North Indian Ocean Basin Research Articles

Evaluation of Track and Intensity Prediction of Tropical Cyclones Over North Indian Ocean Using NCUM Global Model

The performance of the National Centre for Medium Range Weather Forecasting-UK Met office (NCUM) global model in prediction of tropical cyclones (TCs) over the North Indian Ocean (NIO) at 25-km resolution is evaluated on the basis of 43 forecasts for 11 TCs. For this purpose, the analyses are carried out based on (1) basins of formation, (2) straight-moving and recurving/looping TCs, and (3) TC intensity at model initialization. The overall performance of NCUM global model has been found reasonably well in predicting TCs over NIO basin as it demonstrates a good skill irrespective of the region of formation, nature of movement, and intensity. The model has reasonably well predicted the tracks of the TCs in maximum number of the IC runs at different stages of the storms. The mean Direct Position Errors (DPEs) (skill with reference to CLIPER model) over the NIO vary from 97 to 248 km (5–57%) for 12–72-h forecast lengths. The NCUM model is found to be more skillful for track prediction of TCs when initialized at the Severe Cyclone Stage rather than at the Cyclonic Stage or lower. Therefore, the DPEs are lesser with higher model ICs run in each TC case. The model is more capable to predict the landfall location than the landfall time of the storms. The results also show that, on average, forecast tracks as predicted by NCUM lie to the right (i.e., model shows eastward bias of the best-track position) in all simulations for all the basins. The analysis of Along-Track errors reveals that the model forecast positions are biased to the south of (behind) the observed positions. It is evident that the NCUM forecasts are slower relative to the actual translation speed of the system for all forecast lengths, and the NCUM model predicts a delayed landfall. It is observed that the NCUM model has less predictability of intensity prediction of intense storms.

Reprocessing the Most Intense Historical Tropical Cyclones in the Satellite Era Using the Advanced Dvorak Technique

In recent years, a number of extremely powerful tropical cyclones have revived community debate on methodologies used to estimate the lifetime maximum intensity (LMI) of these events. And how do these storms rank historically? In this study, the most updated version of an objective satellite-based intensity estimation algorithm [advanced Dvorak technique (ADT)] is employed and applied to the highest-resolution (spatial and temporal) geostationary satellite data available for extreme-intensity tropical cyclones that occurred during the era of these satellites (1979–present). Cases with reconnaissance aircraft observations are examined and used to calibrate the ADT at extreme intensities. Bias corrections for observing properties such as satellite viewing angle and image spatiotemporal resolution, and storm characteristics such as small eye size are also considered. The results of these intensity estimates (maximum sustained 1-min wind) show that eastern North Pacific Hurricane Patricia (2015) ranks as the strongest storm in any basin (182 kt), followed by western North Pacific Typhoons Haiyan (2013), Tip (1979), and Gay (1992). The following are the strongest classifications in other basins—Atlantic: Gilbert (1988), north Indian Ocean basin: Paradip (1999), south Indian Ocean: Gafilo (2004), Australian region: Monica (2006), and southeast Pacific basin: Pam (2015). In addition, ADT LMI estimates for four storms exceed the maximum allowable limit imposed by the operational Dvorak technique. This upper bound on intensity may be an unnatural constraint, especially if tropical cyclones get stronger in a warmer biosphere as some theorize. This argues for the need of an extension to the Dvorak scale to allow higher intensity estimates.

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.

Characteristics of lightning activity in tropical cyclones developed over North Indian Ocean basin during 2010–2015

