Hurricane Track Research Articles

Performance of an Improved TKE-Based Eddy-Diffusivity Mass-Flux (EDMF) PBL Scheme in 2021 Hurricane Forecasts from the Hurricane Analysis and Forecast System

Abstract Continuous development and evaluation of planetary boundary layer (PBL) parameterizations in hurricane conditions are crucial for improving tropical cyclone (TC) forecasts. A turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF-TKE) PBL scheme, implemented in NOAA’s Hurricane Analysis and Forecast System (HAFS), was recently improved in hurricane conditions using large-eddy simulations. This study evaluates the performance of HAFS TC forecasts with the original (experiment HAFA) and modified EDMF-TKE (experiment HAFY) based on a large sample of cases during the 2021 North Atlantic hurricane season. Results indicate that intensity and structure forecast skill was better overall in HAFY than in HAFA, including during rapid intensification. Composite analyses demonstrate that HAFY produces shallower and stronger boundary layer inflow, especially within 1–3 times the radius of maximum wind (RMW). Stronger inflow and more moisture in the boundary layer contribute to stronger moisture convergence near the RMW. These boundary layer characteristics are consistent with stronger, deeper, and more compact TC vortices in HAFY than in HAFA. Nevertheless, track skill in HAFY is slightly reduced, which is in part attributable to the cross-track error from a few early cycles of Hurricane Henri that exhibited ∼400 n mi (1 n mi = 1.852 km) track error at longer lead times. Sensitivity experiments based on HAFY demonstrate that turning off cumulus schemes notably reduces the track errors of Henri while turning off the deep cumulus scheme reduces the intensity errors. This finding hints at the necessity of unifying the mass fluxes in PBL and cumulus schemes in future model physics development.

Beamforming of Rayleigh and Love Waves in the Course of Atlantic Cyclones

AbstractThe main sources of the ambient seismic wavefield in the microseismic frequency band (peaking in the ∼0.04–0.5 Hz range) are earth's oceans, namely the wind‐driven surface gravity waves (SGW) that couple oscillations into the seafloor and the upper crust underneath. Cyclones (e.g., hurricanes, typhoons) and other atmospheric storms are efficient generators of high ocean waves that in turn generate distinct microseismic signatures. In this study, we perform a polarization (i.e., three‐component) beamforming analysis of microseismic (0.05–0.16 Hz) retrograde Rayleigh and Love waves during major Atlantic hurricanes using a virtual array of seismometers in Eastern Canada. Oceanic hindcasts and meteorological data are used for comparison. No continuous generation of microseism along the hurricane track is observed but rather an intermittent signal generation. Both seismic surface wave types show clear cyclone‐related microseismic signatures that are consistent with a colocated generation at near‐coastal or shallow regions, however the Love wavefield is comparatively less coherent. We identify two different kinds of intermittent signals: (a) azimuthally progressive signals that originate with a nearly constant spatial lag pointing toward the trail of the hurricanes and (b) azimuthally steady signals remaining nearly constant in direction of arrival even days after the hurricane significantly changed its azimuth. This high complexity highlights the need for further studies to unravel the interplay between site‐dependent geophysical parameters, SGW forcing at depth and microseismic wavefield radiation and propagation, as well as the potential use of cyclone microseisms as passive natural sources.

Predicting Subseasonal Tropical Cyclone Activity Using NOAA and ECMWF Reforecasts

Abstract Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.

