Topic 2.2 CYCLOGENESIS: OPERATIONAL FORECASTING PERSPECTIVE

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1 Topic 2.2 CYCLOGENESIS: OPERATIONAL FORECASTING PERSPECTIVE Rapporteur: Christopher W. Landsea National Hurricane Center RSMC Miami SW 17 th Street Miami, FL, 33165, U.S.A. Phone: Working group: Lixion Avila (RSMC Miami), Thierry Dupont (RSMC La Reunion), S. D. Kotal (RSMC New Delhi), and M. Mohapatra (RSMC New Delhi) Abstract: Progress in operational tropical cyclogenesis during the last four years is assessed. Dramatic improvements have occurred in both the skill and the lead-time of genesis forecasts, primarily due to the increased capabilities of global models. Such developments are leading to the eventual issuance of pre-genesis forecasts track, intensity, and size predictions before genesis as well as the possibility of pre-formation Watches and Warnings Introduction This report serves as a summary of forecast activities in the field of tropical cyclone (TC) formation forecasting since the last IWTC in La Reunion in Accurate tropical cyclone genesis forecasting is important because of: The need to provide extended community response planning, especially in remote or large communities The need to provide advisories at extended forecast ranges for offshore and onshore commercial activities The requirement for National Meteorological Services to manage forecasting and reconnaissance resources The potential to reduce future track, intensity, and size errors by more accurately defining the likely genesis location. Within the 2010 IWTC report there was a substantial optimism that genesis forecasting was becoming a solvable problem: Both Ensemble Prediction Systems (EPS) and deterministic Numerical Weather Prediction (NWP) models were routinely capturing TC formation. Improvement in formation forecasts from operational numerical models were expected given advances in various aspects of numerical weather prediction systems, which includes increased resolution, improved parameterization schemes, and improved use of observations, especially satellite data to improve initial conditions. However, tropical cyclone genesis forecast continued to remain a challenging aspect of operational forecasting in the major tropical forecasting centres, as there was still no effective systematic approach in place for genesis forecasting, especially with respect to NWP guidance. 1

2 2.2.1 Definition of tropical cyclogenesis The definition of tropical cyclogenesis from RSMC Miami/Honolulu (North Atlantic and Northeast/North Central Pacific Oceans) is as follows: A warm-core, non-frontal synoptic-scale cyclone, originating over tropical or subtropical waters, with organized deep convection and closed surface wind circulation about a welldefined centre. Note that this definition has no lower bound on the intensity (maximum sustained wind speed). The other regions have similar, but not identical definitions. RSMC La Reunion (Southwestern Indian Ocean) additionally specifies that the maximum of the average wind speed has to be at least 28 kt before a tropical disturbance can be considered a tropical depression. RSMC New Delhi likewise indicates a minimum threshold of maximum winds to be 17 kt. The RSMC and TCWCs of the Southeastern Indian Ocean and Southwestern Pacific Ocean) require maximum winds to reach a minimum threshold of 34 kt before the system is considered a tropical cyclone. RSMC Tokyo, like RSMC Miami/Honolulu, does not have a lower bound wind speed threshold. Such differences make intercomparisons of tropical cyclogenesis across basins somewhat problematic as well as developing and verifying various forecasting methodologies. Regardless of whether a wind speed threshold is used or not, identifying when a TC has formed is a challenging and subjective determination. Even with the benefit of hindsight and post-analysis, the frequent lack of sufficient data and the inherent ambiguity in the definition of a TC introduce ambiguities into the analysis of tropical cyclogenesis. The above definition allows for a wide range of subjective interpretation allowing the forecaster some latitude as far as designating a system a TC Genesis forecast tools Observations and analyses In preparing a prediction for tropical cyclogenesis, forecasters typically assess the largescale conditions and other characteristics associated with TC formation including the following necessary but not sufficient aspects (following Gray 1968, Dvorak 1975, Zehr 1992): A pre-existing disturbance containing abundant deep convection Latitudes poleward ~5 o Adequate ocean thermal energy- SST > 26 o C extending to a depth of 60 m A sufficiently unstable atmosphere & deep layer of moist air Small vertical shear of the horizontal wind Upper-tropospheric anticyclonic outflow over the area Enhanced lower tropospheric relative vorticity Appearance of curved banding features in the deep convection Falling surface pressure: 24-hour pressure changes (falls) of usually 3 mb or more Statistical approaches Cossuth et al. (2013) introduced an operationally derived climatology of tropical disturbances both non-developing and developing - that were analyzed using the Dvorak technique at the National Hurricane Center (NHC) and the Central Pacific Hurricane Center from 2001 to Using these Dvorak intensity estimates, the likelihood of genesis is calculated as a historical baseline for TC prediction (Figure 1). When probabilities of genesis are calculated by a Dvorak current intensity (CI) number, the likelihood of tropical cyclogenesis stratifies well by basin and intensity. Tropical disturbances that are analyzed as being stronger (a higher Dvorak CI number) achieve genesis more often. Further, all else being equal, genesis rates are highest in the eastern Pacific, followed by the Atlantic. Out-of-sample verification of predictive skill shows 2