The characteristics of lightning activity in tropical cyclones (TCs) over North Indian Ocean (NIO) are presented using sample of 21 TCs developed during 2010–2015 using TRMM and World Wide Lightning Location Network (WWLLN) datasets along with information from annual reports of Regional Specialized Meteorological Center (RSMC), New Delhi. The microphysical features such as Polarization Corrected Brightness Temperature (PCT), attenuation corrected reflectivity factor, Ice Water Path (IWP) play a pivotal role in development of convective systems within TCs. The TCs exhibited systematic variation in lightning flashes per day within 300km of estimated center during their life cycle irrespective of their severity and flash rate within 300km of storm center. The lightning flashes ranged from 1 to 3500 flashes per day during pre-cyclone stage, 100 to 8000 flashes per day during cyclone stage and 0 to 4300 flashes per day during post-cyclone stage. The TCs produced maximum flash density in eyewall region (20–40km) and outer rainband region (200–260km). The WWLLN recorded 165,512 flashes within 300km of TC centers during their life cycle. The maximum flashes occurred during cyclone stage followed by pre-cyclone and post-cyclone. The time variation of flash rate for all cyclones was episodic and primarily peaked during late night and early morning hours. The diurnal variation of lightning flashes during TCs due to variation in detection efficiency of WWLLN also controls temporal distribution of lightning activity. During rapid intensification, TCs produced profuse lightning flashes per hour. Episodic lightning flashes per hour occurred during weakening stage and prior to landfall. It is evident that outbreaks of lightning flashes prior to maximum intensity change as manifested in maximum sustained wind speed and fall in estimated central pressure (ECP) indicate potential predictive value of lightning activity for TC intensity change.

Impact of SST on Tropical Cyclones in North Indian Ocean

The impact of the cyclonic activity can be measured based on annual frequency, maximum sustained intensity, landfall, duration, surge height, accumulated cyclone energy and power dissipative index of cyclones. ‘Power dissipative index’ (PDI) is function of duration, frequency and maximum sustained intensity of cyclone (Emanuel 2005). PDI can represent the cyclonic threat better than storm frequency or intensity alone as PDI measures the cyclonic energy dissipation over the basin during a cyclonic event (Emanuel, 2005). PDI of the tropical cyclone over the North Atlantic Ocean basin was related to the climate change by correlating the PDI to sea surface temperature (SST) (Emanuel, 2005). In the present study, an attempt has been made to establish a correlation between the climate change and the cyclonic activities in the North Indian Ocean (NIO) basin. The PDI over the North Indian Ocean basin and individually over Arabian Sea and Bay of Bengal basins were calculated. The PDIs over these basins were correlated to SST obtained from the HadISST1 dataset for these basins. An increasing trend in the SST and tropical cyclone PDI was observed for the past 30 year time period in the North Atlantic Ocean basin by Emanuel (2005). A similar analysis of the cyclonic activity over the North Indian Ocean basin is carried out with the help of available best-tracks of the cyclones from Indian Meteorological Department (IMD) for a period of 23 years (1990-2013). The SST was calculated on temporal period as annual, pre-monsoon and post-monsoon period for study basins. Correlation coefficients for the SSTs over these temporal periods and the PDI of basins were obtained. It was concluded from the study that the SST was not sufficient to establish the changing climate for the cyclonic activities in NIO region for the future.

Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets

Precise forecasting of the track and intensity of tropical cyclones remains one of the top priorities for the meteorological community. In the present study multilayer feed forward neural nets with different architectures are developed to identify the best neural net for forecasting the track and intensity of tropical cyclones over the North Indian Ocean (NIO) with 6, 12 and 24 h lead time. Forecast errors are estimated with each neural net. The result reveals that the neural net architecture 1 (NNA 1) with 10 input layers, 2 hidden layers, 5 hidden nodes and 2 output layers provides the best forecast for both the track and the intensity of the tropical cyclones over NIO. Two cyclones of the same category in Saffir Simpson Hurricane Scale, namely Nargis and Phet, that occurred over the Bay of Bengal and the Arabian Sea of the NIO basin are considered in the present study for validation. The result reveals that the prediction errors (%) with NNA 1 model in estimating the intensity of the cyclones Nargis and Phet during the validation are 3.37, 8.29 and 9.74 as well as 6.38, 11.26 and 18.72 with 6, 12 and 24 h lead time, respectively. The mean track errors for 6, 12 and 24 h forecasts are observed to be 45, 69 and 89 km for cyclone Nargis and 54, 87 and 98 km for cyclone Phet. NNA 1 model is observed to perform better than NNA 2 and NNA 3 models and the existing numerical models.

