24 HR, ALL SITES
80
90
100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
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H 20%ile
30%ile
40%ile
PER
FAR BLO ALT, 0-24 HR, ALL SITES
0
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20
30
40
50
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100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
FA
R 20%ile
30%ile
40%ile
PER
FOH ALT, 0-24 HR, ALL SITES
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100
JAN FEB MAR APR MAY JUN JUL AUG
MONTH
FO
H 20%ile
30%ile
40%ile
PER
POD BLO ALT, 0-24 HR, ALL SITES
0
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20
30
40
50
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JAN FEB MAR APR MAY JUN JUL AUG
MONTH
PO
D 20%ile
30%ile
40%ile
PER
Conclusion
By building expert systems that combine forecaster expertise,
AI, large amounts of data (climatological and current), and
currently available computing power, we can increase forecast
quality and increase forecasting efficiency.
Acknowledgements Thesis Committee – Mohammed El-Hawary, Qigang Gao, Denis Riordan MSC Colleagues – Jim Abraham, Bill Appleby, Michel Béland, Peter Bowyer, Bill Burrows, Luc Corbeil, Daniel Chretien, Stewart Cober, Mike Crowe, Réal Daigle, Eric De Groot, Norbert Dreidger, Jack Dunnigan, Peter Houtekamer, Lorne Ketch, Alister Ling, Ted Lord, Allan MacAfee, Ken Macdonald, Martha McCulloch, Jamie McLean, Jim Murtha, Ewa Milewska, Steve Miller, Desmond O’Neill, George Parkes, Bill Richards, Steve Ricketts, Ray St. Pierre, Henry Stanski, Dave Steenbergen, Val Swail, Herb Thoms, Richard Verret, Bruce Whiffen, Laurie Wilson NRL Colleagues – David Aha, Michael Hadjimichael RAP/NCAR Colleagues – Paul Herzegh, Gerry Wiener
Future: Possible Additions and Improvements
Graphic user interface: let expert forecasters guide the data-mining to test
“what-if” weather scenarios based on various possible conditions.
Links to other software: enable WIND to help with weather watch, proactive
alerting of impending problems. For example, combine with MultiAlert to enable a
smart alert, and thus help forecasters to increase their situational awareness.
More predictors: allow data-mining to be better conditioned, e.g., duration of
precipitation, recent trends (C&V, pcpn, dp/dt), sun factors (length of day,
strength of sun), wind (back trajectory, wind run, source region, cyclonic /
anticyclonic flow), etc.
Data fusion: exploit all available data and employ data fusion techniques 1
to improve nowcasting systems, by intelligently integrating of output of various
models 2 (e.g., GEM and UMOS), forecaster input, and objective nowcasts of
precipitation (based on systems under development), and moving cloud areas
seen / detected on satellite images.
1. Intelligent Weather Systems, RAP, NCAR, http://www.rap.ucar.edu/technology/iws2. Shel Gerding and William Myers, 2003: Adaptive data fusion of meteorological forecast modules, 3rd Conference on Artificial Intelligence Applications to Environmental Science, AMS.
Future: Possible Additions and Improvements
1. Qingmin Shi and Joseph F. JaJa, 2003: Fast Algorithms for a Class of Temporal Range Queries, Proceedings of Workshopon Algorithms and Data Structures, July 30 - August 1, 2003,Ottawa, Canada. and Qingmin Shi and Joseph F. JaJa, 200?: A New Framework for Addressing Temporal Range Queries and Some Preliminary Results, submitted to Theoretical Computer Science.2. Jim Murtha, 1995: Applications of fuzzy logic in operational meteorology, Scientific Services and Professional Development Newsletter, Canadian Forces Weather Service, 42-543. Main Trend in Automation of Nowcasting: Application of Fuzzy Logic, http://bjarne.ca/ideas/trends
Faster retrieval algorithms: use reliable tree-based indexing algorithms for data
retrieval to make data retrieval 1000 times faster. 1 A faster algorithm would help
WIND to scale up and would help us to test a wider range of data retrieval
strategies, e.g., for testing what-if scenarios, forecasters could adjust conditions
with a sliding widget and see a virtually instantaneous response.
Fuzzy rule base: make WIND more of an expert system, to make it systematically
act more “intelligently”, as we learn from experts, experience, and experiments.
Add routines to deal with documented local effects and with special situations
such as radiation fog 2 and blowing snow.
Partnerships: collaborate with the Research Applications Program (RAP), NCAR
and the Aviation Weather Research Program (AWRP) to leverage limited funds,
achieve mutual benefits, and realize the above-listed improvements more quickly. 3
Hybrid Forecast Decision Support Systems
Hybrid forecast system development is a current direction of the
Aviation Weather Research Program (AWRP) 1 and the Research
Applications Program (RAP), 2 NCAR (the main organizers of
AWRP R&D).
AWRP Terminal Ceiling and Visibility Product Development Team
(PDT) project, Consensus Forecast System, a combination of:
COBEL, a physical column model 3
Statistical forecast models, local and regional
Satellite statistical forecast model
1. Aviation Weather Research Program, http://www.rap.ucar.edu/general/awrp_pmr2002
2. Research Applications Program, http://www.rap.ucar.edu
3. Cobel, 1-D model, http://www.rap.ucar.edu/staff/tardif/COBEL
Hybrid Forecast Decision Support Systems
AWRP National Ceiling and Visibility PDT research initiatives: 1
Data fusion: intelligent integration of output of various models, observational data, and forecaster input using fuzzy logic 2, 3
Data mining, C5.0 pattern recognition software for generating decision trees based on data mining, freeware by Ross Quinlan (http://www.rulequest.com), like CART Analog forecasting using Euclidean distance development of daily climatology for 1500+ continental US (CONUS) sites Incorporate AutoNowcast of weather radar in 2004-2005 4
Incorporate satellite image cloud-type classification algorithms 5
1. Gerry Wiener, personal communication, July 2003.
2. Intelligent Weather Systems, RAP, NCAR, http://www.rap.ucar.edu/technology/iws
3. Shel Gerding and William Myers, 2003: Adaptive data fusion of meteorological forecast modules, 3rd Conference on Artificial Intelligence Applications to Environmental Science, AMS.
