Along-track Error Research Articles

Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model

The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.

Barrier-Based Adaptive Line-of-Sight 3-D Path-Following System for a Multijoint Robotic Fish With Sideslip Compensation.

This article proposes a novel barrier-based adaptive line-of-sight (ALOS) three-dimensional (3-D) path-following system for an underactuated multijoint robotic fish. The framework of the developed path-following system is established based on a detailed dynamic model, including a barrier-based ALOS guidance strategy, three integrated inner-loop controllers, and a nonlinear disturbance observer (NDOB)-based sideslip angle compensation, which is employed to preserve a reliable tracking under a frequently varying sideslip angle of the robotic fish. First, a barrier-based convergence strategy is proposed to deal with probable along-track error disruption and suppress the error within a manageable range. Meanwhile, an improved adaptive guidance scheme is adopted with an appropriate look-ahead distance. Afterward, a novel NDOB-based sideslip angle compensation is put forward to identify the varying sideslip angle independent of speed estimation. Subsequently, inner-loop controllers are intended for regulation about the controlled references, including a super-twisting sliding-mode control (STSMC)-based speed controller, a global fast terminal sliding-mode control (GFTSMC)-based heading controller, and a GFTSMC-based depth controller. Finally, simulations and experiments with quantitative comparison in 3-D linear and helical path following are presented to verify the effectiveness and robustness of the proposed system. This path-following system provides a solid foundation for future marine autonomous cruising of the underwater multijoint robot.

A Constant Elevation Search Method for Narrow Beam Radar to Capture Space Targets

Position prediction is the basis of guiding radar to seek, capture, and track space targets, although there’s a risk of insufficient accuracy for narrow beam radar due to the influence of orbit determination and propagation error. Since the along-track error is the main factor that affects the accuracy of position prediction, this paper proposed a Constant Elevation Search (CES) method for narrow beam radar, based on the idea of along-track error compensation, to seek and capture transit targets. With along-track error estimation, all possible positions on a specified elevation can be observed, thus the success rate of capturing transit targets will be improved. Simulation shows that when the position prediction method fails, the CES method still works.

Fixed-time coordinated guidance for containment maneuvering of unmanned surface vehicles under delayed communications: Theory and experiment

The purpose of this paper is to develop a new cooperative controller to solve the containment maneuvering problem for multiple unmanned surface vehicles (USVs) that track one parameterized path in a fixed time. The required velocities and heading angles of the USVs are designed based on Line-of-Sight (LOS) guidance laws. To steer along-track errors and cross-track errors to certain sets in fixed time, the coordinated guidance laws are derived by tan-type barrier Lyapunov functions (BLFs). For the coordination level, time-delayed coordination update laws are proposed for containment maneuvering. Comparing with the current containment maneuvering, the main features are: (i) LOS-based guidance laws are improved with the performance of fixed-time convergence, the convergence time can be determined by user-defined time; (ii) A novel containment maneuvering path variable controller is derived with time delays and without the assumption of setting individual paths following errors to be 0. (iii) the advantages of the proposed containment maneuvering control strategy are illustrated both through theoretical analysis and experimental result.

Nonlinear Positioning Control for Underactuated Unmanned Surface Vehicles in the Presence of Environmental Disturbances

This article presents a nonlinear positioning control method for underactuated marine surface vehicles via the adaptive heading assignment to resist unknown environmental disturbances. A coordinate system rotated about the desired position is established and three control objectives are presented, namely, the along-track error minimization, the yaw-track error minimization, and the rotated adaption of this coordinate system. The last control objective is to minimize the cross-track error by properly making use of the sway disturbance force. The cross-track stability analysis shows that the proposed adaptive law to adjust the rotated coordinate system can make the cross-track system globally bounded and locally exponentially stable when the control gain of the proposed adaptive law is appropriately set. Both simulated and experimental validations demonstrate that the vehicle can keep the preset desired position when the sway disturbance force added to the vehicle hull is close to zero and the vehicle orientation toward the opposite mean disturbance force is maintained at the same time.

