Improving Hurricane Intensity Forecasting with Satellite Data

1. Improving Hurricane Intensity Forecasting Using Satellite Data Presented by Mark DeMaria

2. Requirement, Science, and Benefit • Requirement/Objective • NOAA 5-Year Research Plan • Improve accuracy in intensity forecasts for tropical storms and hurricanes through accelerated tropical cyclone modeling improvements • NWS Government Performance Requirements Act (GPRA) goal • Improve annual average 48-hour tropical cyclone intensity forecast by 14% in 5 years • NOAA Hurricane Forecast Improvement Project (HFIP) Goals • Improve tropical cyclone track and intensity forecasts by 20% in 5 years and 50% in 10 years • Science • How can hurricane intensity forecasts be improved using satellite data? • Benefit • National Hurricane Center (NHC) forecasters and their customers: • Intensity forecasts have impact on hurricane watches/warnings and public response • Under-forecasting can lead to loss of life • Over-forecasting leads to over-warning, unnecessary evacuations and large economic impacts

3. Challenges and Path Forward • Science challenges • Intensity change difficult forecast problem • Multi-scale and highly nonlinear • Possible predictability limits • Next steps • Advanced data assimilation methods for 3-D hurricane models • Extract maximum information from current and future satellite systems • NOAA, NASA, DoD, International • 3-D tropospheric winds (including ocean surface winds) • High vertical and horizontal atmospheric moisture and temperature profiles • Includes cloudy regions • Cloud microphysical variables • Ocean surface and subsurface structure (temperature, salinity, currents) • Statistical model improvements and ensemble approaches 3

4. Long Term Trends in NHC Tropical Cyclone Forecast Errors – Track vs. Intensity 48 hr Track Improvements ~3.7% per year 48 hr Intensity Improvements ~0.6% per year Intensity changes involve wider range of scales and physical processes than track and are much harder to predict .

5. Operational Hurricane Intensity Forecast Guidance Models • Physically-based models • GFDL: 3-dimensional coupled ocean atmosphere model • *HWRF: NCEP Hurricane Weather Research and Forecast Model (HWRF) • Both include coupled ocean model • Empirical models • SHIFOR: Linear regression based on climatology and persistence • Mostly uses as skill baseline • **SHIPS: Linear regression model with atmosphere, ocean predictors • Joint development with NOAA/OAR Hurricane Research Division • **LGEM: Nonlinear statistic model with atmosphere, ocean predictors • Operational implementation dates • SHIFOR 1988, SHIPS 1991, GFDL1995, LGEM 2006, HWRF 2007 • *STAR is assisting with HWRF improvements • **STAR is primary developer of SHIPS/LGEM and is assisting with improvements

6. Operational Hurricane Intensity Forecast Guidance Model Performance Atlantic Intensity Errors (2007-2009) Statistical forecast models have outperformed much more complex coupled ocean-atmosphere models

7. Satellite Data Utilization in Hurricane Models • Initial center location, intensity and structure estimates • Vis/IR Dvorak techniques, passive and active microwave applications • Sea surface temperature products for lower boundary condition • Assimilation of satellite data into numerical models • Atmosphere and ocean • Predictors in statistical intensity forecast models • Oceanic heat content from satellite altimetry • GOES data for convective analysis • Microwave imagery for inner core structure • Lightning data from GOES-R using proxy ground based systems • STAR is contributing in all of these areas

8. The Logistic Growth Equation Model • Uses analogy with population growth modeling • dV/dt = V - (V/Vmpi)nV • (A) (B) (C) • (A) = time change of maximum winds • Analogous to population change • (B) = growth rate term • analogous to reproduction rate • (C) = Limits max intensity to upper bound • Analogous to food supply limit (carrying capacity) • , n = empirical constants • Vmpi= maximum possible intensity (from theory) •  = growth rate (estimated empirically from ocean, atmospheric predictors, satellite data, etc)

9. Recent Improvement to LGEMGrowth rate function of oceanic heat content from satellite altimetry OHC analysis for Hurricane Katrina (left), and improvements to Atlantic SHIPS model, West Pacific STIPS model forecasts with OHC added (right) since 2004. OHC added to LGEM beginning in 2009

10. Future Improvements to LGEMUsing Satellite Data • Total Precipitable Water (TPW) • Naval Research Laboratory blended satellite analyses • NOAA, NASA, DMSP -wave imagery • Eye structure • Hyperspectral soundings from AIRS, IASI • Stability indices and Maximum Potential Intensity estimates • Ground-based lightning as a proxy for GOES-R Geostationary Lightning Mapper (GLM) Cloud to ground lightning strikes for Hurricane Rita 2005 (1 hr composites)

11. Challenges and Path Forward • Science challenges • Intensity change difficult forecast problem • Multi-scale and highly nonlinear • Possible predictability limits • Next steps • Advanced data assimilation methods for 3-D hurricane models • Extract maximum information from current and future satellite systems • NOAA, NASA, DoD, International • 3-D tropospheric winds (including ocean surface winds) • High vertical and horizontal atmospheric moisture and temperature profiles • Includes cloudy regions • Cloud microphysical variables • Ocean surface and subsurface structure (temperature, salinity, currents) • Statistical model improvements and ensemble approaches

12. Challenges and Path Forward • Transition Path • NESDIS/NASA Joint Center for Satellite Data Assimilation • Satellite data assimilation improvements to NCEP global models • NOAA Hurricane Forecast Improvement Project (HFIP) • Model, data assimilation improvements, diagnostic tools transitioned to NCEP operational hurricane model and NHC operations • NOAA Joint Hurricane Testbed • Statistical modeling and simplified products to NHC operations • GOES-R Proving Ground • Evaluation of new hurricane products, improvements to GOES-R baseline products through future version of the SPSRB process • Proposed Satellite Algorithm Testbed • Facilitate transition of satellite products (including hurricane products) to operations