NESDIS Contributions to the Hurricane Forecast Improvement Program (HFIP)

1. NESDIS Contributions to the Hurricane Forecast Improvement Program (HFIP) Mark DeMaria* NOAA/NESDIS/RAMMB HFIP Planning Meeting Silver Spring, MD, 23-24 October 2008 *With contributions from B. Pichel, P. Chang, Z. Jelenak, L. Miller, F. Weng, J. Knaff (NESDIS) B. McNoldy, K. Maclay, I. Jankov, A. Schumacher, R. Brummer (CIRA)

2. Outline • Sounders and Imagers • Coordination with JCSDA on assimilation • Ocean surface winds • Surface wind information content from satellites • Use of NESDIS TC surface wind products for model evaluation • Additional surface and oceanic information • Synthetic Aperture Radar (SAR) and satellite altimetry • Model verification • Evaluation in satellite observation space • Evaluation in the context of statistical models • Forecast impact assessment with NHC’s wind probability model

3. Hurricane Ike 7 Sept 2008

4. Satellite Data for Hurricane Models • Sounders (T, q, SST, land surface properties) • Polar • Microwave and IR • Geostationary • IR • Imagers • Feature track winds • Cloud properties • SST • Cloud/precip structure below cloud top from microwave imagers • Land surface properties • Scatterometers • Ocean surface winds • Satellite altimeters • Sub-surface ocean structure • Lightning mapper (planned for GOES-R)

5. Operational Sounders • GOES • 20 channel IR, 10 km spatial, 1 hr temporal • NOAA POES • -wave (AMSU), 15 T, 5 q channels • 50 km spatial, 12 hr temporal • IR (HIRS), 20 channels • 20 km spatial, 12 hr temporal • Met-Op • AMSU + hyperspectral IR (~8000 channels) • 50/12 km spatial, 12 hr temporal • DMSP • SSM/T µ-wave sounder • NPOESS (~2015) • Advanced microwave + hyperspectral IR • GOES-R (~2015) • No sounder planned

6. Operational Imagers • GOES • 5 channel vis/IR • Meteosat 2nd generation (east Atlantic) • 12 channel SEVERI (vis/IR), east Atlantic • POES • 6 channel vis/IR AVHRR • DMSP • vis, IR, Microwave imagers (SSM/I) • NPOESS (~2015) • Advanced vis/IR imager (VIIRS) • Microwave imagery • GOES-R (~2015) • 16 channel advanced baseline imager (ABI) vis/IR

7. AMSU/AIRS Eye Soundings in Hurricane Isabel (2003)

8. Assimilation of Imager/Sounder Data • Geo – good temporal, spatial, limited spectral (vertical) resolution • Polar – Better spectral (vertical) resolution, limited spatial, temporal resolution • Coordination of HFIP and JCSDA • Community radiative transfer models • Surface emissivity algorithms • Advanced assimilation techniques • Special emphasis for HFIP • Assimilation of “cloudy” radiances • Techniques to utilize high temporal resolution • Assimilation over land • Assimilation of lightning data

9. JCSDA Science Priorities I Improve Radiative Transfer Models II Prepare for Advanced Operational Instruments III Assimilating Observations of Clouds and Precipitation IV Assimilation of Land Surface Observations from Satellites V Assimilation of Satellite Oceanic Observations VI Assimilation for air quality forecasts

10. New Considerations in Cloudy Radiance Assimilation at JCSDA • Develop forward radiative transfer and Jacobian models including clouds and precipitation • Use 1dvar quality control of satellite radiances • Extent the control variables with more hydrometeor parameters • Incorporate cloud and moisture physics in minimization processes • Improve bias corrections using more predictors (e.g. LWP and RWP) from observations and/or moisture physics

11. NESDIS Ocean Surface Vector Wind (OSVW) Activities Relevant to HFIP • Working to establish satellite OSVW as an operationally sustained observing capability - QuikSCAT follow-on mission • Striving to fully address NWS OSW requirements needs including tropical cyclones • NESDIS Ocean Winds flight experiment program - C- and Ku-band profiling radar system on the NOAA P-3 • Product validation and new product development • test and evaluate new remote-sensing techniques/sensors - Real-time data processing, distribution and display system - Close collaboration with NWS, OAR and AOC during hurricane season experiment • Research to Operations training and education - Tailored seminars and training sessions for operational forecasters - Funding a person at IPC and OPC to help facilitate information exchange between operational forecasters and remote-sensing experts • Started with a NOPP funded project (2002), and continued with R20 funding

