Climate Features Climate Features · 2013-01-26 · Atmospheric Temperature Profiles of the...
1
Modeling Atmospheric Temperature Profiles over the Arctic Climatologists concerned with global warming are familiar with the significance of research into the climate of the polar regions. Because the arctic region is the most sensitive to react to climatic changes, the modeling of atmospheric temperature profiles over the arctic region is being investigated. Mean tropospheric temperature profiles from global meteorological data sets are used to model the arctic climatology. Neural network algorithms were applied to archived radiosonde measurements, retrieved temperature profiles from remote sensing methods, standard atmosphere supplement profiles, and monthly solar insolation. For these investigations, we draw upon a wealth of observed global climate data sets which allows us to explore aspects of temperature profiles throughout the arctic seasons. From ground based and satellite observations, it has been observed that seasonal changes can produce temperature profiles that are significantly different for this high latitude geographical region. Parameterization of mean monthly tropospheric temperature profiles from radiosonde stations in the arctic are examined and specific characteristics are analyzed. Radiosonde temperature profiles from various arctic radiosonde stations were used to test the modeled temperature profile performance. ABSTRACT ABSTRACT Features of the Temperature Features of the Temperature Profile Models Profile Models Climatological temperature profile models have been developed that produce mean seasonal temperature profiles with latitudinal dependence for the arctic regions. The models were derived from neural network based algorithms that uses archived temperature profiles, retrieved temperature profiles from remote sensors, radiosonde measurements, and the solar insolation at the top of the atmosphere. Valid from sea level up to 10 kilometers atmospheric height. Conclusion Conclusion Previous research in using neural networks to retrieve atmospheric profiles from remote sensors have shown reasonable results especially in microwave temperature profile retrievals. Using archived radiosonde measurements as ground truth as well as a simulator for temperature profiler measurements, we developed a neural network to test out the feasibility of retrieving upper level temperatures. A procedural test plan has been developed to collect reliable coincident data sets at arctic weather stations closest to the north pole. WMO stations, Alert and Danmarkshavn, were chosen test sites to assess the model's performance for the four seasons. Preliminary results look promising. FOR MORE INFORMATION... POC: Young P. Yee, Mkey Technologies EMAIL: [email protected] • 27 th IIPS conference of the 91 st American Meteorological Society meeting (“A study of climatological temperature profiles in the Northern Hemisphere”, Seattle, WA, Jan 2011) •AIAA Southern New Mexico Technology Symposium, (“Barometric altimeter temperature corrections ”, Las Cruces, NM, Apr2010) •Microwave Radiometer Temperature Retrievals (E. Measure & Y.Yee, ISSSR, 1992) (Y. Yee & E. Measure, SPIE, 1992) •U.S. Standard Atmosphere, 1962, U.S. Government Printing Office, Washington, D.C., 1962. •Emmerson, C., 2010: The Future History of the Arctic , Public Affairs, New York. •Centre for Ocean and Ice -Danish Meteorological Institute-Lyngbyvej 100- 2100 Copenhagen Ø-Denmark. •Serreze, Mark C. and Roger Graham Barry, 2005: The Arctic Climate System , Cambridge University Press, New York. References References Arctic Region defined by 10 deg Isotherm Mkey Technologies, Meteorological Applications Website: www.temperatureprofiles.com Young P. Yee, Kueyson Yee, and Erik Yee Atmospheric Temperature Profiles of the Northern Hemisphere A Compendium of Data Series: Springer Atmospheric Sciences ISBN 978-94-007-4028-0 Alert, Canada (WMO-71082) RMS ERROR 1.80 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -60 -50 -40 -30 -20 -10 0 10 Temperature (Celsius) Height (meters) Temperature Profile - July 2011 Temp Profile Model Alert, Canada (WMO-71082) RMS ERROR 2.08 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -70 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - January 2011 Temp Profile Model Alert, Canada (WMO-71082) RMS ERROR 10.42 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - April 2011 Temp Profile Model Alert, Canada (WMO-71082) RMS ERROR 8.75 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - October 2011 Temp Profile Model Danmarkshavn (WMO-04320) RMS ERROR 2.86 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -80 -70 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - January 2011 Temp Profile Model Danmarkshavn (WMO-04320) RMS ERROR 4.95 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -80 -70 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - April 2011 Temp Profile Model Danmarkshavn (WMO-04320) RMS ERROR 6.22 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -60 -50 -40 -30 -20 -10 0 10 Temperature (Celsius) Height (meters) Temperature Profile - July 2011 Temp Profile Model Danmarkshavn (WMO-04320) RMS ERROR 3.94 o C 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -60 -50 -40 -30 -20 -10 0 Temperature (Celsius) Height (meters) Temperature