Maximum Sustained Surface Wind Research Articles

The impact of coupling a dynamic ocean in the Hurricane Analysis and Forecast System

Coupling a three-dimensional ocean circulation model to an atmospheric model can significantly improve forecasting of tropical cyclones (TCs). This is particularly true of forecasts for TC intensity (maximum sustained surface wind and minimum central pressure), but also for structure (e.g., surface wind-field sizes). This study seeks to explore the physical mechanisms by which a dynamic ocean influences TC evolution, using an operational TC model. The authors evaluated impacts of ocean-coupling on TC intensity and structure forecasts from NOAA’s Hurricane Analysis and Forecast System v1.0 B (HFSB), which became operational at the NOAA National Weather Service in 2023. The study compared existing HFSB coupled simulations with simulations using an identical model configuration in which the dynamic ocean coupling was replaced by a simple diurnally varying sea surface temperature model. The authors analyzed TCs of interest from the 2020–2022 Atlantic hurricane seasons, selecting forecast cycles with small coupled track-forecast errors for detailed analysis. The results show the link between the dynamic, coupled ocean response to TCs and coincident TC structural changes directly related to changing intensity and surface wind-field size. These results show the importance of coupling in forecasting slower-moving TCs and those with larger surface wind fields. However, there are unexpected instances where coupling impacts the near-TC atmospheric environment (e.g., mid-level moisture intrusion), ultimately affecting intensity forecasts. These results suggest that, even for more rapidly moving and smaller TCs, the influence of the ocean response to the wind field in the near-TC atmospheric environment is important for TC forecasting. The authors also examined cases where coupling degrades forecast performance. Statistical comparisons of coupled versus uncoupled HFSB further show an interesting tendency: high biases in peak surface winds for the uncoupled forecasts contrast with corresponding low biases, contrary to expectations, in coupled forecasts; the coupled forecasts also show a significant negative bias in the radii of 34 kt winds relative to National Hurricane Center best track estimates. By contrast, coupled forecasts show very small bias in minimum central pressure compared with a strong negative bias in uncoupled. Possible explanations for these discrepancies are discussed. The ultimate goal of this work will be to enable better evaluation and forecast improvement of TC models in future work.

Ionospheric responses to the tropical cyclones from different oceanic basins over the globe

The perturbations in the ionosphere due to eight tropical cyclones (TCs), namely Iota, Haima, Harold, Willa, Amphan, Gaja, Vadrah and Bulbul, originated and grown in different oceanic basins, are investigated. Total Electron Content (TEC) data, from Global Positioning System (GPS) TEC receiver in operation at Agartala (AGT) or different International GNSS Service (IGS) stations near the cyclone landfall regions, are used in this study. Despite some differences, the ionosphere responds to all tropical cyclones in an almost similar manner. Though the geomagnetic conditions are quiet and there are no perturbations due to any other geophysical phenomena in the active cyclonic storm stage, in all the cases there is a fall in average vertical total electron content (VTEC) deviations below the monthly mean value either on the landfall day or on the following day or even on just previous day. Decrements in Vertical Total Electron Content are found higher for tropical cyclones over North Indian and South Pacific oceanic basins. Recoveries in vertical total electron content values are slower for cyclones over the North Atlantic and North West Pacific basins. Recoveries in vertical total electron content (VTEC) values are slow for tropical cyclones (TCs) over the North Atlantic and North West Pacific basins. But those over other basins are quick. The longer the track of a tropical cyclone (TC), the higher is the reduction in the vertical total electron content (VTEC) value. A negative correlation exists between the maximum sustained surface wind velocities and the total periods of different TCs and also the difference of lowest average differential VTECs with that on the previous day. The observed anomaly in ionospheric responses might be due to the combined effect of TC-inspired gravity waves, ejection of neutral particles from the terminator of a tropical cyclone (TC) and lightning electric fields. To explain the observed results convective activities during TC, with the help of outgoing long wave radiation (OLR) map, are also taken into account. This study provides the primary results regarding regional characteristics and hence a comparative idea for the responses of the ionosphere to different tropical cyclones (TCs) from different geographical positions on the globe, which needs further comprehensive investigation in future.