3 comparable results to that of the NHC, with potential to inform forecasts and provide the first disturbance-centric baseline for tropical cyclogenesis potential. Real-time guidance for the Atlantic and Northeast/North Central Pacific is available on-line: <moe.met.fsu.edu/genesis. Figure 1. Example of 48 hr probability of tropical cyclogenesis based upon the TAFB/CPHC Dvorak number and climatology of tropical disturbances (both developing and non-developing) from Schumacher et al. (2009) developed a new product for estimating the 24 and 48 hr probability of TC formation in individual 5 o x 5 o sub regions of all tropical cyclone basins (Figure 2). This product uses environmental and convective parameters computed from best-track TC positions, National Centres for Environmental Prediction (NCEP) Global Forecasting System (GFS) analysis fields, and water vapour imagery from multiple geostationary satellite platforms. The largest contributors to TC formation probability are climatology, 850-hPa circulation, and distance to an existing TC. The probabilistic forecasts are skilful with respect to climatology, and that there is relatively good agreement between forecast probabilities and observed frequencies. As such, this prediction scheme has been implemented as an operational product called the National Environmental Satellite, Data, and Information Services (NESDIS) Tropical Cyclone Formation Probability (TCFP) product. The TCFP product updates every 6 h and displays plots of TC formation probability and input parameter values on its web site At present, the TCFP provides real-time, objective TC formation guidance for all tropical cyclone basins. Note that the spatial probabilities provided are not easily converted into a disturbance following forecast probability. 3

4 Figure 2. Example of the Tropical Cyclone Formation Probability (TCFP) product for the Indian Ocean basins from 12Z 24 September Left column is the real-time probabilities of formation within a 5 o x 5 o grid space, the middle column is the climatological probabilities for this particular calendar date, and the right column is the anomalous probability. The top row is for 0 to 48 hr percentages, the middle row is the 0 to 24 hr percentages, and the bottom for is the 24 to 48 hr percentages. Deterministic global models High resolution, deterministic global model are increasingly able to explicitly predict genesis of tropical cyclones sometime several days in advance. Upgrades to the horizontal resolution of the models over the past several years likely have improved the capability for providing genesis predictions of these systems that are on the border between mesoscale and synoptic scale. Assimilation of microwave data from polar- and near-equatorial-orbiting satellites gives a better description of the tropospheric moisture field, which is crucial toward realistic depictions of where deep convection should occur. More realistic physics such as prognostic cloud water/ice and a parameterization of cumulus momentum mixing- also are likely to better simulate the tropical circulation. Finally moving to hybrid 3-D variational/ensemble Kalman filter or fully 4-D data assimilation better incorporates non-synoptic time observations. All of these things listed are designed to improve model initial conditions which, in turn, help to provide better genesis forecasts. 4

5 Currently, genesis forecasts issued in operations are inherently subjective the forecaster, after evaluating all available data, provides a probability of formation which is subjective and constrained to some extent by what the previous forecast was same as with track/intensity forecasts. There are no objective schemes as of yet, that have been fully vetted in operations (though examples of ones being developed are shown below). Criteria that the forecasters using to examine the deterministic global models for tropical cyclone genesis: At initial time: No closed isobars No organized cloud pattern (unclassifiable by the Dvorak Technique) Forecast: At least one closed isobar (at 4 mb interval) with lowered central pressure Reasonable structure (closed circulation centre, warm core, deep vertical extent, nonbaroclinic) Longevity of the vortex of at least a day Additionally, assessing deterministic model output for tropical cyclogenesis is sensitive to the thresholds (vorticity, longevity, and baroclinicity) used to identify the cyclones. When less filtered with shallower, shorter longevity, and/or larger baroclinicity thresholds, then additional cyclones are identified and/or earlier genesis is indicated. Figure 3. A 168 hr Global Forecast System forecast of the sea level pressure (yellow contours), 850 mb winds (in blue), and 850 mb vorticity (dashed purple contours) valid at the time of genesis for Katia in The background satellite image provides an infrared picture at the time of genesis. This is an example of a superb global model prediction. 5

