1. Weather Prediction Expert System Approaches
Bulent KISKAC
Harun YARDIMCI

2. Outline Weather Prediction Introduction Case-Based Weather Prediction
Neural-Net Weather Prediction
Hybrid Weather Prediction
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3. Weather Prediction Introduction
Would you like to know the weather in advance?
Automated prediction with distilled information allows non-meteorologist customers to reduce the threat of weather,confidently make decisions and plan activities.
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4. Weather Prediction Motivation
Operational decisions in many organizations are strongly affected by meteorological phenomena.
Day-to-day business processes require detailed weather information in real time and/or short-term predictions formatted to suit user’s needs.
This information has to be reliable, easily understood and thoroughly customized.
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5. Weather Prediction Applications
They can pinpoint areas where weather hazards impact an organization’s operations.
They can detects,predicts and forecasts weather phenomena and hazards, utilizing state-of-the-science technologies, developed and licensed from leading weather research institutions and companies.
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6. Weather Prediction Applications
Provides a complete solution for generating predictions and warnings.
This is made possible by a powerful suite of detection and prediction algorithms that process data from multiple weather data streams.
use a combination of image processing, expert systems, fuzzy logic, and sophisticated statistical techniques.
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7. Weather Prediction Applications
Meteorological data sets assimilated in real time, run a series of algorithms, produced analysis and data that is stored to a database for display and warning.
Provide highly accurate prediction capability for all weather phenomena.(%80-95)
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8. Weather Predictions
Identification and prediction for the following extreme weather phenomena:
Storms
Hail swath
Lightning
Precipitation
Tornado
Flash flood
Damaging Wind
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9. Seamless Suite of Forecasts
Transportation
Forecast Lead Time
Warnings & Alert Coordination
Watches
Forecasts
Threats Assessments
Guidance
Outlook
Protection of Life & Property
Space Operation
Recreation
Ecosystem
State/Local Planning
Environment
Flood Mitigation & Navigation
Agriculture
Reservoir Control
Energy
Commerce
Benefits
Hydropower
Fire Weather
Health
Forecast
Uncertainty
Minutes
Hours
Days
1 Week
2 Week
Months
Seasons
Years
Boundary Conditions
Initial Conditions
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10. Forecasts types TAF Public Forecasts Marine Forecasts
(Hourly,100feet ceiling ,400 meter ground visibility)
Public Forecasts
(Variable cloudiness this morning)
Marine Forecasts
(Fog patches forming this afternoon)
TAF: Forecasts of the height of low cloud ceiling height
are expected to be accurate to within 100 feet.
horizontal visibility on the ground,
when there is dense obstruction to visibility, such
as fog or snow, are expected to be accurate to
within 400 metres
Forecasts of the time of
change from one flying category to another are
expected to be accurate to within one hour.
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11. Application Areas Example
A correctly forecast timing of a ceiling and visibility event could be expected to result in a savings of approximately $480,000 per event at LaGuardia Airport
Based on a related study, the U.S. National Weather Service estimated 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
An examination of the causes and effects of flight delays at the three main airports serving NewYork City concluded that a correctly forecast timing of a ceiling and visibility event (i.e., a significant change) could be expected to result in a savings of approximately $480,000 per event at La Guardia Airport (Allan et al. 2001). Based on a related study, the U.S. National Weather Service estimated 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 (Valdez 2000).
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12. Application Areas Example
When ceiling and visibility at a busy airport are low, in order to maximize safety, the rate of planes landing is reduced.
When ceiling and visibility at a destination airport are forecast to below at a flight's scheduled arrival time, its departure may be delayed in order to minimize traffic congestion and related costs.
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13. Case-Based Reasoning Meteorological view: CBR = analog forecasting
AI view:
CBR = retrieval + analogy + adaptation + learning
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.”
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.
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14. potential endless loop
Classic CBR Flowchart
CBR needs methods for acquiring domain knowledge for retrieval and adaptation.
difficult problem
potential endless loop
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15. k-Nearest Neighbor(s) Technique
For a particular point in question, in a population of points, the k nearest points.”
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.
It may be reasonable 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.”
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16. Fuzzy k-Nearest Neighbor(s) Technique
basic measurement technique is fuzzy.
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.”
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17. Weather Prediction Data Past airport weather observations,
Consists of three parts:
Data – weather observations and model-based guidance.
Fuzzy similarity-measuring algorithm.
Prediction composition – fairly trivial, predictions are based on selected percentiles of cumulative summaries of k nearest neighbors.
Data
Past airport weather observations,
Recent and current observations.
Numeric Weather Prediction based guidance.
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18. Algorithm: Collect Most Similar Analogs, Make Prediction
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.
Ceiling&Visibility evolution
Forecast ceiling and visibility based on outcomes of most similar analogs.
Spread in analogs helps to inform about appropriate forecast confidence.
Climate archive
Analog ensemble . . . .
