scholarly journals Outcomes and challenges of Forecast Demonstration Project (FDP) on landfalling cyclones over the Bay of Bengal

MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 1-12
Author(s):  
M. MOHAPATRA ◽  
B.K. BANDYOPADHYAY ◽  
D.R. SIKKA ◽  
AJIT TYAGI
Keyword(s):  
Bay Of Bengal ◽  
Weather Prediction ◽  
North Indian Ocean ◽  
Demonstration Project ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Pilot Phase ◽  
Ensemble Prediction System ◽  
The North ◽  
Before And After

Cakxky dh [kkM+h esa m".kdfVca/kh; rwQkuksa ds ekxZ vkSj mudh rhozrk ds iwokZuqeku rduhd esa lq/kkj ykus ds fy, iwokZuqeku fun’kZu ifj;kstuk ¼,Q-Mh-ih-½ uked ,d dk;ZØe rS;kj fd;k x;k gSA ,Q-Mh-ih- dk;ZØe dk mÌs’;] ftu {ks=ksa ls vk¡dM+s vO;ofLFkr :i  ls izkIr gksrs gSa ogk¡ muds loaf/kZr izs{k.kksa ds lkFk gh lkFk mRrjh fgUn egklkxj esa pØokrksa ds mRiUu gksus] muds rhoz gksus vkSj mudh xfr dk vkdyu djus ds fy, fofHkUu l[;kRed ekSle iwokZuqeku ¼,u- MCY;w- ih-½ fun’kksZ dh {kerk dk izn’kZu djuk rFkk fo’ks"k :i  ls caxky dh [kkM+h ls lacaf/kr  ogha mlh LFkku ij fy, x, ekiksa ds vk/kkj ij fun’kksZ esa lq/kkj djuk gSA ,Q-Mh-ih- dk;ZØe rhu pj.kksa esa fu/kkfjr fd;k x;k gS uker% ¼i½ izh&ikbyV pj.k ¼15 vDrwcj ls 30 uoacj 2008] 2009½] ¼ii½ ikbyV pj.k ¼15 vDrwcj ls 30 uoacj 2010&2012½ rFkk ¼iii½ vafre pj.k ¼15 vDrwcj ls 30 uoacj 2013&2014½A Hkkjr] fdjk, ds gokbZ tgkt vkSj MªkWilkSansa iz;ksxksa ls 15 vDrwcj ls 30 uoacj 2013&2014 ds nkSjku caxky dh [kkM+h esa cuus okys pØokrksa dk gokbZ tgkt ds tfj, irk yxkus dh ;kstuk cuk jgk gSA bl mÌs’; ds iwfrZ ds fy, ¼i½ izs{k.kkRed mUu;u ¼ii½ pØokr fo’ys"k.k vkSj iwokZuqeku iz.kkyh dk vk/kqfudhdj.k ¼iii½ pØokr fo’ys"k.k vkSj iwokZuqeku izfØ;k ¼iv½ psrkouh mRiknksa dks rS;kj djuk] mudk izLrqrhdj.k rFkk izlj.k ¼v½ fo’oluh;rk mik; vkSj {kerk fuekZ.k ij izkFkfedrk ds vk/kkj ij dk;Z fd, x,A pØokr ds izs{k.k] fo’ys"k.k vkSj iwokZuqeku esa lq/kkj ykus ds fy, fofHkUu dk;Z iz.kkfy;k¡ viukbZ xbZaA o"kZ 2008&11 ds nkSjku ,Q-Mh-ih- vfHk;ku ds izh&ikbyV vkSj ikbyV pj.kksa esa la;qDr izs{k.kkRed] lapkjkRed vkSj ,u-MCY;w-ih- xfrfof/k;ksa esa vusd jk"Vªh; laLFkkuksa us Hkkx fy;kA ,Q-Mh-ih- ds igys vkSj mlds ckn dh izs{k.kkRed iz.kkfy;ksa dh rqyuk ls {ks= esa jsMkj] Lopkfyr ekSle dsUnz ¼,- MCY;w-,l-½] mPp iou xfr fjdkWMjksa esa egRoiw.kZ lq/kkj dk irk pyk gSA bl lq/kkj ls ekWuhVju vkSj iwokZuqeku esa gksus okyh =qfV;ksa esa deh vkbZ gSA th- ,Q- ,l- MCY;w vkj- ,Q] ,p- MCY;w- vkj- ,Q- vkSj vlsEcy iwokZuqeku iz.kkyh ¼bZ- ih- ,l-½  ds vkjaHk gksus ls ,u- MCY;w- ih- funsZ’kksa ds dk;Z fu"iknu esa o`f) gqbZ gSA bl 'kks/k i= esa bl ifj;kstuk dh miyfC/k;ksa ds egRoiw.kZ y{k.kksa lfgr leL;kvksa vkSj laHkkoukvksa dks izLrqr fd;k x;k gS rFkk mudh foospuk dh xbZ gSA pØokrksa dk gokbZ tgkt }kjk irk yxkus ds fy, ckj&ckj fd, x, iz;klksa ds ckotwn ;g dk;Z vHkh laHko ugha gks ldk gSA o"kZ 2013&14 ds nkSjku Hkkoh vfHk;ku ds le; ;g ,d eq[; pqukSrh gksxhA A programme has been evolved for improvement in prediction of track and intensity of tropical cyclones over the Bay of Bengal resulting in the Forecast Demonstration Project (FDP). FDP programme is aimed to demonstrate the ability of various Numerical Weather Prediction (NWP) models to assess the genesis, intensification and movement of cyclones over the north Indian ocean with enhanced observations over the data sparse region and to incorporate modification into the models which could be specific to the Bay of Bengal based on the in-situ measurements. FDP Programme is scheduled in three phases, viz., (i) Pre-pilot phase (15 Oct - 30 Nov 2008, 2009, (ii) Pilot phase (15 Oct - 30 Nov, 2010-2012) and (iii) Final phase (15 Oct - 30 Nov, 2013-14). India is planning to take up aircraft probing of cyclones over the Bay of Bengal during 15 Oct - 30 Nov, 2013-14 with hired aircraft and dropsonde experiments. To accomplish the above objective, the initiative was carried out with priorities on (i) observational upgradation, (ii) modernisation of cyclone analysis and prediction system, (iii) cyclone analysis and forecasting procedure, (iv) warning products generation, presentation & dissemination, (v) confidence building measures and capacity building. Various strategies were adopted for improvement of observation, analysis and prediction of cyclone. Several national institutions participated for joint observational, communicational & NWP activities during the pre-pilot and pilot phases of FDP campaign during 2008-11. The comparison of observational systems before and after FDP indicates a significant improvement in terms of Radar, Automatic Weather Station (AWS), high wind speed recorders over the region. It has resulted in reduction in monitoring and forecasting errors. The performance of NWP models have increased along with the introduction of NWP platforms like IMD GFS, WRF, HWRF and ensemble prediction system (EPS). Salient features of achievements along with the problems and prospects of this project are presented and discussed in this paper. With repeated attempts, the aircraft probing of cyclones could not be possible till now. It is a major challenge for the future campaign during 2013-14.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

