The document describes an algorithm for short-term temperature and hourly precipitation prediction using neural networks. It involves collecting data from various weather stations, preprocessing the data by interpolating missing values and normalizing it. The data is then segmented and fed into separate neural networks for temperature and precipitation prediction. Different combinations of input variables, network architectures and hyperparameters are tested. The best model is selected and used to make online predictions through a web interface. Future work may involve improving prediction accuracy through additional modeling techniques.

1. Short Term Temperature and Hourly
Precipitation Prediction System
Nithyakumaran Gnanasekar
Under the Guidance of
Arthur Helmicki, Victor Hunt and Paul Talaga

2. Agenda
•Motivation
•Background
•Suggested Algorithm
•Results and discussion
•Future Work

3. Motivation
• During winter, ice accumulates onto the bridge stays,
which later drops down on the traffic when the
temperature increases.
• In Dec 19 2012 one such incident was occurred,
where a huge piece of ice to crashed on a vehicle’s
windshield
• Huge amount of multi dimensional data is collected
from the bridge and local weather station every few
minutes.
• UCII has designed and developed a warning system
providing timely warning and alert messages.

4. Background
• Prediction System can be classified into
• Univariate models
• Multivariate models
• The models can be further classified into
• Statistical method
• neural network based methods.
• Use of pre trained neural networks to provide online prediction is
exploited in this work.
• Once the network is trained the multivariate prediction can be
performed in real time.

5. Background
List of Inputs
Temperature
Humidity
Wind speed
Solar Radiation
Hourly Precipitation
Input
5*24= 120 Input Neurons
Output
8 Temperature neurons
Jain, Abhishek. Predicting air temperature for frost warning using artificial neural networks. Diss. uga, 2003
• Jain, Abhishek in his work used single layer neural
network to predict temperature for next eight
hours
• The Temperature Prediction was performed only
during early spring during sunrise.
• To warn the farmers about morning frost that
damage orchids

6. Suggested Algorithm
• Temperature Prediction System
• Hourly Precipitation Prediction
System
• Data Collection
• Data Segmentation
• Neural Network Design
• Train Network
• Decay and Weights
• Input/output variable selection
• Predict Data
• Algorithm
• Variable Selection
• Stations Selection
• Number of hours of input data
• Output representation
• Bagging

7. Algorithm- Temperature Prediction System
Data Collection Segment Data Train
Network
Predict SectionTrain Section
Predict Data Web Interface

8. Data Collection
Stations Type Time Interval
Local Weather Stations 5,10,12,15 min
Airports 60 – 30 min
On Bridge Sensors 10,15 min
Data Collection:
• Data is collected at irregular Time intervals
• Cubic Spline interpolation is used to fix
missing values
• The data is aggregated to be hourly
frequency.
Station 1
Station 2
Station 15
South Tower
Cubic Spline Imputation
Aggregate to Hourly Data
.
.
.
.
.
.
.
.
.DB
Stations, Airports, Sensors

9. Algorithm- Temperature Prediction System
Data Collection
Segment Data Train
Network
Predict Data Web Interface.
.
Training Section Cubic Spline Interpolation
Predict Section

10. Segment Data
Data
-7 to 0
0-15
15-28
28-34
Hourly Aggregated data Classifier
Normalized data with Min Max Information
Normalize
Normalize
Normalize
Normalize
Data
Data
Data
Data
• The Hourly Aggregated Data from Data
Collection section is used as input.
• The Data is first segmented into four buckets.
• This is accordance with neural network design.
• The Mean temperature for every 24 hours is
calculated and is used for classification
• Every variable is then normalized to read
between 1-0.
• The Min Max information required for de-
normalization is attached along with the data.
Temperature
Hourly
Precipitation Humidity
Wind
Speed
Solar
Radiation
Max 7.016667 3.704762 97 35.30526 643.75
Min -10.1696 0 35.1875 0 0
Min Max Information Stored along with Data

11. Expert System Design
-7 to 0
0-15
15-28
28-34
Classifier
Output
The Classifier sends the input based on
mean temperature to corresponding
network.
Every input is given to only one neural
network for training.
Only one output is computed at any
given time.
This system performs well and reduces
error due to large denormalization
* All four Blocks are classical back propagation network with one hidden layer

12. Algorithm- Temperature Prediction System
Data Collection
Segment Data Train
Network
Predict Data Web Interface
Training Section Cubic Spline Interpolation Segment Data
Predict Section Normalized Data with Min Max info
Segment Data
.
.