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19. Knowledge Representation
Category
temporal
cloud ceiling and visibility
wind
precipitation
spread and temperature
pressure
Attribute
date
hour
cloud amount(s) cloud ceiling height visibility
wind direction wind speed
precipitation type precipitation intensity
dew point temperature dry 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 ³ 6/10ths cloud cover horizontal visibility in metres
degrees from true north knots
nil, rain, snow, etc. nil, light, moderate, heavy
degrees Celsius degrees Celsius
kiloPascal × hour -1
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20. Prediction System – Data Structure and Case Retrieval
Compose present case: recent obs + NWP
Collect most similar past cases
Present Case
Recent past
Time zero
Future
a(t0-p)
...
a(t0)
...
guidance
Traversing Case Base
Similarity measurement
...
...
b(t0-p)
...
b(t0)
...
b(t0+p)
...
...
Past Cases

21. Solution Features Fuzzy Set
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26. Ceiling and Visibility Forecast
Forecast: ceiling and visibility based on 30%ile of analogs
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27. Ceiling and Visibility Forecast
Probabilistic forecast: 10 %ile to 50%ile cig. and vis. from analogs.
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28. Verification Method
Forecasts verified using standard performance measurement method, 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.
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29. Artificial Neural Networks Motivation
-Real-Time Operation: Neural network computations can be carried out in parallel.
-Fault Tolerance by Redundant Information Coding: Destruction of parts of a network leads to the degradation of performance. However, some network capabilities in neural networks can be retained even with major network damage.
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30. Biological Neural Networks
This figure displays the essential structure of a neuron:
Effective
connections
activation
function
input
output
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31. Biological Neural Networks
Neurons constantly receive signals from these inputs and then perform their function.
The neurons evaluate the voltages inputted to it and then, if the evaluated value is greater than some threshold value (meaning the excitatory influences are more dominant than the inhibitory influences acting on the neuron), the neurons fire.
When firing, a voltage signal is generated and outputted along a structure called an axon.
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32. Basic Neuron Model Inputs xi arrive through pre-synaptic connections
Synaptic efficacy is modeled using real weights wi
The response of the neuron is a nonlinear function f of its weighted inputs
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33. Network Topology Feedforward Inputs Outputs Inputs Feedback Outputs
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34. Differences In Networks
Feedforward Networks
Solutions are known
Weights are learned
Evolves in the weight space
Used for:
Prediction
Classification
Function approximation
Feedback Networks
Solutions are unknown
Weights are prescribed
Evolves in the state space
Used for:
Constraint satisfaction
Optimization
Feature matching
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35. Inputs To Neurons Arise from other neurons or from outside the network
Nodes whose inputs arise outside the network are called input nodes and simply copy values
An input may excite or inhibit the response of the neuron to which it is applied, depending upon the weight of the connection
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36. Weights
Represent synaptic efficacy and may be excitatory or inhibitory
Normally, positive weights are considered as excitatory while negative weights are thought of as inhibitory
Learning is the process of modifying the weights in order to produce a network that performs some function
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37. Output The response function is normally nonlinear Samples include
Sigmoid
Piecewise linear
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38. The Backpropagation Network
The backpropagation network (BPN) is for classification or function approximation applications.
Three (sometimes more) layers of neurons,
Only feedforward processing: input layer  hidden layer  output layer,
Sigmoid activation functions
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39. The Backpropagation Network
BPN units and activation functions:
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40. Backpropagation Preparation
Training Set A collection of input-output patterns that are used to train the network
Testing Set A collection of input-output patterns that are used to assess network performance
Learning Rate-η A scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments
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41. Network Error Total-Sum-Squared-Error (TSSE)
Root-Mean-Squared-Error (RMSE)
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42. Learning in the BPN
Before the learning process starts, all weights (synapses) in the network are initialized with pseudorandom numbers.
We also have to provide a set of training patterns. They can be described as a set of ordered vector pairs {(x1, y1), (x2, y2), …, (xP, yP)}.
Then we can start the backpropagation learning algorithm.
This algorithm iteratively minimizes the network’s error by finding the gradient of the error surface in weight-space and adjusting the weights in the opposite direction (gradient-descent technique).
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43. Learning in the BPN
Gradient-descent example: Finding the absolute minimum of a one-dimensional error function f(x):
Repeat this iteratively until for some xi, f’(xi) is sufficiently close to 0.
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44. A Pseudo-Code Algorithm
Randomly choose the initial weights
While error is too large
For each training pattern (presented in random order)
Apply the inputs to the network
Calculate the output for every neuron from the input layer, through the hidden layer(s), to the output layer
Calculate the error at the outputs
Use the output error to compute error signals for pre-output layers
Use the error signals to compute weight adjustments
Apply the weight adjustments
Periodically evaluate the network performance
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45. Learning in the BPN Gradients of two-dimensional functions:
the gradient is always pointing in the direction of the steepest increase of the function. In order to find the function’s minimum, we should always move against the gradient.
