Nexrad in Space Workshop Vision and Goals

1. Nexrad in SpaceWorkshop Vision and Goals Gregory J. Tripoli University of Wisconsin

2. Vision • To fundamentally transform our ability to observe, model and predict the space time variability of hurricanes and tropical storms through the synergistic use of revolutionary technology and science advancement.

3. 20th Century Paradigm for NWP • Deterministic Prediction of Energy Containing Waves • Initialize governing equations and advance to a future time • Horizontal grid resolution ~ 10’s of kilometers • Input data: rawisonde ~400 km horizontal spacing, 1 mb vertical spacing, 12 hr time--- resolves so-calledsynoptic- scale flow, i. e. baroclinic waves • Scales of disturbances predicted: ~100’s -1000’s km (space), ~1-6 days time • Predictability < 1 lifetime of any energy-containing disturbance disturbance, (e.g. up to 6 days for a baroclinic wave)

4. Conventional “Synoptic” Observations

5. Model Grid Spacing

6. Model Resolution

7. 20th Century Paradigm for NWP • Deterministic Prediction of Subordinate Disturbances • Initialize model with deterministic flow • Predict mesoscale features created by the interaction of predictable features with definable surface characteristics • Mesoscale features take on the predictability of the synoptic scale flw

8. 20th Century Paradigm for NWP • Simulation of Subordinate Disturbances • Initialize model with deterministic flow • Predict mesoscale features created by the interaction of predictable features with definable surface characteristics • Simulated mesoscale features have independent behavior, but may be used to explain the behavior of simulated phenomena

9. Model Resolution

10. Gap between “synoptic” Observations and Model Resolution

11. Tropical Cyclone NWP Paradigm Genesis of Fefa • Tropical Cyclone prediction does not fit the 20th Century paradigm • Form over oceanic regions where synoptic data is scarce • There may or may not be a parent synoptic scale disturbance like an easterly wave • Initial development from an organization of convective plumes • Bottom-up disturbance, i.e. energy is released by convective disturbances or eye-wall circulation that is not resolved by conventional synoptic observations • Whereas a baroclinic disturbance results from [PE] => PE’ , where both [PE] and PE’ are observable, TC’s come prom PE’’ => PE’, which is not definable from conventional observations

12. Predictability Issues in Age of Cloud Resolving Models • Deterministic predictability remains confined to time scales less than 1 lifecycle period of the energy containing disturbance • Baroclinic Cyclone ~ 6 days • Cumulus cloud ~ 20- 60 minutes • Eye Wall ~ 20-40 hours • Rainband ~ 10-20 hours • Probabilistic predictability of a feature confined to the predictability of the environment that sustains that feature • Generally this will be the slow manifold, balanced portion of the flow field

13. What Observations are Needed to Drive New paradigm of Cloud Resolving TC Models? • Mass Versus Wind Observations • Balanced portion of disturbance is defined by: • Mass (thermal) fields scales > Rossby Radius • Wind fields scales < Rossby Radius • As model resolution crosses the Rossby Radius threshold, fine scale observations of the wind field, become important

14. NEXRAD • The envy of the world in radar coverage • Limited vertical resolution • Gaps in coverage

15. Satellite • LEO (low time resolution, good spatial resolution) • GEO (good time resolution, less spatial resolution) Goes sounder (moisture)

16. Tropical Cyclone NWP Paradigm Hurricane Georges • How do we initialize TCs? • Bogus idealized TC’s into a synoptically initiated simulation • Adjust according to reconnaissance observations of • Central pressure • Radius of maximum wind • Artificial asymmetry Generic Hurricane insert

17. Hurricane Prediction Model Initialization • Global models (GFS)~30-50 km equivalent resolution • Only resolve gross features of hurricane • Parameterize convection, precipitation • Use reconnaissance missions to create bogus observations • Use a “cookie cutter” to remove existing storm • Assimilate bogus data to initialize storm • GFDL Hurricane Model • Simulate an axi-symmetric hurricane in similar conditions using reconnaissance information to tweak intensity and size • Use “cookie cutter” to remove existing storm from GFS initialized 3D model • Remove asymmetries from “cut out storm” • Add assymetries into “symmetric “ storm and insert into 3D model

18. Employing NEXRAD for NWP • Use cloud resolving model’s to predict weather • Explicit (bulk) microphysics • Horizontal resolution ~4km or less • Clouds exist explicitly

19. NEXTAD for for TC NWP • Cloud Resolving Model Prediction • Assimilation of Radar and Reconnaissance Radar vs HWRF of Rita

20. Can we fill the gap?

21. Still a Gap!

22. Space Borne RadarTRMM14:22 GMTSeptember 22, 2005

23. Results From TRMM Satellite : PR / TMI Results From DC-8 A/C : PR-2 Vertical structure cross-sections of Hurricane Humberto obtained at 14 & 35 GHz by PR-2 A/C Radar along nadir-track. Obtained during CAMEX-4 on 25 September 2001. Zhh @ 14 GHz Zhh @ 35 GHz VR @ 14 GHz VR @ 35 GHz TRMM Products

24. CloudSat

25. How Can A NEXRAD in SpAce Help?

26. NIS may be the answer

27. Theoretical Considerations Support the NEXRAD in SPACE Concept for the 21st Century NWP Paradigm • Four-Dimensional , regional coverage, of TC dynamic and microphysical structure in regions of precipitation • Regions of precipitation are associated with most of the major mesoscale circulations • Four-dimensional view of Doppler velocity from nadir is optimal information for defining divergent part of the wind and the dynamics driving it --- providing unambiguous depiction of transient (unbalanced) flow • Four-dimensional view of reflectivity factor provides (through assimilation) important information concerning cloud structure and the storm dynamics that are needed to create that structure • Multivariate four-dimensional observations of the storm and its environment provide unambiguous information for cloud resolving prediction system to build in similar characteristics through four-dimensional multivariate assimilation

28. Measurement of Doppler velocity and Doppler width at the scales where wind controls probabilistic evolution • enables more accurate probabilistic prediction of transient features

29. Real Time Prediction • Improved ability for models to continuously pick up on TC’s evolving dynamical structure • Improved ability for forecasters to pick up on TC’s evolving dynamical structure • Improved depiction of storm representation yields improved prediction of attendant weather and sea surface conditions

30. Scientific Investigations of Tropical Cyclones • Four-Dimensional multivariate observations of each tropical cyclone provides a wealth of information defining storm evolution and the variability among storms • Unambiguously define the internal dynamics occurring and their evolution over time • Reveal evolution and variability of precipitation processes • Reveal role of convection • Observations of mechanisms connecting outflow with core dynamics • Virtually every storm in the region of NIS coverage will be captured

31. Goal of this Workshop • Bring the tropical cyclone community together to introduce NIS technology and the potential it harnesses for future advances in TC science • Reach a consensus within the TC and cloud-resolving NWP research community present on: • Usefulness of NIS technology to improvements in TC prediction and understanding • Specific technical requirements for this technology to attain optimal usefulness • The appropriate roadmap to follow with regards to further development and future implementation of this technology in the form of a mission