13. Train Network
• The train network can be tuned for
parameter
• Decay
• Number of weights
• The network splits the training set into two set
• 90% data for Training
• 10% for evaluating.
• The 10% of evaluation sample is a
random set picked from training set.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
RMSE(C)
Decay Parameter
Decay Parameter
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
0 5 10 15 20 25 30 35
RMSE(C)
Number of Neurons
Number of Neurons

14. Train Network
• The decay and weights can be
provided as sequence.
• The network would iterate through
the combination
• Provide a report on such a data set. S.no neuron decay rmse sse maxerror meanerror
1 1 1.00E-01 2.290552 23588.85 8.499794 0.30368227
2 1 1.00E-05 2.572076 29743.62 10.92812 -0.09750547
3 1 1.00E-02 2.472744 27490.63 10.154952 -0.07979373
4 2 1.00E-01 2.283635 23446.58 8.616908 0.49384269
5 2 1.00E-05 3.73943 62869.07 14.439244 -0.97010542
6 2 1.00E-02 2.284121 23456.56 8.953333 -0.08007818
7 3 1.00E-01 2.157424 20926.54 7.961871 0.39193959
8 3 1.00E-05 6.009436 162365.5 20.995079 2.84870665
9 3 1.00E-02 2.322157 24244.28 11.334819 0.87290343
10 4 1.00E-01 2.143207 20651.63 7.892792 0.36539226
11 4 1.00E-05 3.81584 65464.6 12.887219 -0.53080024
12 4 1.00E-02 2.210153 21961.95 10.485526 0.57920437
13 5 1.00E-01 2.079384 19439.97 7.621765 0.32835461
14 5 1.00E-05 3.700756 61575.39 12.738093 0.48186848
15 5 1.00E-02 2.173325 21236.15 10.482595 0.32212886
16 6 1.00E-01 2.047381 18846.18 7.54331 0.28953601
17 6 1.00E-05 5.057563 115002.9 17.145608 0.18968488
18 6 1.00E-02 2.102648 19877.39 7.163767 -0.17827613
19 7 1.00E-01 2.064283 19158.65 7.525051 0.26114947
20 7 1.00E-05 4.861489 106258.8 12.917473 -1.98795079
$net
a 144-10-8 network with 1538 weights options were - decay=0.1
$minMax
row Temperature HourlyPrecipitation Humidity WindSpeed SolarRadiation diff
1 Max 7.016667 3.704762 97.0000 35.30526 643.75 2.413333
2 Min -10.169565 0.000000 35.1875 0.00000 0.00 -2.520000
$bestNet
"Best selected configuration has hidden neuron 10 and decay 0.1"
$rmse 2.290552
Network
Description
Min Max
Information Chosen
Network
Configuration
Detailed
Report for all
combination

15. Neural Network Training Section Cubic Spline Interpolation Normalized Data with Min Max info
Online Prediction and Web Section Trained Neural Network Segment Data
Algorithm- Temperature Prediction System
Data Collection
Train
Network
Predict Data Web Interface
Segment Data Train Network
Segment Data.
.

16. Output/Input Selection
• In the previous works listed in the
background
• All the variables collected were fed to
neural network as input.
• No were performed on the correlation
between the variables.
• Correlation analysis between the variables
led to using better set of input and in
improving prediction result.

