1. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Improving Hurricane Intensity Forecasting Using Satellite Data Presented by Mark DeMaria Presented by Mark DeMaria

2. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 2 Requirement, Science, and Benefit Requirement/Objective Weather & Water: –Increase lead time and accuracy for weather and water warnings and forecasts –Reduce uncertainty associated with weather and water forecasts, assessments, and decision tools –Increase development, application, and transition of advanced science and technology to operations and services NWS Government Performance Requirements Act (GPRA) goal –Improve annual average 48 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 (contribution to meeting requirement) 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. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 3 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.

4. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 4 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

5. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 5 Operational Hurricane Intensity Forecast Guidance Model Performance Statistical forecast models have outperformed much more complex coupled ocean-atmosphere models

6. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 6 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

7. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 7 The Logistic Growth Equation Model Uses analogy with population growth modeling dV/dt =  V -  (V/V mpi ) n V (A) (B) (C) –(A) = time change of maximum winds Analogous to population change –(B) = growth rate term a analogous to reproduction rat e –(C) = Limits max intensity to upper bound Analogous to food supply limit (carrying capacity ) , n = empirical constants V mpi = maximum possible intensity (from theory)  = growth rate (estimated empirically from ocean, atmospheric predictors, satellite data, etc)

8. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 8 Recent Improvement to LGEM Growth 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

9. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 9 Cloud to ground lightning strikes for Hurricane Rita 2005 (1 hr composites) Future Improvements to LGEM Using 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)

10. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 10 Challenges and Path Forward Continuing science challenges –Intensity change difficult forecast challenge –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

11. Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 11 Challenges and Path Forward Paths into applications/operations –NESDIS/NASA Joint Center for Satellite Data Assimilation Satellite data assimilation –NOAA Hurricane Forecast Improvement Project (HFIP) Model, data assimilation improvements, diagnostic tools –NESDIS PSDI, NOAA Joint Hurricane Testbed Statistical modeling and simplified products –GOES-R Proving Ground Evaluation of new hurricane products –Proposed Satellite Algorithm Testbed Facilitate transition of satellite products (including hurricane products) to operations