6 Figure 4. Same as Figure 3, but for Irenes s genesis in This is an example of a quite poor global model prediction. Figures 3 and 4 provide example of an excellent and poor global model forecast several days into the future. Ensemble predictions systems (EPS) Single model ensemble prediction systems currently include Environment Canada s Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Global Environmental Model (NAVGEM), and the Met Office global model (UKMET). These have the drawback compared to the deterministic parent models because of the decreased resolution, but have an advantage in the dozens of model runs can be performed from each initial time. Additionally, the issues of thresholds of vorticity, longevity, and baroclinicity mentioned for deterministic model output also are an issue for single model ensemble output. One method for using single model ensemble output is shown in Figure 5. Such compositing of several model runs on one image provides information on genesis as well as potential tracks after formation. Note that the spatial probabilities provided are not easily converted into a disturbance following forecast probability. These output are available for the Atlantic, Northeast/North Central Pacific, and Northwest Pacific at the following website - Ensemble modelling systems available include the GFS, NAVGEM, CMC, and ECMWF. 6

7 Figure 5. Example of forecast output from the GFS ensemble system. Black lines indicate potential tropical cyclones with dots indicating position at 00 and 12 UTC. The number shown at the beginning of each track provides the lead time that the cyclone was detected in the model. The colours indicate the probability that a cyclone will be within 200 km within the 00 to 120 h forecast time. Figure 6. Example of the output from the ECMWF ensemble for a predicted tropical cyclone in the Atlantic basin. The gray lines indicate potential cyclones that are all coherent members likely representing the same system. The red line is the composite track, the red dots are points 12 hours apart, and the text every two days indicates the date/time, number of members available, and the average intensity of the members. 7

8 Elsberry et al. (2011a,b) developed an automated technique for the detection and tracking of tropical cyclone like vortices (TCLV) in numerical weather prediction models, and especially for ensemble-based models. A TCLV is detected in the model grid when selected dynamic and thermodynamic fields meet specified criteria. A backward-and-forward extension from the mature stage of the track is utilized to complete the track. In addition, a fuzzy logic approach is utilized to calculate the TCLV fuzzy combined-likelihood value (TFCV) for representing the TCLV characteristics in the ensemble forecast outputs. They demonstrated that this algorithm efficiently extracts western North Pacific TCLV information from the vast amount of ensemble data from the NCEP Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF). Depending on the specified domain size and the ensemble track numbers to define a forecast event, some skill is indicated in predicting the named TC activity out to 30 days. Elsberry et al. (2014) also demonstrated the utility of the technique to the Atlantic basin Figure 6 - from the ECWMF ensembles. This methodology is not routinely available at the present time. Statistical-dynamical hybrid approaches Figure 7. Example output of the Tropical Cyclone Genesis Index (TCGI) Dunion (2013) developed a disturbance-following tropical cyclone (TC) genesis index (TCGI) to provide forecasters with an objective tool for identifying the 0-48hr and 0-120hr probability of TC genesis in the North Atlantic basin. Predictors from a variety of sources were tested and potentially integrated into this new scheme and included Dvorak T-number / CI value estimates, environmental and convective parameters currently used in the NESDIS TC Formation 8

9 Probability (TCFP) product (fixed grid scheme), environmental parameters from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) that are relevant to TC genesis, and total precipitable water (TPW) retrievals from microwave satellites. Six robust TCGI predictors were identified and have been incorporated into an experimental real-time version of TCGI. Figure 7 provides output from TCGI for a tropical disturbance in the Atlantic basin on September 11 th, These are available experimentally for the Atlantic basin: Figure 8. Example of statistical-dynamical output for TC genesis. The top panel indicates the probability of a disturbance becoming a tropical cyclone within 120 hr along with the forecast track. The bottom panel indicates the combined probability of genesis within 120 hr based upon output from three deterministic global models. 9