17. Correlation Plot Between Variables
http://10.39.8.247/predict/correlation.php#tabs-1

18. Temperature Difference Correlation plot
http://10.39.8.247/predict/correlation.php#tabs-2

19. Output/Input Selection
List of Inputs
Temperature
Hourly Precipitation
Solar Radiation
Pressure
Wind Speed
Humidity
Temperature Difference
Input
7*24= 168 Input Neurons
Output
8 Temperature Difference neurons
AfterCorrelationAnalysisandsimulation
http://10.39.8.247/predict/TemperatureDiff.php
0
5
10
15
20
25
30
Numberofneurons
Neuron
0.39
0.4
0.41
0.42
0.43
0.44
0.45
0.46
0.47
(C)
Root Mean Square Error for Variable Combination

20. Neural Network Training Section Cubic Spline Interpolation Normalized Data with Min Max info
Online Prediction and Web Section Trained Neural Network Segment Data
Algorithm- Temperature Prediction System
Data Collection
Predict Data Web Interface
Segment Data Train Network
Train
Network
Segment Data.
.

21. Predict Data
[1] [2]Data Classifier
[1] Data Collection Section [2] Data Segmentation Section Trained Neural Network
data w/o minMax Normalized Data with MinMax
Data
• Last 24 hours of data is picked. It is passed through data
collection section
• This data is then sent to classifier to identify the
appropriate Neural network
• This provides the minMax Information for normalization
• The data is then passed thorough segmentation section
• This is then provided to network to predict newer values
*Note Every Hour has 8 predicted values

22. Neural Network Training Section Cubic Spline Interpolation Normalize Data Multiple Sections
Online Prediction and Web Section Trained Neural Network Segment Data
Algorithm- Temperature Prediction System
Data Collection
Web Interface
Segment Data Train Network
Train
Network
Segment Data.
.

23. Web Interface
• Configuration
• PHP 5.4
• Zend Framework
• D3.js for plotting
• Uses MYSQL Database
• Complaint with UCII site.

24. Algorithm- Temperature Prediction System
Data Collection Segment Data Train Network Predict Data Web Interface
Train
Network
Segment Data.
.
Neural Network Training Section Cubic Spline Interpolation Normalize Data Multiple Sections
Online Prediction and Web Section Trained Neural Network Segment Data

25. Suggested Algorithm
• Temperature Prediction System
• Hourly Precipitation Prediction
System
• Data Collection
• Data Segmentation
• Neural Network Design
• Train Network
• Decay and Weights
• Input/output variable selection
• Predict Data
• Algorithm
• Variable Selection
• Stations Selection
• Number of hours of input data
• Output representation
• Bagging

26. Algorithm- Hourly Prediction System
Data Collection Train Network Predict Data Web Interface
Train
Network
Normalize Data.
.
Neural Network Training Section Cubic Spline Interpolation Multiple Sections
Online Prediction and Web Section Trained Neural Network Aggregate Data

27. Variable Selection-Correlation
• Output
• 4 hours of Probability data for
Hourly Precipitation
http://10.39.8.247/predict/correlation.php#div-1
All Variables
Temperature
Humidity
Dew point
Pressure
Wind Speed
Wind Speed Gust
Wind Direction
Wind Direction Degree
Daily Rain
Hourly Precipitation
Selected Variables
Hourly Precipitation
Humidity
Wind Direction
Daily Rain
Solar Radiation
Temperature

28. Cross Correlation Between Variables
HP Vs Humidity HP Vs Pressure HP Vs Solar Radiation
HP Vs Temperature HP Vs Wind Direction Degree HP Vs Wind Speed
*HP- Hourly Precipitation

29. Variable Selection-Cross Correlation
HP Vs Humidity HP Vs Pressure HP Vs Solar Radiation
HP Vs Temperature HP Vs Wind Direction Degree HP Vs Wind Speed
Selected Variables
Hourly Precipitation
Humidity
Wind Direction
Daily Rain
Solar Radiation
Temperature
All Variables
Temperature
Humidity
Dew point
Pressure
Wind Speed
Wind Speed Gust
Wind Direction
Wind Direction Degree
Daily Rain
Hourly Precipitation
Selected Variables
Hourly Precipitation
Humidity
Daily Rain
Wind Direction
Correlation
Cross Correlation
*Daily Rain is Picked for further analysis as it similar to Hourly Precipitation