Typhoon-related changes in moisture pathways and sources for precipitation in Eastern China during the three major rainy seasons

Typhoons (TYs) and their interactions with large-scale ambient flow might exert an important control on moisture transport and precipitation. In this study, we used a Lagrangian model, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), to quantify changes in moisture pathways and sources associated with TY activity in relation to precipitation in eastern China during the three major rainy seasons. We compared TY-active and TY-inactive periods, i.e., the periods with and without TYs (maximum sustained surface wind speeds of up to 32.7 m/s), over the western North Pacific (WNP) to investigate TY-associated changes for the period 1979–2020. During the pre-summer rainy season in South China, anomalous moisture exhibited a north–south dipole pattern, with moisture contributions from the region extending from the South China Sea to the adjacent tropical WNP showing a 17.0% decrease during TY-active compared with TY-inactive periods (34.7% versus 41.8%, respectively), and contributions from the region extending from southwestern China to the northwestern North Pacific showing a 18.9% increase (17.0% versus 14.3%, respectively) and those from the Bay of Bengal showing a 16.6% increase (17.6% versus 15.1%, respectively). The moisture anomaly during the Meiyu rainy season in the Yangtze–Huaihe River Basin had an east–west dipole structure, with the moisture contribution from the Bay of Bengal reduced by 21.8% as compared with TY-inactive periods, and that from the WNP increased by 62.3%. During the rainy season of North China, the moisture contribution from the Indian Ocean decreased by 7.2%, while that from the WNP increased by 8.7% and from Northeast China increased by 10.0%. In addition, during TY-active periods, moisture contributions with local origins showed a slight increase.

The impact of spume droplets induced by the bag-breakup mechanism on tropical cyclone modeling

Spume, large-radius seawater droplets that are ejected from the ocean into the atmosphere, can exchange moisture and heat fluxes with the surrounding air. Under severe weather conditions, spume can substantially mediate air-sea fluxes through thermal effects and thus needs to be physically parameterized. While the impact made by spume on air-sea interactions has been considered in bulk turbulent air-sea algorithms, various hypotheses in current models have resulted in uncertainties remaining regarding the effect of spume on air-sea coupling. In this study, we extended a classic bulk turbulent air-sea algorithm with a “bag-breakup” physical scheme of spume generation parameterizations to include spume effects in a complicated numerical model. To investigate the impact of spume on air-sea coupling, we conducted numerical experiments in a simulation of Tropical Cyclone Narelle. We observed a significant improvement in the ability to model minimum central pressure and maximum sustained surface wind speed when including the bag-breakup spume scheme. In particular, the impact of the bag breakup–generated spume is observed in the intensity, structure, and size of the tropical cyclone system through the modulation of local wind speed (U10), wave height (Hs), and sea surface temperature.

Revisiting the Definition of Rapid Intensification of Tropical Cyclones by Clustering the Initial Intensity and Inner‐Core Size

AbstractRapid intensification (RI) of tropical cyclones (TCs) provides a great challenge in operational forecasting and contributes significantly to the development of major TCs. RI is commonly defined as an increase in the maximum sustained surface wind speed of at least a certain threshold within 24 hr. The most widely used threshold is 30 kt (15.4 m/s), which was determined statistically. Here we propose a new definition for RI by objectively clustering TCs using the intensification rate, initial intensity, and radius of the maximum wind speed (RMW). A group of 770 samples is separated at a threshold of 45 kt (23.2 m/s). The threshold is 40 kt (20.6 m/s) for the western North Atlantic, where TC size measurements are more reliable. Monte Carlo experiments demonstrate that the proposed threshold is robust even considering the uncertainty in RMW of as high as 30 km. We show that, when a TC undergoes RI, its maximum wind speed is approximately 60 ± 15 kt (30.9 ± 7.7 m/s) and the RMW is 45 ± 20 km. The new threshold outperforms the conventional threshold of 30 kt/24 hr in describing the bimodal distribution of lifetime maximum intensity and explaining the annual count of Category 5 TCs. This new definition provides a more physically based threshold and describes a more reliable representation of extreme events. Although more comparisons are needed for operational application, it is likely to be desirable for case‐based process studies and could provide a more valuable metric for TC intensification classification and research.

