Hurricane Weather Research And Forecasting Research Articles

High-Definition Hurricanes: Improving Forecasts with Storm-Following Nests

Abstract To forecast tropical cyclone (TC) intensity and structure changes with fidelity, numerical weather prediction models must be “high definition,” i.e., horizontal grid spacing ≤ 3 km, so that they permit clouds and convection and resolve sharp gradients of momentum and moisture in the eyewall and rainbands. Storm-following nests are computationally efficient at fine resolutions, providing a practical approach to improve TC intensity forecasts. Under the Hurricane Forecast Improvement Project, the operational Hurricane Weather Research and Forecasting (HWRF) system was developed to include telescopic, storm-following nests for a single TC per model integration. Subsequently, HWRF evolved into a state-of-the-art tool for TC predictions around the globe, although its single-storm nesting approach does not adequately simulate TC–TC interactions as they are observed. Basin-scale HWRF (HWRF-B) was developed later with a multistorm nesting approach to improve the simulation of TC–TC interactions by producing high-resolution forecasts for multiple TCs simultaneously. In this study, the multistorm nesting approach in HWRF-B was compared with a single-storm nesting approach using an otherwise identical model configuration. The multistorm approach demonstrated TC intensity forecast improvements, including more realistic TC–TC interactions. Storm-following nests developed in HWRF and HWRF-B will be foundational to NOAA’s next-generation hurricane application in the Unified Forecast System.

The NOAA Track-Wise Wind Retrieval Algorithm and Product Assessment for CyGNSS

A novel approach in addressing cyclone global navigation satellite system (CyGNSS) intersatellite and GPS-related calibration issues is proposed, based on a track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction method. This method makes use of both ancillary data from numerical weather prediction models and a semiempirical geophysical model function. Care is taken, so the track-wise <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> bias correction maintains CyGNSS signal sensitivity. Both intersatellite and GPS-related calibration issues are removed after correction. Long-term <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> downward trend, observed throughout the CyGNSS mission, is greatly reduced. Using the corrected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma ^{o}$ </tex-math></inline-formula> measurements, a wind retrieval method is also presented and its product thoroughly assessed for a three-year period against European Centre for Medium-Range Weather Forecasts (ECMWFs), Advanced Scatterometer (ASCAT) A/B, Advanced Microwave Scanning Radiometer (AMSR)-2, GMI, WindSat, hurricane weather research and forecasting (HWRF) model, and the stepped frequency microwave radiometer (SFMR) winds. The overall wind speed bias and standard deviation of the error (stde) against ECMWF are 0.16 and 1.19 m/s, while these are −0.11 and 1.12 m/s against ASCAT A/B, respectively. The same metrics against AMSR-2/GMI/WindSat (combined) are −0.19 and 1.11 m/s, respectively. The bias and stde against soil moisture active passive (SMAP) are −0.38 and 1.90 m/s, respectively. In the tropical cyclone environment, the bias and stde against HWRF are −0.54 and 2.90 m/s, and −4.71 and 5.88 m/s with SFMR. Finally, CyGNSS wind performance is gauged in the presence of rain. Below 10 m/s, the bias between CyGNSS and ECMWF increases as the rain rate increases. Between 10 and 15 m/s, biases are mostly absent. Above 15 m/s, results are inconclusive due to the low number of collocated rain samples. Overall, the presented CyGNSS wind speed product both exhibits consistency and reliability, showing promise of using GNSS-R derived winds for operational purposes.

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

AbstractShort-term spinup 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 to be helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU). In the traditional 4DIAU, increments are predetermined by subtracting background forecasts from analyses. Such predetermined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapidly 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 the oscillation model and HWRF Model show that 1) the predetermined 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 overfiltering; 3) relocating initial background before the incremental update can improve the corresponding traditional methods; and 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.