30. Simulation –Variable Combination
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
HP-WD-DR-T
HP-P-WD-H-T
HP-P-WD-T
HP-WD-H-T
HP-SR-P-WS
HP-P-WD-DR-T
HP-SR-P-WD-WS
HP-P-WS-DR
HP-T
HP-H-T
HP-SR-WS-H-DR
T-HP-SR-P-WD-WS-DR
HP-SR-WS-H-T
HP-SR-P-WS-H
HP-WS-DR-T
HP-WD-DR
HP-SR-T
T-HP-SR-P-WD-H-DR
HP-P-WD
HP-SR-WD-WS-DR-T
HP-SR-P-WD-WS-T
HP-SR-P-WD-H-T
HP-WS-H-DR-T
HP-SR-H-DR-T
HP-SR-P-H-T
HP-P-WD-H
HP-P-WS
HP-SR-P-H
HP-P-T
HP-SR-P
HP-P-WD-WS-H-T
HP-SR-WD-H-DR-T
MeanAbsoluteError
Mean Absolute Error
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0 1 2 3 4 5 6 7 8
MeanAbsoluteError
Number of Variables used
Mean Absolute Error
• List of Selected Variables Combinations
• HP-WD-H-DR
• HP-WD-DR-T
• HP-WD-H-DR-T
• HP-P-WD-WS-DR
• HP-P-WD-H-T
• Legend
• T- Temperature
• HP- Hourly Precipitation
• WD- Wind Direction
• WS- Wind Speed
• P- Pressure
• H- Humidity
• DR- Daily Rain
• SR- Solar Radiation
• HP-SR-WD-DR
• HP-P-WD-DR
• HP-P-WD-WS-H
• HP-P-WD-T
• HP-WD-WS-H-T

31. Variable Selection-Simulation
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
HP-WD-DR-T
HP-P-WD-WS-DR
HP-P-WD-DR
HP-WD-WS-H-T
HP-WD-H-T
HP-WD-WS-DR-T
HP-P-WD-H-DR
HP-SR-H-DR
HP-SR-P-WD-WS
HP-SR-DR-T
HP-SR
HP-SR-WS-DR
HP-H-T
HP-SR-WS-DR-T
HP-SR-WD-WS
HP-H
HP-SR-WS-H-T
HP-SR-WD-H-DR
T-HP-SR-P-WS-…
HP-SR-P-WD-H
HP-WD-DR
HP-SR-P-WD-…
HP-SR-WS-H-DR-T
HP-P-WS-H-T
HP-P-WD
HP-SR-H
HP-SR-P-WD-DR-T
HP-SR-WD-WS-…
HP-SR-P-WD-H-T
HP-WD-WS
HP-P-H-DR
HP-SR-P-H-DR
HP-SR-P-H-T
HP-WD-WS-H-DR
HP-WD-WS-H
HP-SR-WS-H
HP-SR-P-H
HP-H-DR
HP-P-H-DR-T
HP-WD
HP-P-WD-WS-H-T
HP-SR-P-WS-DR-T
MeanAbsoluteError
Mean Absolute Error
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0 1 2 3 4 5 6 7 8
MeanAbsoluteError
Number of Variables used
Mean Absolute ErrorSelected Variables
Hourly Precipitation
Humidity
Wind Direction
Daily Rain
Solar Radiation
Temperature
All Variables
Temperature
Humidity
Dew point
Pressure
Wind Speed
Wind Speed Gust
Wind Direction
Wind Direction Degree
Daily Rain
Hourly Precipitation
Selected Variables
Hourly Precipitation
Humidity
Daily Rain
Wind Direction
Selected Variables
Hourly Precipitation
Humidity
Daily Rain
Wind Direction
Correlation
Cross Correlation
Simulation

32. Stations that can be used as Good Predictors
0
5
10
15
20
25
-7 -6 -5 -4 -3 -2 -1
Burna10
CWWK
Ibccoqui5
Ibcpittm3
Surre10
Surre6