4. AutoNowcast, http://www.rap.ucar.edu/projects/nowcast
5. Tag, Paul M., Bankert, Richard L., Brody, L. Robin. 2000: An AVHRR Multiple Cloud- Type Classification Package. Journal of Applied Meteorology: Vol. 39, No. 2, pp. 125-134.
Fuzzy LogicIntegrationAlgorithm
ProductGenerator
User
HumanInput
(> 15 min)
SelectiveClimatological
Input
Real-TimeData
Algorithms
ModelOutput
Algorithms
Data AssimilationMesoscale Model
Real-Time DataPreprocessing
QualityControl
SensorSystems
AIworkshere
Graphic UserInterface
1. RAP, Intelligent Weather Systems, www.rap.ucar.edu/technology/iws/design.htm
WeatherRadar
Nowcasts
RAP, Thunderstorm Auto-Nowcasting, www.rap.ucar.edu/projects/nowcast
Intelligent Weather Systems (RAP/NCAR) 1
“Smart Alert” Concept
Impendingproblem
Bust
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St. John’s
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CeilingVisibilityDirectionSpeedTime…Weather
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Search Make
INTEGRATION
CLIMATEARCHIVE
data
PRODUCTDISPLAY(editable)
HEADS-UPALERT &DISPLAY
ACTUALWEATHER
MAP(animated)
GUIDANCEDISPLAY(satellite,
NWP, etc.)
FORECASTER(interacts, intervenes)
awareness and knowledge
PREDICTION
UPPER AIR
SATELLITE
METAR
REAL-TIMEOBSdata
RAW, QC’dWEATHER
data
MODELLEDWEATHER
NWPdata
PRODUCTGENERATION
PRODUCTSinformation
MODELLEDWEATHER
MAP(editable)
DSS(interaction withintegration and
prediction)
PRODUCTSPECIFICATIONS
CONSISTENCYCHECKING
TRANSLATION
FORECAST
EXTRAPOLATION
PROJECTEDOBS
AIknowledge
USER
MODEL-BASEDWEATHERELEMENTS
VERIFICATION
0 time
official forecast
actual trend
!
Graphic interventionFirst resort
Direct interventionLast resort
data and information• up-to-the-minute intelligent data fusion• abstract features• derived fields• intelligently composed “interest fields”
RADAR
DAdata
information• special interests• cost-based decision-making models
DECISION SUPPORT SYSTEMS *
POST-PROCESSING
Battleboard raises forecaster’s situational awareness
GUI leverages forecaster’s actions
* Forecaster Workstation User Requirements Working Group meeting notes, 2000: Decision support systems for weather forecasting based on modular design, updated slightly for Aviation Tools Workshop in 2003.
Decision Support Systems Design
Generic: no-name, conceptual design that could link and
integrate the most useful elements of WIND, AVISA, MultiAlert,
SCRIBE, FPA, URP, and so on in evolving WSP application, NinJo.
Modular: shows where distinct sub-tools / agents can be developed.
Working in this way, individual developers could work on isolated
sub-problems and anticipate how to plug their results into a larger
shared system. As technology inevitably improves, improved modules
can be easily installed and quickly implemented.
User-centered: forecast decision support systems from forecaster's
point of view, designed to increase situational awareness.
Hybrid: combines complementary sources of knowledge, forecasters
and AI, to increase the quality of input data and output information.
Intelligent integration of data, information, and model output, and
use of adaptive forecasting strategies are intrinsic in this design.
A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html
Since we can assign numeric values to linguistic expressions, it follows that we can also combine such expressions into rules and evaluate them mathematically.A typical fuzzy logic rule might be:
If temperature is warm and pressure is low then set heat to high
Fuzzy logic
A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html
How Rules Relate to a Control Surface
A fuzzy associative matrix (FAM) can be helpful to be sure you are not missing any important rules in your system. Figure shows a FAM for a control system with two inputs, each having three labels. Inside each box you write a label of the system output. In this system there are nine possible rules corresponding to the nine boxes in the FAM. The highlighted box corresponds to the rule:
If temperature is warm and pressure is low then set heat to high
A graphical illustration to fuzzy logic, http://www.mcu.motsps.com/lit/tutor/fuzzy/fuzzy.html
The input to output relationship is precise and constant. Many engineers were initially unwilling to embrace fuzzy logic because of a misconception that the results were not repeatable and approximate. The term fuzzy actually refers to the gradual transitions at set boundaries from false to true.
Three Dimensional Control Surface
CBRInference Engine
CBRKnowledge Base
Problem Input
Assign Indices Indexing Rules
Case Base Input + Indices
Case Retrieve MatchMemory Rules
Retrieved CaseStore
Adapt AdaptationAssign Indices Rules
Proposed SolutionNew Case Test New Solution
Solution Failure Description Repair Repair
RulesExplain Causal Analysis
PredictiveFeatures
1. Adapted from Riesbeck and Schank 1989
difficult problem
potential endless loop
CBR needs methods for acquiring domain knowledge for retrieval and adaptation.
Classic CBRFlowchart 1