Influence of atmospheric density and area-to-mass errors on orbit prediction accuracy

Abstract Due to the fact that the space is becoming increasingly congested, there is a high requirement for the orbit prediction accuracy of space targets to support collision warning tasks. This is especially true for space debris with high area-to-mass ratio and low orbit altitude. This paper focuses on the along-track position error caused by the atmospheric density error and area-to-mass ratio error for targets with near-circular orbit in the orbit prediction. We derive a semi-analytical expression to describe the along-track position error, and the expression’s validity is evaluated by a goodness-of-fit index compared with the simulated “true” along-track position error. The simulation is based on Encke’s special perturbation method, in which the drag error is treated as a perturbation. The drag error is modeled by the difference between the JB2008 and MISIS density model and the sinusoidal oscillation error model concerning the inexact area-to-mass ratio. Furthermore, targets with various orbit altitudes ranging from 300km to 800km are selected to investigate whether the along-track position error can meet the collision warning requirements.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019 & 2020

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

Analytic Method for Estimating Aircraft Fix Displacement From Gyroscope’s Allan-Deviation Parameters

The noise and drift requirements for a navigation-grade gyroscope are widely known, yet there is no simple analytic model of how the noise and drift of a gyroscope influence the fix displacement error (FDE) of an inertial navigation system (INS). This work derives simple analytical expressions for the cross-track and along-track errors of an aircraft whose INS consists solely of a three-axis gyroscope system with perfect knowledge of the vertical direction. The error signal of each gyroscope is Gaussian white noise and drift modeled as a first-order Markov random walk. These expressions provide a straightforward mean of calculating the FDE of an aircraft as a function of the flight duration, velocity, noise amplitude, drift amplitude, and drift’s time constant. These expressions are validated with Monte-Carlo simulations of long flights. This model quantifies the noise-versus-drift trade-off for a gyroscope in an inertial navigation system. It can save time when estimating the noise and drift that a gyroscope must exhibit to satisfy a given position-error requirement, or vice versa. They are used in particular to confirm the values, often cited without demonstration, of the noise and drift required to meet the maximum position error of an aircraft imposed by the Federal Aviation Administration’s required navigation performance 10 specification. Finally, it demonstrates that using the minimum in the measured Allan deviation of a gyroscope as a metric of the drift is incorrect, because it fails to capture the drift’s time constant. The proper metric is the maximum in the Allan deviation.

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

2-D Spatially Variant Motion Error Compensation for High-Resolution Airborne SAR Based on Range-Doppler Expansion Approach

Motion errors are inevitable in real-world scenarios and introduce significant phase errors in airborne synthetic aperture radar (SAR) imaging. Generally, these errors consist of the cross-coupling and spatially variant components. Cross-coupling errors can usually be eliminated by motion compensation (MoCo), whereas the latter are seldom addressed, which deteriorate the imaging qualities, especially for the high-resolution cases. To solve the problem, a novel approach based on 2-D range-Doppler expansion is proposed. First, an accurate range equation of the aircraft is obtained based on the inertial navigation system (INS) data. Then, the range-Doppler expansion corresponding to the slant range and Doppler centroid are performed, by which the echo signal is decoupled into two spatially variant parts in range and azimuth directions. Finally, the chirp-z transforms (CZTs) are employed to remove the range and azimuth spatial variations introduced, respectively, by the cross-track and along-track errors. Different from the conventional methods, our approach can greatly decrease the cross-coupling and spatially variant effects brought by motion errors in high-resolution cases. Computer simulation and real data experiments demonstrate the effectiveness of the proposed approach.

Precise re-entry and landing of propellantless spacecraft

• Propellant-free spacecraft ground impact point targeting algorithm. • Guidance and feedback control targeting strategy using exclusively aerodynamic drag. • Monte Carlo simulation results proving algorithm robustness and performance. Spacecraft returning scientific samples or humans from space must be capable of surviving re-entry and landing in a desired location. Traditionally, this has been accomplished via a propulsive de-orbit burn. However, it is not always possible to mount a propulsion system on board a satellite or a capsule. In the case of small satellites deployed from the International Space Station, for example, on-board propulsion systems are forbidden for safety reasons. Our work proposes a new technological solution for re-entering and landing a spacecraft in a desired location from a low Earth orbit using exclusively aerodynamic drag and eliminating the need for chemical propulsion. First, an iterative procedure is utilized to compute the desired state at the re-entry interface (100 km) such that a propagation of the vehicle dynamics in the nominal re-entry drag configuration from this initial state leads to a landing at a desired latitude and longitude on the surface of the Earth. Next, a re-entry point targeting algorithm is utilized to determine the on-orbit ballistic coefficient profile necessary to target the desired re-entry point. Finally, the ballistic coefficient profile during the final hours of the trajectory before the re-entry interface is iteratively modified to correct any remaining along-track error in the landing location. The proposed solution is applied to a small satellite system that is jettisoned from the ISS and is equipped with a deployable heat shield that also serves as a drag device.

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.