Impact of Model Resolution on Secondary Eyewall Formation and Eyewall Replacement Cycle

AbstractDepicting Secondary Eyewall Formation (SEF) and Eyewall Replacement Cycle (ERC) in a numerical model is important for tropical cyclone (TC) forecasting. However, there is no consensus about what resolutions are appropriate to describe SEF/ERC within a full‐physics mesoscale model. In this study, numerical experiments are conducted to examine the impact of the horizontal and vertical resolutions on SEF/ERC. The mesoscale model is configured through nesting to the horizontal grid spacings of 6, 4, 2, 1.33, 0.67‐km, and with 27‐ and 54‐levels on an f‐plane in a quiescent environment. In addition, there are more levels below 1.5‐km to better describe the TC boundary layer (TCBL). The simulations with 6 and 4‐km grid spacings show no obvious SEF/ERC regardless of the number of vertical levels. When the horizontal grid spacings decrease to 2‐km or smaller, the simulations manifest SEF/ERC. These results are supported by a few simulations with the ARW model using similar configurations. Furthermore, the spectra of kinetic energy and vertical velocity from various resolutions confirm that the grid spacings should be smaller than 4‐km to resolve SEF/ERC. The impact of doubling vertical levels on the SEF/ERC is not as significant as doubling the horizontal resolutions. Finally, we discuss the coupling between the balanced/unbalanced flows (above/in the TCBL), and their effect on SEF. It is proposed that the coupled balanced/unbalanced processes that generate the quasi‐steady cooling zone in the primary eyewall and two warming regions inside and beyond the cooling zone are essential for SEF.

The Influence of Radiation on the Prediction of Tropical Cyclone Intensification in a Forecast Model

AbstractThis study examines the influence of radiative heating on the prediction of tropical cyclone (TC) intensification in the Hurricane Weather Research and Forecasting (HWRF) model. Previous idealized modeling and observational studies demonstrated that radiative heating can substantially modulate the evolution of TC intensity. However, the relevance of this process under realistic conditions remains unclear. Here, we use observed longwave radiative heating to explore the performance of TC forecasts in HWRF simulations. The performance of TC intensity forecasts is then investigated in the context of radiative heating forecasts. In observations and HWRF forecasts, high clouds near the TC center increase the convergence of radiative fluxes. A sharp spatial gradient (≥60 W/m2) in the flux convergence from the TC center outward toward the environment is associated with subsequent TC intensification. More accurate simulation of the spatial structure of longwave radiative heating is associated with more accurate TC intensity forecasts.

Automatic Center Detection of Tropical Cyclone Using Image Processing Based on the Operational Radar Network

This study presents the algorithm ACTION, defined as automatic center detection of tropical cyclone (TC) using image processing based on the operational radar network. Based on the high visibility of weather radar imagery, a TC’s motion vector is calculated from the continuous image change using optical flow, producing the TC’s rotation center. The algorithm performance was verified by analyzing the typhoons (TCs in the northwestern Pacific) that affected the Korean Peninsula from 2018–2021, demonstrating a high detection rate of 80.8% within an error distance of 40 km against the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. As ACTION is capable of performing real-time calculations that are directly applied to rapidly generated weather radar images, it is currently being utilized by the KMA. The high temporal resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting.

Evaluating western North Pacific tropical cyclone forecast in the subseasonal to seasonal prediction project database

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.

Impacts of Observation Forward Operator on Infrared Radiance Data Assimilation with Fine Model Resolutions

