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

2. 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

3. 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.

4. Autonomous Observing Systems Automatic weather stations (AWS) Unmanned aerial vehicles (UAV)

5. 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

6. Draft

7. 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

8. 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

9. Wingspan 3 meters Weight 15 kg Payload Capacity 2-5 kg Endurance 12-17+ hrs Range 1000+ km Altitude 100-6000 m Communications via 900 MHz radio and Iridium Flies in fully autonomous mode with user-controlled capability Aerosonde Aerosonde UAV

10. Wind Speed/Direction Pitot with GPS RH/Temp/Pressure Standard Radiosonde Met Sensors Ocean /Ice Skin Temperature Infrared Thermometer Ocean/Ice Visible Imagery Still Digital Camera Net Shortwave Radiation Pyranometer Net Longwave Radiation Pyrgeometer RH/T/P/wind profiles Dropsondes Altitude and Surface Waves Laser Altimeter Aerosonde Measurements

11. The Challenges Cold temperatures – Impacted: Engine Parts failure Communication failures Wind – Take-off / landing – In flight winds Aircraft icing

12. Temperature 100-600 m layer: ~2 K warming SHF Profile 1-2: ~580 W/m 2 (10.6 km) SHF Profile 2-3: ~400 W/m 2 (11.8 km) SHF Profile 3-4: ~60 W/m 2 (24.1 km) SHF Profile 1-4: ~250 W/m 2 (46.5 km)

13. Relative Humidity 100-600 m layer: 125% inc. in specific humidity LHF Profile 1-2: ~90 W/m 2 (10.6 km) LHF Profile 2-3: ~140 W/m 2 (11.8 km) LHF Profile 3-4: ~80 W/m 2 (24.1 km) LHF Profile 1-4: ~100 W/m 2 (46.5 km)

14. Wind Speed

16. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 SUMO: Atmospheric profiling http://www.gfi.uib.no/

17. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 SUMO operation

18. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 SUMO measurement sites: Spitsbergen

19. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 LYR old aurora station, 31.03.-01.04.2009

20. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 WRF model validation

21. © J. Reuder, COST ES0802 Workshop, Cambridge, 22.09.2010 WRF model validation – “cold” cases Old Auroral Station LYR airport

22. 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

24. 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

25. 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

26. 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