A Unified Scheme of Stochastic Physics and Bias Correction in an Ensemble Model to Reduce Both Random and Systematic Errors

2019 ◽  
Vol 34 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Yu Xia ◽  
Jing Chen ◽  
Jun Du ◽  
Xiefei Zhi ◽  
Jingzhuo Wang ◽  
...  
Keyword(s):  
Bias Correction ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Ensemble Prediction ◽  
Ensemble Model ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Stochastic Physics

Abstract This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System

2009 ◽  
Vol 66 (3) ◽  
pp. 603-626 ◽  
Author(s):  
J. Berner ◽  
G. J. Shutts ◽  
M. Leutbecher ◽  
T. N. Palmer
Keyword(s):  
Kinetic Energy ◽  
Prediction Models ◽  
Positive Impact ◽  
Weather Prediction ◽  
Coarse Grained ◽  
Ensemble Prediction ◽  
Spectral Space ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Integration Schemes

Abstract Understanding model error in state-of-the-art numerical weather prediction models and representing its impact on flow-dependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscale-propagating errors caused by unresolved subgrid-scale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarse-grained high-resolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for Medium-Range Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kinetic-energy spectra, a better representation of forecast-error growth, improved flow-dependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for large-anomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flow-dependent formulations of the unresolved processes.

scholarly journals Impact of Surface Parameter Uncertainties within the Canadian Regional Ensemble Prediction System

2013 ◽  
Vol 141 (5) ◽  
pp. 1506-1526 ◽  
Author(s):  
Christophe Lavaysse ◽  
Marco Carrera ◽  
Stéphane Bélair ◽  
Normand Gagnon ◽  
Ronald Frenette ◽  
...  
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Initial Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Surface Parameter ◽  
Ensemble Prediction System ◽  
Surface Parameters ◽  
Screen Level ◽  
The Impact

Abstract The aim of this study is to assess the impact of uncertainties in surface parameter and initial conditions on numerical prediction with the Canadian Regional Ensemble Prediction System (REPS). As part of this study, the Canadian version of the Interactions between Soil–Biosphere–Atmosphere (ISBA) land surface scheme has been coupled to Environment Canada’s numerical weather prediction model within the REPS. For 20 summer periods in 2009, stochastic perturbations of surface parameters have been generated in several experiments. Each experiment corresponds to 20 simulations differing by the perturbations at the initial time of one or several surface parameters or prognostic variables. The sensitivity to these perturbations is quantified especially for 2-m temperature, 10-m wind speed, cloud fraction, and precipitation up to 48-h lead time. Spatial variability of these sensitivities over the North American continent shows that soil moisture, albedo, leaf area index, and SST have the largest impacts on the screen-level variables. The temporal evolution of these sensitivities appears to be closely linked to the diurnal cycle of the boundary layer. The surface perturbations are shown to increase the ensemble spread of the REPS for all screen-level variables especially for 2-m temperature and 10-m wind speed during daytime. A preliminary study of the impact on the ensemble forecast has shown that the inclusion of the surface perturbations tends to significantly increase the 2-m temperature and 10-m wind speed skill.

