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

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.

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band multi-parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was performed by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity factor. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of an ensemble prediction system (EPS). Cross-validation was performed using field-survey data, radar observations, and Himawari-8 imageries. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results are in agreement with ground-based data, where the radar-based estimated grain size distribution falls within the range of in situ grain size. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

Application of Bias Correction to Improve WRF Ensemble Wind Speed Forecast

Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed forecast were diurnally variated and clustered with the usage of the planetary boundary layer (PBL) scheme. To correct the error characteristics of the ensemble wind speed forecast, three system-bias representations with decaying average algorithms were studied. One of the three system-bias representations is represented by the forecast error of the ensemble mean (BC01), and others are assembled from each PBC group (BC03) as well as an independent member (BC20). System bias was calculated daily and updated within a 5-month duration, and the verification was conducted in the last month, including 316 gauges around Taiwan. Results show that the mean of the calibrated ensemble (BC03) was significantly improved as the calibrated ensemble (BC20), but both demonstrated insufficient ensemble spread. However, the calibrated ensemble, BC01, with the best dispersion relation could be extracted as a more valuable deterministic forecast via the probability matched mean method (PMM).

Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

ENSO predictability over the past 137 years based on a CESM ensemble prediction system

In this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models.

The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

Rainfall prediction over Ambon Meteorological Station using Multi-Physics Ensemble WRF-ARW

One of the methods to create good forecast using WRF-ARW modelling is tuning the parameterization. However, this method cannot provide rainfall event probability. Current research result revealed that it was able to simulate and forecast some weather parameters. However, based on the verification results, there were some weather parameters which still had low accuracy. Due to such low accuracy on some weather parameters, the authors were interested in performing post-processing methods in forecasting the weather during extreme weather at Pattimura Ambon Meteorological Station. In this study, we employed multi-physics ensemble prediction system (MEPS) by combining 20 WRF-ARW parameterization schemas, which were processed to obtain the ensemble mean, ensemble spread, and basic probability to get the uncertainty from each weather parameters. Verification process was done by using spreads, skill method and ROC curves. It was discovered that MEPS products have a better skill compared to the forecast control, the correlation value of MEPS products is larger and has the lowest error value. In addition, the result of ROC curves shows that the MEPS has an ability to predict weather condition during cloudy and extreme rain.

Early warning of heavy rainfall event using time-lagged ensemble prediction system (case study February 15th 2019)

Convective clouds can be related to the development of intense storms that produce various extreme weather. The development of extreme weather could involve strong nonlinear interactions of many factors in the atmosphere, hence the ability to forecast extreme weather especially heavy rainfall and issued an early warning, becomes very important. BMKG has developed a time-lagged ensemble prediction system by utilizing the initial time difference, which is considered capable of providing data updates more closely to the forecasts final results. This study examines the percentile classification in the ensemble prediction system, to look for an extreme values distribution, then used it as extreme threshold. The extreme threshold was tested in a heavy rain case on February 15th 2019, on D-7, D-3, and D-1 of early warning dissemination. Based on this research, it was found that the use of the 90th and 95th percentile classification method was able to show a signal of extreme events on D-7 and D-3 events with a consistent probability pattern. In the D-1 prediction period, the probability value increases and the average precipitation value exceeds the extreme threshold.