1. Scripps Institution of Oceanography
Targeted In-Situ Tropical Cyclone Observations from Air-Deployed Drifters
Luca R. Centurioni
Scripps Institution of Oceanography
Third Capacity Building Workshop of the WMO/IOC Data Buoy Cooperation Panel (DBCP) for the North Pacific Ocean and Its Marginal Seas (NPOMS-4)
2-4 November 2015, Busan, Korea

2. Outline
Why are tropical cyclones targeted with drifters that collect observations in the ocean/atmosphere boundary layers?
Technology and methodology. Intruemnts and operations
Examples of deployments, data and scientific results.
Upcoming technical developments

3. Background
Improvements in track forecast have been made in the past few years. Much less improvement was achieved w.r.t. intensity forecast
The track of a storm is largely dictated by the large scale atmospheric flow, therefore track forecast depends crucially from the accuracy of the global circulation models
Not all model have the same skills, which depend on the sills as well as on the way observations are assimilated
The intensity of a storm is controlled by external processes that are regulated by the large scale flow and by internal processes, that also include TC/ocean interaction that controls the enthalpy and the momentum fluxes
TC initialization is a challenge and the data to constrain the models are seldom available (N Atlantic is sometimes the exception). Satellite data and global observing system data should be fully exploited

4. Background: Inner Core and Ocean Data
Observations within storms are needed at several stages of its evolution, from early stages (i.e. tropical depression) through intensification/weakening events
Aircraft inner core observations may be helpful but assimilating the data efficiently is still a challenge
Impact of assimilation of aircraft inner core observations is not always significant and it depends on models (2013 HFIP report)
The ocean is often poorly represented and only recently the ocean modules are being upgraded to better models, such as HYCOM. But, ALL ocean models need to be constrained by observations, especially on the scale of a TC inner core (tens of kms…)

5. HFIP (Hurricane Forecast Improvement Project) The project calls, amongst others, for:
Improvements in model’s physics and resolution
Improvements in high resolution data assimilation techniques in regional models
Enhanced observational strategies

6. Why Collect Ocean obs?
TC draw their energy from the warm ocean, but in doing so also change the ocean in a broad swath around their track by direct cooling and through the action of the ocean waves and currents generated by TC winds (Leipper 1967; Price 1981; Black 1983; Shay 2010; and references therein). This affects the evolution of the TC and also leaves an imprint on the ocean that can last long after the storm has passed (D’Asaro et al., 2013)
Create a database that can be used to hindcast (through model validation/ data assimilation) and understand the contribution of the ocean in intensity forecast.
For these reasons the Global Drifter Program maintains a hurricane drifter array that is deployed in TC that threatens the US mainland or have a scientific interest.

8. SVP-2T

9. Sensor Accuracy
Barometer / mb
Thermistor / °C
Wind Direction +/- 2°
Wind Speed m/s (0-60m/s)
Sampling every 15min
Averaging sec
Data per day ~ 85% of 24 hr
Battery Life ~ 5 months

10. Conceptual Schematics of Drifter Deployment

11. Air-Deployment by 53rd Hurricane Hunter Squadron of Air National Guard

12. Operational deployments. Hurricane Isaac. First deployment map (2012)

13. Operational deployments. Hurricane Isaac
Operational deployments. Hurricane Isaac. Second and final deployment map (2012)

14. Air-Deployment by 53rd Hurricane Hunter Squadron of Air National Guard

15. Hurricane Isaac. Actual crossing.

16. Drifters observations within ISAAC
Drifters observations within ISAAC. All data were available in real-time through the GTS
Ten ocean drifters deployed during Hurricane Isaac on August 26, The drifter positions/tracks (left) and continuous observations of sea-level pressure (hPa, top-right), surface wind speed (kts, mid-right), and SST (C, bottom-right) are shown from August 26-September 11, 2012.