scholarly journals Sensitivity of Ensemble Forecast Verification to Model Bias

2018 ◽  
Vol 146 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Jun Du ◽  
Yutao Zhang ◽  
Yu Xia ◽  
...  
Keyword(s):  
Bias Correction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Bias ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Forecast Bias ◽  
Probabilistic Forecasts ◽  
Before And After

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

scholarly journals Prediction of Lake-Effect Snow Using Convection-Allowing Ensemble Forecasts and Regional Data Assimilation

2017 ◽  
Vol 32 (5) ◽  
pp. 1727-1744 ◽  
Author(s):  
Seth Saslo ◽  
Steven J. Greybush
Keyword(s):  
Data Assimilation ◽  
Great Lakes ◽  
Forecast Error ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Prediction ◽  
Great Lakes Region ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Lake Effect

Abstract Lake-effect snow (LES) is a cold-season mesoscale convective phenomenon that can lead to significant snowfall rates and accumulations in the Great Lakes region of the United States. While limited-area numerical weather prediction models have shown skill in prediction of warm-season convective storms, forecasting the sharp nature of LES precipitation timing, intensity, and location is difficult because of model error and initial and boundary condition uncertainties. Ensemble forecasting can incorporate and quantify some sources of forecast error, but ensemble design must be considered. This study examines the relative contributions of forecast uncertainties to LES forecast error using a regional convection-allowing data assimilation and ensemble prediction system. Ensembles are developed using various methods of perturbations to simulate a long-lived and high-precipitation LES event in December 2013, and forecast performance is evaluated using observations including those from the Ontario Winter Lake-Effect Systems (OWLeS) campaign. Model lateral boundary conditions corresponding to weather conditions beyond the Great Lakes region play an influential role in LES precipitation forecasts and their uncertainty, as evidenced by ensemble spread, particularly at lead times beyond one day. A strong forecast dependence on regional initial conditions was shown using data assimilation. This sensitivity impacts the timing and intensity of predicted precipitation, as well as band location and orientation assessed with an object-based verification approach, giving insight into the time scales of practical predictability of LES. Overall, an assimilation-cycling convection-allowing ensemble prediction system could improve future lake-effect snow precipitation forecasts and analyses and can help quantify and understand sources of forecast uncertainty.

Sensitivity of ensemble forecast verification to model bias

2020 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Jun Du
Keyword(s):  
Bias Correction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Bias ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Forecast Bias ◽  
Probabilistic Forecasts ◽  
Before And After

<p>        This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent "strong" and "weak" bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread' spatial structure is much less; the spread-skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.</p>

scholarly journals Experiments with EOF-Based Perturbation Methods and Their Impact on the CPTEC/INPE Ensemble Prediction System

2009 ◽  
Vol 137 (4) ◽  
pp. 1438-1459 ◽  
Author(s):  
Antônio Marcos Mendonça ◽  
JoséPaulo Bonatti
Keyword(s):  
South America ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Simultaneous Application ◽  
Ensemble Prediction System ◽  
Perturbation Versus ◽  
The Impact

Abstract The impact of modifications of the perturbation method based on empirical orthogonal functions (EOFs) used operationally upon the ensemble prediction system (EPS) at the Center for Weather Prediction and Climate Studies/National Institute for Space Research (CPTEC/INPE) is evaluated. The main changes proposed in this study are to apply the EOF method to perturb the midlatitudes, apply additional perturbations to the surface pressure (P) and specific humidity (Q) fields, and compute regional perturbations over South America. The impact of these modifications in the characteristics of the initial perturbations and in the quality of the EPS forecasts is investigated. The EPS forecasts are evaluated through average statistical scores over the period 15 December 2004–15 February 2005. The statistical scores used in the evaluation are pattern anomaly correlation, root-mean-square error, ensemble spread, Brier skill score, and perturbation versus error correlation analysis (PECA). Results indicate that with the inclusion of perturbations on P and Q, EOF-based perturbations acquire a more baroclinic structure. It is also observed that the simultaneous application of additional perturbations both in the extratropics and to the P and Q fields improves the performance of CPTEC EPS and enhances the quality of forecast perturbations. Moreover, regional EOF-based perturbations computed over South America have positive impact on the ensemble forecasts over the target region.

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