Tropical cyclones prediction by numerical models in India Meteorological Department

lkj & bl 'kks/k&Ik= esa Hkkjr ekSle foKku foHkkx ¼Hkk- ekS- fo- fo-½ esa viukbZ xbZ pØokr izfr:fir djus dh dfYir rduhdksa ij ppkZ dh xbZ gSA vDrwcj 1999 esa mM+hlk esa vk, egkpØokr ds izkjfEHkd {ks=ksa esa dkYifud Hkzfeyrk dk mi;ksx djds] pØokr ds fof’k"V ekWMy] Doklh ySaxjfx;u ekWMy ¼D;w- ,y- ,e-½ ls 72 ?kaVs ds iwokZuqeku vkSj Hkkjr ekSle foKku foHkkx ds lhfer {ks= fun’kZ ¼,y- ,- ,e-½ ls 36 ?kaVs ds iwokZuqeku izfr:fir fd, x,A bl 'kks/k esa] 26 ls 28 vDrwcj rd dh izkjafHkd fLFkfr;ksa ds vk/kkj ij D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh vkSlr =qfV;k¡ 24 ?kaVs ds fy, 21 fd-eh-] 48 ?kaVs ds fy, 91 fd-eh- vkSj 72 ?kaVs ds fy, 179 fd-eh- jghA 1998&2004 rd ds fiNys lkr o"kksZa ds nkSjku D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh =qfV;ksa ds vk¡dM+ksa ij Hkh blesa ppkZ dh xbZ gSA blds vykok] ,y- ,- ,e- ls fd, x, iwokZuqeku ij izkjafHkd fLFkfr;ksa ds izHkko dh Hkh tk¡p dh xbZA fofHkUu izkjafHkd fLFkfr;ksa ls rS;kj fd, x, vkSlr ¼lesfdr½ iwokZuqeku ls 24 ?kaVs ds iwokZuqeku esa 123 fd-eh- vkSj 36 ?kaVs ds iwokZuqeku esa 81 fd-eh- dh =qfV;k¡ ikbZ xbZ] tks ,dek= iwokZuqeku dh rqyuk esa de jghA bu iz;ksxksa ls ;g irk pyk fd dkYifud Hkzfeyrk okys D;w- ,y- ,e- ekWMy ls pØokr ds ekxZ dk lVhd iwokZuqeku izkIr fd;k tk ldrk gS tks vHkh rd la[;kRed ekWMyksa ls miyC/k gks ikrk FkkA In the present paper, the cyclone bogusing techniques followed in India Meteorological Department (IMD) were discussed. Using the idealized vortex in the initial fields for Orissa super cyclone October 1999, the specialized cyclone model, Quasi-Lagrangian Model (QLM) 72 hours track forecast and also 36 hours forecast with IMD limited area model (LAM) were simulated. In this case, the QLM average track forecast errors based on 26-28 October initial conditions were 21 km for 24 hours, 91 km for 48 hours and 179 km for 72 hours. Also the QLM track forecast error statistics during the last 7 years 1998-2004 are discussed. In addition, the impact of initial conditions on the LAM forecast was examined. It was observed that the mean (ensemble) forecast generated from different initial conditions was shown track error of 123 km in 24 hours and 81 km in 36 hours forecast which is less than individual forecast. These experiments have established that the QLM model, with idealized vortex, provides track forecast within an accuracy level that was currently available from numerical models.

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Impact of Refractivity Profiles from a Proposed GNSS-RO Constellation on Tropical Cyclone Forecasts in a Global Modeling System

Monthly Weather Review ◽

10.1175/mwr-d-19-0360.1 ◽

2020 ◽

Vol 148 (7) ◽

pp. 3037-3057

Author(s):

Michael J. Mueller ◽

Andrew C. Kren ◽

Lidia Cucurull ◽

Sean P. F. Casey ◽

Ross N. Hoffman ◽

...

Keyword(s):

Tropical Cyclone ◽

North Atlantic ◽

Forecast Error ◽

Good Control ◽

Track Forecast ◽

Forecast Errors ◽

Random Errors ◽

Error Statistics ◽

The North ◽

The North Atlantic

Abstract A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (Vmax), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, Vmax, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and Vmax errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade Vmax and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and Vmax improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest Vmax degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

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Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches

Weather and Forecasting ◽

10.1175/waf-d-11-00131.1 ◽

2012 ◽

Vol 27 (5) ◽

pp. 1249-1263 ◽

Cited By ~ 57

Author(s):

Ling-Feng Hsiao ◽

Der-Song Chen ◽

Ying-Hwa Kuo ◽

Yong-Run Guo ◽

Tien-Chiang Yeh ◽

...

