scholarly journals Current status of tropical cyclone track prediction techniques and forecast errors

MAUSAM ◽  
2021 ◽  
Vol 57 (1) ◽  
pp. 151-158
Author(s):  
AKHILESH GUPTA
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Weather Forecasting ◽  
Model Development ◽  
Synthetic Data ◽  
India Meteorological Department ◽  
Indian Region ◽  
Current Status ◽  
Forecast Errors ◽  
Rapid Improvement

lkj & fiNys dqN n’kdksa esa HkweaMyh; m".kdfVca/kh; pØokrksa ds iwokZuqeku esa gksus okyh =qfV;ksa esa mUur vk¡dM+k laxzg.k rduhdksa] lrr ekWMy fodkl] mPp foHksnuksa vkSj Hkzfey fof’"Vrk ds QyLo:Ik dkQh deh vkbZ gSA ;g ns[kk x;k gS fd lewps fo’o esa iwokZuqeku dh =qfV;ksa esa izR;sd le; ds vuqHkoksa ds vk/kkj ij izfro"kZ 1 ls 2 izfr’kr rd dh deh vkbZ gS ftuds ifj.kkeLo:Ik yEch vof/k ¼48 ?kaVs ls vf/kd½ ds iwokZuqekuksa esa vR;ar rhoz xfr ls lq/kkj gks jgk gSA vVykafVd vkSj iz’kkar egklkxj tSls csfluksa esa ;|fi fofHkUu dkj.kksa ls iwokZuqeku dh =qfV;ksa esa dkQh vf/kd deh gqbZ gS rFkkfi Hkkjrh; {ks= esa ;g izo`fŸk dkQh lk/kkj.k jgh gSA bl {ks= esa iwokZuqeku dh =qfV;ksa esa deh vkus dk ,dek= dkj.k Hkkjr ekSle foKku foHkkx ¼Hkk- ekS- fo- fo-½ vkSj jk"Vªh; e/;e vof/k ekSle iwokZuqeku dsUnz ¼,u- lh- ,e- vkj- MCY;w- ,Q-½ tSls izpkyukRed ,u- MCY;w- ih- dsUnzksa }kjk muds {ks=h; vkSj HkweaMyh; ekWMyksa ds fo’ys"k.k esa la’ysf"kr Hkzfeyrk dk vf/kdkf/kd iz;ksx gks jgk gSA varZjk"Vªh; m".kdfVca/kh; pØokr vuqla/kku esa bl rF; ij vc vf/kd cy fn;k tk jgk gS fd fo’ks"k :Ik ls cgqr de le; esa gh vikjEifjd vk¡dM+ksa ds vf/kd mi;ksx] eslksLdsy fo’ys"k.kksa] Hkzfey fof’k"Vrk ds fy, la’ysf"kr vk¡dM+ksa ds mi;ksx ls vkSj mPp ekWMy foHksnu esa HkkSfrd izkpyhdj.k ds fu"iknu }kjk m".kdfVca/kh; pØokr ds ekxZ dk iwokZuqeku vf/kd lVhd cu ldsA pØokrksa ds ySaMQkWy ds laca/k esa igys ls py jgh gjhdsu MCY;w- vkj- ,Q- ifj;kstuk ij fo’ks"k :i ls py jgs vuqla/kku vkSj izpkyukRed dk;ksZa ls vkus okys o"kksZa esa Hkkjrh; {ks= dks ykHk gksus dh laHkkouk gSA rFkkfi] Hkkjrh; {ks= }kjk ekWMy ds fodkl ds lefUor iz;klksa ds vykok ekWMy ds fo’ys"k.k ds fy, ikjEifjd vkSj vikjEifjd vk¡dM+ksa dh vf/kdre miyC/krk rFkk mUur vk¡dM+k laxzg.k rduhd ds mi;ksx dks vf/kd izkFkfedrk nh tkuh pkfg,A Thanks to advanced data assimilation techniques, continuous model development, higher resolutions, and vortex specification, there has been considerable progress in the reduction of global tropical cyclone forecast errors during past few decades. It has been observed that world-wide rate of reduction of forecast errors was of the order of 1%-2% per year for all time horizons, with most rapid improvement at longer durations (beyond 48 hours). While other basins like Atlantic and Pacific oceans reported greater rate of decline of these errors due to various factors, the trend has been quite modest for Indian region. The only factor responsible for reduction of errors in the region was the greater use of synthetic vortex by operational NWP centres like India Meteorological Department (IMD) and National Centre for Medium Range Weather Forecasting (NCMRWF) in their Regional & Global model analyses. The current emphasis of international tropical cyclone research is to achieve greater accuracy of TC track prediction, especially in the short range, by maximizing the use of non-conventional data, meso-scale analysis, use of synthetic data for vortex specification, and the performance of physical parameterization at higher model resolution. The current research and operational emphasis of the ongoing  Hurricane WRF project for land falling cyclones, is expected to benefit the Indian region in the years to come. Nevertheless, the Indian region needs to assign higher priority to the greater availability of conventional & non-conventional data and use of advanced data assimilation technique for model analysis besides its concerted efforts on model developments.  