15. Surface Wind Information Content from Satellite Data • Surface Wind Information • QuikSCAT, ASCAT • GOES IR derived proxy flight level winds • Relates inner core IR structure to surface winds • GOES feature track winds • Nonlinear balance winds from AMSU T/q retrievals • Simple surface reductions over water/land • Combine input in a variational objective analysis system • Demonstrates wind information in satellite data • New NESDIS operational product in 2009 • Global tropical cyclone satellite surface wind analysis

16. Example: Hurricane Boris GOES-IR Flight-Level Proxy AMSU 2-d 700 hPa Winds

17. Example: Hurricane Boris GOES winds (P > 600hPa) Scatterometer

18. Example: Hurricane Boris Surface Analysis IR Image

19. Hurricane Dean (2007) Example

20. Analysis With Aircraft Obs. Satellite-Only Satellite+Flight-Level +SFMR Satellite-only product useful for model verification for cases with no aircraft

21. Synthetic Aperture Radar (SAR) • Based on cm radar backscatter functional dependence on ocean roughness • Early SAR missions had very narrow swath (~100 km) • Wider swath (~500 km) available since 1995 • Canadian Radarsat and European Envisat • Sub-km spatial resolution • Inner core surface structure • Wave information • Hurricane applications being developed by NESDIS/StAR (Bill Pichel) + Others

22. Danielle 31 Aug ‘98 Dennis 27 Aug ‘99 Dennis 29 Aug ‘99 Dennis 31 Aug ‘99 Floyd 15 Sep ‘99 Alberto 17 Aug ‘00 Florence 13 Sep ‘00 Dalila 26 Jul ‘01 Flossie 29 Aug ‘01 Flossie 1 Sep ‘01 Erin 11 Sep ‘01 Erin 13 Sep ‘01 Felix 17 Sep ‘01 Humberto 26 Sep ‘01 Juliette 27 Sep ‘01 Olga 28 Nov ‘01 Hurricane Eye Structure Hurricane Eye Wall Studies 100 km [CSA Hurricane Watch Project]

23. Ocean Swell Direction and Wavelength (C) CSA 1998 Monitoring Storm-generated Swell Waves Hurricane Bonnie25 AUG 1998 23:18 UTC

24. Satellite Altimetry • Accurate satellite measurements of ocean surface height provide sub-surface structure • NHC has utilized Ocean Heat Content (OHC) analyses qualitatively and in SHIPS statistical model since 2003 • Assimilation techniques being developed for ocean models

25. OHC Analysis for Hurricane Ivan (2004)

26. Impact of OHC on Operational SHIPS Forecasts (2004-2007) 34 kt or greater, Atlantic storms west of 50oW

27. Altimeter Missions 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 2-f MR & 66-deg, 10-day, 315-km Jason-2/OSTM TOPEX-Poseidon JASON Jason Jason-3 SWOT 2017-20? AltiKa on SARAL 98-deg, 35-day, 80-km – failure of on-board recording ERS-2 Sentinel 3 – 3-sat series ENVISAT HY-2 series Nearing total failure 108-deg, 17-day, 160-km data access? GEOSAT Follow-on In orbit Approved Planned/Pending Approval

28. Model Verification in Satellite Space • Traditional NHC track/intensity verification provides little insight into causes of errors • In situ measurements for verification limited • Use forward models to create “synthetic” satellite data from model output • Compare synthetic and real satellite data • Example from WRF model forecast of an “atmospheric river” from Isadora Jankov et al • Similar technique under development at CIRA from NCEP/EMC project

29. An Evaluation of Various WRF-ARW Microphysics Using Simulated GOES Imagery for an Atmospheric River Event Affecting the California Coast Isidora Jankov, Manajit Sengupta, Louis Grasso, Daniel Coleman, Dusanka Zupanski, Milija Zupanski, Lindsey Daniel, and Renate Brummer

30. Atmospheric River Events • During the winter season significant precipitation events in California are often caused by land-falling “atmospheric rivers” associated with extra tropical cyclones in the Pacific. • Atmospheric rivers are elongated regions of high values of vertically integrated water vapor over the Pacific and Atlantic oceans that extend from the tropics and subtropics into the extratropics and are readily identifiable using SSM/I. • Due to the terrain steepness and soil characteristics in the area, a high risk of flooding and landslides is often associated with these events.

