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

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/


SUMO operation


SUMO measurement sites: Spitsbergen


LYR old aurora station, 31.03.-01.04.2009


WRF model validation


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

Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.

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