10 Halperin et al. (2013) analyzed TC genesis forecasts from five global models [Environment Canada s Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the models is based on a combination of methods used previously in the literature and newly defined objective criteria. All model indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics are being used experimentally (Figure 8) from CMC, GFS, and UKMET to provide probabilistic TC genesis forecasts for disturbances in the Atlantic and Northeast Pacific: Operational Genesis Products With the growing number of tools with increased skill, some of the RSMCs/TCWCs are providing genesis predictions to the public. These are detailed in Table 1. Figure 9 provides examples of the public genesis products being produced routinely for the Atlantic and Northeast Pacific basins. Table 1. Operational genesis products issued for the public by the various RSMCs/TCWCs Region Genesis Type of forecast Location Frequency lead time Atlantic and 0 to 48 hr Probabilistic (10% Cone graphic conveys Four times daily Northeast/Central 0 to 120 hr increments) formation location (00, 06, 12, and Pacific possibilities 18Z) Northwest Pacific No genesis predictions North Indian No genesis predictions Southwest Indian 0 to 120 hr Qualitative Qualitative Once daily (12Z) Southeast 0 to 72 hr Probabilistic (Very low Qualitative Once daily (04Z) Indian/South Pacific [<5%], Low [5-20%], Moderate [20-50%], High [>50%]) 10

11 11

12 Figure 9. Examples of public products issued for genesis predictions for the Atlantic/Northeast Pacific basins. The top panel provides the location of any current disturbances ( X ) colour coded for the short-term (0 to 48 hr) genesis probability overlaid on the current infrared satellite image. The middle panel provides the long-term (0 to 120 hr) genesis probability colour coded along with the location (hatched) where the cyclone may form. An X is indicated in this figure if there is an initial disturbance that can be tracked. The bottom panel provides the accompanying text product with the 0 to 48 hr and 0 to 120 hr genesis predictions. Figure 10. Example of reliability diagram for genesis predictions issued for the Northeast Pacific basin during 2013 by RSMC Miami. The solid lines indicate the mean verifying percentage for each 10% forecast bin. The dotted lines indicate the number of forecasts issued for each 10% forecast bin. The blue lines are for the 0 to 48 hr genesis predictions and the red lines are for 0 to 120 hr. 12

13 Quantitative genesis predictions have been issued for the Atlantic and Northeast/North Central Pacific basins for a couple of years. Figure 10 provides an example of the skill in these predictions as verified in a reliability diagram. For the Northeast Pacific basin, the 0 to 48 hr forecasts were relatively reliable with an indication of some low bias (i.e., the probabilities issued should have been somewhat higher), while the 0 to 120 hr forecasts were less successful with both a low bias but also with no discernment for the forecasts issued for the 40 to 70% ranges Potential Genesis Products With improvement to genesis becoming available in deterministic, ensemble, and dynamical statistical techniques, enhanced genesis products to be issued to the public may be possible. Currently, watches and warning are tied administratively to tropical cyclone advisories. The possibility of issuing pre-genesis advisories would provide allow for watches/warnings to be issued for coastal regions in the cases of TCs forming near the coast followed immediately by intensification. However, it can be difficult to determine placement of watch and warning areas when the system is poorly defined and track and intensity prognosis is highly uncertain. One possible product would be to provide a figure indicating current disturbance position along with a cone indicating the formation area along the coast with watches/warnings in place (Figure 11). Figure 11. Example of possible pre-genesis prediction. Location of the disturbance at the current time is denoted by the X. The enclosed area represents the potential formation area. The colours indicate the coastal watches/warnings in place. In this case, a Tropical Storm Watch would be issued along the Texas, United States coast. 13