17. Drifter data/model comparison
ADOS drifter observations (black) and UMCM model forecasts (coupled Atmosphere-Ocean model in green, Atmosphere-Wave-Ocean model in red) of SST (top-left), SLP (hPa, bottom-left), surface wind speed (m/s, top-right) and direction (bottom-right)

18. Sub-surface Data - Rita SST changes before storm center arrives

19. Gradient Wind Balance above Boundary Layer: Comparison with Dropsonde wind speed

20. Drifter Deployments during last 10 Years, 2003 – 2015 (update table)
Target TC
N drifter
CAT
Min Dist
2003
Fabian 16 3
48 km
2004
Frances 39 4
30 km
2005
Rita 20
12 km
2007
Dean 12 5
2008
Gustav 2
13 km
Ike 9
3 km
Hagupit 1
14 km
Jangmi
18 km
2010
Fanapi 53
11 km
Malakas -
2012
Isaac 10
20 km
Total of 207 Drifters deployed, 92% success rate

21. Typhoon Fanapi CAT 3 Landfall in Taiwan on 9/19
tens of casualties between China and Taiwan
954 mm rain in Pingtung county (TW)
Significant damage (orders of
hundreds of M of USD)

22. ITOP
Assets in ITOP

23. Drifters Observations During ITOP
Deployments

24. Drifters Observations During ITOP
METHODS: Analysis
Bath calibration of T-P sensors, std criteria & median filtering (spikes), manual editing
10 m wind scaled by 1.8 (1.6) for values greater (less) than 15 ms-1 (field calibration)
Lagrangian tracks end velocity obtained from standardized Kriging algorithms
Backrotated near-inertial velocity and harmonic fit

25. Drifters Observations During ITOP
RESULTS. 16 drifters deployed ahead of TY Fanapi measured the air pressure at the sea level. All pressure data were posted to the GTS in real-time
P_min=937 mbar
-Very good agreement with dropwindsondes
-excellent temporal resolution from drifter’s scan

26. Drifters Observations During ITOP
RESULTS: 8 drifters deployed ahead of TY Fanapi measured the wind velocity at the sea level. To note is wind intensification and good agreement with sondes

27. Drifters Observations During ITOP RESULTS: Cold wake formation
PREDICTED SST COOLING WITH T100: 1.9°C
MAXIMUM OBSERVED SST COOLING: 2.7°C
~30% LOW (SEE ALSO D’ASARO ET AL. 2013)

28. ITOP Drifters Observations During ITOP
RESULTS: near-inertial currents observations
Few observations of near-inertial currents generated by TS exist. Inertial currents are indeed strongest to the right of the storm. E-folding scale for strong inertial currents (RHS) for Fanapi was ~5d (Hormann et al. GRL, 2014)

29. Wake properties: summary of ITOP results (from D’Asaro et al 2013)

30. Development of Compact-ADOS
Existing ADOS (z=150m), 160 kg, 120cm3 box, for C-130 deployment
New configuration, 12 kg, 10cm x 100cm tube, for P-3,G-IV or small aircraft
First sea tests conducted off San Diego
Compact-ADOS prototype: z=150m, 10 T-sensor spool (left), T-sensor (center) and container for GPS/Iridium antenna and electronics (right). Looking into adding air and humidity sensor too.

31. CONCLUSIONS
An “operational” system for targeted C-130 air deployments of various GDP drifters exists.
“High quality” measurements of Pa, SST, sea-level wind velocity and T(z=150m) through tropical cyclones can be made
Meteorological research and operational hurricane monitoring aircraft cannot deploy these large packages
New “operational” mini system for acquisition tropical cyclone surface and subsurface data is in development at SIO

32. ITOP Drifters Observations During ITOP
RESULTS: cold wake evolution and surface currents
Cold wake surface cap (warm water) occurs in ~3 days. Cold wake was gone in (at least) 23 days (see Mrvaljevic et al for full discussion). Note the strong advection of the cold wake by surface currents .