Wind-wave Climate Projections for the Indian Ocean from Satellite Observations

The oceans play a key role in climate change and their impact has profound implications on the marine ecosystem and multitude activities around the globe. The effects due to climate change can have long-term repercussion. The latest report on the Intergovernmental Panel on Climate Change had identified the importance of wind-wave climate and its key role in global climate models. The present study investigates the impact of climate change on variability of maximum significant wave height and wind speeds over the Indian Ocean basin. The study is based on analysis from the daily observation of satellite altimeter measured wind and waves derived from eight satellite missions covering a period of 21 years from 1992 until 2012. The results signify that the Southern Ocean belt encompassing latitudinal belts between 40°S – 55°S experienced the highest variability due to impact from climate change. Both wind and wave activity has shown an increasing trend in the Southern Ocean, and this rise is more conspicuous in the current decade. The implications from increased wave activity in the Southern Ocean results in swell field that can influence the local wind-generated waves in the North Indian Ocean basin. The wind-wave activity in certain sectors of the tropical North Indian Ocean also increased from impact of climate change.

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

AbstractThe performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

Global Distribution of Hot Towers in Tropical Cyclones Based on 11-Yr TRMM Data

Abstract Global distribution of hot towers in tropical cyclones (TCs) is statistically quantified using an 11-yr Tropical Rainfall Measuring Mission (TRMM) Tropical Cyclone Precipitation Feature (TCPF) database. From 6003 individual TRMM overpasses of 869 TCs, about 1.6% of TC convective systems are found to penetrate 14 km and about 0.1% of them even reach the 380-K potential temperature level. Among six TC-prone basins, the highest population of TC convective systems and those with hot towers are found over the northwest Pacific (NWP) basin. However, the greatest percentage of TCPFs that are hot towers [overshooting TCPFs (OTCPFs)] is found over the North Indian Ocean basin. Larger overshooting distance and ice mass are also found in this basin. The monthly variation of OTCPFs resembles that of TC activities in each basin. The percentage of OTCPFs is much higher in the inner core (IC) region (10%) than that in the inner rainband (IB; 2%) and outer rainband (OB; 1%) regions. OTCPFs in the IC region have much larger overshooting distance, area, volume, and ice mass than those in the IB and OB regions. The percentage of OTCPFs in the IC region increases as both TC intensity and intensification rate increase. About 17% of IC features in rapidly intensifying storms penetrate over 14 km, while the percentage is down to 11% for slowly intensifying, 9% for neutral, and 8% for weakening storms. A very good linear relationship is found between TC intensification rate and the percentage of TCPFs that are hot towers in the IC region.

North Indian Ocean tropical cyclone activities influenced by the Indian Ocean Dipole mode

Using Joint Typhoon Warning Center tropical cyclone (TC) track data over the North Indian Ocean (NIO), National Centers for Environmental Prediction monthly reanalysis wind and outgoing long-wave radiation data, and National Oceanic and Atmospheric Administration sea surface temperature data from 1981 to 2010, spatiotemporal distributions of NIO TC activity and relationships with local sea surface temperature (SST) were studied with statistical diagnosis methods. Results of empirical orthogonal function (EOF) analysis of NIO TC occurrence frequency show that the EOF1 mode, which accounts for 16% of total variance, consistently represents variations of TC occurrence frequency over the whole NIO basin. However, spatial distributions of EOF1 mode are not uniform, mainly indicating variations of westward-moving TCs in the Bay of Bengal. The prevailing TC activity variation mode oscillates significantly on a quasi-5 year interannual time scale. NIO TC activity is notably influenced by the Indian Ocean dipole (IOD) mode. When the Indian Ocean is in a positive (negative) phase of the IOD, NIO SST anomalies are warm in the west (east) and cold in the east (west), which can weaken (strengthen) convection over the Bay of Bengal and eastern Arabian Sea, and cause anticyclonic (cyclonic) atmospheric circulation anomalies at low levels. This results in less (more) TC genesis and reduced (increased) opportunities for TC occurrence in the NIO. In addition, positive (negative) IOD events may strengthen (weaken) westerly steering flow over the Bay of Bengal, which further leads to fewer (more) westward-moving TCs which appear in regions west of 90°E in that bay.