Air-sea strongly coupled data assimilation for tropical cyclone prediction

2021 ◽  
Author(s):  
Xingchao Chen
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Forecast Error ◽  
Regional Scale ◽  
Forecast Errors ◽  
State Variables ◽  
Strongly Coupled ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
Tc Prediction

<p>Air-sea interactions are critical to tropical cyclone (TC) energetics. However, oceanic state variables are still poorly initialized, and are inconsistent with atmospheric initial fields in most operational coupled TC forecast models. In this study, we first investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018) using a 200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model. Meaningful and dynamically consistent cross domain ensemble error correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update model state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation (DA). A regional-scale strongly coupled DA system based on the ensemble Kalman filter (EnKF) is then developed for TC prediction. The potential impacts of different atmospheric and oceanic observations on TC analysis and prediction are examined through observing system simulation experiments (OSSEs) of Hurricane Florence (2018). Results show that strongly coupled DA resulted in better analysis and forecast of both the oceanic and atmospheric variables than weakly coupled DA. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, strongly coupled DA reduces the forecast errors of TC track and intensity. Results show promise in potential further improvement in TC prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based strongly coupled DA system.</p>

Intercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)

2014 ◽  
Vol 142 (9) ◽  
pp. 3347-3364 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Hybrid System ◽  
Ensemble Kalman Filter ◽  
Computational Cost ◽  
Wrf Model ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Verification Methods

This study examines the performance of ensemble and variational data assimilation systems for the Weather Research and Forecasting (WRF) Model. These methods include an ensemble Kalman filter (EnKF), an incremental four-dimensional variational data assimilation (4DVar) system, and a hybrid system that uses a two-way coupling between the two approaches (E4DVar). The three methods are applied to assimilate routinely collected data and field observations over a 10-day period that spans the life cycle of Hurricane Karl (2010), including the pregenesis disturbance that preceded its development into a tropical cyclone. In general, forecasts from the E4DVar analyses are found to produce smaller 48–72-h forecast errors than the benchmark EnKF and 4DVar methods for all variables and verification methods tested in this study. The improved representation of low- and midlevel moisture and vorticity in the E4DVar analyses leads to more accurate track and intensity predictions by this system. In particular, E4DVar analyses provide persistently more skillful genesis and rapid intensification forecasts than the EnKF and 4DVar methods during cycling. The data assimilation experiments also expose additional benefits of the hybrid system in terms of physical balance, computational cost, and the treatment of asynoptic observations near the beginning of the assimilation window. These factors make it a practical data assimilation method for mesoscale analysis and forecasting, and for tropical cyclone prediction.

scholarly journals Potential of INSAT-3D Sounder Derived Total Precipitable Water Product for Weather Forecast

2018 ◽  
Author(s):  
Shailesh Parihar ◽  
Ashim Kumar Mitra ◽  
Rajiv Bhatla
Keyword(s):  
Weather Forecasting ◽  
Correlation Coefficients ◽  
India Meteorological Department ◽  
Precipitable Water ◽  
Weather Forecast ◽  
Satellite System ◽  
Indian Region ◽  
Data Consistency ◽  
Total Precipitable Water ◽  
Global Navigation Satellite