Comparative performance of HWRF model coupled with POM and HYCOM for tropical cyclones over North Indian Ocean

The demonstration of air-sea interaction in the high-resolution numerical weather prediction (NWP) model is the necessary condition to predict the extreme event like tropical cyclone. It is already established that the coupled mesoscale models are capable of forecasting the track and intensity of tropical cyclones with reasonable skill over North Indian Ocean (NIO) basin and over other ocean basins as well. The Hurricane Weather Research and Forecasting (HWRF) model is consequently operational by India Meteorological Department (IMD) for the forecasting tropical cyclones like many other National Meteorological and Hydrological Services. Two different coupled versions of HWRF modeling systems with feature based Princeton Ocean Model (POM) and eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) are employed for the real-time forecasting of tropical cyclones over NIO. In this study, the comparison of forecast skills between two coupled variants in predicting track and intensity for all eight cyclones during 2019 has been carried out. The results show that the track prediction of all cyclones by HWRF model coupled with HYCOM is comparatively better than that with POM. But the intensity prediction skills of both coupled models do not portray any obvious superiority for all forecast hours. The absolute intensity errors of HWRF model with POM coupling are large compared to HYCOM during initial phase up to 60 hours of forecast except for Vayu cyclone. But, the errors in intensity exhibited by POM are a bit smaller compared to HYCOM for longer lead period. However, the standard deviation is very large for both models and numbers of forecasts verified are also less. In case of two cyclones of 2020, the ability of coupled variant with HYCOM is superior to POM in forecasting the tracks of cyclones. Considering the skills of both the models for intensity prediction, it is clear that the use of coupled HCOM model for short-lived weaker storms is advantageous but the performance of the model is not consistently maintained with longer lead time for intense and long-lived cyclone. A brief assessment of surface fluxes and Sea Surface Temperature reveals that HYCOM portray more realistic ocean condition in turn surface fluxes compared to POM and therefore the coupled variant of HWRF with HYCOM is superior to the variant with POM while forecasting cyclones over deep Ocean.

Understanding the rapid intensification of tropical cyclone Titli using Hurricane WRF model simulations

Understanding Rapid Intensification (RI) is crucial for improving the Tropical Cyclone (TC) intensity forecast skill and TC induced disaster preparedness. The physical processes that lead to RI are not well studied over the North Indian Ocean (NIO) region. Two TC cases, i.e., Titli (exhibited RI) and Phethai (not exhibited RI) from the 2018 post-monsoon season are considered to understand the environmental conditions responsible for making RI happen. The Hurricane Weather Research and Forecasting (HWRF) model produced simulations for Titli and Phethai are analyzed to understand the intensification changes. The purpose of this study is to investigate the environmental and storm structure characteristics that led to the RI of TC Titli when compared to TC Phethai. The Phethai and Titli simulations are initially validated against the best estimations by India Meteorological Department (IMD) and compared in terms of the environmental vertical wind shear as this parameter is often negatively correlated with TC intensification. Area averages of the deep-layer and mid-layer vertical wind shear over the TC environment suggest that shear did not affect the intensification of the Titli simulation. Initially, Titli contained higher magnitudes of relative humidity throughout the troposphere within the storm’s circulation. This steered to a higher upward mass flux in the troposphere and rapid intensification of relative vorticity (circulation) in the middle troposphere prior to any significant change ensued at the surface. After a deep vortex was established, the mass flux (and lower-tropospheric convergence) continued to rise, leading to RI. These results offer a basis for future research to understand better and forecast the development of RI.

A Statistical Analysis of High-Frequency Track and Intensity Forecasts from NOAA’s Operational Hurricane Weather Research and Forecasting (HWRF) Modeling System

AbstractA statistical analysis is performed on the high-frequency (3⅓ s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) Model for all tropical cyclones (TCs) in the North Atlantic Ocean basin over a 3-yr period (2017–19). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax, with average values of 6–8 km. The Vmax fluctuations were as high as 20 kt (10.3 m s−1) in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt (2.8 m s−1) for category-2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running-mean windows of 3–9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

A Comparative Modeling Study of Supertyphoons Mangkhut and Yutu (2018) Past the Philippines with Ocean-Coupled HWRF

