scholarly journals Real time forecasting of the Bay of Bengal cyclonic storm “RASHMI” of October 2008 – A statistical-dynamical approach

MAUSAM ◽  
2021 ◽  
Vol 61 (1) ◽  
pp. 1-10
Author(s):  
S. D. KOTAL ◽  
S. K. ROY BHOWMIK ◽  
B. MUKHOPADHYAY
Keyword(s):  
Real Time ◽  
Bay Of Bengal ◽  
Position Error ◽  
Potential Parameter ◽  
Japan Meteorological Agency ◽  
Time Error ◽  
Cyclonic Storm ◽  
Dynamical Approach ◽  
Intensity Prediction ◽  
Genesis Potential Parameter

A four-step statistical-dynamical approach is applied for real time forecasting of the Bay of Bengal cyclonic storm “RASHMI” of October 2008 which made landfall near Khepupara (Bangladesh) around 2200 UTC of 26 October 2008. The four-step approach consists of (a) Analysis of Genesis Potential Parameter (GPP), (b) Track prediction, (c) Intensity Prediction by Statistical Cyclone Intensity Prediction (SCIP) model and (d) Prediction of decaying intensity after the landfall. The results show that the analysis of Genesis Potential Parameter (GPP) at early stages of development strongly indicated that the cyclone “RASHMI” had enough potential to reach its cyclone stage.  The 48 hours landfall forecast position error based on 0000 UTC on 25 October shows that the error varies from around 10 km to 95 km and landfall time error varies from 12 hours early to 23 hours delay by different numerical models (NWP). The consensus forecast (ensemble) based on these NWP models shows that landfall forecast position error is around 10 km and landfall time error is around 2 hours delay. The updated 24 hours forecast based on 0000 UTC of 26 October shows improvement in the forecast. The model predicted landfall position error varies from around 10 km to 55 km with landfall time 6 hours early to 3 hours delay. The Multiple Model Ensemble (MME) forecast shows that the landfall forecast position is close to observed landfall point and the landfall time is early by 2 hours. The JMA (Japan Meteorological Agency) and ensemble forecasts are found to be consistent both in terms of 24-hourly forecasts position, landfall point and landfall time. The 12–hourly intensity prediction up to 24 hours forecasts based on 0000 UTC on 26 October show that the model (SCIP) could pick up the intensification of the cyclone. The model forecasts till the landfall point show that there is an underestimation of intensity by 2 knots and 8 knots at 12 hour and 24 hour forecasts respectively. The 6-hourly decaying intensity forecast after the landfall shows an overestimation of 6 knots and 10 knots at 6-hour and 12-hour forecasts respectively. The approach provided useful guidance to the forecasters for real time forecasting of the cyclone.

Real-time prediction of movement, intensity and storm surge of very severe cyclonic storm Hudhud over Bay of Bengal using high-resolution dynamical model

2016 ◽  
Vol 81 (3) ◽  
pp. 1771-1795 ◽  
Author(s):  
Raghu Nadimpalli ◽  
Krishna K. Osuri ◽  
Sujata Pattanayak ◽  
U. C. Mohanty ◽  
M. M. Nageswararao ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Storm Surge ◽  
Bay Of Bengal ◽  
Dynamical Model ◽  
Cyclonic Storm ◽  
Time Prediction ◽  
Movement Intensity ◽  
Severe Cyclonic Storm

Further Improvements to the Statistical Hurricane Intensity Prediction Scheme Using Tropical Cyclone Rainfall and Structural Features

2018 ◽  
Vol 33 (6) ◽  
pp. 1587-1603 ◽  
Author(s):  
Udai Shimada ◽  
Hiromi Owada ◽  
Munehiko Yamaguchi ◽  
Takeshi Iriguchi ◽  
Masahiro Sawada ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Heat Content ◽  
Absolute Error ◽  
Structural Features ◽  
Japan Meteorological Agency ◽  
Intensity Change ◽  
Hurricane Intensity ◽  
Intensity Prediction ◽  
Prediction Scheme

Abstract The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple regression model for forecasting tropical cyclone (TC) intensity [both central pressure (Pmin) and maximum wind speed (Vmax)]. To further improve the accuracy of the Japan Meteorological Agency version of SHIPS, five new predictors associated with TC rainfall and structural features were incorporated into the scheme. Four of the five predictors were primarily derived from the hourly Global Satellite Mapping of Precipitation (GSMaP) reanalysis product, which is a microwave satellite-derived rainfall dataset. The predictors include the axisymmetry of rainfall distribution around a TC multiplied by ocean heat content (OHC), rainfall areal coverage, the radius of maximum azimuthal mean rainfall, and total volumetric rain multiplied by OHC. The fifth predictor is the Rossby number. Among these predictors, the axisymmetry multiplied by OHC had the greatest impact on intensity change, particularly, at forecast times up to 42 h. The forecast results up to 5 days showed that the mean absolute error (MAE) of the Pmin forecast in SHIPS with the new predictors was improved by over 6% in the first half of the forecast period. The MAE of the Vmax forecast was also improved by nearly 4%. Regarding the Pmin forecast, the improvement was greatest (up to 13%) for steady-state TCs, including those initialized as tropical depressions, with slight improvement (2%–5%) for intensifying TCs. Finally, a real-time forecast experiment utilizing the hourly near-real-time GSMaP product demonstrated the improvement of the SHIPS forecasts, confirming feasibility for operational use.

Real-time error detection techniques based on FPGA

2013 ◽  
Vol 33 (5) ◽  
pp. 1459-1462
Author(s):  
Xiaoming JU ◽  
Jiehao ZHANG ◽  
Yizhong ZHANG
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Time Error ◽  
Detection Techniques

Augmented Reality Overlay System for Computer-guided Needle Procedures

1969 ◽  
Author(s):  
Zachary Baum
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Region Of Interest ◽  
Calibration Method ◽  
Position Error ◽  
Ct Image ◽  
Entire System ◽  
Projected Image ◽  
Needle Interventions

Purpose: Augmented reality overlay systems can be used to project a CT image directly onto a patient during procedures. They have been actively trialed for computer-guided procedures, however they have not become commonplace in practice due to restrictions of previous systems. Previous systems have not been handheld, and have had complicated calibration procedures. We put forward a handheld tablet-based system for assisting with needle interventions. Methods: The system consists of a tablet display and a 3-D printed reusable and customizable frame. A simple and accurate calibration method was designed to align the patient to the projected image. The entire system is tracked via camera, with respect to the patient, and the projected image is updated in real time as the system is moved around the region of interest. Results: The resulting system allowed for 0.99mm mean position error in the plane of the image, and a mean position error of 0.61mm out of the plane of the image. This accuracy was thought to be clinically acceptable for tool using computer-guidance in several procedures that involve musculoskeletal needle placements. Conclusion: Our calibration method was developed and tested using the designed handheld system. Our results illustrate the potential for the use of augmented reality handheld systems in computer-guided needle procedures. 

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