Predicting the rapid intensification and dynamics of pre-monsoon extremely severe cyclonic storm ‘Fani’ (2019) over the Bay of Bengal in a 12-km global model

The present study investigates into genesis, intensification, dynamical behavior and the prediction of Extremely Severe Cyclonic Storm (ESCS) ‘Fani' over the Bay of Bengal (BoB) in the National Centre for Medium-Range Weather Forecasting (NCMRWF) global numerical weather prediction (NWP) modeling system ‘NCUM.’ The global model is adopted from the UK Met office's Unified Model (UM). The ESCS ‘Fani' formed from 26 April–04 May 2019, over the warm waters of BoB. In the criterion of landfalling tropical cyclones (TCs), which crossed the Orissa coast, it was the most intense cyclonic storm during pre-monsoon season since 1965. The TC exhibited a markedly different characteristic having genesis very near to the equator (near 2.7°N and 88.7°E) and possessing one of the longest tracks (~3030-km) over the BoB region. The NCUM global model was operationally run during the occurrence of the TC. The diagnosis of the TC's genesis and rapid intensification (RI) in the model is carried out using various metrics such as Genesis potential parameter (GPP), winds at 850-hPa, vertically integrated moisture flux, potential vorticity (PV) at isentropic level 315 K, Severe Weather Threat (SWEAT) index and daily averaged latent heat flux, etc. It is found that the early genesis, structure, RI, and movement of the TC were well captured by the model in advance. The Model predicted TC tracks for a total of seven initial conditions (from 27 April to 03 May 2019, at 24-h interval) are found closely co-related with the observed best track of the TC provided by India Meteorological Department (IMD). The lower values of Direct Position Errors (DPEs), along-track errors (ATEs), and cross-track errors (CTEs) indicate the skillful prediction of the TC by the model. The structure, amplitude, and location (SAL) simulated precipitation of the TC over the BoB and along the central-east coast of India are in agreement with the observed systems. Our detailed diagnostic analysis suggests that the TC formation, development, and intensification was mostly controlled by the warmer BoB sea surface temperatures.

An Investigation of Large Along-Track Errors in Extratropical Transitioning North Atlantic Tropical Cyclones in the ECMWF Ensemble

Abstract The synoptic evolution and mechanisms for the largest medium-range (72–120 h) along-track errors of tropical cyclones (TC) are investigated. The mean along-track errors (ATEs) of the 51-member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble are evaluated for 393 forecasts (85 TCs) during the 2008 to 2016 North Atlantic seasons. The 27 unique forecasts within the upper quintile of most negative ATEs (i.e., slow bias greater than 500 km by 72 h) are inherently fast-moving TCs that undergo extratropical transition as they recurve and interact with a 300-hPa upstream trough and a downstream ridge. Both the trough and ridge are underamplified by only 5–10 m ~60 h before the time of largest ATE. The height errors then grow rapidly due to underpredicted 300–200-hPa potential vorticity advection by both the nondivergent wind and the irrotational wind from the TC’s outflow. Both wind components are underpredicted and result in weak biases in the trough’s developing potential vorticity gradient and associated jet streak. The underamplification of the upstream trough is exacerbated by underpredicted 700-hPa cold advection extending from beneath the trough into the TC at 48–36 h before the largest ATE. Standardized differences are consistent with the mean errors and reveal that weaker divergent outflow is driven by underpredicted near-TC precipitation, which corresponds to underpredicted 700-hPa moisture fluxes near the TC at ~108 h before the largest ATE. The ensemble member ATEs at 72–120 h generally show little correlation with their ATEs before 36 h, suggesting that initial position uncertainty is not the primary source of ATE variability later in the forecast.

Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System

AbstractBecause of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency.

Trajectory Prediction Sensitivity Analysis Using Monte Carlo Simulations Based on Inputs’ Distributions

To facilitate the increasing amount of air traffic, current and future decision support tools for air traffic management require an efficient and accurate trajectory prediction. With uncertainty inherent to almost all inputs of a trajectory predictor, the accurate prediction is not a simple task. In this study, Monte Carlo simulations of a ground-based trajectory predictor are performed to estimate the prediction uncertainty up to 20 min look-ahead time and to assess the correlation between inputs and prediction errors. Selected inputs are aircraft bank angle, constant calibrated airspeed and Mach number speed settings, vertical speed, temporary level-offs, air temperature, lapse rate, wind, and air traffic control intent. These inputs are provided in the form of their distribution functions obtained from observed data such as surveillance data, weather forecasts, and air traffic controllers’ inputs. Simulations are performed for heavy and medium wake turbulence category aircraft. Results indicate that with 20 min look-ahead time, when outliers are not considered, along-track errors can reach up to 18 nmi, whereas altitude errors can reach up to around 13,000 ft. Cross-track errors in cruise highly depend on the lateral deviations due to Air Traffic Control instructions, and, in this study, are within 10 nmi. Wind conditions, vertical speed, calibrated airspeed, Mach number speed setting, and temporary level-offs are determined to be the most influential inputs.