14 A more explicit method would be to provide a full advisory package with the tropical cyclone track, intensity, and size forecasts for a disturbance. Figure 12 provides an example of what a five day pre-genesis track prediction would have looked like. For pre-genesis predictions, it is expected that the track, intensity, and size forecasts would be more uncertain and have larger errors that that issued for a developed tropical cyclone. Moreover, there would be false alarms for such pre-genesis systems, as not all would become tropical cyclones. Considerations for issuing pre-genesis predictions would include: likelihood of genesis (medium or high probability), leadtime (three day or five day), and location of system (issue only for land-threatening pre-genesis cases or issue for all pre-genesis cases). Figure 12. Example of an experimental 5-day prediction of a disturbance that became Hurricane Irene. The white line and symbols provide the best track of Irene, and the black line shows what a pre-genesis prediction of the track could have provided Recommendations Researchers should develop methods for providing quantitative (probabilistic) genesis prediction guidance via deterministic, ensemble, and statistical-dynamical models Researchers should develop methods for provide methods for providing pre-genesis prediction guidance of TC track, intensity and size via deterministic, ensemble, and statistical-dynamical models RSMCs/TCWCs should explore issuing quantitative genesis predictions to the public RSMCs/TCWCs should explore increasing lead-time of existing quantitative genesis predictions to the public RSMCs/TCWCs should explore the possibility of issuing pre-genesis tropical cyclone track, intensity, and size forecasts 14

15 References Cossuth, J. H., R. D. Knabb, D. P. Brown, R. E. Hart, 2013: Tropical cyclone formation guidance using pregenesis Dvorak climatology. Part I: Operational forecasting and predictive potential. Wea. Forecasting, 28, Dunion, J., 2013: Development of a probabilistic tropical cyclone genesis prediction scheme. Joint Hurricane Testbed Final Report, Dvorak, V.F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, Elsberry, R. L., H.-C. Tsai, M. S. Jordan, 2014: Extended-range forecasts of Atlantic tropical cyclone events during 2012 using the ECMWF 32-day ensemble predictions. Wea. Forecasting, 29, Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, Halperin, D. J., H. E. Fuelberg, R. E. Hart, J. H. Cossuth, P. Sura, and R. J. Pasch, 2013: An evaluation of tropical cyclone genesis forecasts from global numerical models. Wea. Forecasting, 28, Schumacher, A. B., M. DeMaria, J. A. Knaff, 2009: Objective estimation of 24-h probability of tropical cyclone formation. Wea. Forecasting, 24, Tsai, H.-C., K.-C. Lu, R. L. Elsberry, M.-M. Lu, and C.-H. Sui, 2011: Tropical cyclone-like vortices detection in the NCEP 16-day ensemble system over the western North Pacific in 2008: Application and forecast evaluation. Wea. Forecasting, 26, Zehr, R.M., 1992: Tropical cyclogenesis in the Western North Pacific. NOAA Technical Report NESDIS 61, U. S. Department of Commerce, Washington, DC, 181 pp. 15

16 APPENDIX ADVANCES IN TROPICAL CYCLONE GENESIS PREDICTION OVER THE NORTH INDIAN OCEAN - RESEARCH AND OPERATIONAL SUMMARY S. D. Kotal sdkotal.imd@gmail.com and M. Mohapatra mohapatraimd@gmail.com India Meteorological Department Mausam Bhavan, Lodi Road, New Delhi INTRODUCTION The North Indian Ocean (NIO) basin is a smaller oceanic basin compared to the other vast oceanic basins of globe and, it is further divided into two sub-regions, viz., Bay of Bengal (BOB) and the Arabian Sea (AS) with intervening South Asian land areas. The average life period of a Cyclonic Disturbance (CD) over the NIO is about 3-5 days only in contrast to larger life periods over other oceanic basins. If a depression (Maximum Sustained Surface Wind Speed (MSW): knots and two closed isobars at the interval of 2 hpa in a 5 x5 latitude / longitude box) forms over the NIO from a pre-existing low pressure area, cyclogenesis is said to have occurred. Forecasting of cyclogenesis is highly challenging for TC forecasters. It becomes more challenging if we consider lack of continuous meteorological observations over the oceanic region. During the recent past, this aspect has been addressed to some extent with the availability of ocean buoy data and several satellites based high resolution data and products on real time to the forecasters (Bhatia and Sharma, 2013). During the last two decades, cyclone forecasting all over the world basins has greatly benefited from the numerical weather prediction (NWP) models. Significant improvement in accuracy and reliability of NWP products has been driven by sophisticated numerical techniques, advanced data assimilation algorithms, new observing systems, the phenomenal increase in satellite observations, physical parameterizations, and advancements in computing power allowing for higher spatial resolution and longer forecast lead time. Recent advances in monitoring and prediction of cyclogenesis over the NIO is presented and analysed in following sections. 2. CYCLOGENESIS The tropical cyclone genesis has been attributed to both thermodynamic and dynamical factors. Palmen (1948) showed that hurricanes form over regions where Sea Surface Temperature (SST) is greater than 26 o C. In addition to SST, other important factors for genesis of tropical cyclones are: large Coriolis force, high low level relative vorticity, weak vertical wind shear (VWS), moisture in the middle troposphere and convective instability (Gray 1975). Zehr (1992) used vorticity at 850 hpa, divergence at 850 hpa and vertical wind shear to derive a genesis parameter (GP). He showed that this genesis parameter was useful in differentiating between non-developing 16

