Showing papers by "National Centre for Medium Range Weather Forecasting published in 2013"

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

Abstract: The performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007‐11. The analyses are carried out with respectto1)basinsofformation,2)straight-movingandrecurvingTCs,3)TCintensityatmodelinitialization, and 4) season of occurrence. The impact of high resolution (18 and 9km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375km (7%‐51%) for a 12‐72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9km) predictions yield an improvement in mean track error for the NIO Basin by about 4%‐10% and 8%‐24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ;13%‐28% and 5%‐15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%‐40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

Gridded daily Indian monsoon rainfall for 14 seasons: Merged TRMM and IMD gauge analyzed values

Abstract: Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1°×1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.

Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3

Abstract: The regional climate model (RegCM3) from the Abdus Salam International Centre for Theoretical Physics has been used to simulate the Indian summer monsoon for three different monsoon seasons such as deficit (1987), excess (1988) and normal (1989). Sensitivity to various cumulus parameterization and closure schemes of RegCM3 driven by the National Centre for Medium Range Weather Forecasting global spectral model products has been tested. The model integration of the nested RegCM3 is conducted using 90 and 30-km horizontal resolutions for outer and inner domains, respectively. The India Meteorological Department gridded rainfall (1° × 1°) and National Centre for Environment Prediction (NCEP)–Department of Energy (DOE) reanalysis-2 of 2.5° × 2.5° horizontal resolution data has been used for verification. The RegCM3 forced by NCEP–DOE reanalysis-2 data simulates monsoon seasons of 1987 and 1988 reasonably well, but the monsoon season of 1989 is not represented well in the model simulations. The RegCM3 runs driven by the global model are able to bring out seasonal mean rainfall and circulations well with the use of the Grell and Anthes–Kuo cumulus scheme at 90-km resolution. While the rainfall intensity and distribution is brought out well with the Anthes–Kuo scheme, upper air circulation features are brought out better by the Grell scheme. The simulated rainfall distribution is better with RegCM3 using the MIT-Emanuel cumulus scheme for 30-km resolution. Several statistical analyses, such as correlation coefficient, root mean square error, equitable threat score, confirm that the performance of MIT-Emanuel scheme at 30-km resolution is better in simulating all-India summer monsoon rainfall. The RegCM3 simulated rainfall amount is more and closer to observations than that from the global model. The RegCM3 has corrected its driven GCM in terms of rainfall distribution and magnitude over some parts of India during extreme years. This study brings out several weaknesses of the RegCM model which are documented in this paper.

Observational challenges in evaluating climate models

Abstract: Activities like the Coupled Model Intercomparison Project (CMIP) have revolutionized climate modelling in terms of our ability to compare models and to process information about climate projections and their uncertainties. The evaluation of models against observations is now considered a key component of multi-model studies. While there are a number of outstanding scientific issues surrounding model evaluation, notably the open question of how to link model performance to future projections, here we highlight a specific but growing problem in model evaluation - that of uncertainties in the observational data that are used to evaluate the models. We highlight the problem using an example obtained from studies of the South Asian Monsoon but we believe the problem is a generic one which arises in many different areas of climate model evaluation and which requires some attention by the community.

Seasonal prediction of the Indian summer monsoon rainfall using canonical correlation analysis of the NCMRWF global model products

Abstract: In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981-2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave-three-out cross-validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981-2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006-2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east- and northeast India as well as many parts of peninsular region especially over west coast of India.

Improvement of Monsoon Depressions Forecast with Assimilation of Indian DWR Data Using WRF-3DVAR Analysis System

Abstract: An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track.