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46. Possible Data Structures
Two-dimensional arrays
Weights (at least for input-to-hidden layer and hidden-to-output layer connections)
Weight changes (Dij)
One-dimensional arrays
Neuron layers
Cumulative current input
Current output
Error signal for each neuron
Bias weights
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47. Learning in the BPN
If we choose the type and number of neurons in our network appropriately, after training the network should show the following behavior:
If we input any of the training vectors, the network should yield the expected output vector (with some margin of error).
If we input a vector that the network has never “seen” before, it should be able to generalize and yield a plausible output vector based on its knowledge about similar input vectors.
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48. Apply Inputs From A Pattern
Apply the value of each input parameter to each input node
Input nodes computer only the identity function
Feedforward
Inputs
Outputs
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49. Calculate Outputs For Each Neuron Based On The Pattern
The output from neuron j for pattern p is Opj where
and
k ranges over the input indices and Wjk is the weight on the connection from input k to neuron j
Feedforward
Inputs
Outputs
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50. Calculate The Error Signal For Each Output Neuron
The output neuron error signal dpj is given by dpj=(Tpj-Opj) Opj (1-Opj)
Tpj is the target value of output neuron j for pattern p
Opj is the actual output value of output neuron j for pattern p
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51. Calculate The Error Signal For Each Hidden Neuron
The hidden neuron error signal dpj is given by
where dpk is the error signal of a post-synaptic neuron k and Wkj is the weight of the connection from hidden neuron j to the post-synaptic neuron k
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52. Calculate And Apply Weight Adjustments
Compute weight adjustments DWji at time t by DWji(t)= η dpj Opi
Apply weight adjustments according to Wji(t+1) = Wji(t) + DWji(t)
Some add a momentum term a*DWji(t-1)
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53. An Example: Exclusive “OR”
Training set
((0.1, 0.1), 0.1)
((0.1, 0.9), 0.9)
((0.9, 0.1), 0.9)
((0.9, 0.9), 0.1)
Testing set
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54. An Example (continued): Network Architecture
inputs
output(s)
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55. An Example (continued): Network Architecture
Target output
0.9
0.1
Sample input 1
0.9 1 1
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56. Feedforward Network Training by Backpropagation: Process Summary
Select an architecture
Randomly initialize weights
While error is too large
Select training pattern and feedforward to find actual network output
Calculate errors and backpropagate error signals
Adjust weights
Evaluate performance using the test set
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57. An Example (continued): Network Architecture??
Actual output
???
Target output
0.9
0.1 ?? ??
Sample input 1 ??
0.9 ?? 1 ?? 1
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59. iteration
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60. iteration
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61. Supervised Learning in ANNs
If an ANN has too few neurons, it may not have enough degrees of freedom to precisely approximate the desired function.
If an ANN has too many neurons, it will learn the exemplars perfectly, but its additional degrees of freedom may cause it to show implausible behavior for untrained inputs; it then presents poor ability of generalization.
Unfortunately, there are no known equations that could tell you the optimal size of your network for a given application; you always have to experiment.
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62. Rainfall Forecasting Test
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63. RainFall
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64. Rainfall Forecasting Test
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65. Hybrid Weather Prediction
Two basic methods to predict weather:
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.
In practice, hybrid methods used: Models + Observations
Statistical methods infer estimated expected distributions under specified conditions. Theoretical distributions are fit to scanty data, e.g. normal distributions.
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66. Hybrid Weather Prediction
Hybrid methods = Models + Observations
Statistical methods infer estimated expected distributions under specified conditions.
Theoretical distributions are fit to scanty data, e.g. normal distributions.
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67. Hybrid Forecast Decision Support Systems
Hybrid forecast system development is a current direction of the Aviation Weather Research Program
AWRP Terminal Ceiling and Visibility Product Development Team (PDT) project, Consensus Forecast System, a combination of:
a physical column model
Statistical forecast models, local and regional
Satellite statistical forecast model
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68. Hybrid Forecast Decision Support Systems
AWRP National Ceiling and Visibility PDT research initiatives
Data fusion: intelligent integration of output of various models, observational data, and forecaster input using fuzzy logic
Data mining, C5.0 pattern recognition software for generating decision trees based on data mining
Analog forecasting using Euclidean distance development of daily climatology.
Incorporate AutoNowcast of weather radar.
Incorporate satellite image cloud-type classification algorithms.
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70. 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
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.
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71. Weather Radar Nowcasts Graphic User Interface
Intelligent Weather Systems
Weather Radar Nowcasts
RAP, Thunderstorm Auto-Nowcasting,
Human Input (> 15 min)
Graphic User Interface
AI works here
Real-Time Data Algorithms
Real-Time Data Preprocessing
Fuzzy Logic Integration Algorithm
Sensor Systems
Quality Control
Product Generator
User
Model Output Algorithms
Data Assimilation Mesoscale Model
Selective Climatological Input
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72. Conclusion Many decision makers who are responsible for
outdoor activities, transportation and travel planning.
When weather can seriously influence the
operations of an organization, precisely prediction weather information on which to base management decisions that avoid injury, mitigate weather-related risk and leverage
knowledge to competitive advantage.
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73. End of Presentation
QUESTIONS?
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