Profile - October 2011 Temp Profile Model Danmarkshavn Alert (Reference: Wikimedia Commons) ALERT, CANADA DANMARKSHAVN, GREENLAND SAMPLE NEURAL NETWORK TRAINING ARCTIC TEST LOCATIONS METHODOLOGY METHODOLOGY Neural network techniques are being used to develop models of atmospheric temperature profiles based on latitude and seasons in the arctic region. Back-propagation neural networks are constructed using archived radiosonde upper air temperature measurements as inputs. The desired outputs are retrieving upper level tropospheric temperature profiles based on latitude and the season of the year. The network is trained with radiosonde measurements as truth-values. ... • Long, cold winters and short, cool summers •Most parts of the Arctic is covered with some form of ice (sea ice, glacial ice, snow) •Arctic region is primarily ocean moderated by ice and water •High latitude regions experience extreme variability in solar radiation in both summer and winter Climate Features Climate Features of the Arctic of the Arctic Sets From Selected Locations Collect Geographical Information Screen Data Files to Remove Incomplete and Defective Data Records Train Neural Network Interpolate Meteorological Measurements to Specific Atmospheric Height Intervals Assemble Training and Testing Sets Apply Network to Measurements at Selected Test Sites Sets From Selected Locations Collect Geographical Information Screen Data Files to Remove Incomplete and Defective Data Records Train Neural Network Interpolate Meteorological Measurements to Specific Atmospheric Height Intervals Assemble Training and Testing Sets Collect Radiosonde Data Sets From Selected Locations Collect Geographical Information Screen Data Files to Remove Incomplete and Defective Data Records Train Neural Network Interpolate Meteorological Measurements to Specific Atmospheric Height Intervals Evaluate Performance of Neural Network Assemble Training and Testing Sets
Transcript of Climate Features Climate Features · 2013-01-26 · Atmospheric Temperature Profiles of the...
Modeling Atmospheric Temperature Profiles over the Arctic
Climatologists concerned with global warming are familiar with the
significance of research into the climate of the polar regions. Because the arctic
region is the most sensitive to react to climatic changes, the modeling of
atmospheric temperature profiles over the arctic region is being investigated.
Mean tropospheric temperature profiles from global meteorological data sets are
used to model the arctic climatology. Neural network algorithms were applied to
archived radiosonde measurements, retrieved temperature profiles from remote
sensing methods, standard atmosphere supplement profiles, and monthly solar
insolation. For these investigations, we draw upon a wealth of observed global
climate data sets which allows us to explore aspects of temperature profiles
throughout the arctic seasons. From ground based and satellite observations, it
has been observed that seasonal changes can produce temperature profiles that
are significantly different for this high latitude geographical region.
Parameterization of mean monthly tropospheric temperature profiles from
radiosonde stations in the arctic are examined and specific characteristics are
analyzed. Radiosonde temperature profiles from various arctic radiosonde
stations were used to test the modeled temperature profile performance.
ABSTRACTABSTRACT
Features of the Temperature Features of the Temperature
Profile ModelsProfile Models
Climatological temperature profile models have been developed that produce mean seasonal temperature profiles with latitudinal dependence for the arctic regions.
The models were derived from neural network based algorithms that uses archived temperature profiles, retrieved temperature profiles from remote sensors, radiosonde measurements, and the solar insolation at the top of the atmosphere.
Valid from sea level up to 10 kilometers atmospheric height.
ConclusionConclusion
Previous research in using neural networks to retrieve atmospheric profiles from remote sensors have shown reasonable results especially in microwave temperature profile retrievals. Using archived radiosonde measurements as ground truth as well as a simulator for temperature profiler measurements, we developed a neural network to test out the feasibility of retrieving upper level temperatures. A procedural test plan has been developed to collect reliable coincident data sets at arctic weather stations closest to the north pole. WMO stations, Alert and Danmarkshavn, were chosen test sites to assess the model's performance for the four seasons. Preliminary results look promising.
FOR MORE INFORMATION...
POC: Young P. Yee, Mkey Technologies
EMAIL: [email protected]
• 27th IIPS conference of the 91st American Meteorological Society meeting
(“A study of climatological temperature profiles in the
Northern Hemisphere”, Seattle, WA, Jan 2011)
•AIAA Southern New Mexico Technology Symposium,
(“Barometric altimeter temperature corrections”, Las Cruces, NM, Apr2010)
•Microwave Radiometer Temperature Retrievals
(E. Measure & Y.Yee, ISSSR, 1992)
(Y. Yee & E. Measure, SPIE, 1992)
•U.S. Standard Atmosphere, 1962, U.S. Government Printing Office, Washington, D.C.,
1962.
•Emmerson, C., 2010: The Future History of the Arctic, Public Affairs, New York.