33. Cross Correlation Between Stations for Hourly Precipitation
SURRE21-COQUI5 SURRE21-SURRE6 SURRE21-PITTM3
SURRE21-BURNA 10 SURRE21-SURRE10

34. Cross Correlation Between Stations for Hourly Precipitation- Daily Rain
SURRE21-Surre10 SURRE21-Surre6
SURRE21-COQUI5 SURRE21-BURNA10

35. Cross Correlation Between Stations for Hourly Precipitation- Humidity
SURRE21-BURNA10 SURRE21-CWMM SURRE21-CWWK
SURRE21-COQUI5 SURRE21-SURRE6 SURRE21-SURRE10

36. Input Selection –Station Combination
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0 1 2 3 4 5 6 7
MAE
Number of Input Variables
MEAN Absolute Error
Best Combination
Surre21,Burna 10, Ibccoqui5
Surre21,Burna 10, PITM3
Surre21,PITM3, Surre6
0
0.05
0.1
0.15
0.2
0.25
0.3
Sur21-NA10-IBC5
Sur21-PIT3-Sur10-CWMM
Sur21-NA10-IBC5-PIT3-Sur6
Sur21-NA10-PIT3-Sur10-CWMM-CWWK
Sur21-NA10-IBC5-Sur10-Sur6
Sur21-NA10-IBC5-PIT3-Sur6-CWWK
Sur21-NA10-PIT3-Sur6-CWMM-CWWK
Sur21-NA10-IBC5-Sur6-CWWK
Sur21-IBC5-Sur10-Sur6
Sur21-IBC5-Sur6
Sur21-IBC5-PIT3-Sur10-Sur6
Sur21-NA10-IBC5-PIT3-Sur6-CWMM
Sur21-IBC5-Sur10-Sur6-CWWK
Sur21-Sur10-CWMM-CWWK
Sur21-PIT3-Sur10-CWWK
Sur21-IBC5
Sur21-NA10-IBC5-PIT3-Sur10
Sur21-NA10-PIT3-CWMM
Sur21-IBC5-PIT3-Sur10-CWWK
Sur21-IBC5-CWMM
Sur21-NA10-PIT3-Sur10-CWWK
Sur21-Sur10-Sur6-CWWK
Sur21-IBC5-PIT3-Sur6
Sur21-Sur6-CWMM
Sur21-IBC5-Sur10-CWWK
Sur21-PIT3-Sur10-Sur6-CWMM
Sur21-IBC5-Sur6-CWMM
Sur21-NA10-Sur10-CWWK
Sur21-IBC5-Sur6-CWWK
Sur21-IBC5-Sur6-CWMM-CWWK
Sur21-Sur10-Sur6-CWMM
Sur21-NA10-Sur10-CWMM-CWWK
Sur21-NA10-IBC5-PIT3-CWMM
Sur21-Sur10-CWMM
Sur21-PIT3
MeanAbsoluteError
Combination Plot for Stations

37. Number of Hours of Input Data
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0 5 10 15 20 25 30
MAE
Number of hours
Number of Input hours of data required
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
0 5 10 15 20 25 30 35
MAE
Number of Neurons
Number of Neurons
• Number of Hours input data- 24
• Number of hidden Neurons=13

38. Input/Output Representation
• Input is 24 hour Normalized data from three stations and 3 variables from
the stations.
• Output is 4 variable. Each variable indicating probability of rain for every
hour ahead.
Date Time Hour1 Hour2 Hour3 Hour4
… 0 0 0 0
…. 0 1 0 0
… 1 0 0 0
…. 0 0 0 1
• Sample Actual Output Data Used for Training
• Rainfall>1(mm) ? Output=1 : Output =0
2^4= 16 different Combinations of possible Output
Distribution of Actual Output Classes
2011-2013

39. Bagging
• Bagging is way of
boosting the system
performance by
altering the class
distribution in the
training set.
• This ensures
prediction is not
always skewed
towards the largest
occurring class