Abstract All-sky radiance assimilation often has non-Gaussian observation error distributions, which can be exacerbated by high model spatial resolutions due to better resolved nonlinear physical processes. For ensemble Kalman filters, observation ensemble perturbations can be approximated by the linearized observation operator (LinHx) that uses the observation operator Jacobian of ensemble mean rather than the full observation operator (FullHx). The impact of observation operator on infrared radiance data assimilation is examined here by assimilating synthetic radiance observations from channel 1025 of GIIRS with increased model spatial resolutions from 7.5 km to 300 m. A tropical cyclone is used, while the findings are expected to be generally applied. Compared to FullHx, LinHx provides larger magnitudes of correlations and stronger corrections around observation locations, especially when all-sky radiances are assimilated at fine model resolutions. For assimilating clear-sky radiances with increasing model resolutions, LinHx has smaller errors and improved vortex intensity and structure than FullHx. But when all-sky radiances are assimilated, FullHx has advantages over LinHx. Thus, for regimes with more linearity, LinHx provides stronger correlations and imposes more corrections than FullHx; but for regimes with more nonlinearity, LinHx provides detrimental non-Gaussian prior error distributions in observation space, unrealistic correlations, and overestimated corrections, compared to FullHx. Significance Statement Assimilating satellite radiances has been essential for numerical weather prediction. All-sky radiance assimilation can improve the analyses and forecasts of tropical cyclones, but it often has non-Gaussian observation error distributions. With increased model resolutions, nonlinear physical processes can be better resolved, which leads to non-Gaussian error distributions of state variables. Nonlinearity and non-Gaussianity impose great challenges for data assimilation (DA), since most DA theories assume linear processes and Gaussian error distributions. As the spatial resolutions of observations and numerical models will keep increasing, the potential issues of assimilating high-spatial-resolution observations given fine model resolutions need to be examined. The linearized observation forward operator, as an alternative to remedy non-Gaussianity for radiance DA, is investigated. With model resolution increasing from 7.5 km to 300 m, the linearized observation operator has advantages over the full observation operator for assimilating clear-sky radiances, but the opposite is true for assimilating all-sky radiances.

Examining the sensitivity of ocean response to oceanic grid resolution in COAMPS-TC during Hurricane Irma (2017)

Using the Navy's Coupled Ocean-Atmospheric Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC), we investigate the sensitivity of upper ocean momentum processes to varying ocean horizontal and vertical resolutions during Hurricane Irma (2017). This study aims to examine the impact of ocean resolution sensitivity on the representation of the ocean mixed layer (OML) and thermocline heat and salt budget when the tropical cyclone (TC) is at maximum intensity east of the Lesser Antilles. Results indicate that changes in ocean resolution over the forecast period do not greatly affect TC track but have variable influence on TC intensity. OML budget analyses reveal that at coarser horizontal resolutions, the overall OML response is sensitive to vertical ocean resolution. Additionally, the dominant OML processes change as horizontal resolution increases with the dependence shifting from horizontal advection to vertical mixing. Examining budget tendencies with respect to TC quadrant reveals that the right-rear quadrant ocean response is least sensitive to grid spacing. However, the OML response within the front quadrants is most sensitive to resolution changes. Moreover, thermocline downwelling is apparent in both front quadrants and the left-rear quadrant. This downwelling response is caused by a mass convergence within the OML, pumping surface water into the ocean interior. Ocean grid resolution sensitivities in coupled TC forecast models are vital to understand since OML response is indicative of air-sea response and, thus, may contribute to forecasted TC intensity. • Modeled TC intensity varies up to 12 kt at 24 h by only changing ocean grid resolution. • Dominant oceanic processes are sensitive to ocean grid resolution and change with respect to TC quadrant. • Consider increasing upper ocean vertical resolution for horizontal resolutions ≥ 10 km.

Factors Influencing the Track of Hurricane Dorian (2019) in the West Atlantic: Analysis of a HAFS Ensemble