17 and developing tropical disturbances in the western North Pacific. Roy Bhowmik (2003) observed that a low pressure system with GP value around 20x10-12 s -1 at T.No. 1.5 has the potential to intensify into a severe cyclonic storm, while one with GP value greater than 45 x10-12 s -1 at T.No. 2.0 have the potential to intensify into a very severe cyclonic storm over the Indian Sea. 2.1 Genesis potential parameter (GPP) Every year a good number of low pressure systems form over the Indian Sea, but only a few of them intensify into a cyclonic storm. For the operational practice, there is need to specify a genesis parameter for the NIO, which could indicate the formation and potential of the system for intensification into a cyclonic storm at early stages of development. In this context, Kotal et al. (2009) developed a Genesis Potential Parameter (GPP) consisting of both dynamical as well as thermodynamic variables for differentiating non-developing and developing low pressure systems over the Indian Sea at their early development stages when T-number is 1.0, 1.5 or 2.0. The genesis potential parameter (GPP), for the NIO basin has been developed in terms of four variables, namely vorticity at lower levels, middle tropospheric relative humidity, middle tropospheric instability, and the VWS. The new insight in this area is the real time grid point analysis of the GPP to locate the most potential zone for cyclogenesis and to understand the potential for intensification (Developing or Non-developing system) of the system (Kotal and Bhattacharya, 2013a). The grid point analysis and forecast of the genesis parameter up to seven days are generated for monitoring and forecasting of cyclogenesis over the NIO in real-time and found to be very useful. Grid point analysis and forecasts of GPP is able to predict the formation and location of a system before 168 hours of its formation (Kotal et al 2013b). 2.2 ECMWF Ensemble Prediction System for Extended Range Tropical Cyclones genesis and track forecasts operational at SHAR (Sriharikota) CFAN's (Climate Forecast Applications Network) tropical cyclone forecast products include a sophisticated analysis of the ECMWF Variable Ensemble Prediction System (VarEPS) 1-15 day and monthly forecast products that are integrated into a multi-model analysis with forecasts from other global and regional models. During the period , VarEPS has undergone a number of important changes that include increasing the horizontal and vertical resolutions of the modelling system, expanding data assimilation procedures to include a greater number of satellite radiance measurements, updating the model physics (including cumulus convection parameterization schemes), and changing how initial and stochastic perturbations are generated using singular vectors. VarEPS includes the ECMWF global model that is run at TL1279 spectral truncation (horizontal resolution -16 km) with 91 vertical levels out to 10 days along with 51 ensemble members (50 perturbed members, 1 control run) at TL639 spectral truncation (horizontal resolution-32 km) with 62 vertical levels. Stochastic perturbations are also added during the model integration to account for the uncertainty in parameterized physical processes. Five additional singular vectors are computed and perturbed in the six grid spaces enclosing each TC using a diabatic, adjoint version of the ECMWF global atmospheric model at TL42 spectral truncation with 42 vertical levels. To increase the intensity retrievals from VarEPS, 10-m winds are replaced with winds averaged in the lower troposphere (10 m, 925 hpa, and 850 hpa). 2.3 Satellite based Cyclone Observation and Real-time Prediction over the North Indian Ocean (SCORPIO) at Satellite Application Centre (SAC), India Coupled-HWRF model is run at Satellite Application Centre (SAC), India. The model is run on real time based on 00 UTC initial conditions to provide 12 hourly track and intensity forecasts up to 144 hours. Satellite based cyclone observations and real time predictions over the north Indian Ocean (SCORPIO, developed by the ISRO (Indian Space Research Organisation) ( The SCORPIO system is designed to predict the genesis of the 17