Influence of the ocean on tropical cyclone intensity using a high resolution coupled atmosphere–ocean model: A case study of very severe cyclonic storm Ockhi over the North Indian Ocean

AbstractA very severe cyclonic storm (VSCS), Ockhi was conceived in the Bay of Bengal and moved over the Arabian Sea undergoing rapid intensification (RI) in the early stages of its life cycle. Ockhi is simulated using the high‐resolution coupled Hybrid Coordinate Ocean Model and the Hurricane Weather Research Forecast model. The study suggests that sea surface temperature warmer than the seasonal climatological mean with the availability of a large tropical cyclone heat potential (TCHP) has acted as a potential cause for RI when the TC moved over that region. We have conducted experiments with (CP) and without (UCP) ocean coupling. The predicted tracks of UCP and CP experiments agree well with the India Meteorological Department's best track data. However, CP showed a significant improvement in intensity prediction with 61 and 19% for mean sea level pressure and maximum sustained surface wind at 10 m respectively in comparison to UCP. The presence of thermal and haline stratification in the subsurface affects surface cooling. The subsurface turbulent heat flux calculated for the high‐TCHP region under the TC suggests that the upward transport of heat, which increases the enthalpy flux, becomes necessary to bring about RI. The primary circulation, secondary circulation, inflow, thermal structure, moisture distribution and mass flux within the TC environment during the 24‐hr RI period are analyzed to investigate the evolution of TC characteristics for both UCP and CP experiments. The analyses suggest that the UCP experiments show higher values of intensity as compared to CP experiments. Further, a more organized evolution of updrafts is present in the CP simulation, maybe due to the formation of a stable boundary layer. The study shows the significant influence of coupling on surface enthalpy controlling boundary layer characteristics and thus ultimately producing a more organized TC.

Investigation of Machine Learning Using Satellite-Based Advanced Dvorak Technique Analysis Parameters to Estimate Tropical Cyclone Intensity

AbstractSeveral simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

On occurrence of rapid intensification and rainfall changes in tropical cyclones over the North Indian Ocean

AbstractThe present study examines the climatological characteristics and possible triggers of rapid intensification (RI) of tropical cyclones (TCs) during 1990–2019 over the North Indian Ocean (NIO). RI is defined as an increase in maximum sustained surface wind speed of 30 knots (15.4 m⋅s−1) or more in a 24 hr duration. In the NIO basin, the threshold of 24 hr intensity change represents the 93rd percentile using a 3‐min sustained wind, while, it is 95th percentile in the Atlantic basin, where 1‐min sustained wind is used. A total of 46 TCs (~38% of the total TCs) have exhibited RI at an average rate of 1–2 TCs year−1 over the NIO. A significant increase of RI‐TCs is seen from the year 2000 onwards over the region. The maximum RI‐TCs occurrence is found in the post‐monsoon season. The majority of TCs (~48%) undergo the RI phase within a 12–24 hr time during the depression stage. About ~35% (26%) of the TCs are retained in the RI phase for the duration of at least 24 hr (36 hr). Most of the RI‐TCs move northwestward (38%) and westward (31%) direction 6 hr before the RI onset with slow/normal translation speeds. During the RI phase, ~72% of TCs travels a distance of ~150–450 km. The TC inner‐core region receives heavy rainfall, and a ~ 3 mm⋅hr−1 increment is noticed 12 hr before the RI to the RI onset. Most of the RI‐TCs made landfall over eastern states of India showing the vulnerability of the regions. Composite analysis demonstrated that higher precipitable water (~55 mm), surface flux (500 W⋅m−2), cyclone heat potential (50–60 kJ⋅cm−2) in the moderate shear (6–8 m⋅s−1) environment favours the RI process. This study highlights the significance of RI‐TCs and triggering conditions for the RI onset over the NIO region.

Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

The prediction of the intensity, location and time of the landfall of a tropical cyclone well advance in time and with high accuracy can reduce human and material loss immensely. In this article, we develop a Long Short-Term memory based Recurrent Neural network model to predict intensity (in terms of maximum sustained surface wind speed), location (latitude and longitude), and time (in hours after the observation period) of the landfall of a tropical cyclone which originates in the North Indian ocean. The model takes as input the best track data of cyclone consisting of its location, pressure, sea surface temperature, and intensity for certain hours (from 12 to 36 hours) anytime during the course of the cyclone as a time series and then provide predictions with high accuracy. For example, using 24 hours data of a cyclone anytime during its course, the model provides state-of-the-art results by predicting landfall intensity, time, latitude, and longitude with a mean absolute error of 4.24 knots, 4.5 hours, 0.24 degree, and 0.37 degree respectively, which resulted in a distance error of 51.7 kilometers from the landfall location. We further check the efficacy of the model on three recent devastating cyclones Bulbul, Fani, and Gaja, and achieved better results than the test dataset.

Prediction of tropical cyclone trajectories over the Northern Indian Ocean using COSMO

The present study investigates the performance of a regional numerical weather prediction model; namely, the Consortium for Small-scale Modelling (COSMO) in the prediction of the tropical cyclone (TC) trajectories for varying intensities of the storm. A total of 8 TCs formed over the Northern Indian Ocean from 2017 to 2019 are chosen for the evaluation of the COSMO model. The central pressure ( $$P_\mathrm{Central}$$ ), pressure drop ( $$\varDelta P$$ ), and maximum sustained surface wind speed (MSW) simulated by the COSMO model are validated against the concurrent observations from India Meteorological Department. The forecasted mean track errors are 95 km for a lead time of 24 h, whereas it was about 140 km for a lead time of 48 h. The mean initial positional error in identification of the storm was about 50 km. The intensity of a storm is underestimated in terms of $$\varDelta P$$ and MSW, especially for a lead time of 0–24 h, whereas the model shows a consistent overestimation for a lead time of more than 24 h. During the initial stage of a storm, when its intensity is categorized as a Deep Depression, we notice a maximum amount of uncertainty in the prediction of cyclone track. The COSMO model yields improved predictability of the tracks for storms categorized as Very Severe Cyclonic Storms. As the intensity of a storm increases from a Deep Depression to a Very Severe Cyclonic Storm, the track errors associated with model simulations tend to decrease. Results of the present study illustrate the predictability of TCs from COSMO in terms of the trajectory and intensity of the storm.

Impact of WRF Parameterization Schemes on Track and Intensity of Extremely Severe Cyclonic Storm “Fani”

