PowerPoint Presentation

TASK 3: Evaluating Observation Strategies

Tropical Cyclone Cold WakesChris Old and Chris MerchantSchool of GeoSciences, The University of EdinburghESA Data Assimilation Projects, Progress Meeting 215th July 2013, The University of Reading

0OverviewTropical cyclones represent a challenge for many forecast models because:TC development strongly coupled to ocean [Cione & Uhlhorn, 2003; Lloyud & Vecchi, 2011]Gradients generated are poorly resolved at typical model resolutions [Bender & Ginis, 2000]Development of TC dependent on: [Jacob & Shay, 2003; Vincent et al., 2012]depth of pre-existing ocean surface mixed layerstrength of surface mixed layer stratification prior to storms passageTC intensity is driven by a evaporation/condensation heat engine.Direct feedback between TC and ocean surface layer through (near-inertial) wind-driven overturning.Warm surface waters forced downward, cool deep water forced upward, increasing column integrated PE.Overturning leads to cooling of surface water, reducing evaporation, weakening the heat engine. Cold wake forms if TC translation speed exceeds phase speed of first baroclinic mode. [Jaimes & Shay, 2010]Limited impact on pole-ward heat transport. (Winter mixed layer releases heat) [Jansen et al., 2010; Vincent et al., 2012]Measurable impact on amplitude of seasonal SST (10% reduction). [Vincent et al., 2012]Seasonal SST buffering may have climatic impact.Tropical cyclones present a strongly coupled process suitable for testing a coupled model system.1Introduction

1Microwave vs TIR Based SSTs2Introduction

Thermal Infrared The majority of SST products are based on thermal infrared measurements of the surface temperature.TIR sensors do not measure through cloud, so will not measure the SST in the vicinity of tropical cyclones.TIR only SST products will underestimate the surface cooling associated the passage of tropical cyclones.MicrowaveMicrowave based sensor can measure through cloud so can measure TC cold wakes. [Wentz et al., 2000]MW has broader footprint compared with TIR sensors.SST estimates contaminated by land signals from with ~75km of the coast.SST retrieval poor in regions of high wind speed, sun glint, rainfall, and near sea ice.Available MW Based SST Products [Chelton & Wentz, 2005]TMI (TRMM Microwave Imager) December 1997 to PresentNon-sun-synchronous orbit, inclination 35, global coverage between 40S and 40N.Footprint 46km, scan swath 878km.Gridded SST product at 0.25 0.25, accuracy 0.5C.AMSR-E (Advanced Microwave Scanning Radiometer for EOS) June 2002 to December 2010Sun-synchronous orbit, inclination 98.14, global coverage between 90S and 90N.Footprint 56km, scan swath 1450km. Gridded SST product at 0.25 0.25, accuracy 0.4C.2Microwave SST Products3Introduction

Gridded AMSR-E Coverage ( 2 June 2003 )OI AMSR-E Coverage ( 2 June 2003 )Gridded AMSR-E dataSeparated in ascending/descending.Equator Crossing Times (local): 1:30 PM ascending 1:30 AM descending Ground track repeat: 16 days

Daily record - incomplete coverage.Includes TC cold wake obs.

Optimally Interpolated AMSR-E dataRepresents daily minimum SST.Corresponds to ~8 AM SST.Diurnal warming removed empirically.SST values > 3 of mean from all data within 100km of obs location removed.

Daily record - complete coverage.Extreme TC cold wake obs removed.3Tropical Cyclone TracksIBTrACS archive provides best available tropical cyclone track data from a number of agencies.Archive covers 1848 to present day for tropical depressions through to super storms (Cat. 5 Hurricanes).Records include time, position, max. sustained wind, mean sea-level pressure, basin of origin, nature of storm.Data available from www.ncdc.noaa.gov/oa/ibtracs/ [Knapp et al., 2010]4Introduction

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Test Case: Hurricane Frances, 20045ERA-Interim / MW SST Comparison

Aim to compare AMSR-E and ERA-Interim SST over TC cold wake.Extract TC track locations from IBTrACS archive.Extract time series of SST data at each track location for all points within 200km of each location.Calculate spatial average SST time series at each location.Remove climatology to get anomaly (used Pathfinder).Find time of minimum SST anomaly.5AMSR-E ERA-Interim SST Comparison6ERA-Interim / MW SST Comparison

6Tropical Cyclone Cold Wake Diagnostic7TC Cold Wake Diagnostic

Need to generate adjustments to TIR-based SST products accounting for missed period of cold wake SSTs.

Adjustment needs to capture both development and decay of tropical cyclone impact on SSTs.

IBTrACS archive used to identify location of tropical cyclones for generating local adjustments.

OI TMI/AMSR-E SST products are used as basis for adjustment to obtain daily full global coverage.

Gridded TMI/AMSR-E SST data used, when available, to compensate for smoothing of OI SSTs.

SST adjustment product generated at TMI/AMSR-E data resolution ( i.e. 0.25 0.25 ).

SST cold wake adjustment product will be daily record.

As a demonstration of the diagnostic adjustments to the ERA-Interim SSTs for 2004 were constructed.7Methodology8TC Cold Wake DiagnosticExtract all tropical storm track segments around the globe from IBTrACS archive for the day being processed.

