Tropical Cyclone Modeling and Data Assimilation
Jason Sippel NOAA AOML/HRD 2021 WMO Workshop at NHC Outline
• History of TC forecast improvements in relation to model development
• Ongoing developments
• Future direction: A new model History: Error trends
Official TC Track Forecast Errors: • Hurricane track forecasts 1990-2020 have improved markedly
300 • The average Day-3
forecast location error is 200 now about what Day-1
error was in 1990 100 • These improvements are 1990 2020 largely tied to improvements in large- scale forecasts
History: Error trends
• Hurricane track forecasts have improved markedly
• The average Day-3 forecast location error is now about what Day-1 error was in 1990
• These improvements are largely tied to improvements in large- scale forecasts
History: Error trends
Official TC Intensity Forecast Errors: 1990-2020 • Hurricane intensity 30 forecasts have only
recently improved
20
• Improvement in intensity
10 forecast largely
corresponds with commencement of 0 1990 2020 Hurricane Forecast Improvement Project HFIP era
History: Error trends
HWRF Intensity Skill 40 • Significant focus of HFIP has been the 20 development of the HWRF better 0 Climo better HWRF model
-20
-40 • As a result, HWRF intensity has improved
Day 1 Day 3 Day 5 significantly over the past decade HWRF skill has improved up to 60%!
Michael
Talk focus: How better use of data, particularly from recon, has helped improve forecasts Michael
Talk focus: How better use of data, particularly from recon, has helped improve forecasts History: Using TC Observations
• US has used dropsondes Dropsonde impact on GFS TC track for TC model forecast
20 improvement since 1997
With drops better 0 • Aberson (2010, 2011) With drops worse examined impact of
-20 dropsondes in GFS
Day 1 Day 3 Day 5 • Significant track Impact of dropsondes in September 2008 improvement globally
History: Using TC Observations
Observations Analysis • Starting in 2008, it became apparent that assimilating 88D Doppler velocity could improve coastal TC forecasts Observations Analysis • Assimilating radar data significantly improved analyses and forecasts of Hurricane Humberto
History: Using TC Observations
Fcst. & Obs. Maximum winds
40 Observation Forecasts • Starting in 2008, it became apparent that
20 assimilating 88D Doppler
velocity could improve NO Doppler coastal TC forecasts 0 40 • Assimilating radar data
significantly improved 20 analyses and forecasts of Hurricane Humberto WITH Doppler 0 Date è
History: Using TC Observations
Experimental & Operational Operational Wind Errors (No TDR) • Subsequent work 20 showed forecast
15 improvements from assimilating tail Doppler 10 radar (TDR) velocity from NOAA recon 5 Experimental (with TDR)
Day 1 Day 3 Day 5 • These results led to a
Maximum wind errors from operational dedicated effort to forecasts (no TDR) and an experimental system that assimilated TDR data. assimilate TDR operationally
History: Using TC Observations
Fcst. & Obs. Maximum winds
80 NO TDR DATA • HWRF forecast TDR data began being assimilated in HWRF in
2013 40 * * Observed • For weak storms like 80 WITH TDR DATA Karen (left), there was
substantial improvement of a positive intensity 40 * bias in HWRF *
Day 1 Day 3 Day 5
History: Using TC Observations
• Results worse over larger sample 2013 HWRF recon impact: Intensity
• Major problem No recon 20 Recon was short-term forecast degradation
10
• Cause was physics Larger errors and data with recon assimilation 0 deficiencies for Day1 Day2 Day3 Day4 Day5 strong storms
History: HWRF improvements
Fcst. & Obs. Maximum winds • Increasing resolution AND improving physics
80 (diffusion/mixing) are
necessary
40 • The challenge is to make
Observation physics changes that HWRF: CTRL don’t make every TD a HWRF: High-res + Improve Phys. 0 Cat 5
Experimental OU HWRF forecasts of RI of Hurricane Patricia
History: HWRF improvements
Experimental & Operational Intensity Errors • Data assimilation
10 improvements are also Operational HWRF necessary OU: 3D-EnsVar OU: 4D-Ensvar
6 • Experimental OU system with better data
assimilation system 2 performs much better
Day 1 Day 3 Day 5 Vmax errors in operational HWRF vs the experimental OU HWRF system History: HWRF improvements History: HWRF improvements