Oceansat-2 and RAMA buoy winds: A comparison

Abstract: Sea surface vector winds from scatterometers onboard satellites play an important role to make accurate Numerical Weather Prediction (NWP) model analysis over the data sparse oceanic region. Sea surface winds from Oceansat-2 scatterometer (OSCAT) over the Indian Ocean were validated against the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoy winds to establish the accuracy of OSCAT winds. The comparison of OSCAT winds against RAMA buoy winds for a period of one year (2011) shows that the wind speeds and directions derived from OSCAT agree with RAMA buoy winds. The monthly mean wind speeds from both OSCAT and RAMA buoy show maximum value during the monsoon period as expected. In the complete annual cycle (2011), the monthly mean root mean square differences in the wind speed and wind direction were less than ∼2.5 ms−1 and ∼20°, respectively. The better match between the OSCAT and RAMA buoy wind is observed during Indian summer monsoon (June–September). During monsoon 2011, the root mean square differences in wind speed and wind direction were less than 1.9 ms−1 and 11°, respectively. Collocation of scatterometer winds against equatorial and off-equatorial buoys clearly brought out the monsoon circulation features. Collocation of Advanced Scatterometer (ASCAT) winds on-board European Space Agency (ESA) MeTop satellite with respect to RAMA buoy winds during monsoon 2011 also showed that the OSCAT wind statistics are comparable with that of ASCAT over the Indian Ocean, and indicates that the accuracy of both the scatterometers over the Indian Ocean are essentially the same.

Impact of SSM/I retrieval data on the systematic bias of analyses and forecasts of the Indian summer monsoon using WRF assimilation system

Abstract: Assimilation and forecast experiments have been carried out in this study using conventional observations as well as total precipitable water and surface wind data retrieved from the Special Sensor for Microwave Imaginary SSM/I sensors. The main objectives of this study were to document the bias in short-range predictions of the Weather Research and Forecasting WRF version 3.1 model over the Indian region during the summer monsoon season and the impact of SSM/I data. All the experiments were carried out in the monsoon seasons of 2001 as a part of pilot phase studies for the South Asian Regional Reanalysis SARR project. It is seen that the model has strong bias in wind forecasts over the Arabian Sea and the Indian Ocean. A cyclonic bias in the forecasts exists over south-west India. Over the equatorial Indian Ocean, a strong southerly bias towards the Bay of Bengal is noticed. The model has a systematic bias to increase moisture over most parts of the equatorial Indian Ocean. Except over the Gangetic plains, the model exhibits dry bias with reduced moisture over most parts of India in 24 hour forecasts. The impact of assimilation of SSM/I products has been to increase the moisture over the Bay of Bengal, where the model has shown dry bias. The moisture content over the equatorial Indian Ocean western sector reduced significantly after assimilation of SSM/I data, where the model has a tendency to enhance moisture. Major rainfall zones during the monsoon season are brought out well in 6 hour forecasts by the model; however, the rainfall amount increased over the Bay of Bengal due to the assimilation of SSM/I data. These features are consistent with the moisture and wind differences between the two assimilation experiments. A quantitative verification of model rainfall in terms of equitable threat scores indicate that the accuracy of rainfall products is higher when SSM/I data are assimilated. It is seen that the general pattern of rainfall tendency in 24 hour forecasts remains the same irrespective of whether the forecast initial conditions are with or without SSM/I data. Examination of a case of monsoon depression showed that assimilation of SSM/I data improved the analysis.

Impact of cyclone bogusing and regional assimilation on tropical cyclone track and intensity predictions