Weather Research and Forecasting (WRF) model version 4.1.3 is configured in the present study to understand the impact of microphysical parameterization schemes on the track and intensity of extremely severe cyclonic storm (ESCS) “Fani” that occurred during 26 April to 4 May 2019 in the Bay of Bengal. The model is customized with six different microphysical parameterization schemes along with Kain–Fritsch cumulus for convection and the Yonsei University planetary boundary-layer scheme for computation of heat and moisture transport. The simulations are conducted at 27-km horizontal resolution with 41 levels in the vertical for 150 h (00UTC 28 April to 06UTC 4 May 2019). Model-simulated features such as the track, track error, minimum central sea-level pressure (MSLP), maximum sustained surface wind (MSW), and precipitation are validated against India Meteorological Department (IMD) and Tropical Rainfall Measuring Mission (TRMM) observation data. Although all the schemes predict track patterns similar to observation, the MP3 scheme shows the least track error even after landfall, and its track length is maximum in comparison with other schemes, albeit less than the observed track. The MP3 scheme exhibits a better track for “Fani,” followed by MP14. The lowest mean direct position error (along-track error) of 85 km (68.5 km) is noted for the MP3 scheme, followed by 112.5 km (82.3 km) for the MP8 scheme. Analysis of the MSLP and MSW using the t-test suggests that the MP2 scheme has better forecast ability, followed by MP6. Analysis of the quantitative/spatial matching of the rainfall forecasts and TRMM observations using the Method for Object-Based Diagnostic Evaluation (MODE) tool suggests that the MP8 (MP14) scheme is capable of simulating the rainfall beyond (up to) 48 h. Analysis of the zonal cross-section of horizontal and vertical winds shows that intense convection is well simulated by the MP2, MP3, and MP4 schemes during the change of intensity from cyclonic storm to severe cyclonic storm (SCS), by MP6 and MP14 from SCS to very severe cyclonic storm (VSCS) and from VSCS to ESCS, and by MP8 at ESCS level. All the schemes can capture the eyewall and simulate the westerly that allowed the system to move north-northeastwards. The synoptic flow at different pressure levels is reproduced by all the schemes. However, assessing the impact of the microphysics parameterizations on the synoptic flow of “Fani” at low resolution was difficult. Analysis of Qc and Qr indicates definite impacts of the microphysical parameterizations on the vertical structure of “Fani” in producing rainfall. The MP3 scheme exhibits a better cloud pattern up to upper troposphere level, followed by the MP2 scheme. Analysis of the latent heat flux predicted using the different microphysics schemes suggests that the latent heat flux release at VSCS intensity level or above contributes to further intensification of the TC, and this feature is very well simulated by the MP2 scheme.

Understanding the characteristics of rapid intensity changes of Tropical Cyclones over North Indian Ocean

North Indian Ocean (NIO), which comprises of Bay of Bengal (BoB) and Arabian Sea (AS) basins, is one of the highly potential regions for Tropical Cyclones (TCs) in the world. Significant improvements have been achieved in the prediction of the movement of TCs, since the last decade. However, the prediction of sudden intensity changes becomes a challenging task for the research and operational meteorologists. Hence, the present study focuses on finding the climatological characteristics of such intensity changes over NIO regions. Rapid Intensification (RI) is defined as the 24-h maximum sustained surface wind speed rate equal to 30 knots (15.4 ms−1). The results suggest that the TCs formed over the NIO basin are both seasonal and basin sensitive. Since 2000, a significant trend is observed in RI TCs over the basin. At least one among three cyclones getting intensified is of RI category. More number of RI cases have been identified in the BoB basin than the AS. The post-monsoon season holds more RI and rapid decay cases, with 63% and 90% contribution. Most of the TCs are attaining RI onset in their initial stage. Further, India is receiving more landfalling RI TCs, followed by Bangladesh and Oman. The east coast of India, Tamil Nadu, and Andhra Pradesh are the most vulnerable states to these RI TCs. The cyclogenesis locations associated with RI cases hold higher moisture, and sea surface temperature as compared to the Non-RI cases.

An assessment of a very severe cyclonic storm in the Arabian sea using the COSMO model

Accurate and reliable representation of convective processes is one of the major sources of uncertainty in numerical weather prediction (NWP) models, especially for those operating in the grey zone resolutions. The performance of NWP models become more sensitive to their grid resolutions, when they are used for simulation of severe weather events, such as a cyclonic storm. In this paper, we present a detailed assessment of an intense convective episode with heavy precipitation associated with the passage of a very severe cyclonic storm “OCKHI” using the Consortium for Small-scale Modelling (COSMO). A set of distinct numerical simulations are carried out using COSMO to address the impact of grid resolution and the treatment of explicit and implicit convection. Results obtained from the present investigation indicate that explicit treatment of convection in the COSMO model led to improved prediction of the cyclonic event in terms of sea level pressure, maximum sustained surface wind speeds and the accumulated rainfall, but reduction of the spatial grid resolution from 7 to 3 km did not show appreciable differences in the forecast fields. Conclusively, the current study recommends switching off the convection parameterization scheme at a grid resolution of 7 km for improved predictability of tropical cyclones.

Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction

Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events.

A Long Short-Term Memory Model for Global Rapid Intensification Prediction

ABSTRACTRapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.

Climatology of Passive Microwave Brightness Temperatures in Tropical Cyclones and their Relations to Storm Intensities as Seen by FY-3B/MWRI

A new database, the tropical cyclones passive microwave brightness temperature (TCsBT) database including 6273 overpasses of 503 tropical cyclones (TC) was established from 6-year (2011–2016) Fengyun-3B (FY-3B) Microwave Radiation Imager (MWRI) Level-1 brightness temperature (TB) data and TC best-track data. An algorithm to estimate the TC intensity is developed using MWRI TB’s from the database. The relationship between microwave TB and the maximum sustained surface wind (Vmax) of TCs is derived from the TCsBT database. A high correlation coefficient between MWRI channel TB and Vmax is found at the radial distance 50–100 km near the TC inner core. Brightness temperatures at 10.65, 18.70, 23.8, and 36.5 GHz increase but 89 GHz TB’s and polarization corrected TB at 36.5 GHz (PCT36.50) and PCT89 decrease with increasing TC intensity. The TCsBT database is further separated into the 5063 dependent samples (2010–2015) for the development of the TC intensity estimation algorithm and 1210 independent samples (2016) for algorithm verification. The stepwise regression method is used to select the optimal combination of storm intensity estimation variables from 12 candidate variables and four parameters (10.65h, 23.80v, 89.00v and PCT36.50) were selected for multiple regression models development. Among the four predictors, PCT36.50 contributes the most in estimating TC intensity. In addition, the errors are lower for estimating 6-h and 12-h future Vmax than estimating the current Vmax.

Performance assessment of WRF model in simulating the very severe cyclonic storm “TITLI” in the Bay of Bengal: A case study

The present study has been conducted to assess the performance of the Weather Research and Forecasting – Advanced Research (WRF-ARW, hereinafter named as WRF) model in simulating the very severe cyclonic storm (VSCS) “Titli” which formed over the Bay of Bengal during 08–13 October, 2018. The simulated meteorological variables viz. 10-m wind vectors, surface latent heat flux, rainfall, track and intensity based on minimum sea level pressure (MSLP), maximum sustained surface wind (MSW) are assessed to evaluate the model performance. The results are compared with India Meteorological Department (IMD) observations along with verification analyses [FiNaL analysis (FNL)]. The model simulated 24- h accumulation of rainfall verification is carried out by using the Method for Object based Diagnostic Evaluation (MODE) tool of Model Evaluation Tool Version 8 (MET-V8). The various statistical skill scores obtained from the MODE clearly indicates that the overall performance of WRF model in predicting the rainfall is reasonably good, mainly at lower forecast length (36–60 hr). The intensity and spatial distribution of rainfall are well matched with the verified analysis as well as with the Tropical Rainfall Measuring Mission (TRMM) observations. The analysis of maximum latent heat flux in the region of strongest surface wind suggests that the circulation is maintained due to increased transport of heat and moisture from the ocean. The track error analysis shows more error during the model initialization, which gradually decreases with the advance of forecast time. Efficacy of the WRF model in simulating the tropical cyclone (TC) is very distinct from the present study.