Abstract. INSAT-3D satellite objectives to upgrade the meteorological observation, monitoring of earth surface atmosphere for weather forecasting and disaster warning. The amount of the water vapor present in atmospheric column in the form of total precipitable water (TPW) derived product from atmospheric sounding system is one such weather monitoring capability in the INSAT-3D payload. The current study is based on INSAT-3D satellite sounder derived TPW and corresponding TPW from radiosonde observations (RS) and National Oceanic and Atmospheric Administration (NOAA), N-18 and N-19 have been used to assess retrieval performances. The RS TPW from 34 India Meteorological Department (IMD) stations over the Indian region from May to September 2016 has been considered for the validation. The analysis is performed on daily, monthly, sub-divisional and overall basis over the Indian region. On daily and monthly scale against RS TPW, the root mean square error (RMSE) and correlation coefficients (CC) of INSAT-3D TPW are in and around of 8 mm and above 0.8 respectively. However, on sub-divisional and overall scale, the RMSE found to be in the range of 1 to 2 mm and CC was around 0.9 in comparison with RS and NOAA. The spatial distribution of INSAT-3D TPW with actual rainfall observation is also been investigated. In general, INSAT-3D TPW correspond well with rainfall observation however, heavy rainfall events occurs in the presence of high TPW values. Furthermore, a case study with INSAT-3D TPW and ground based Global Navigation Satellite System (GNSS) receiver network have been demonstrated. It is noticed that, INSAT-3D TPW can be considered as a precursor for mesoscale activity very well. The purpose of this study is to investigate the potential use of operational INSAT-3D sounder derived TPW to weather forecast. However, the major source of improvement in INSAT-3D TPW is mainly applying the GSICS calibration corrections (Global Space-based Inter-Calibration System) on Infra-Red (IR) sounder channels at IMDPS, New Delhi, which aims to produce corrections, ensuring the data consistency and allowing them to be used to produce globally homogeneous products for environmental monitoring. The current TPW from INSAT-3D satellite can be utilized operationally for weather purpose and it can also offer substantial opportunities for improvement in now casting studies.

scholarly journals Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

2011 ◽  
Vol 50 (11) ◽  
pp. 2309-2318 ◽  
Author(s):  
Howard Berger ◽  
Rolf Langland ◽  
Christopher S. Velden ◽  
Carolyn A. Reynolds ◽  
Patricia M. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
North Pacific ◽  
Western North Pacific ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Rapid Scan ◽  
Atmospheric Motion ◽  
The Impact

AbstractEnhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

scholarly journals Microwave Sounder Cloud Detection Using a Collocated High-Resolution Imager and Its Impact on Radiance Assimilation in Tropical Cyclone Forecasts

2016 ◽  
Vol 144 (10) ◽  
pp. 3937-3959 ◽  
Author(s):  
Hyojin Han ◽  
Jun Li ◽  
Mitch Goldberg ◽  
Pei Wang ◽  
Jinlong Li ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hurricane Sandy ◽  
Advanced Technology ◽  
Forecast Errors ◽  
Cloud Detection ◽  
Process Data ◽  
Radiance Assimilation

Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.

scholarly journals A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

2014 ◽  
Vol 7 (4) ◽  
pp. 1819-1828 ◽  
Author(s):  
H. Wang ◽  
X.-Y. Huang ◽  
D. Xu ◽  
J. Liu
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Domain Size ◽  
Small Scale ◽  
Forecast Errors ◽  
Blending Method ◽  
Environmental Prediction ◽  
Regional Forecasts ◽  
The Impact

Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scale-dependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to reintroduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a 2-week-long period in September 2012. It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently reintroduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

scholarly journals A scale-dependent blending scheme for WRFDA: impact on regional weather forecasting

2014 ◽  
Vol 7 (2) ◽  
pp. 2455-2482
Author(s):  
H. Wang ◽  
X.-Y. Huang ◽  
D. Xu ◽  
J. Liu
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Domain Size ◽  
Small Scale ◽  
Forecast Errors ◽  
Blending Method ◽  
Environmental Prediction ◽  
Regional Forecasts ◽  
The Impact

