Some Thoughts on HFIP Bob Gall
Thanks! For putting up with me for the last six years , For helping to create a Hurricane Project that I believe has been very successful • I will be mostly retiring at the end of the year • Vijay will be taking over as Development Director Rather than the usual outline of HFIP technical achievements in the past year—much of which you will hear from the team reports-- I thought I would outline some of my thoughts and comments about HFIP
Reasons for the success of HFIP • It had significant funding—you can’t make much happen without sufficient resources • It brought together a broad community -- research to development to operational implementation to work collectively on the hurricane problem. • It facilitated communication within a large group – Bi-Weekly telecons, annual meeting, workshops • Community participation is developing project plans—the teams • It developed facilities – DTC for code access and testing—provides basic link R2O – A dedicated large computer facility The community effort allowed a large group of scientists to focus on a single problem
Some general Comments • Initialization remains HFIP’s biggest problem • Don’t ignore the global model • A question about statistical significance • Don’t ignore Ensembles • More community focus on developing physics packages • Comments on Recent HFIP Performance
Don’t ignore the global model • You will hear a recommendation from the SRC to cease any focus on the global model problem – I am not sure that is good advice – SRC feels we should focus on initialization/physics and the first 1-3 days—focus on the regional model • But the global model is still a central part of any regional system—to the HFIP goals • There is a sense within NOAA that the NGGPS project will take care of the global model • But there problems/issues with the global models that are unique to the hurricane problem – There needs to be some way to insure that they are appropriately addressed
The HFIP Project Vision/Goals • Vision – Organize the hurricane community to dramatically improve numerical forecast guidance to NHC in 5-10 years • Goals – Reduce numerical forecast errors in track and intensity by 20% in 5 years, 50% in 10 years – Extend forecast guidance to 7 days with skill comparable to 5 days at project inception – Increase probability of predicting rapid intensification at day 1 to 90% and 60% at day 5 6 6
NCEP vs ECMWF for Atlantic 2006-2008 % gain over HFIP baseline (track) GFS ECMWF 7
NCEP vs ECMWF for Atlantic 2012 % gain over HFIP baseline (track) GFS ECMWF 8
The Initialization Problem • There is no doubt that initialization remains a major problem for the program – The problem pretty much eliminates the value of the regional model forecasts in the 0-2 day range (intensity) – Forecasts of RI by a model during these first 2 days have little reliability • The problem is likely mostly related to initial conditions that are inconsistent with the model dynamics/physics • The initialization will likely ultimately be solved through data assimilation – But the resultant initial flow will need to be model consistent somehow • Improved data will help but it isn’t the main problem
Stream 2.0 Skill (AL Intensity) ------ Smaller sample size ------ Decay SHIPS shows highest model skill ----- APSI skill gain is largest through 72 h -----
Stream 1.5 Skill (AL Intensity) ------ Statistical- dynamical configurati ons show highest skill including SPC3 ----- Dynamical models transition from (-) to (+) skill with lead time ----- CXTI and UW4I show lowest skill
Stream 1.5 Skill (EP Intensity) ------ CXTI and HWFI show highest skill ----- Statistical- dynamical configuratio ns generally lose skill with lead time ----- HFIP 5-yr skill goal met intermittent ly -----
Question about statistical significance • Recently there has been a lot of emphasis on looking at the statistical significance of error comparisons – Such as the impact of some change compared to a control run • There is no doubt that this is very important in some settings • But note that almost all tests of the impact of some change in the model at NCEP are not statistically significant – Yet they are used to make decisions on model changes – And the models get better.
Impact of Radar Data
Don’t Ignore Ensembles • I probably don’t have to say this to ensemble people – But they seem to want to focus on probabilities • But most folks want to know when and where NHC thinks the hurricane is going to hit and how strong it will be when it does— which is a deterministic forecast • Ensembles give some information like that—ensemble mean – But we are throwing away a huge amount of information that can be used to improve a deterministic forecast from both multi-model and single ensembles. • This isn’t a criticism of forecasters – It is a criticism of the project that hasn’t put enough emphasis on developing simple tools for extracting this information from the ensemble and presenting them to the forecaster
Emphasis on Physics Packages • The two primary areas where we can improve the models is Initialization and improved physics packages • In my opinion we need more focus within the broad community (outside the operational centers) on developing/testing/improving physics packages – Particularly the university community – I am not sure why physics gets less emphasis in the research community than say data assimilation (and cores)
Comments on Recent HFIP Performance
Operational Intensity Forecast Trends* and HFIP Goals *Courtesy NHC: 2013 results are preliminary, subject to revision
HWRF Intensity ATL Basin Cumulative Forecast Improvements Improving 15-20% per year since 2011 2013 version is approaching 5 year goal 23
Stream 1.5 Skill (AL Intensity) ------ Statistical- dynamical configuratio ns show highest skill including SPC3 ----- Dynamical models transition from (-) to (+) skill with lead time ----- CXTI and UW4I show lowest skill most lead times -----
Stream 2.0 Skill (AL Intensity) ------ Smaller sample size ------ Decay SHIPS shows highest model skill ----- APSI skill gain is largest through 72 h -----
Stream 1.5 Skill (AL Track) ------ GPMI lowest skill for most lead times, but still higher than HFIP 5-yr skill goal ----- HWRF highest skill among operation al models
Stream 2.0 Skill (AL Track) ------ Smaller sample size ----- HWRF and FIM configurati ons show highest skill most lead times ----- HFIP 5-yr skill goal surpassed for most lead times -----
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