Impact of very severe cyclonic storm Phailin on shoreline change along South Odisha Coast

The present study makes a qualitative and quantitative assessment of inundation limit, the structural damage and shoreline change due to very severe cyclonic storm Phailin and also estimates the rate of recovery along different parts of south Odisha coast. At the time of landfall of Phailin along Odisha and adjoining Andhra Pradesh coast on October 12, 2013, maximum sustained surface wind speed reached up to 200–210 kmph gusting to 220 kmph with an estimated pressure drop 66 mbar at the center. Significant wave heights reached up to more than 7 m with a mean period range between 4 and 12 s from southeast direction during the event. Strong gale wind and high wave followed by massive rainfall brought irreparable damage to the coastal structures and shoreline along south Odisha. Real-time kinematics global positioning system and differential global positioning system Arcpad were used to monitor the shoreline change before and after Phailin and the inundation limit. The study indicates maximum inundation at southernmost part of Odisha coast (Ramayapatnam) followed by inside the Gopalpur Port area and minimum near Gopalpur port north. Shoreline change from pre- (September 2013) to post-storm period (October 2013) is landward all along south Odisha coast with maximum (48.7 m) near Rushikulya turtle nesting beach and minimum (12.6 m) near south of Gopalpur tourist beach. The recovery of the beach and dune areas assessed during October 2014 (after 1 year from the post-storm observation) is uneven. Percentage of recovery is maximum at south side of Gopalpur port, while recovery is minimum on north of the port.

Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations

AbstractA statistical passive microwave intensity estimation (PMW-IE) algorithm for estimating the intensity of tropical cyclones (TCs) in the North Atlantic and northeastern and central Pacific basins is developed and tested. The algorithm is derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to provide objective estimates of current maximum sustained surface winds (Vmax) and 6-h future Vmax of TCs. The full record of TRMM data (1998–2013) including 2326 TMI overpasses of 503 TCs is separated into dependent samples (1998–2010) for model development and independent samples (2011–13) for model verification. The best track intensities are used as dependent variables in a stepwise multiple-regression approach. Separately for each basin, three regression models are derived using selected 1) 85-GHz-only variables, 2) rain-rate-only variables, and 3) combined 85-GHz and rain variables. The algorithms are evaluated using independent samples and those with contemporaneous aircraft-reconnaissance measurements. Rain-only and combined models perform better than the 85-GHz-only model. Lower errors are found for estimating the 6-h future Vmax than estimating the current Vmax using all three models. This suggests that it is optimal to use passive-microwave-retrieved rain variables observed a few hours earlier to estimate TC intensity. The MAE (RMSE) of 6-h future Vmax is 9 (12) kt (1 kt ≈ 0.51 m s−1) when testing the combined models with ATL and EPA independent samples. Aircraft-reconnaissance-based independent samples yields a MAE of 9.6 kt and RMSE of 12.6 kt for estimating 6-h future Vmax.

Dependence of Tropical Cyclone Intensification Rate on Sea Surface Temperature, Storm Intensity, and Size in the Western North Pacific

Abstract This study extends the statistical analysis on the dependence of tropical cyclone (TC) intensification rate (IR) on sea surface temperature (SST), storm initial intensity (maximum sustained surface wind speed Vmax), and storm size, in terms of the radius of maximum wind (RMW), the radius of 34-kt (AR34; 1 kt = 0.51 m s−1) wind, and the outer-core wind skirt parameter DR34 (= AR34 − RMW), for North Atlantic TCs to western North Pacific (WNP) TCs during 1982–2015. Results show that the relationship between the TC maximum potential intensification rate (MPIR) and SST also exists in the WNP. TC IR depends strongly on TC intensity and structure, consistent with the findings for North Atlantic TCs. TC IR is positively (negatively) correlated with storm intensity when Vmax is below (above) 70 kt and negatively correlated with the RMW. Rapid intensification (RI) occurs only in a relatively narrow range of parameter space in storm intensity and both inner- and outer-core sizes, with the highest IR appearing for Vmax = 70 kt, RMW ≦ 40 km, AR34 = 150 km, and DR34 = 100 km. The highest frequency of occurrence of intensifying TCs occurs for Vmax ~ 40–60 kt, RMW ~ 20–60 km, AR34 = 200 km, and DR34 = 120 km. Overall, these values are very similar to those for TCs in the North Atlantic. These results suggest the need for the realistic initialization of TC structure in numerical models and the inclusion of size parameters in statistical TC intensity prediction schemes.