Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.
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Transcript of Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.
Autonomous Polar Atmospheric Observations
John J. CassanoUniversity of Colorado
Research Topics for Improved NWP• Atmospheric dynamics and physics
– Cloud processes– Radiative transfer– Turbulence and boundary layer processes– Surface energy budget– Mesoscale circulations
• polar lows, topographically forced flows• Coupling of atmosphere with other climate system
components– Ex. atmosphere-ocean-sea ice coupling
• NWP model evaluation• NWP data assimilation
Needed Observations
• Atmospheric state• Surface and aloft
• Boundary layer properties• Surface energy budget
• Turbulent and radiative fluxes• Clouds• Precipitation• Non-atmospheric features
• Sea ice, snow cover, etc.
Autonomous Observing Systems
• Automatic weather stations (AWS)• Unmanned aerial vehicles (UAV)
Automatic Weather Stations
– Measurements:• Observations of temperature, pressure, wind, humidity• Additional observations at some sites
– Network:• Need observations over a broad area to get a
representation of different regions• Higher spatial resolution networks may be needed for
specific meteorological studies• Surface observations can be made with AWS• Upper air observations (esp. in the Antarctic and over the
Arctic Ocean) are more problematic
Draft
Unmanned Aerial Vehicles
• Lower cost than manned research flights– But cost can vary from $1-10k to over $1M
• Fly under adverse weather conditions– Ex. Antarctic night
• Use for mesoscale and boundary layer studies
Polar Boundary Layer
• Polar boundary layers are poorly represented in NWP models
• Important for topographically forced flows• Important air-sea exchange for polar lows• UAVs provide one option for detailed
boundary layer measurements
WingspanWingspan 3 meters3 meters
WeightWeight 15 kg15 kg
Payload Payload CapacityCapacity
2-5 kg2-5 kg
EnduranceEndurance 12-17+ hrs12-17+ hrs
RangeRange 1000+ km1000+ km
AltitudeAltitude 100-6000 m100-6000 m
Communications via 900 MHz radio and IridiumCommunications via 900 MHz radio and Iridium
Flies in fully autonomous mode with user-controlled capability Flies in fully autonomous mode with user-controlled capability
AerosondeAerosonde UAV
Wind Speed/DirectionWind Speed/Direction Pitot with GPSPitot with GPS
RH/Temp/PressureRH/Temp/Pressure Standard Radiosonde Met Standard Radiosonde Met SensorsSensors
Ocean /Ice Skin Ocean /Ice Skin TemperatureTemperature
Infrared ThermometerInfrared Thermometer
Ocean/Ice Visible Ocean/Ice Visible ImageryImagery
Still Digital CameraStill Digital Camera
Net Shortwave Net Shortwave RadiationRadiation
PyranometerPyranometer
Net Longwave Net Longwave RadiationRadiation
PyrgeometerPyrgeometer
RH/T/P/wind profilesRH/T/P/wind profiles DropsondesDropsondes
Altitude and Surface Altitude and Surface WavesWaves
Laser AltimeterLaser Altimeter
Aerosonde MeasurementsAerosonde Measurements
The Challenges
• Cold temperatures– Impacted:
• Engine• Parts failure
• Communication failures• Wind
– Take-off / landing– In flight winds
• Aircraft icing
Temperature
100-600 m layer: ~2 K warmingSHF Profile 1-2: ~580 W/m2 (10.6 km)SHF Profile 2-3: ~400 W/m2 (11.8 km)SHF Profile 3-4: ~60 W/m2 (24.1 km)SHF Profile 1-4: ~250 W/m2 (46.5 km)
Relative Humidity
100-600 m layer: 125% inc. in specific humidity
LHF Profile 1-2: ~90 W/m2 (10.6 km)LHF Profile 2-3: ~140 W/m2 (11.8 km)LHF Profile 3-4: ~80 W/m2 (24.1 km)LHF Profile 1-4: ~100 W/m2 (46.5 km)
Wind Speed
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
SUMO: Atmospheric profiling
http://www.gfi.uib.no/
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
SUMO operation
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
SUMO measurement sites: Spitsbergen
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
LYR old aurora station, 31.03.-01.04.2009
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
WRF model validation
© J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010
WRF model validation – “cold” cases
Old Auroral Station LYR airport
Model evaluation• Need to evaluate models on several scales
- At largest scales can compare to reanalyses
- At smaller scales can compare model to point observations
Model Evaluation: Physical Processes
It is important to not only evaluate the model state but to evaluate if the model reproduces observed relationships between variables
Conclusions
• Automatic weather stations– Provide broad coverage– Install dense networks for focused studies– Lack of data over oceans / sea ice– Provide important information for model
evaluation– Observations for data assimilation
• Need accurate elevation for pressure assimilation
Conclusions
• Unmanned aerial vehicles– Can provide mesoscale and
boundary layer observations– Cost can range from inexpensive ($1-10k) to very
expensive ($1M)– Useful for IOPs, more difficult for long term use– Potential for targeted obs for data assimilation
• Model evaluation– Evaluate model state as well as processes