Abstract Hurricane Dorian (2019), a category-5 tropical cyclone (TC), was characterized by a large spread in track forecasts as it moved northwest. A set of 80 ensemble forecasts from the Hurricane Analysis and Forecast System (HAFS) was produced to evaluate Dorian’s track spread and the factors that contributed to it. Track spread was particularly critical at long lead times (5–7 days after initialization near the Lesser Antilles), because of the uncertainty in the location of landfall and hazards. Four clusters of members were analyzed based on the 7-day track, characterized by Dorian moving: 1) slowly near the northern Bahamas (closest to reality), 2) across the Florida Peninsula, 3) slowly into Florida’s east coast, and 4) quickly north of the Bahamas. Ensemble sensitivity techniques were applied to identify areas that were most critical for Dorian’s track. Key differences were found in the strength of the subtropical ridge over the western Atlantic Ocean with a weaker ridge and slower easterly steering flow in the offshore groups. Subtle differences in the synoptic pattern over the United States also appeared to affect the timing of Dorian’s northward turn, specifically the strength of a shortwave trough moving over the Ohio Valley. Despite some early track differences, the correlation between early and late track errors was not significant. An examination of four members further highlights the differences in steering and the strength of the subtropical ridge. This study demonstrates the utility of ensemble datasets for studying TC forecast uncertainty and the importance of medium-range modeling of synoptic-scale steering features to accurately predict the track of tropical cyclones. Significance Statement Hurricane Dorian was a catastrophic hurricane for the Bahamas and got very close to Florida without directly impacting the state. Some early forecasts showed the storm moving directly into or across Florida; others correctly showed the storm stalling over the Bahamas and then turning northward. This track forecast uncertainty made preparations in Florida challenging; therefore, we wanted to better understand why Dorian took the track that it did, to see what this tells us about the factors that affect hurricane tracks, and learn for future storms. We looked at an ensemble of 80 runs of a hurricane model, initiated at the same time. Some runs showed a Florida landfall; others showed Dorian stalling over the Bahamas. The strength of the subtropical ridge over the Atlantic north of Dorian and an upper-level trough of low pressure over the United States were key influences on storm path. These two large-scale features were better forecast in the ensemble members that correctly showed Dorian stalling and turning northward. This study shows how useful ensembles can be for understanding the processes driving hurricane motion and also shows that it is critical to forecast multiple synoptic-scale features correctly to accurately predict a hurricane’s track 5–7 days in advance.

Analysis of CYGNSS Soil Moisture Retrieval Based on ANN over a Selected Region in Ethiopia

Soil moisture (SM) plays a vital role in agriculture, ecosystem functioning, water conservation, weather predictions and climate models. High spatial and temporal frequency data of soil moisture is crucial for agricultural and other important applications. Recent advancements have brought attention to the possibility of using GNSS reflectometry (GNSS-R) for applications on land such as snow sensing, soil moisture retrieval, sea surface monitoring and other applications in addition to positioning, navigation, and timing applications of GNSS. Cyclone Global Navigation Satellite System (CYGNSS) is designed to improve hurricane forecasting by studying the interaction between the ocean and the atmosphere within tropical cyclones. However recent studies show the opportunity of this system for high spatio-temporal soil moisture retrieval. This study presents a machine learning-based approach to get SM at a selected region in Ethiopia using CYGNSS data and analysis of the result. Artificial Neural Network (ANN) model is developed and used to predict soil moisture. The Soil Moisture Active Passive (SMAP) global soil moisture data have been used as reference data in the ML algorithm. The proposed approach has achieved a good correlation between predicted values of soil moisture and reference values from SMAP.

Understanding Meteorological Changes Following Severe Defoliation during a Strong Hurricane Landfall: Insights from Hurricane Michael (2018)

Abstract Despite prompting persistent meteorological changes, severe defoliation following a tropical cyclone (TC) landfall has received relatively little attention and is largely overlooked within hurricane preparedness and recovery planning. Changes to near-track vegetation can modify evapotranspiration for months after tropical cyclone passage, thereby altering boundary layer moisture and energy fluxes that drive the local water cycle. This study seeks to understand potential spatial and temporal changes in defoliation-driven meteorological conditions using Hurricane Michael (2018) as a testbed. In this sensitivity study, two Weather Research and Forecasting (WRF) Model simulations, a normal-landscape and a post-TC scenario, are compared to determine how a defoliation scar placed along Michael’s path alters surface heat fluxes, temperature, relative humidity, and precipitation near the storm’s track. In the month following the foliage reduction, WRF resolves a 0.7°C 2-m temperature increase, with the greatest changes occurring at night. Meanwhile, the simulations produce changes to the sensible and latent heat fluxes of +8.3 and −13.9 W m−2, respectively, while average relative humidity decreases from 73% to 70.1%. Although the accumulated precipitation in the defoliated simulation was larger along a narrow corridor paralleling and downwind of the hurricane track, neither simulation satisfactorily replicated post-Michael precipitation patterns as recorded by NCEP Stage IV QPE, casting doubt as to whether the downwind enhancement was exclusively due to the defoliation scar. This sensitivity analysis provides insight into the types of changes that may be possible following rapid and widespread defoliation during a TC landfall.

