a. FY12-13 GIMPAP Project Proposal Title Page version 25 October 2011 Title: Combining Probabilistic and Deterministic Statistical Tropical Cyclone Intensity Forecast Models Status: New Duration: 2 years Project Leads: John Knaff, NESDIS/STAR/RAMMB Mark DeMaria, NESDIS/STAR/RAMMB Other Participants: Andrea Schumacher, CIRA/CSU, James Franklin, NCEP/NHC, 1

b. Project Summary Statistical-Dynamical intensity forecast models (SHIPS and LGEM) have been the most skillful over the past few years –Don’t perform as well for rapid intensification cases Rapid Intensification Index (RII) –Complements SHIPS/LGEM forecasts –Provides probability of RI (next 24 hr) –Subset of SHIPS/LGEM predictors most related to RI and RW –GOES input much more important than in SHIPS/LGEM Develop method to improve LGEM for rapid intensity cases by including RII as predictor 2

c. Motivation / Justification 3 NHC and JTWC Forecast Priorities** Improved Guidance on Rapid Intensification is No. 1 Priority of NHC and JTWC **From OFCM Working Group For Tropical Cyclone Research Presentation, Interdepartmental Hurricane Conference, March 2011

Atlantic Intensity Model Error (homogeneous sample) 48 hr Predicted (LGEM) vs. Observed Intensity Changes Atlantic Sample

d. Methodology RII identifies combination of predictors most conducive to rapid intensity changes –Provides quantitative estimate of probability of rapid intensification and rapid weakening Assimilate RII probabilities into deterministic LGEM forecast model 5

LGEM forecasts for Adrian (2011) 6 RII > 50%

Modification of LGEM Growth  Based on RII and RWI* 7 Increase  at 0-24 hr when RII > X Decrease  at 0-24 hr when RWI > Y

Procedure for Adjusting  Assume functional form for adjusted  =  a (t) –  a (t) =  (t) + [1 - H(t - t i )][a*P ri + b*P rw ] –H(t - t i ) = Heaviside function H=0 for t < t i, H=1 for t ≥ t i –P ri = probability of Rapid Intensification –P rw = probability of Rapid Weakening –H(t - t i ) Find values of model parameters t i, a, b to minimize Cost Function for many forecast cases Cost function E = ∑(V pred – V obs ) 2 Adjoint of LGEM provides gradients for minimization: ∂E/∂a, ∂E/∂b, ∂E/∂t i 8

Increased Influence of GOES Input in LGEM hr Average LGEM Normalized Growth Rate Predictor Coefficients Normalized RII Discriminant Function Coefficients

e. Expected Outcomes Improved intensity forecasts from LGEM –Especially for RW and RI cases Better use of GOES for its ability to identify RW and RI cases 10

e. Possible Path to Operations LGEM and RII are already operational at NHC –West Pac version under development for JTWC Experimental real time tests through Proving Ground or Joint Hurricane Testbed If successful, transition improved LGEM through coordination with NHC and JTWC 11

f. Milestones Year 1 –Modify LGEM adjoint to include gradients needed for RW, RI inclusion –Perform parameter estimation analysis for Atlantic and east/central Pacific –Test on independent cases Year 2 –Repeat analysis for western North Pacific –Perform real time tests Leverage Proving Ground and Joint Hurricane Testbed projects at CIRA –Evaluate real time tests, adjust algorithm as needed –Implement in NHC/JTWC operations if appropriate 12

g. Funding Request (K) Funding SourcesProcurement Office Purchase Items FY12FY13 GIMPAPStAR Total Project Funding 49,00050,000 StAR Grant to CIRA 46,30046,000 StAR Federal Travel 2,7002,000 StAR Federal Publication 2,000 StAR Federal Equipment StAR Transfers to other agencies Other Sources 13

g. Spending Plan FY12 $49,000 Total Project Budget 1.Grant to CIRA – 46, CIRA FTE 2.Federal Travel – 2,700 (M. DeMaria to AMS tropical conf., Ponte Vedra, FL, Apr 2012) FY13 $50,000 Total Project Budget 1.Grant to CIRA – 45, CIRA FTE 2.Federal Travel – 2,200 (PI to Interdepartmental Hurricane Conference) 3.Federal Publications – 2,000 14