Abstract. Due to limitation of the domain size and limited observations used in regional data assimilation and forecasting systems, regional forecasts suffer a general deficiency in effectively representing large-scale features such as those in global analyses and forecasts. In this paper, a scale-dependent blending scheme using a low-pass Raymond tangent implicit filter was implemented in the Data Assimilation system of the Weather Research and Forecasting model (WRFDA) to re-introduce large-scale weather features from global model analysis into the WRFDA analysis. The impact of the blending method on regional forecasts was assessed by conducting full cycle data assimilation and forecasting experiments for a two-week long period in September 2012. It is found that there are obvious large-scale forecast errors in the regional WRFDA system running in full cycle mode without the blending scheme. The scale-dependent blending scheme can efficiently re-introduce the large-scale information from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analyses, and keep small-scale information from WRF analyses. The blending scheme is shown to reduce analysis and forecasting error of wind, temperature and humidity up to 24 h compared to the full cycle experiments without blending. It is also shown to increase precipitation prediction skills in the first 6 h forecasts.

Improve Tropical Cyclone Prediction of TWRF with the Application of Advanced Observation Data

2020 ◽  
Author(s):  
Der-Song Chen ◽  
Ling-Feng Hsiao ◽  
Jia-Hong Xie ◽  
Jing-Shan Hong ◽  
Chin-Tzu Fong ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Radar Data ◽  
Initial Time ◽  
Forecast Errors ◽  
Model Resolution ◽  
Weather Bureau ◽  
Four Dimensional Data Assimilation ◽  
Central Weather Bureau

<p>With violent wind and severe rainfall, the tropical cyclone is one of the most disastrous weather systems over ocean and the coastal area. To provide accurate tropical cyclone (TC) track and intensity forecasts is one of the most important tasks of the national weather service of countries affected. Taiwan is one of the areas frequently influenced by tropical cyclones. Improving the tropical cyclone forecast is the highest priority task of Taiwan’s Central Weather Bureau (CWB).</p><p>Recent improvement of the TC forecast is due to the improvement of the numerical weather prediction. A version of the Advanced Research Weather Research and Forecasting Model (WRF), named TWRF (Typhoon WRF), was developed and implemented in CWB for operational TC forecasting from 2011. During the years, partial update cycling, cyclone bogus scheme, relocation scheme, 3DVAR with outer loop, analysis blending scheme, new trigger Kain–Fritsch cumulus scheme, and so on have been studied and applied in TWRF (Hsiao et al. 2010, 2012, 2015) to improve the model. We also improved the model by changing the TWRF configuration from a triple nested to a double nested grid and increasing the model resolution from 45/15/5 km 45-levels to 15/3 km 52-levels from 2016. Results showed increasing the model resolution improving the track, intensity and rainfall forecast. However, The averaged 24/48/72 hours TC track forecast errors of TWRF are 91/147/223, 84/133/197, 74/127/215, 64/122/202, 70/120/194 and 70/122/180 km in year 2014, 2015, 2016, 2017, 2018 and 2019 respectively.</p><p>In this study, WRF Four-dimensional data assimilation (FDDA) is adopted to assimilate the temperature, pressure, water vapor content which processed from the FORMOSAT-7 constellation, high-temporal frequency atmospheric motion vector (AMV) retrieved from Himawari-8 satellite images and radar data to generate a model balanced TC structure and thermodynamic state at the initial time. The specific goal is to improve the track, structure and intensity prediction of TCs and their associated rainfall distribution in Taiwan. The detail will be presented in the conference.</p><p>Keywords: tropical cyclone, Himawari-8 AMV, Four-dimensional data assimilation, FORMOSAT-7, radar data.</p><p>Corresponding author address:</p><p>Der-Song Chen,  [email protected]</p><p>Central Weather Bureau, 64 Gongyuan Rd., Taipei, Taiwan, R.O.C., 10048.</p>

scholarly journals Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Weather Forecasting ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Medium Range ◽  
Gust Speed

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.

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