40. Agenda
•Motivation
•Background
•Suggested Algorithm
•Results and discussion
•Future Work

41. Results –Temperature Prediction System
Evening Morning On a Rainy day
Before rainfall During Snowfall

42. Error Plot For Jan ‘14 to March ‘14
y = 0.9791x + 0.1056
RMSE=0.569079
-10
-5
0
5
10
15
20
-10 -5 0 5 10 15 20
PredictedTemperature(C)
Actual Temperature (C)
Predicted Temperature 1-2 (hour)
y = 0.9519x + 0.2233
RMSE= 0.875618
-10
-5
0
5
10
15
20
-10 -5 0 5 10 15 20
ActualTemperature(C)
Predicted Value (C)
Predicted Temperature 3-4 th (hour)
y = 0.927x + 0.3126
RMSE=1.083171
-10
-5
0
5
10
15
20
-10 -5 0 5 10 15 20
ActualTemperature(C)
Predicted Temperature (C)
Predicted Temperature 5-6 (hour)
y = 0.9022x + 0.4126
RMSE=1.302739
-10
-5
0
5
10
15
20
-10 -5 0 5 10 15 20
ActualTemperature Predicted Temperature (C)
Predicted Temperature 7-8(hour)

43. Error Plots
Jan 14
April 14
Feb 14
Jan- Apr 14

44. Hourly Precipitation Results
0
0.5
1
1.5
2
2.5
3
0
10
20
30
40
50
60
70
80
90
100
12:00AM
1:00AM
2:00AM
3:00AM
4:00AM
5:00AM
6:00AM
7:00AM
8:00AM
9:00AM
10:00AM
11:00AM
12:00PM
1:00PM
2:00PM
3:00PM
4:00PM
5:00PM
6:00PM
Precipitation(mm)
Probability(%)
Low Rain 1/7/2014
0
0.5
1
1.5
2
2.5
3
3.5
4
0
10
20
30
40
50
60
70
80
90
100
2:00AM
3:00AM
4:00AM
5:00AM
6:00AM
7:00AM
8:00AM
9:00AM
10:00AM
11:00AM
12:00PM
1:00PM
2:00PM
3:00PM
4:00PM
5:00PM
6:00PM
7:00PM
8:00PM
Precipitation(mm)
Probability(%)
Average Rain 1/2/2014
0
1
2
3
4
5
6
7
8
0
20
40
60
80
100
Precipitation(mm)
Probability(%)
Heavy Rain 1/11/2014
0
0.5
1
1.5
2
2.5
3
0
10
20
30
40
50
60
70
80
90
100
6:00AM
7:00AM
8:00AM
9:00AM
10:00AM
11:00AM
12:00PM
1:00PM
2:00PM
3:00PM
4:00PM
5:00PM
6:00PM
7:00PM
8:00PM
9:00PM
10:00PM
11:00PM
Precipitation(mm)
Probability(%)
Snow Fall 2/22/2014
Legend
Precipitation(mm)- Right axis
Probability (%) -Left axis

45. Error Plot
Predicted
Error Computed for Random Sample data 2011-2013
*This data was not used for Training
Value 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Output Class 0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
Predicted
actual actual
counts percentage

46. Agenda
•Motivation
•Background
•Suggested Algorithm
•Results and discussion
•Future Work

47. Future Work
• Extending Hourly Precipitation algorithm to other stations and bridge
sensors
• Study of using predicted data as an input to another prediction to
creating a feed back network.
• Using radar data to predict hourly precipitation to increase accuracy.