The ocean-coupled Hurricane Weather Research and Forecasting (HWRF) system was used to investigate the evolution of Supertyphoons Mangkhut and Yutu (2018) over the Philippines Sea and near landfall in the northern Philippines. The simulation results indicate that Mangkhut at a deepening stage has a smaller track sensitivity to the use of different physics schemes but greater intensity sensitivity, which becomes reversed for Yutu at a weakening stage. When both upstream tracks are well simulated with some specific suite of physics schemes, sensitivity experiments indicate that both track deviations near the northern Philippines are only weakly modified by the air–sea interaction (ocean-coupled or uncoupled processes), the topographic effects of the Philippines terrain (retained or not), and the initial ocean temperature change along both typhoon tracks. The interactions between the internal typhoon vortex and the large-scale flow play an important role in the overall movement of both typhoons, which were explored for their structural and convective evolutions near the terrain. The wavenumber-one potential vorticity (PV) tendency budget of the typhoon vortex was analyzed to explain the induced typhoon translation from different physical processes. The west-northwestward translation for the stronger Mangkhut near the northern Philippines is primarily induced by both horizontal and vertical PV advection but with the latter further enhanced to dominate the northward deflection when closing in to the terrain. However, the northwestward translation and track deflection near landfall for the weaker Yutu are driven by the dominant horizontal PV advection. Differential diabatic heating is relatively less important for affecting the movement of both typhoons near landfall.

A Comparison of HWRF Six-Hourly 4DEnVar and Hourly 3DEnVar Assimilation of Inner Core Tail Dopper Radar Observations for the Prediction of Hurricane Edouard (2014)

Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) DA methods for the analyses and forecasts of hurricanes with rapidly evolving BEC. Both methods account for evolving BEC in a hybrid EnVar DA system. In order to compare these methods, experiments are conducted by assimilating inner core Tail Doppler Radar (TDR) wind for Hurricane Edouard (2014) and by running the Hurricane Weather Research and Forecasting (HWRF) model. In most metrics, 1H-3DEnVar and 6H-4DEnVar analyses and forecasts verify better than 6H-3DEnVar. 6H-4DEnVar produces better thermodynamic analyses than 1H-3DEnVar. Radar reflectivity shows that 1H-3DEnVar produces better structure forecasts. For the first 24–48 h of the intensity forecast, 6H-4DEnVar forecast performs better than 1H-3DEnVar verified against the best track. Degraded 1H-3DEnVar forecasts are found to be associated with background storm center location error as a result of underdispersive ensemble storm center spread. Removing location error in the background improves intensity forecasts of 1H-3DEnVar.

Uncertainty of Tropical Cyclone Wind Radii on Sea Surface Temperature Cooling

AbstractThe sea surface temperature (SST) beneath a tropical cyclone (TC) is of great importance to its dynamics; therefore, understanding and accurately estimating the magnitude of SST cooling is of vital importance. Existing studies have explored important influences on SST of TC translation speed, maximum surface winds, ocean thermal condition, and ocean stratification. But the influence of the TC wind radii (or collectively called the TC size) on SST has been largely overlooked. In this study, we assess the influence of wind radii uncertainty on SST cooling by a total of 15,983 numerical simulations for the western North Pacific during the 2014–2018 seasons. Results show a 6%–20% SST cooling error induced using wind radii from the Joint Typhoon Warning Center official forecast and a 35%–40% SST cooling error using wind radii from the operational runs of the Hurricane Weather Research and Forecasting (HWRF) model. Our results indicate that SST cooling is most sensitive to the radius of 64 kt winds (R64) due to its effects on the integrated kinetic energy of the TC and subsequent mixing of the ocean surface layer. It is also found that the correlation between SST cooling induced by the TC and its size is 0.49, which is the highest among all the parameters tested. This suggests that it is extremely important to get TC size correct in order to predict the SST cooling response, which then impacts TC evolution in numerical weather prediction models.