Keyword(s):

Data Assimilation ◽

North Pacific Ocean ◽

Limited Area ◽

Track Forecast ◽

Forecast Errors ◽

Outer Loop ◽

Typhoon Track ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

Abstract In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of suppressing the growth of the systematic model error. A typhoon prediction model using the Advanced Research core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions.

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A Rational Numerical Method for Simulation of Drop-Impact Dynamics of Oleo-Pneumatic Landing Gear

Applied Sciences ◽

10.3390/app11094136 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4136

Author(s):

Rosario Pecora

Keyword(s):

Numerical Method ◽

Initial Conditions ◽

Impact Load ◽

Numerical Models ◽

Landing Gear ◽

Design Stage ◽

Impact Dynamics ◽

State Variables ◽

Adopted Model ◽

The Impact

Oleo-pneumatic landing gear is a complex mechanical system conceived to efficiently absorb and dissipate an aircraft’s kinetic energy at touchdown, thus reducing the impact load and acceleration transmitted to the airframe. Due to its significant influence on ground loads, this system is generally designed in parallel with the main structural components of the aircraft, such as the fuselage and wings. Robust numerical models for simulating landing gear impact dynamics are essential from the preliminary design stage in order to properly assess aircraft configuration and structural arrangements. Finite element (FE) analysis is a viable solution for supporting the design. However, regarding the oleo-pneumatic struts, FE-based simulation may become unpractical, since detailed models are required to obtain reliable results. Moreover, FE models could not be very versatile for accommodating the many design updates that usually occur at the beginning of the landing gear project or during the layout optimization process. In this work, a numerical method for simulating oleo-pneumatic landing gear drop dynamics is presented. To effectively support both the preliminary and advanced design of landing gear units, the proposed simulation approach rationally balances the level of sophistication of the adopted model with the need for accurate results. Although based on a formulation assuming only four state variables for the description of landing gear dynamics, the approach successfully accounts for all the relevant forces that arise during the drop and their influence on landing gear motion. A set of intercommunicating routines was implemented in MATLAB® environment to integrate the dynamic impact equations, starting from user-defined initial conditions and general parameters related to the geometric and structural configuration of the landing gear. The tool was then used to simulate a drop test of a reference landing gear, and the obtained results were successfully validated against available experimental data.

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A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

Weather and Forecasting ◽

10.1175/waf-d-10-05033.1 ◽

2012 ◽

Vol 27 (1) ◽

pp. 124-140 ◽

Cited By ~ 11

Author(s):

Bin Liu ◽

Lian Xie

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Dynamical Downscaling ◽

Weather Forecasting ◽

Forecast Error ◽

Regional Model ◽

Small Scale ◽

Limited Area ◽

Track Forecast ◽

Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

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ROAD TEMPERATURE MODELLING WITHOUT IN-SITU SENSORS

The Baltic Journal of Road and Bridge Engineering ◽

10.3846/bjrbe.2017.30 ◽

2017 ◽

Vol 12 (4) ◽

pp. 241-247 ◽

Cited By ~ 1

Author(s):

Karol Opara ◽

Jan Zieliński

Keyword(s):

Energy Balance ◽

Urban Areas ◽

Initial Conditions ◽

Forecast Error ◽

Weather Data ◽

Forecast Errors ◽

The Road ◽

In Situ Sensors ◽

Pavement Temperature

Modelling of the pavement temperature facilitates winter road maintenance. It is used for predicting the glaze formation and for scheduling the spraying of the de-icing brine. The road weather is commonly forecasted by solving the energy balance equations. It requires setting the initial vertical profile of the pavement temperature, which is often obtained from the Road Weather Information Stations. The paper proposes the use of average air temperature from seven preceding days as a pseudo-observation of the subsurface temperature. Next, the road weather model is run with a few days offset. It first uses the recent, historical weather data and then the available forecasts. This approach exploits the fact that the energy balance models tend to “forget” their initial conditions and converge to the baseline solution. The experimental verification was conducted using the Model of the Environment and Temperature of Roads and the data from a road weather station in Warsaw over a period of two years. The additional forecast error introduced by the proposed pseudo-observational initialization averages 1.2 °C in the first prediction hour and then decreases in time. The paper also discusses the use of Digital Surface Models to take into account the shading effects, which are an essential source of forecast errors in urban areas. Limiting the use of in-situ sensors opens a perspective for an economical, largescale implementation of road meteorological models.