31. SIMULATIONS • * 12302005 event • 4km inner nest • 4 different Microphysics: • Lin • WSM6 • Thompson • Schultz • * YSU PBL • NAM Initial • and LBCs ATA CZD * *

32. CZD 24hr precipitation accumulation starting 30 December, 2005 at 12UTC Observations Lin Schultz WSM6 Schultz Thompson

33. Observations Lin WSM6 Thompson Schultz Observed and Simulated Brightness Temperatures Valid at 12312005 12UTC

34. Brightness Temperature’s Probability of Occurrence Observations Lin WSM6 Thompson Schultz

36. Dynamical Model Evaluation in the Context of Statistical Models • Statistical models such as SHIPS and LGEM rely on empirical relationships between intensity change and storm environmental variables • SST, shear, etc • Do dynamical models properly evolve the large scale environment? • Do dynamical models show the same statistical relationships with intensity change? • Current HWRF pilot study: • Assemble large scale predictor and intensity change database for evaluation

37. Atlantic Intensity Model Performance 2008 Atlantic Intensity Errors (Arthur-Omar) Atlantic Intensity Model with Smallest 48 hr Intensity Error 1988-2008

38. Evaluation of HWRF Forecasts for Hurricane OMAR

39. Observed and HWRF Forecasts of SST and Vertical Shear for Omar N = 22, 17, 11 at 0, 36 and 72 hr

40. HFIP Applications of NHC’s Wind Probability Model • Developed by NESDIS/RAMMB and CIRA • Replaced “Strike” probability program in 2006 • Possible mechanism to tie ensemble forecasts to watches/warning • Evaluation of HFIP forecast improvement

41. The Monte Carlo Wind Probability Model • Include uncertainty in TC track, intensity and structure forecasts • Interaction of track, intensity structure errors, especially near land, made fitting error distributions to analytic distributions inaccurate • Monte Carlo approach useful for situations with complicated geometry, but well-defined interaction rules • Originally developed for scattering problems • Developed by RAMMB, operational in 2006 • Text and graphical products • Versions for Atlantic and east/central/western N. Pacific

42. The Monte Carlo Wind Probability Model • 1000 track realizations from random sampling NHC track error distributions • Serial correlation and bias of errors accounted for • Intensity of realizations from random sampling NHC intensity error distributions • Serial correlation and bias of errors accounted for • Special treatment near land • Wind radii of realizations from radii CLIPER model and its radii error distributions • Serial correlation included • Probability at a point from counting number of realizations passing within the wind radii of interest

43. MC Probability Example Hurricane Dean 17 Aug 2007 18 UTC • Major Hurricane • Non-major Hurricane • Tropical Storm • Depression 1000 Track Realizations 64 kt 0-120 h Cumulative Probabilities

44. Objective Guidance for U.S. Hurricane Warnings • Preliminary rules based on probability analysis for NHC hurricane warnings • Use 48 h cumulative 64 kt probabilities • Add breakpoint if P ≥ 10 % • Remove breakpoint if P < 1% • Minimum warning length = 40 n mi • Except near U.S. boundaries • Update every 6 hours • Rules for Watches under development

45. 3 Hurricane Ivan 2004 Example NHC Objective Guidance 13 Sep 00 Z 15 Sep 06 Z (24 h before landfall )

46. Ivan Hurricane Warning Lengths • Objective Guidance • Warnings issued a little earlier • Warning lowered a little earlier • Warnings for Dry Tortugas • Gulf coastline length about right

47. Evaluation of Impact of Model Improvements on Watches/Warnings • Hurricane Ivan case • What is impact on warnings from a 20% and 50% reduction in track error? • Adjust track errors in MC model by 20% and 50% • Run automated warning program using adjusted probabilities • Calculate reduction of coastline length for each case

48. Warning Length Reductions (n mi) Due to Reduced Track Errors Note: Duration of the warnings also reduced by 6 to 18 hours

49. Forecast-Dependent Probabilities • Operational MC model uses basin-wide track error distributions • Can situation-dependent track distributions be utilized? Track plots courtesy of J. Vigh, CSU

50. 72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE*(2002-2006) *GPCE is Goerss Predicted Consensus Error, which depends on track model spread