Important factors in the tracking of tropical cyclones in operational models

AbstractMultiple configurations of the Geophysical Fluid Dynamics Laboratory vortex tracker are tested to determine a setup that produces the best representation of a model forecast tropical cyclone center fix for the purpose of providing track guidance with the highest degree of accuracy and availability. Details of the tracking algorithms are provided, including descriptions of both the Barnes analysis used for center-fixing most variables and a separate scheme used for center-fixing wind circulation. The tracker is tested by running multiple configurations on all storms from the 2015-2017 hurricane seasons in the Atlantic and eastern Pacific Basins using forecasts from two operational National Weather Service models, the Global Forecast System (GFS) and the Hurricane Weather Research and Forecast (HWRF) model. A configuration that tracks only 850 mb geopotential height has the smallest forecast track errors of any configuration based on an individual parameter. However, a configuration composed of the mean of eleven parameters outperforms any of the configurations that are based on individual parameters. Configurations composed of subsets of the eleven parameters and including both mass and momentum variables provide results comparable to or better than the full 11-parameter configuration. In particular, a subset configuration with thickness variables excluded generally outperforms the 11-parameter mean, while one composed of variables from only the 850 mb and near-surface layers performs nearly as well as the 11-parameter mean. Tracker configurations composed of multiple variables are more reliable in providing guidance through the end of a forecast period than are tracker configurations based on individual parameters.

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.

Understanding the Role of Mean and Eddy Momentum Transport in the Rapid Intensification of Hurricane Irma (2017) and Hurricane Michael (2018)

The prediction of rapid intensification (RI) in tropical cyclones (TCs) is a challenging problem. In this study, the RI process and factors contributing to it are compared for two TCs: an axis-symmetric case (Hurricane Irma, 2017) and an asymmetric case (Hurricane Michael, 2018). Both Irma and Michael became major hurricanes that made significant impacts in the United States. The Hurricane Weather Research and Forecasting (HWRF) Model was used to examine the connection between RI with forcing from the large-scale environment and the subsequent evolution of TC structure and convection. The observed large-scale environment was reasonably reproduced by HWRF forecasts. Hurricane Irma rapidly intensified in an environment with weak-to-moderate vertical wind shear (VWS), typically favorable for RI, leading to the symmetric development of vortical convective clouds in the cyclonic, vorticity-rich environment. Conversely, Hurricane Michael rapidly intensified in an environment of strong VWS, typically unfavorable for RI, leading to major asymmetries in the development of vortical convective clouds. The tangential wind momentum budget was analyzed for these two hurricanes to identify similarities and differences in the pathways to RI. Results suggest that eddy transport terms associated with convective processes positively contributed to vortex spin up in the early stages of RI and inhibited spin up in the later stages of RI in both TCs. In the early stages of RI, the mean transport terms exhibited notable differences in these TCs; they dominated the spin-up process in Irma and were of secondary importance to the spin-up process in Michael. Favorable aspects of the environment surrounding Michael appeared to aid in the RI process despite hostile VWS.

The Irreplaceable Utility of Sequential Data Assimilation for Numerical Weather Prediction System Development: Lessons Learned from an Experimental HWRF System

AbstractLimited-area numerical weather prediction models currently run operationally in the United States and 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 preprocessing 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.

Validation of Ensemble-Based Probabilistic Tropical Cyclone Intensity Change

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h−1; 15.4 m s−1 24 h−1) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecasts.

Estimating Error Variances of a Microwave Sensor and Dropsondes aboard the Global Hawk in Hurricanes Using the Three-Cornered Hat Method

AbstractThis study estimates the random error variances and standard deviations (STDs) for four datasets: Global Hawk (GH) dropsondes (DROP), the High-Altitude Monolithic Microwave Integrated Circuit Sounding Radiometer (HAMSR) aboard the GH, the fifth European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), and the Hurricane Weather Research and Forecasting (HWRF) Model, using the three-cornered hat (3CH) method. These estimates are made during the 2016 Sensing Hazards with Operational Unmanned Technology (SHOUT) season in the environment of four tropical cyclones from August to October. For temperature and specific and relative humidity, the ERA5, HWRF, and DROP datasets all have similar magnitudes of errors, with ERA5 having the smallest. The error STDs of temperature and specific humidity are less than 0.8 K and 1.0 g kg−1 over most of the troposphere, while relative humidity error STDs increase from less than 5% near the surface to between 10% and 20% in the upper troposphere. The HAMSR bias-corrected data have larger errors, with estimated error STDs of temperature and specific humidity in the lower troposphere between 1.5 and 2.0 K and between 1.5 and 2.5 g kg−1. HAMSR’s relative humidity error STD increases from approximately 10% in the lower troposphere to 30% in the upper troposphere. The 3CH method error estimates are generally consistent with prior independent estimates of errors and uncertainties for the HAMSR and dropsonde datasets, although they are somewhat larger, likely due to the inclusion of representativeness errors (differences associated with different spatial and temporal scales represented by the data).