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

In this research, a hybrid dead reckoning error correction scheme is developed based on extended Kalman filter (EKF) and map matching (MM) to improve the positioning accuracy for vehicle self-localization. The developed method aims at obtaining accurate positions when the GPS signals are occasionally unavailable or weakened. First, the heading data collected from an odometer and an optical fiber gyroscope are integrated by an EKF to reduce the random errors in dead reckoning. Then a modified topological MM algorithm is developed to reduce the systematic errors in dead reckoning. In this work, both cross-track errors and along-track errors are considered to improve positioning accuracy of MM. The errors are finally corrected using the results achieved from both the dead reckoning and the MM when the driving distance of a vehicle exceeds a predefined length or the vehicle turns in an intersection. Experiments have been conducted to evaluate the developed method and the results show that the maximum error and average error of dead reckoning can be respectively reduced to 15.4 m and 5.2 m during the experiment with total distance of 43 km. This positioning accuracy is even better than the accuracy of the low-cost GPSs which are usually at the order of 15–20 m (95%). The developed method is effective to achieve the positions of the vehicle when the GPS signals are occasionally unavailable or weakened.

Genesis and track prediction of pre-monsoon cyclonic storms over North Indian Ocean in a multi-model ensemble framework

An attempt has been made in this study to evaluate the performance of a multi-model ensemble prediction system (MMEPS) in the extended range prediction of genesis and track of cyclonic storms (CS) over North Indian Ocean (NIO) during the onset phase of Indian summer monsoon. The MMEPS comprises of National Centres for Environmental Prediction Climate Forecast System version 2 and its atmospheric component, Global Forecast System version 2, at two different resolutions. In this study, we analyse the performance of this MMEPS in the prediction of six CS cases formed around the onset phase of the Indian summer monsoon over NIO. Along with this, we evaluate the usefulness of the genesis potential parameter used by India Meteorological Department (IMD) for the real-time cyclogenesis prediction. ERA-Interim re-analysis data sets are used to compare MME predictions from two initial conditions (IC) close to the genesis date of each storm. Results show that genesis forecasts from nearest available IC is capturing the vorticity and associated features of the storm better than far-IC. A modified version of vortex tracking scheme developed by Geophysical Fluid Dynamics Laboratory is used to obtain the mean track positions from MME outputs and compare with IMD-best tracks. Track verification is done by calculating average direct position error, cross-track error and along-track error against corresponding IMD-best tracks. It is found that track predictions from both near and far ICs have lower average errors initially with near-IC having lesser errors as compared to far-IC. Along-track error for pre-genesis track prediction from far-IC implies that the predicted tracks remarkably lag behind observed tracks for most of the cases. These errors could be contributed from the inability of the models in realistically capturing the region of evolution and intensification of both thermodynamic and dynamic parameters with increasing lead time.

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.

Impact of Vortex Initialization with Satellite Data Assimilation for Predicting Track and Intensity of Tropical Cyclone Chapala

The present study evaluates the impact of vortex initialization (VI) with assimilation of satellite observations in the National Centre for Medium Range Weather Forecasting-UK Met Office Unified (NCUM) global Model for prediction of extremely severe cyclonic storm (ESCS) Chapala formed over the Arabian Sea (AS) during 28 October–03 November 2015. For this purpose, two numerical experiments viz. Vort-Gts (assimilation of only Global Telecommunication System (GTS) observations with VI) and Vort-Rad (assimilation of GTS plus satellite observations with VI) are conducted. The model is run with a total of five different initial conditions (ICs) through 24 h cycles. The standard metrics of cyclone track errors such as Direct Position Errors (DPEs), Cross-Track Errors (CTEs), and Along-Track Errors (ATEs) are computed and found to have improved by 47, 40, and 57%, respectively in Vort-Rad over Vort-Gts. The intensity and land fall (LF) errors are significantly reduced in Vort-Rad in comparison with the Vort-Gts. The statistical skill scores such as equitable threat score (ETS) and bias are better represented in the Vort-Rad.