Abstract: lkj & bl v/;;u dk mIs’; vYi vof/k iwokZuqeku esa pOokr ds iFk vkSj mldh rhozrk dk iwokZuqeku yxkus ds fy, MCY;w-vkj-,Q- lehdj.k vkSj iwokZuqeku iz.kkyh esa m".kdfVca/kh; dkYifud pOokr ds vk/kkj ij mlds izHkko dk fu/kkZj.k djuk gSA bl izHkko dks pOokr ds izHkko dh =qfV] dsUnzh; nkc vkSj vf/kdre lrr iou xfr ds :i esa crk;k x;k gSA ;g v/;;u o"kZ 2010 esa cus rhu pOokrksa uker% ‘ySyk’ ¼caxky dh [kkM+h½] ‘fxjh’ ¼caxky dh [kkM+h½ vkSj ‘QsV’ ¼vjc lkxj½ ij vk/kkfjr gSA MCY;w- vkj- ,Q- ekWMy izpkyukRed ,u-lh-,e- vkj-MCY;w-,Q- Vh382 ,y 64 ds fo’ys"k.k vkSj iwokZuqekuksa dk mi;ksx djrk gS vkSj bl ekWMy dks pOokr ds iFk vkSj bldh rhozrk dk iwokZuqeku yxkus ds fy, 72 ?kaVs rd lekdfyr fd;k x;k gSA bl ijh{k.k ds pkj lSVksa dh tkip dh xbZ ¼i½ fu;a=.k ijh{k.k ¼lh-,u-Vh-,y-½ ftlesa uk rks lehdj.k vkSj uk gh dkYifud pOokr dks vk/kkj ekuk x;k gSA bl ekWMy dk vkjaHk varoZsf’kr HkweaMyh; ekWMy fo’ys"k.k dk mi;ksx djrs gq, fd;k x;kA ¼ii½ lehdj.k ijh{k.k ¼oh-,-vkj-½ esa MCY;w- vkj- ,Q- oh- ,- vkj- vkidM+k lehdj.k iz.kkyh ¼fcuk dkYifud vk/kkj ij ekuk x;k pOokr½ dk mi;ksx djrs gq, ekWMy dh vkjafHkd fLFkfr;ki rS;kj dh xbaZA ¼iii½ pOokr ds ijh{k.k ¼ch-vks-th-½ lehdj.k ds fcuk dsoy dkYifud pOokr dks ekurs gq, dkYifud vk/kkj ij pOokr ds iz;ksx fd, x, gSaA bl ekeys esa dkYifud vk?kkj ij pOokr dk mi;ksx djrs gq, ekWMy ds izFke vuqeku dks la’kksf/kr fd;k x;k vkSj bldk vkjafHkd fLFkfr;ksa ds :i esa mi;ksx fd;k x;k gSA ¼iv½ pkSFks ijh{k.k esa dkYifud vk/kkj ij pOokr ds ckn MCY;-w vkj- ,Q- vkidM+k lehdj.k ¼ch- vks- th- oh- ,- vkj-½ nksuksa dk mi;ksx djrs gq, ekWMy dh vkjafHkd fLFkfr;ki rS;kj dh xbZA buls izkIr gq, ijh.kkeksa ls vkjafHkd fLFkfr;ksa esa dkYifud pOokr ds mYys[kuh; izHkko dk irk pyk gSA ;s rhuksa gh pOokr dkYifud ¼ch-vks-th- vkSj oh-,-vkj-½ iz;ksxksa dh vkjafHkd fLFkfr;ksa ¼0000 ;w- Vh- lh-½ esa ik, x, tk ldrs gSa tks vU;Fkk dkYifud vk/kkj ij rS;kj fd, x, pOokrksa ds vHkko esa ¼oh- ,-vkj- vkSj lh- ,u- Vh- ,y-½ iz;ksx esa ugha gksrh gSA ch- vks- th- oh- ,- vkj- ijh{k.k ds iFk =qfV;ksa esa mYys[kuh; deh ns[kh xbZ gSA oh- ,- vkj- dh rqyuk esa ch- vks- th- oh- ,- vkj- esa iFk =qfV esa vf/kdre deh Oe’k% ‘ySyk’ esa 76-8 izfr’kr] ‘fxjh’ esa 87-3 izfr’kr vkSj ‘QsV’ esa 51-5 izfr’kr jghA ‘ySyk’ vkSj ‘fxjh’ ds fy, oh-,-vkjdh rqyuk esa ch-vks-th-oh-,-vkj- esa fy, x, izs{k.k vf/kdre lrr@Ofed iou xfr vkSj vf/kdre dsUnzh; nkc ds fudV gSaA

Validation of Kalpana-1 atmospheric motion vectors against upper air observations and numerical model derived winds

Abstract: Validation of Kalpana-1 atmospheric motion vectors AMVs against upper air radiosonde RS winds and numerical model-derived winds National Centre for Medium Range Weather Forecasting's NCMRWF's T382L64 first guess during the monsoon season of 2011 was attempted in this study. This was the first attempt to compare Kalpana-1 AMVs with model-derived winds. An AMV validation against RS winds showed that the mean AMV speed is always higher than that of the mean RS speed, except in high-level cloud motion vectors CMVs. In the southwest monsoon season of 2011, the maximum speed bias in Kalpana-1 AMV with respect to RS winds was observed in the middle level ∼5 m s−1. The root mean square vector difference RMSVD of Kalpana-1 AMV with respect to the collocated RS winds ∼5–7 m s−1 has been found to be in the same range as those of other geostationary satellites, especially over the northern hemisphere and the tropics. The validation of Kalpana-1 AMVs against first guess revealed more erroneous low-level and middle-level AMVs, but the vector difference in the high-level winds was found to be smaller than the same in the low-and middle-level winds. The uncertainty in the empirical genetic algorithm GA used to derive the Kalpana-1 AMVs, which does not use model background fields, may be the reason for the high RMSVD of Kalpana-1 AMVs with respect to RS winds and high bias with respect to first guess. The mean observed AMV clearly depicted monsoonal features such as low-level westerly jet LLWJ and tropical easterly jet TEJ. The speed bias density plots of Kalpana-1 high-level CMVs 400–100 hPa and water vapour channel winds WVWs above ∼500 hPa with respect to first guess showed that the bias was higher for WVWs; however, the standard deviations of high-level CMVs and WVWs are comparable.