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

AbstractAlthough 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 the 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 circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts.

Land-Surface Diurnal Effects on the Asymmetric Structures of a Postlandfall Tropical Storm.

After a tropical storm makes landfall, its vortex interacts with the surrounding environment and the underlying surface. It is expected that diurnal variation over land will affect storm structures. However, this has not yet been explored in previous studies. In this paper, numerical simulation of postlandfall Tropical Storm Bill (2015) is conducted using a research version of the NCEP Hurricane Weather Research and Forecasting (HWRF) model. Results indicate that during the storm's interaction with midlatitude westerlies over the Great Plains, the simulated storm with the SLAB land‐surface scheme is stronger, with faster eastward movement and attenuation, and more asymmetric structures than that with the NOAH land‐surface scheme. More symmetric structures correspond with a slower weakening and slower eastward movement of the storm over land. Further diagnoses suggest an obvious response of the storm's asymmetric structures to diurnal effects over land. Surface diabatic heating in the storm environment is important for the storm's symmetric structures and intensity over land. Specifically, during the transition from nighttime to daytime, the evident strengthening of convective instability, atmospheric baroclinicity, and the lateral advection of high θe air in the storm environment, associated with the rapid increase in surface diabatic heating, are conducive to the development of vertical vorticity and storm‐relative helicity, thus contributing to the maintenance of the storm's symmetric structures and intensity after landfall.

Ice Water Content as a Precursor to Tropical Cyclone Rapid Intensification

AbstractThis study examines how the structure and amount of cloud ice water content are related to rates of tropical cyclone (TC) intensification using CloudSat profiling radar measurements and simulations from the Hurricane Weather Research and Forecasting (HWRF) model. Observational studies have demonstrated the signal of TC intensification in the passive satellite measurements of frozen water concentration. However, the vertical and horizontal resolution of passive satellite observations are limited. CloudSat measurements and HWRF simulations provide high‐resolution data sets of ice water content to better understand its relationship with the rate of TC intensification. It is found that rapidly intensifying TCs have larger ice water content compared to TCs with slower intensification rates. Similar results are obtained even after accounting for the effect of initial TC intensity. Such precursors of rapidly intensifying TCs may be used to better understand and improve the prediction of TC intensification.

A Review and Evaluation of Planetary Boundary Layer Parameterizations in Hurricane Weather Research and Forecasting Model Using Idealized Simulations and Observations

This paper reviews the evolution of planetary boundary layer (PBL) parameterization schemes that have been used in the operational version of the Hurricane Weather Research and Forecasting (HWRF) model since 2011. Idealized simulations are then used to evaluate the effects of different PBL schemes on hurricane structure and intensity. The original Global Forecast System (GFS) PBL scheme in the 2011 version of HWRF produces the weakest storm, while a modified GFS scheme using a wind-speed dependent parameterization of vertical eddy diffusivity (Km) produces the strongest storm. The subsequent version of the hybrid eddy diffusivity and mass flux scheme (EDMF) used in HWRF also produces a strong storm, similar to the version using the wind-speed dependent Km. Both the intensity change rate and maximum intensity of the simulated storms vary with different PBL schemes, mainly due to differences in the parameterization of Km. The smaller the Km in the PBL scheme, the faster a storm tends to intensify. Differences in hurricane PBL height, convergence, inflow angle, warm-core structure, distribution of deep convection, and agradient force in these simulations are also examined. Compared to dropsonde and Doppler radar composites, improvements in the kinematic structure are found in simulations using the wind-speed dependent Km and modified EDMF schemes relative to those with earlier versions of the PBL schemes in HWRF. However, the upper boundary layer in all simulations is much cooler and drier than that in dropsonde observations. This model deficiency needs to be considered and corrected in future model physics upgrades.

A Forward Model for Data Assimilation of GNSS Ocean Reflectometry Delay-Doppler Maps

Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples.