1. “OLYMPEX” Physical validation Precipitation estimation Hydrological applications Field Experiment Proposed for November-December 2014 4th International Workshop for GPM Ground Validation 21-23 June 2010, Helsinki, Finland R. Houze & S. Medina

2. The Olympic Peninsula is a natural “precipitation laboratory” Persistent southwesterly flow during the winter provides a reliable source of moisture Extremely large precipitation accumulation produced as the moist SWly flow impinges on coastal terrain Low 0ºC level  rain at low elevations, snow at higher levels

3. The Olympic Peninsula is a natural “precipitation laboratory” Persistent southwesterly flow during the winter provides a reliable source of moisture NCEP long-term mean sea level pressure (mb) for winter (December to January) and topography

4. The Olympic Peninsula is a natural “precipitation laboratory” Extremely large precipitation accumulation produced as the moist SWly flow impinges on coastal terrain Annual average precipitation (PRISM) Maximum

5. The Olympic Peninsula is a natural “precipitation laboratory” Low 0ºC level  rain at low elevations, snow at higher ones Distribution of Nov-Jan 0°C level for flow that is onshore and moist at low levels (KUIL sounding) Mean 0°C level during storms = 1.5 km See this full range in individual storms! Frequency of occurrence 0°C level Plot provided by Justin Minder

6. Resources and experience in the region 1965-2000: Cascade Project, CYCLES, COAST 2001: IMPROVE field experiment 2004-2008: Detailed observing network across a southwestern Olympics ridge 2009: NOAA Mobile Atmospheric River Monitoring System in Westport 2011/12: NWS Coastal radar expected to be in place Ongoing: Regional Environmental Prediction

7. Resources and experience in the region 2001: IMPROVE-1 field experiment (Stoelinga et al. 2003) Coastline 

8. Resources and experience in the region 2001: IMPROVE-2 field experiment (Stoelinga et al. 2003) Woods et al. 2005

9. 2004-2008: Detailed observing network across a southwestern Olympics ridge (Minder et al. 2008) Resources and experience in the region Detailed gauge network SNOTEL RAWS sites COOP site Anemometers Disdrometers

10. Resources and experience in the region 2009: NOAA Mobile Atmospheric River Monitoring System in Westport  Time Height Signal-to- noise ratio Radial velocity Data from vertically-pointing S-band radar

11. Resources and experience in the region 2011/12: NWS Coastal radar expected to be in place Dark gray areas indicate regions where the 0.5° elevation scans are blocked Example of Olympic Mountain slopes views from coastal radar Current radar coverage Radar coverage with coastal radar

12. Resources and experience in the region Ongoing: Regional Environmental Prediction-- WRF, hydrology, air quality, etc (Mass et al. 2003) Real-time mesoscale numerical simulations dx = 4 kmdx = 36 km

13. Resources and experience in the region Ongoing: Regional Environmental Prediction-- WRF, hydrology, air quality, etc (Mass et al. 2003) Runs in ensemble mode 1.33 km spatial resolution coming soon

14. Resources and experience in the region Ongoing: Regional Environmental Prediction-- WRF, hydrology, air quality, etc (Mass et al. 2003) Verified by gauges: Minder et al. 2008 Long period of continuous mesoscale simulations provides model climatology e.g., 5-yr MM5 Nov-Jan precipitation climatology (mm)

15. Resources and experience in the region Ongoing: Regional Environmental Prediction-- WRF, hydrology, air quality, etc (Mass et al. 2003) Ensemble forecasting  probabilistic information e.g., probability that the precipitation accumulated in a 3 h period > 0.1in

16. Resources and experience in the region Ongoing: Regional Environmental Prediction-- WRF, hydrology, air quality, etc (Mass et al. 2003) Hydrological prediction: Mesoscale numerical output drives a distributed hydrological model  basin streamflow forecast

17. GPM components that are feasible to address in the Olympic Peninsula Physical validation of algorithms Rain and snow measurement Hydrological applications

18. Physical validation (i.e. experimentation with physical assumptions in GPM algorithms) a.How well do retrieval algorithms handle transitions from rain to snow on sloping terrain? b.How does the melting layer affect algorithm performance? c.How do algorithms perform in different sectors of storms passing over mountains and in different types of precipitation?

19. Precipitation estimation (i.e. validation of its accuracy from satellite instruments mounted on aircraft) a.Do precipitation algorithms give realistic rainfall transition from ocean to land? b.Do precipitation algorithms yield accurate orographic enhancement of rain amounts? c.How can satellite rain measurements be downscaled accurately relative to the topography?

20. Hydrological applications (i.e. testing the efficacy of GPM to improve streamflow forecasting in complex terrain) a.Can satellite rain estimates over mountains provide useful input to real-time hydrologic forecasting? b.Does downscaling relative to topography improve hydrologic forecasting? c.Can assimilation of satellite rain estimates into regional forecasting models improve hydrological forecasts?

21. Possible field experiment configuration NPOL would have an unimpeded view of the Quinault valley and the Olympic mountains DC8 ER2 GH P3

22. Conclusions The Olympic Peninsula is an ideal natural laboratory –Persistence of moist flow –complex terrain –huge precipitation amounts –low 0°C level Existing and planned resources plus past experience –provide a strong framework for a field campaign Crucial NASA & NOAA facilities could be added –NPOL, aircraft, profilers, etc Can address –Physical validation of GPM algorithms –Rain and snow measurement –Hydrological applications

24. This research was supported by NSF Grant ATM-0820586 and NASA Grants NNX07AD59G and NNX10AH70G