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TC Modelling and Data Assimilation 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 .
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