Multi-Model Ensemble (MME) prediction of rainfall using canonical variates during monsoon season in India

Abstract: ,f’k;k {ks= dh xzh"dkyhu ekulwu o"kkZ dk fHkUu fHkUu fnDddkfyd iSekuksa ij iwokZuqeku djuk vHkh Hkh ,d dfBu dk;Z gSA vHkh gky ds o"kksZa esa e/; va{kk’k dh rqyuk esa ekWulwu o"kkZ dk e/; vof/k iwokZuqeku nsus dh n{krk esa lekuqikfrd lq/kkj esa deh jgh gSA ekulwu vkSj m".kdfVca/kh; iwokZuqeku esa vkxs vkSj lq/kkj ds fy, Xykscy ekWMyl vkSj vkidM+ vkesyu rduhd dk mi;ksx fd;k tk jgk gSSA cgjgky cgq fun’kZ bUlsEcy ¼,e-,e-bZ-½ iwokZuqeku dh yksdfiz;rk c<+ jgh gS D;ksafd iwokZuqeku esa ,d:irk vkSj fun’kZ dh vfuf’prrkvksa esa deh dks ns[krs gq, ikrs gaS fd blesa O;kogkfjd iwokZuqeku djus ds fy, vf/kd lwpuk djkus dh laHkkouk gSA tSlk fd izeq[k dsUnz okLrfod le; ds ekWMy izfrQyksa dk vkil esa vknku iznku djrs gS blesa ,e-,e-bZ- iwokZuqeku dkS’ky dks c<+kusa esa O;kogkfjd :i ls fdQk;rh rjhdk gSA lk/kkj.k bUlsEcy vkSlr dks NksM+dj ,u-lh-,e-vkj-MCY;w-,Q-@i`Foh foKku ea=ky;] Hkkjr }kjk o"kZ 2009 ds ekulwu ds nkSjku e/; vof/k ekulwu o"kkZ iwokZuqeku ds fy, ,e-,e-bZ- iwokZuqeku dk vf/kd mi;ksx fd;k x;kA blds fy, izekf.kd fopj rduhd dk iz;ksx fd;k x;kA izkIr fd, x, izfrQyksa dh x.kuk djus ls irk pyk gS fd cgq fun’kZ bUlsEcy iwokZuqeku dh n{krk fdlh ,d ekWMy iwokZuqekuksa dh vis{kk vf/kd lgh gS vkSj ;g vkerkSj ij lk/kkj bUlsEcy vkSlr ls Hkh vf/kd lgh gSA gkykifd Xykscy ekWMyl dh n{krk rhu fnu ds vkxs rd dh gksrh gS ijUrq ,e-,e-bZ- rduhd dks 5 fnuksa dh vof/k rd ykxw djus ij blesa egRoiw.kZ lq/kkj ns[kk tk ldrk gSA The prediction of Asian summer monsoon rainfall at various space-time scales is still a difficult task. Compared to mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range had been less in recent years. Global models and data assimilation techniques are being further improved for monsoon and tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real-time, MME is a viable inexpensive way for enhancing the forecast skill. During monsoon 2009, apart from simple ensemble mean ,the MME predictions of large-scale monsoon precipitation in medium range was carried out NCMRWF/MoES, India. The canonical variates technique is used for it. The skill scores are computed, which indicate that multi-model ensemble forecast has higher skill than individual model forecasts and also higher than the simple ensemble mean in general. Although the skill of the global models falls beyond day-3, but a significant improvement could be seen by employing the MME technique up to day-5.