QuickOSSE Research on the Impact of Airship-Borne Doppler Radar Radial Winds to Predict the Track and Intensity of a Tropical Cyclone

Different from the aircraft TC reconnaissance flight missions before, a tropical cyclone (TC) field campaign project with a Doppler radar equipped on an airship that could hang over on the top of a TC (about 20 km) has been recently carried out in China. To understand the impact of airship-borne radar radial wind observations in TC forecasting, this work conducted quick observation simulation system experiments (QuickOSSE) by assimilating simulated airship-borne Doppler radar radial winds with an Ensemble Kalman Filter (EnKF) algorithm. The results show that airship-borne radial winds assimilation reproduces the forecasted track and minimum sea level pressure of the nature run. The forecast of dynamic and thermodynamic TC structures, such as tangential wind, secondary circulation, and warm core, are also improved. In addition, two determining factors, the radar depression angle (D-ang) and the distance from the airship to the TC center (DIS), are found to primarily affect the forecast of the TC track and intensity, respectively. Benefiting from a larger horizontal coverage, observations under a smaller D-ang improved the track more significantly. Meanwhile, the intensity forecast error with a DIS around the radius of the maximum wind is the smallest among several sensitive experiments, which may because the peak-velocity winds representing the TC’s intensity could be observed by radar. The results are expected to help establish an observational strategy for upcoming airship flight missions in practice.

Advanced Machine Learning Methods for Major Hurricane Forecasting

Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined).

Real-time forecast of hurricane-induced damage risk to envelope systems of engineered buildings through metamodeling

The growing intensity and frequency of extreme wind events, together with the continued economic and population growth of coastal areas, has created an urgent need for methodologies that can inform emergency responders and managers of the increasing risk to the infrastructure of these areas. To this end, this work outlines a real-time risk forecast framework for hurricane-induced damage to building envelope systems of engineered buildings. Damage is quantified through a recently introduced multi-demand and coupled progressive fragility model, with a full range of uncertainty in structural properties, capacities, and wind stochasticity. To enable real-time assessment, an efficient Kriging metamodel is introduced to capture the damage statistics conditioned on intensity measures. From official real-time hurricane advisories, site-specific intensity measures are forecast based on a parametric wind field model while considering the uncertainty in, among others, the hurricane track, pressure deficit, and filling model. Damage risk is predicted through propagating uncertainty by Monte Carlo simulation through the Kriging metamodels calibrated to forecast intensity measures. For illustration, the real time damage risk of the envelope system of a 45-story building located in Miami, Fl, was estimated for hurricanes Matthew (2016), Irma (2017), and Dorian (2019). The efficiency and accuracy of the proposed framework is demonstrated.

A Bias Correction Scheme with the Symmetric Cloud Proxy Variable and Its Influence on Assimilating All-Sky GOES-16 Brightness Temperatures

Abstract All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.

Creatively Destructive Hurricanes: Do Disasters Spark Innovation?

We investigate whether disasters can lead to innovation. We construct a US county-level panel of hurricane damages using climate data, hurricane tracks, and a wind field model and match these to patent applications by the location of their inventor over the last century in the United States. We examine both general innovation and patents that explicitly mention the terms ’hurricane’ or ’storm.’ In line with the current literature that hypothesizes innovative activity driven by shocks, in particular innovation intended to mitigate future shocks, we find that hurricanes lead to temporary boost in damage-mitigating patents a few years after the event. However, we also show there is long-term, lasting over two decades, general reduction of innovation after a damaging storm. We conclude that hurricanes, and possibly other types of disasters, cannot be viewed as a ’benefit in disguise,’ and that these events are unlikely to generate longer-term beneficial dynamics in an adversely affected location.

