Improving the Four-Dimensional Incremental Analysis Update (4DIAU) with the HWRF 4DEnVar Data Assimilation System for Rapidly Evolving Hurricane Prediction

Short-term spin-up for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU).In the traditional 4DIAU, increments are pre-determined by subtracting background forecasts from analyses. Such pre-determined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapid-evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with hurricane Patricia (2015).Results from both oscillation model and HWRF model show that: 1. the pre-determined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution. 2. 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the over-filtering. 3. Relocating initial background before the incremental update can improve the corresponding traditional methods. 4. the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments.

Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model

A new data set of tropical cyclone winds (‘TC-winds’) through rain as observed by the WindSat and AMSR2 microwave radiometers has been developed by making use of a linear combination of C- and X-band frequency channels. These winds, along with tropical cyclone winds from the SMAP L-band radiometer, are compared with the Hurricane Weather Research and Forecasting (HWRF) model. Due to differences in spatial scales between the satellites and the high-resolution HWRF model, resampling must be performed on the model winds before comparisons are done. Various ways of spatial resampling are discussed in detail, and an optimal method is determined. Additionally, resampled model winds must be temporally interpolated to the time of the satellite before direct comparisons are made. This interpolation can occasionally result in un-physical 2D wind fields, especially for fast-moving storms. To assist users with this problem, a methodology for handling un-physical wind features is detailed. Results of overall comparisons between the satellites and HWRF for 19 storms between 2017 and 2020 displayed consistent storm features, with overall average biases less than 1 m/s and standard deviations below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. Differences were seen when the comparisons were performed separately for the Atlantic and Pacific basins, with biases and standard deviations between the satellites and HWRF showing better agreement in the Atlantic. The impact of rain on the satellite wind retrievals is discussed, and no systematic bias was seen between the three sensors, despite the fact that they use different frequency channels in their tropical cyclone winds-through-rain retrieval algorithms.

The irreplaceable utility of sequential data assimilation for numerical weather prediction system development: Lessons learned from an experimental HWRF system

Limited-area numerical weather prediction models currently run operationally in the United States follow a “partially-cycled” schedule, where sequential data assimilation is periodically interrupted by replacing model states with solutions interpolated from a global model. While this strategy helps overcome several practical challenges associated with real-time regional forecasting, it is no substitute for a robust sequential data assimilation approach for research-to-operations purposes. Partial cycling can mask systematic errors in weather models, data assimilation systems, and data pre-processing techniques, since it introduces information from a different prediction system. It also adds extra heuristics to the model initialization steps outside the general Bayesian filtering framework from which data assimilation methods are derived. This study uses a research-oriented modeling system, which is self-contained in the operational Hurricane Weather Research and Forecasting (HWRF) model package, to illustrate why next-generation modeling systems should prioritize sequential data assimilation at early stages of development. This framework permits the rigorous examination of all model system components—in a manner that has never been done for the HWRF model. Examples presented in this manuscript show how sequential data assimilation capabilities can accelerate model advancements and increase academic involvement in operational forecasting systems at a time when the United States is developing a new hurricane forecasting system.

Impact of Increasing Horizontal and Vertical Resolution during the HWRF Hybrid EnVar Data Assimilation on the Analysis and Prediction of Hurricane Patricia (2015)

<p>Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking.  In this study, using the ensemble-variational DA system for Hurricane Weather Research and Forecasting (HWRF) model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015).  The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia.</p><p>The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage.  These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage.  Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages.  Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary, secondary circulation, and warm core structures via the increased resolution in DA lead to improved TC intensity forecasts.</p>

A New Horizontal Mixing-Length Formulation for Numerical Simulations of Tropical Cyclones

A new physically-based horizontal mixing-length formulation is introduced and evaluated in the Hurricane Weather and Research Forecast (HWRF) model. Recent studies have shown that the structure and intensity of tropical cyclones (TCs) simulated by numerical models are sensitive to horizontal mixing length in the parameterization of horizontal diffusion. Currently, many numerical models including the operational HWRF model formulate the horizontal mixing length as a fixed fraction of grid spacing or a constant value, which is not realistic. To improve the representation of the horizontal diffusion process, the new formulation relates the horizontal mixing length to local wind and its horizontal gradients. The resulting horizontal mixing length and diffusivity are much closer to those derived from field measurements. To understand the impact of different mixing-length formulations, we analyze the evolutions of an idealized TC simulated by the HWRF model with the new formulation and with the current formulation (i.e., constant values) of horizontal mixing length. In two real-case tests, the HWRF model with the new formulation produces the intensity and track forecasts of Hurricanes Harvey (2017) and Lane (2018) that are much closer to observations. Retrospective runs of hundreds of forecast cycles of multiple hurricanes show that the mean errors in intensity and track simulated by HWRF with the new formulation can be reduced approximately by 10%.

Impact of increasing horizontal and vertical resolution during the HWRF hybrid EnVar data assimilation on the analysis and prediction of Hurricane Patricia (2015)

Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for Hurricane Weather Research and Forecasting (HWRF) model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia.The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary, secondary circulation, and warm core structures via the increased resolution in DA lead to improved TC intensity forecasts.

Vortex Initialization in the NCEP Operational Hurricane Models

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model).

Evaluation of the Grell–Freitas Convective Scheme in the Hurricane Weather Research and Forecasting (HWRF) Model

The Developmental Testbed Center (DTC) tested two convective parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model and compared them in terms of performance of forecasting tropical cyclones (TCs). Several TC forecasts were conducted with the scale-aware Simplified Arakawa Schubert (SAS) and Grell–Freitas (GF) convective schemes over the Atlantic basin. For this sample of over 100 cases, the storm track and intensity forecasts were superior for the GF scheme compared to SAS. A case study showed improved storm structure for GF when compared with radar observations. The GF run had increased inflow in the boundary layer, which resulted in higher angular momentum. An angular momentum budget analysis shows that the difference in the contribution of the eddy transport to the total angular momentum tendency is small between the two forecasts. The main difference is in the mean transport term, especially in the boundary layer. The temperature tendencies indicate higher contribution from the microphysics and cumulus heating above the boundary layer in the GF run. A temperature budget analysis indicated that both the temperature advection and diabatic heating were the dominant terms and they were larger near the storm center in the GF run than in the SAS run. The above results support the superior performance of the GF scheme for TC intensity forecast.

Influence of Assimilating CYGNSS Ocean Surface Wind Data on Tropical Cyclone Analyses and Predictions

<div> <div><em>For the analysis and forecasting of tropical cyclones, the main benefits of data from the CYGNSS constellation of satellites are the increased revisit frequency compared with polar-orbiting satellites and the ability to provide ocean surface wind observations through convective precipitation. Consequently, CYGNSS delivers an improved capability to observe the structure and evolution of ocean surface winds in and around tropical cyclones. This study quantifies the impact of assimilating CYGNSS delay-Doppler maps, CYGNSS retrieved wind speeds and derived CYGNSS wind vectors on 6-hourly analyses and 5-day forecasts of developing tropical cyclones, using the 2019 version of NOAA's operational Hurricane Weather Research and Forecasting (HWRF) model.</em></div> </div>