Extract 30 day time series of OI TMI/AMSR-E SST data and TIR-only SST data for all points within a fixed distance of each track segment, covering 7 days prior to 22 days after the storm passage.

8Methodology (cont.)9TC Cold Wake DiagnosticCalculate median SST ( ) for each time series; (Median not biased by outliers.)Used to adjust for relative bias (season and location dependent) between MW and TIR SST data.

Calculate anomalies about

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Methodology (cont.)10TC Cold Wake DiagnosticCalculate cold wake adjustment

Find SSTmin and tmin within 5 days of TC passage, and set SST(x, y, t) = SSTmin ; { tpass t tmin }

Add observations from gridded TMI/AMSR-E data where available within this SST minimum period. Only use data between 4 PM and 8 AM (local time) to avoid diurnal warming effects.

10Methodology (cont.)11TC Cold Wake DiagnosticStore time series results in corresponding daily fields of the final data product.

11Threshold For Cold Wake Adjustment12TC Cold Wake DiagnosticExpected uncertainty in SST is

To capture missed cold wake signal accept only SST -0.7 C ( elsewhere set to SST = 0.0 C )

12Time Series of Wake Adjustments13TC Cold Wake Diagnostic

13Discussion14DiscussionUse of TC Cold Wake Adjustment productCan be added to SST data used for model boundary conditionCan be used to test whether coupled system produces better representation of TC SSTsThese apply equally to the CERA and the single column model developments.

Next StepIdentify SST product to be used to drive CERA model during development.Identify which years to generate the adjustments for.Generate adjustment product for SST data.

NOTE: The IDL code used to generate the example adjustment for the ERA-Interim SST can be applied to any SST product.

The code takes approximately 30 minutes to generate a year of daily cold wake SST adjustments.

14References15Useful References:Bender, M. A., and I. Ginis, (2000), Real-case simulations of hurricane-ocean interaction using a high resolution coupled model: Effects on hurricane intensity, Mon. Weather Rev., 128, 917-946Bosart, L. F., C. S. Velden, W. E. Bracken, J. Molinari, and P. G. Black, (2000), Environmental influences of the rapid intensification of hurricane Opal (1995) over the Gulf of Mexico, Mon. Weather Rev., 128, 322-352Chelton, D. B., and F. J. Wentz, (2005), Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research, Bull. Amer. Meteor. Soc, 86, 1097-1115, doi:10.1175/BAMS-86-8-1097Cione, J. J., and E. W. Uhlhorn, (2003), Sea surface temperature variability in hurricanes: Implications with respect to intensity change, Mon. Weather Rev., 131, 1783-1796Dare, R. A., and J. L. McBride, (2012), Sea surface temperature response to tropical cyclones, Mon. Wea. Rev., 139, 3798-3808, doi:10.1175/MWR-D-10-05019.1Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtild, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hlm, L. Isaksen, P. Kllberg, M. Khler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peuby, P. de Rosnay, C. Tavolato, J. N. Thpaut, and V. Vitart, (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553-597, doi:10.1002/qj.828Donlon, C. J., M. Martin, J. Stark, J. Roberts-Jones, E. Fiedler, and W. Wimmer, (2012), The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sens. Environ., 116, 140-158, doi:10.1016/j.rse.2010.10.017Emanuel, K., C. DesAutels, C. Holloway, and R. Korty, (2004), Environmental control of tropical cyclone intensity, J. Atmos. Sci., 61, 843-858Gentemann, C. L., F. J. Wentz, C. A. Mears, and D. K. Smith, (2004), In situ validation of tropical rainfall measuring mission microwave sea surface temperature, J. Geophys. Res., 109, C04021, doi:10.1029/2003JC002092Hong, X., S. W. Chang, S. Raman, L.K. Shay, and R. Hodur, (2000), The interaction between hurricane Opal (1995) and a warm core ring in the Gulf of Mexico, Mon. Weather Rev., 128,1347-1365Jacob, S. D., and L. K. Shay, (2003), The role of oceanic mesoscale features on the tropical cyclone-induced mixed layer response: A case study, J. Phys. Oceanogr., 33, 649-676Jaimes, B., and L. K. Shay, (2010), Near-inertial wave wake of hurricanes Katrina and Rita over mesoscale oceanic eddies, J. Phys. Oceanogr., 40, 1320-1337, doi:10.1175/2010JPO4309.1Jansen, M. J., R. Ferrari, and T. A. Mooring, (2010), Seasonal versus permanent thermocline warming by tropical cyclones, Geophys. Res. Lett., 37, L03602, doi:10.1029/2009/GL041808Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, (2010), The International Best Tracks Archive for Climate Stewardship (IBTrACS),Bull. Amer. Meteor. Soc., 91, 363-376, doi:10.1175/2009BAMS755.1Lloyd, I. D., and G. A. Vecchi, (2011), Observational evidence of oceanic controls on hurricane intensity, J. Climate, 24, 1138-1153, doi:10.1175/2010JCLI3763.1Reynolds, R. W., and T. M. Smith, (1994), Improved global sea surface temperature analysis using optimum interpolation, J. Climate, 7, 929-948Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, (2002), An improved in situ and satellite SST analysis for climate, J. Climate, 15, 1609-1625, doi:10.1175/1520-0442(2002)015