48. Questions?

49. Thank you

50. Additional Materials

51. Hourly Precipitation Vs Humidity
6-23-13
5-11-13
1-3-13

52. Hourly Precipitation Vs Pressure
6-23-13
5-11-13
1-3-13

53. Hourly Precipitation Vs Solar Radiation
6-23-13
5-25-13
1-3-13

54. Hourly Precipitation Vs Temperature
6-23-13
5-25-13
1-3-13

55. Hourly Precipitation Vs Wind Direction
6-23-13
5-25-13
1-3-13
Back time lagged correlation

56. Hourly Precipitation Vs Wind Direction
Back time lagged correlation
0
50
100
150
200
250
300
350
Degree
Direction Vs Degress
2013- 6 Hours Prior to Rainfall- Wind Direction Distribution

57. Temperature Prediction - Evening Drop
-3
-2
-1
0
1
2
3
4
5
6
12:00:00PM
2:24:00PM
4:48:00PM
7:12:00PM
9:36:00PM
12:00:00AM
2:24:00AM
4:48:00AM
7:12:00AM
9:36:00AM
(C)
Temperature Prediction 1-4-14
Temperature Prediction 1 Prediction2 Prediction3 Prediction 4 Prediction 5 Prediction 6 Prediction 7 Prediction 8
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
12:00:00 PM 2:24:00 PM 4:48:00 PM 7:12:00 PM 9:36:00 PM 12:00:00 AM 2:24:00 AM 4:48:00 AM 7:12:00 AM 9:36:00 AM
(C)
Mean Error

58. Temperature Prediction- Morning Rise.
-2
-1
0
1
2
3
4
19:12
0:00
4:48
9:36
14:24
19:12
0:00
(C)
Temperature Prediction (1-6-14)
Temperature Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Predict 7 Predict 8
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0 5 10 15 20 25 30
(C)
Mean Error

59. Temperature Prediction for Complete rainy
day
6
6.5
7
7.5
8
7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00 2:24
(C)
Temperature Prediction 1-11-14
Temperature Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
7:12 9:36 12:00 14:24 16:48 19:12 21:36 0:00 2:24
(C)
Mean Error

60. Temperature Prediction Before Rainfall
0
1
2
3
4
5
0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48
(C)
Temperature Prediction 2-10-14
Temperature Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0 2 4 6 8 10 12 14 16 18 20
(C)
DateTime
Mean Error

61. Temperature Prediction During Snow
-2
-1
0
1
2
3
9:36 12:00 14:24 16:48 19:12 21:36 0:00 2:24 4:48
(C)
Temperature Prediction 02-22-14
Temperature Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0 2 4 6 8 10 12 14 16 18
(C)
Date Time
Mean Error

62. Hourly Precipitation – Low rain
0
0.5
1
1.5
2
2.5
3
0
10
20
30
40
50
60
70
80
90
100
12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM
Precipitation(mm)
Probability(%)
Rainfall Prediction 1/7/2014
Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8 Precipitation

63. Hourly Precipitation – Average rain
0
0.5
1
1.5
2
2.5
3
3.5
4
0
10
20
30
40
50
60
70
80
90
100
2:00
AM
3:00
AM
4:00
AM
5:00
AM
6:00
AM
7:00
AM
8:00
AM
9:00
AM
10:00
AM
11:00
AM
12:00
PM
1:00
PM
2:00
PM
3:00
PM
4:00
PM
5:00
PM
6:00
PM
7:00
PM
8:00
PM
PRECIPITATION(MM)
PROBABILITY(%)
Rainfall Prediction 1/2/2014
Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8 Precipitation

64. Hourly Precipitation – Heavy rain
0
1
2
3
4
5
6
7
8
0
20
40
60
80
100
4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 8:00 PM
Precipitation(mm)
Probability(%)
Rainfall Prediction 1/11/2014
Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8 Precipitation

65. Hourly Precipitation – Snow Fall
0
0.5
1
1.5
2
2.5
3
0
10
20
30
40
50
60
70
80
90
100
6:00
AM
7:00
AM
8:00
AM
9:00
AM
10:00
AM
11:00
AM
12:00
PM
1:00
PM
2:00
PM
3:00
PM
4:00
PM
5:00
PM
6:00
PM
7:00
PM
8:00
PM
9:00
PM
10:00
PM
11:00
PM
PRECIPITATION(MM)
PROBABILITY(%)
Rainfall Prediction 2/22/2014
Predict 1 Predict 2 Predict 3 Predict 4 Predict 5 Predict 6 Preict 7 Predict 8 Precipitation