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Intercomparison of an Ensemble Kalman Filter with Three- and Four-Dimensional Variational Data Assimilation Methods in a Limited-Area Model over the Month of June 2003

Monthly Weather Review ◽

10.1175/2010mwr3610.1 ◽

2011 ◽

Vol 139 (2) ◽

pp. 566-572 ◽

Cited By ~ 48

Author(s):

Meng Zhang ◽

Fuqing Zhang ◽

Xiang-Yu Huang ◽

Xin Zhang

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Forecast Error ◽

Wrf Model ◽

Warm Season ◽

Variational Data Assimilation ◽

Lead Times ◽

Limited Area ◽

Forecast Errors

Abstract This study compares the performance of an ensemble Kalman filter (EnKF) with both the three-dimensional and four-dimensional variational data assimilation (3DVar and 4DVar) methods of the Weather Research and Forecasting (WRF) model over the contiguous United States in a warm-season month (June) of 2003. The data assimilated every 6 h include conventional sounding and surface observations as well as data from wind profilers, ships and aircraft, and the cloud-tracked winds from satellites. The performances of these methods are evaluated through verifying the 12- to 72-h forecasts initialized twice daily from the analysis of each method against the standard sounding observations. It is found that 4DVar has consistently smaller error than that of 3DVar for winds and temperature at all forecast lead times except at 60 and 72 h when their forecast errors become comparable in amplitude, while the two schemes have similar performance in moisture at all lead times. The forecast error of the EnKF is comparable to that of the 4DVar at 12–36-h lead times, both of which are substantially smaller than that of the 3DVar, despite the fact that 3DVar fits the sounding observations much more closely at the analysis time. The advantage of the EnKF becomes even more evident at 48–72-h lead times; the 72-h forecast error of the EnKF is comparable in magnitude to the 48-h error of 3DVar/4DVar.

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Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting

Monthly Weather Review ◽

10.1175/2007mwr1904.1 ◽

2007 ◽

Vol 135 (12) ◽

pp. 4117-4134 ◽

Cited By ~ 118

Author(s):

Brian Ancell ◽

Gregory J. Hakim

Keyword(s):

Variance Reduction ◽

Initial Conditions ◽

Sampling Error ◽

Forecast Error ◽

Single Point ◽

Error Variance ◽

Initial Time ◽

Forecast Errors ◽

Adjoint Sensitivity ◽

Time Optimal

Abstract The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

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Diagnosis and Optimization of Ensemble Forecasts

Monthly Weather Review ◽

10.1175/2007mwr2153.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 1054-1074 ◽

Cited By ~ 2

Author(s):

Tomislava Vukicevic ◽

Isidora Jankov ◽

John McGinley

Keyword(s):

Initial Conditions ◽

Forecast Error ◽

Absolute Error ◽

Ensemble Forecast ◽

Stochastic Representation ◽

Ensemble Forecasts ◽

Ensemble Technique ◽

Consensus Forecasts ◽

Almost All ◽

The Impact

Abstract In the current study, a technique that offers a way to evaluate ensemble forecast uncertainties produced either by initial conditions or different model versions, or both, is presented. The technique consists of first diagnosing the performance of the forecast ensemble and then optimizing the ensemble forecast using results of the diagnosis. The technique is based on the explicit evaluation of probabilities that are associated with the Gaussian stochastic representation of the weather analysis and forecast. It combines an ensemble technique for evaluating the analysis error covariance and the standard Monte Carlo approach for computing samples from a known Gaussian distribution. The technique was demonstrated in a tutorial manner on two relatively simple examples to illustrate the impact of ensemble characteristics including ensemble size, various observation strategies, and configurations including different model versions and varying initial conditions. In addition, the authors assessed improvements in the consensus forecasts gained by optimal weighting of the ensemble members based on time-varying, prior-probabilistic skill measures. The results with different observation configurations indicate that, as observations become denser, there is a need for larger-sized ensembles and/or more accuracy among individual members for the ensemble forecast to exhibit prediction skill. The main conclusions relative to ensembles built up with different physics configurations were, first, that almost all members typically exhibited some skill at some point in the model run, suggesting that all should be retained to acquire the best consensus forecast; and, second, that the normalized probability metric can be used to determine what sets of weights or physics configurations are performing best. A comparison of forecasts derived from a simple ensemble mean to forecasts from a mean developed from variably weighting the ensemble members based on prior performance by the probabilistic measure showed that the latter had substantially reduced mean absolute error. The study also indicates that a weighting scheme that utilized more prior cycles showed additional reduction in forecast error.