CURRENT OBSERVATIONS ASSIMILATED BY HWRF INCLUDE: • Conventional observations (radiosondes, dropwindsondes, aircraft, ships, buoys, surface observations over land, scatterometer, etc) • NEXRAD 88-D Doppler velocity • ALL reconnaissance (HDOB, TDR) • Atmospheric motion vectors • Clear-sky satellite radiance observations History: HWRF improvements
• Recon benefit assessed in 2016-2018 Intensity error in 2019 HWRF high impact storms No recon 20 Recon
• Many major hurricanes
in this sample 10
• Recon has a clear 0 positive impact on Day1 Day2 Day3 Day4 Day5 intensity, 10-15% improvement through 72h History: Recent Performance
Intensity skill: Near-CONUS • Model intensity skill varies HWRF greatly by region 60 CTCX IVCN • Highest skill is where we have 40
the most data (esp. HWRF) 20
0 Where the P3 flies (circles) Intensity skill: MDR HWRF 60 CTCX Near- IVCN CONUS 40 MDR 20
0 Day 1 Day 3 Day 5
History: Recent Changes
Example of end-point drop positions “End-point” dropsondes from USAF C-130 missions • Dropsondes at end-points of “alpha” pattern from C-130 missions tested in 2017
• Data denial tests suggested a Impact on intensity skill 10% impact on intensity skill 30
15 • Based on these results, this Positive 0 practice was implemented Negative -15 operationally in 2018 -30 Day 1 Day 3 Day 5
Brief summary
• Track and intensity errors are both improving
• DA & Physics improvements jointly improve model performance
• Significant improvements in HWRF DA system and data usage Outline
• History of TC forecast improvements in relation to model development
• Ongoing developments
• Future direction: A new model Ongoing developments
• Upgrade to GFSV16 in March included better Additional recon impact on GFS track HB20 (basin-scale H220) use of dropsondes and HB20 – no dropsondes flight-level data 12
8
• Added data improves 4 Added data better entire NATL sample track 0 Mesonet test: Intensity Error (kt) Added data worse
by ~5% -4 Day 1 Day 3 Day 5 Day 7 • Higher impact in cycles with data & strong storms Ongoing developments
• Ongoing work assessing how best to deploy dropsondes Dropsonde Test: Intensity Error using basin-scale HWRF ALL DROPSONDES NO DROPSONDES
• Dropsondes directly benefit track by 5-10% and intensity by 10-15% Mesonet test: Intensity Error (kt)
• Removing dropsondes anywhere (e.g., inner core vs. environment, etc.) has Day 1 Day 3 Day 5 negative consequences Ongoing developments
Mesonet test: Track Error (km) • Majority of HWRF H221 development thus far has 200 H221 + MESONET/METAR
focused over ocean 100
• Known physics issues over 0 land need to be addressed Mesonet test: Intensity Error (kt)
20 • Major sources of data over land not currently assimilated 10
0 Day 1 Day 3 Day 5
Ongoing developments
Mesonet test: Track Error (km) • Ongoing work is examining H221 the impact of mesonet and 200 H221 + MESONET/METAR
METAR data on HWRF 100
• Initial results show a large 0 positive track benefit and Mesonet test: Intensity Error (kt) smaller benefit for intensity 20 and other metrics
10
0 Day 1 Day 3 Day 5
Ongoing developments
Improving the DA system improves analyses
High-frequency full cycling alleviates imbalance.
3DEnVAR – 6h 3DEnVAR – 1h
Courtesy Xuguang Wang, HFIP partner Ongoing developments
Improving the DA system improves analyses
4DEnVAR alleviates imbalance as well.
3DEnVAR – 6h 4DEnVAR – 6h
Courtesy Xuguang Wang, HFIP partner Outline
• History of TC forecast improvements in relation to model development
• Ongoing developments
• Future direction: A new model Future direction: HAFS (Hurricane Analysis and Forecast System)
Future direction: HAFS (Hurricane Analysis and Forecast System)
MAJOR BENEFITS OF HAFS: • More flexible / capable data assimilation system than HWRF • Much better use of satellite data than HWRF • Realistic storm interaction, not possible in HWRF
RESULT: • Better initialization of vortex and environment • Improved track and intensity forecasts
Conclusions
• NOAA TC prediction is undergoing dramatic advancements, lead by improvements in global models and HWRF
• We are using more of the available data in DA
• Long term plans address ongoing issues and allow for greater data usage
• The above factors should contribute to intensity improvement in particular