•Centre for Ocean and Ice -Danish Meteorological Institute-Lyngbyvej 100-
2100 Copenhagen Ø-Denmark.
•Serreze, Mark C. and Roger Graham Barry, 2005: The Arctic Climate System,
Cambridge University Press, New York.
ReferencesReferences
Arctic Region defined
by 10 deg Isotherm
Mkey Technologies, Meteorological Applications Website: www.temperatureprofiles.com
Young P. Yee, Kueyson Yee, and Erik Yee
Atmospheric Temperature Profiles of the Northern Hemisphere A Compendium of Data Series: Springer Atmospheric Sciences ISBN 978-94-007-4028-0
Alert, Canada (WMO-71082)RMS ERROR 1.80 oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-60 -50 -40 -30 -20 -10 0 10
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - July 2011
Temp Profile Model
Alert, Canada (WMO-71082)RMS ERROR 2.08 oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-70 -60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
Temperature Profile- January 2011
Temp Profile Model
Alert, Canada (WMO-71082)RMS ERROR 10.42 oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - April 2011
Temp Profile Model
Alert, Canada (WMO-71082)RMS ERROR 8.75oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - October2011Temp Profile Model
Danmarkshavn (WMO-04320)RMS ERROR 2.86oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-80 -70 -60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - January2011Temp Profile Model
Danmarkshavn (WMO-04320)RMS ERROR 4.95oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-80 -70 -60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - April 2011
Temp Profile Model
Danmarkshavn (WMO-04320)RMS ERROR 6.22oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-60 -50 -40 -30 -20 -10 0 10
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - July 2011
Temp Profile Model
Danmarkshavn (WMO-04320)RMS ERROR 3.94oC
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
-60 -50 -40 -30 -20 -10 0
Temperature (Celsius)
He
igh
t (m
ete
rs)
TemperatureProfile - October2011Temp Profile Model
Danmarkshavn
Alert
(Reference: Wikimedia
Commons)
ALERT, CANADA
DANMARKSHAVN, GREENLAND
SAMPLE NEURAL
NETWORK TRAINING
ARCTIC TEST LOCATIONS
METHODOLOGYMETHODOLOGY
Neural network techniques are being used to develop models of atmospheric temperature profiles based on latitude and seasons in the arctic region. Back-propagation neural networks are constructed using archived radiosonde upper air temperature measurements as inputs. The desired outputs are retrieving upper level tropospheric temperature profiles based on latitude and the season of the year. The network is trained with radiosondemeasurements as truth-values.
ConclusionConclusion
Previous research in using neural networks to retrieve atmospheric profiles from remote sensors have shown reasonable results especially in microwave temperature profile retrievals. Using archived radiosonde measurements as ground truth as well as a simulator for temperature profiler measurements, we developed a neural network to test out the feasibility of retrieving upper level temperatures. A procedural test plan has been developed to collect reliable coincident data sets at arctic weather stations closest to the north pole. WMO stations, Alert and Danmarkshavn, were chosen test sites to assess the model's performance for the four seasons. Preliminary results look promising.
...
• Long, cold winters and short, cool summers
•Most parts of the Arctic is covered with some form
of ice (sea ice, glacial ice, snow)
•Arctic region is primarily ocean moderated by ice
and water
•High latitude regions experience extreme variability
in solar radiation in both summer and winter
Climate Features Climate Features
of the Arcticof the Arctic
Apply Network to Measurements
at Selected Test Sites
Collect Radiosonde Data
Sets From Selected
Locations
Collect Geographical
Information
Screen Data Files to Remove
Incomplete and Defective
Data Records
Train Neural Network
Interpolate Meteorological Measurements to Specific
Atmospheric Height Intervals
Evaluate Performance of
Neural Network
Assemble Training and Testing Sets
Apply Network to Measurements
at Selected Test Sites
Collect Radiosonde Data
Sets From Selected
Locations
Collect Geographical
Information
Screen Data Files to Remove
Incomplete and Defective
Data Records
Train Neural Network
Interpolate Meteorological Measurements to Specific
Atmospheric Height Intervals
Evaluate Performance of
Neural Network
Assemble Training and Testing Sets
Collect Radiosonde Data
Sets From Selected
Locations
Collect Geographical
Information
Screen Data Files to Remove
Incomplete and Defective
Data Records
Train Neural Network
Interpolate Meteorological Measurements to Specific
Atmospheric Height Intervals
Evaluate Performance of
Neural Network
Assemble Training and Testing Sets
![Polar Climate Change as Manifest in Atmospheric …...tainty in projected climate change [ 4]. This review forms part of a Topical Collection on Climate Change and Atmospheric Circulation.](https://static.fdocuments.in/doc/165x107/5f47ba734b9e0419c1183f85/polar-climate-change-as-manifest-in-atmospheric-tainty-in-projected-climate.jpg)