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Experimental Tropical Cyclone Forecasts Using a Variable-Resolution Global Model

Monthly Weather Review ◽

10.1175/mwr-d-15-0159.1 ◽

2015 ◽

Vol 143 (10) ◽

pp. 4012-4037 ◽

Cited By ~ 21

Author(s):

Colin M. Zarzycki ◽

Christiane Jablonowski

Keyword(s):

High Resolution ◽

Tropical Cyclone ◽

Initial Conditions ◽

Weather Prediction ◽

Horizontal Resolution ◽

Hurricane Sandy ◽

Track Forecast ◽

Time Step ◽

Variable Resolution ◽

The Impact

Abstract Tropical cyclone (TC) forecasts at 14-km horizontal resolution (0.125°) are completed using variable-resolution (V-R) grids within the Community Atmosphere Model (CAM). Forecasts are integrated twice daily from 1 August to 31 October for both 2012 and 2013, with a high-resolution nest centered over the North Atlantic and eastern Pacific Ocean basins. Using the CAM version 5 (CAM5) physical parameterization package, regional refinement is shown to significantly increase TC track forecast skill relative to unrefined grids (55 km, 0.5°). For typical TC forecast integration periods (approximately 1 week), V-R forecasts are able to nearly identically reproduce the flow field of a globally uniform high-resolution forecast. Simulated intensity is generally too strong for forecasts beyond 72 h. This intensity bias is robust regardless of whether the forecast is forced with observed or climatological sea surface temperatures and is not significantly mitigated in a suite of sensitivity simulations aimed at investigating the impact of model time step and CAM’s deep convection parameterization. Replacing components of the default physics with Cloud Layers Unified by Binormals (CLUBB) produces a statistically significant improvement in forecast intensity at longer lead times, although significant structural differences in forecasted TCs exist. CAM forecasts the recurvature of Hurricane Sandy into the northeastern United States 60 h earlier than the Global Forecast System (GFS) model using identical initial conditions, demonstrating the sensitivity of TC forecasts to model configuration. Computational costs associated with V-R simulations are dramatically decreased relative to globally uniform high-resolution simulations, demonstrating that variable-resolution techniques are a promising tool for future numerical weather prediction applications.

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The Impact of MetOp and Other Satellite Data within the Met Office Global NWP System Using an Adjoint-Based Sensitivity Method

Monthly Weather Review ◽

10.1175/mwr-d-12-00232.1 ◽

2013 ◽

Vol 141 (10) ◽

pp. 3331-3342 ◽

Cited By ~ 55

Author(s):

Sangwon Joo ◽

John Eyre ◽

Richard Marriott

Keyword(s):

Satellite Data ◽

Forecast Error ◽

Weather Prediction ◽

Satellite System ◽

Error Reduction ◽

Forecast Errors ◽

Data Types ◽

Satellite Platform ◽

Atmospheric Sounding ◽

The Impact

Abstract The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various subsets of observations are compared, with emphasis on space-based observations, particularly those from instruments on board the European Organisation for the Exploitation of Meteorological Satellites Meteorological Operational-A (MetOp-A) platform. Satellite data are found to account for 64% of the short-range global forecast error reduction, with the remaining 36% coming from the assimilation of surface-based observation types. MetOp-A data are measured as having the largest impact of any individual satellite platform (about 25% of the total impact on global forecast error reduction). Their large impact, compared to that of NOAA satellites, is mainly due to MetOp's additional sensors [Infrared Atmospheric Sounding Interferometer (IASI), Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), and the Advanced Scatterometer (ASCAT)]. Microwave and hyperspectral infrared sounding techniques are found to give the largest total impacts. However, the GPS radio occultation technique is measured as having the largest mean impact per profile of observations among satellite types. This study demonstrates how the FSO technique can be used to assess the impact of individual satellite data types in NWP. The calculated impacts can be used to guide improvements in the use of currently available data and to contribute to discussions on the evolution of future observing systems.

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