Use of Incremental Analysis Updates in 4D-Var Data Assimilation

The four-dimensional variational （4D-Var） data assimilation systems used in most operational and research centers use initial condition increments as control variables and adjust initial increments to find optimal analysis solutions. This approach may sometimes create discontinuities in analysis fields and produce undesirable spin ups and spin downs. This study explores using incremental analysis updates （IAU） in 4D-Var to reduce the analysis discontinuities. IAU-based 4D-Var has almost the same mathematical formula as conventional 4D-Var if the initial condition increments are replaced with time-integrated increments as control variables. The IAU technique was implemented in the NASA/GSFC 4D-Var prototype and compared against a control run without IAU. The results showed that the initial precipitation spikes were removed and that other discontinuities were also reduced, especially for the analysis of surface temperature.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

Satellite Data Assimilation of Upper-Level Sounding Channels in HWRF with Two Different Model Tops

The Advanced Microwave Sounding Unit-A（AMSU-A） onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites（EUMETSAT）Met Op-A, the hyperspectral Atmospheric Infrared Sounder（AIRS） onboard Aqua, the High resolution Infra Red Sounder（HIRS） onboard NOAA-19 and Met Op-A, and the Advanced Technology Microwave Sounder（ATMS） onboard Suomi National Polar-orbiting Partnership（NPP） satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting（HWRF） system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

REVIEW OF RECENT PROGRESS IN TROPICAL CYCLONE TRACK FORECASTING AND EXPRESSION OF UNCERTAINTIES

The Ninth International Workshop on Tropical Cyclones(IWTC-9) took place in Hawaii, USA in December 2018. This review paper was presented at the Workshop under the Tropical Cyclone Track topic.The forecasting of tropical cyclone(TC) track has seen significant improvements in recent decades both by numerical weather prediction models and by regional warning centres who issue forecasts having made use of these models and other forecasting techniques. Heming and Goerss(2010) gave an overview of forecasting techniques and models available for TC forecasting, including evidence of the improvement in performance over the years. However, the models and techniques used for TC forecasting have continued to develop in the last decade. This presentation gives an updated overview of many of the numerical weather prediction models and other techniques used for TC track prediction. It includes recent performance statistics both by the models and the regional warning centres.

A DEVELOPMENTAL FRAMEWORK FOR IMPROVING HURRICANE MODEL PHYSICAL PARAMETERIZATIONS USING AIRCRAFT OBSERVATIONS

As part of NOAA’s Hurricane Forecast Improvement Program(HFIP),this paper addresses the important role of aircraft observations in hurricane model physics validation and improvement.A model developmental framework for improving the physical parameterizations using quality-controlled and post-processed aircraft observations is presented,with steps that include model diagnostics,physics development,physics implementation and further evaluation.Model deficiencies are first identified through model diagnostics by comparing the simulated axisymmetric multi-scale structures to observational composites.New physical parameterizations are developed in parallel based on in-situ observational data from specially designed hurricane field programs.The new physics package is then implemented in the model,which is followed by further evaluation.The developmental framework presented here is found to be successful in improving the surface layer and boundary layer parameterization schemes in the operational Hurricane Weather Research and Forecast(HWRF) model.Observations for improving physics packages other than boundary layer scheme are also discussed.

REAL-TIME FORECASTS OF TYPHOON RAPID INTENSIFICATION IN THE NORTH WESTERN PACIFIC BASIN WITH THE NCEP OPERATIONAL HWRF MODEL

This study presents the real-time performance of the United States(US) National Centers for Environmental Prediction(NCEP) operational Hurricane Weather Research and Forecast(HWRF) model in predicting rapid intensification(RI) of typhoons in the North Western Pacific(WPAC) basin in 2013. Examination of all RI cases in WPAC during 2013 shows that the HWRF model captures a consistent vortex structure at the onset of all RI as seen in previous idealized studies with HWRF. However, HWRF has issues with predicting RI when the model vortex is initialized with intensity greater than hurricane strength. Further verification of the probability of detection(POD) and the false alarm rate(FAR) of RI forecasts shows that the HWRF model outperforms all other models used by the US Navy’s Joint Typhoon Warning Center, possessing highest POD and lowest FAR in 2013. Examination of the intensity change forecasts at different forecast lead times also confirms that the HWRF model has superior performance, particularly at the 72-h lead time with the POD index ~0.91 and the FAR index ~0.33. Such unique performance of the HWRF model demonstrates its role in helping operational agencies improve their official intensity(and RI) forecasts for tropical cyclones in the WPAC basin.

HWRF BASED ENSEMBLE PREDICTION SYSTEM USING PERTURBATIONS FROM GEFS AND STOCHASTIC CONVECTIVE TRIGGER FUNCTION

In this study, an ensemble prediction system(EPS) for the operational Hurricane Weather Research and Forecast(HWRF) model at the Environmental Modeling Center(EMC) of National Centers for Environmental Prediction(NCEP) is introduced and evaluated. The HWRF-EPS takes into account two main sources of uncertainties related to the initial/boundary conditions and the model physics by 1) using the large scale fields from NCEP Global Ensemble Forecast System(GEFS);and 2) stochastically perturbing the convective trigger function in the cumulus convection parameterization scheme.Verification for the 2011-2012 North Atlantic hurricane seasons shows that HWRF-EPS outperforms its deterministic versions at all lead times for both track and intensity forecast errors. Statistical characteristics are investigated and analyzed to demonstrate the effectiveness and robustness of the HWRF-EPS. The relationship between ensemble spread and forecast error for track and intensity in the HWRF-EPS indicated that the spread is likely more useful as a predictor of forecast error when it has moderately low values. Rank histogram analysis shows that the HWRF-EPS is well dispersed in both track and intensity forecasts except for the systematic errors inherited from the deterministic version. Further comparison with 2012 hurricane season’s top-flight models shows improved track and intensity forecasts from the HWRF-EPS.

ADVANCING THE STATE OF THE ART IN OPERATIONAL TROPICAL CYCLONE FORECASTING AT NCEP

Regional Hurricane modeling systems developed and implemented into operations at National Centers for Environmental Prediction(NCEP)of National Oceanic and Atmospheric Administration(NOAA)National Weather Service(NWS)are now used for tropical cyclone forecast guidance in all ocean basins of the world.Lately,HWRF(Hurricane Weather Research and Forecast)modeling system has made significant improvements to the state of the art in numerical guidance for tropical cyclone track,intensity,size,structure and rainfall forecasts.These improvements come from advances in various components of the modeling system that are incorporated into the model in yearly upgrade cycles.NWS/NCEP/Environmental Modeling Center’s hurricane team has also developed another non-hydrostatic hurricane model in NOAA Environmental Modeling System(NEMS)framework known as HMON(Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic)model which was implemented at NCEP operations this past year.Development of HMON is consistent with,and a step closer to developing Next Generation Global Prediction System(NGGPS)chosen Finite Volume Cubed-Sphere(FV3)dynamic core based global to local scale coupled models in a unified modeling framework.In this paper,operational configuration details of this new HMON model are discussed along with operational HWRF model upgrades,and their forecast performance is compared to other models.We also discuss plans for hurricane model improvements in the next two to five years.