Effect of Flow-Dependent Unbalanced Background Error Variances on Tropical Cyclone Forecasting

The background error variance in variational data assimilation can significantly affect a model’s initial field. Around extreme weather events, the variance of the unbalanced control variables have contributed highly to the total variance. This study investigates the effect of flow-dependent unbalanced variance on tropical cyclone (TC) forecasts using the ensemble of data assimilation (EDA) method. The analysis of TC Saudel (October 2020) shows that flow-dependent unbalanced variances can better represent the uncertainty in the background error, which is investigated in terms of magnitude and distribution. The vertical distribution of the temperature-explained variance ratio also shows that the contribution of the vorticity-balanced variance around Saudel is lower than the global average (in the troposphere). Single-observation experiments reveal that the structured flow-dependent errors of unbalanced control variables can also introduce corresponding structural information in analysis increments. As expected, the experiments in which the variances of all variables are flow-dependent in the one-month TC forecast performed better overall. Compared with the reference, these forecasts reduce the average absolute track and intensity errors by approximately 31% and 9%, respectively. The results demonstrate that EDA-based unbalanced variances can indeed improve the mean forecast skills of TC tracks and intensities despite instability at some lead times by improving the forecast of the circulation situation and providing a more appropriate balance relationship between variables.

A Comparative Study on the Performances of Spectral Nudging and Scale-Selective Data Assimilation Techniques for Hurricane Track and Intensity Simulations

It is a common practice to use a buffer zone to damp out spurious wave growth due to computational error along the lateral boundary of limited-area weather and climate models. Although it is an effective technique to maintain model stability, an unintended side effect of using such buffer zones is the distortion of the data passing through the buffer zone. Various techniques are introduced to enhance the communication between the limited-area model’s inner domain and the outer domain, which provides lateral boundary values for the inner domain. Among them, scale-selective data assimilation (SSDA) and the spectral nudging (SPNU) techniques share similar philosophy, i.e., directly injecting the large-scale components of the atmospheric circulation from the outer model domain into the interior grids of the inner model domain by-passing the lateral boundary and the buffer zone, but the two methods are taking different implementation approaches. SSDA utilizes a 3-dimensional variational data assimilation procedure to accomplish the data injection objective, whereas SPNU uses a nudging process. In the present study, the two approaches are evaluated comparatively for simulating hurricane track and intensity in a pair of cases: Jeanne (2004) and Irma (2017) using the Weather Research and Forecasting (WRF) model. The results indicate that both techniques are effective in improving tropical cyclone intensity and track simulations by reducing the errors of the large-scale circulation in the inner model domain. The SSDA runs produced better simulations of temperature and humidity fields which are not directly nudged. The SSDA runs also produced more accurate storm intensities in both cases and more realistic structure in Hurricane Jeanne’s case than those produced by the SPNU runs. It should be noted, however, that extending these case study results to more general situations requires additional studies covering a large number of additional cases.

Ground-Space-Sky Observing System Experiment during Tropical Cyclone Mulan in August 2022

Forecasting tropical cyclone track and intensity is a great challenge for the meteorological community, and safeguarding the life and property of people living near the coast is an important issue. One major reason for challenging forecasts is the lack of observations over the vast oceans. During tropical cyclone Mulan between 8 and 10 August 2022 over the northern part of the South China Sea, the meteorological authority and research institutes of Chinese mainland collaborated with the meteorological service in Hong Kong on conducting the first-ever ground-space-sky observing system experiment on tropical cyclone Mulan. The enhanced targeted observations collected during the experiment include Geostationary Interferometric Infrared Sounder, round-trip radiosondes, and aircraft-launched dropsondes. This paper describes the campaign, technical details of the meteorological models used, and impact of the additional targeted observation data on the tropical cyclone forecast. Ideally, similar enhanced observation campaigns could be conducted in the future, not only in the northern part of the South China Sea, but also in other ocean basins.