18 tropical cyclone system with a lead time of ~60 90 hours. Major components of SCORPIO system are: (a) detection of cyclogenesis over the north Indian Ocean using round-the-clock automated analysis of Ocean surface wind vectors. Once a convective system is detected to be a potential case of cyclogenesis, a number of other algorithms are activated that include (b) fixing the location and estimation of intensity using 30-minute KALPANA infrared images, and (c) prediction of the cyclone track for up to 120-hours using a Lagrangian Advection Model that uses high resolution (0.5 ) global forecast model fields as input. 2.4 Tropical Cyclone Formation Probability Guidance Product of CIRA, RAMMB, NOAA Tropical Cyclone Formation Probability Guidance Product of CIRA, RAMMB, NOAA ( phase and amplitude forecasts of Madden-Julien Oscillation (MJO) by NCEP and other meteorological agencies are useful inputs for prediction of cyclogenesis. 2.5 NWP models Non-hydrostatic mesoscale WRF and HWRF model at resolution 27 km and 9 km, and the Global Forecast System (GFS) at resolution T574L64 (~23 km) are being run for operational forecasting in IMD. For the day-to-day weather forecasting, IMD also makes use of NWP products prepared by some other operational NWP centres including, European Center for Medium range Weather Forecasts (ECMWF), NCEP (National Center for Environmental Prediction) and Japan Meteorological Agency (JMA), NCMRWF(India)-GFS, NCMRWF-UM, NCMRWF-GEFS, WRF run at Indian Institute of Technology-Delhi, HWRF-NCEP, ARP (MeteoFrance). Guidance from these NWP models is available for forecast lead time 72h to 240h. 3. OPERATIONAL MONITORING AND PREDICTION OF CYCLOGENESIS RSMC New Delhi follows a Standard Operating Procedure (SOP) for monitoring and prediction of cyclogenesis (India Meteorological Department (IMD), 2013) and the current status of monitoring and prediction of cyclogenesis over NIO is presented below. The basic inputs for monitoring of cyclogenesis include satellite data and products synoptic data and guidance from Numerical Weather Prediction (NWP) models and comprises of the following steps: (i) Continuous monitoring of the convective cloud clusters using INSAT-3D and Kalpana satellite imageries - (a) The areal extent, depth, number of cloud clusters with high reflectivity and cloud top temperatures < -40 C and organisation of cloud clusters are monitored at halfhourly intervals. (b) Animation of cloud clusters for past 24 hrs are closely watched for their persistence, areal expansion, increase in depth and improvement in organisation. IR/WV/Microwave imageries of various geostationary and polar orbiting satellites are closely monitored for the above purpose. (ii) In case of prevailing favourable convection over the sea area, the Low Level Cyclonic Circulation (LLCC) is monitored using scatterometer wind data (OSCAT / ASCAT winds) and available buoy and ship observations. Wind speed, temperature and moisture in the vicinity of the LLCC are also regularly monitored using WINDSAT and SSMI data. 18

19 (iii) (iv) (v) The LLCC is then matched with surface derived pressure and circulation patterns. When cyclogenesis occurs close to the coast, Radar observations take the highest priority to find out the LLCC (Raghavan, 2013). The environmental factors governing cyclogenesis, viz., SST, Ocean Thermal Energy, VWS, Vorticity, Upper level Divergence, Lower level Convergence, middle level relative humidity, moist static stability etc. are monitored using satellites derived products from NOAA-AOML, Co-operative Institute of Meteorological Satellite Studies (CIMSS), University of Wisconsin, USA and INCOIS (Indian National Centre for Ocean Information Services), India and numerical models analyses in addition to bouy, ship, coastal observations. NWP based Mean Sea Level Pressure (MSLP), wind, moisture and temperature fields are analysed regularly to compare the model analyses with actual observations for selecting the model with best initial conditions and hence to follow the guidance from that model. The forecaster may follow the numerical model with better performance in the past also for predicting genesis. NWP analysis and forecast fields include outputs from various models such as IMD-GFS, NCMRWF(India)-GFS, ECMWF, UKMO, JMA, ARP (MeteoFrance), IMD-WRF, WRF run at Indian Institute of Technology - Delhi, NCMRWF-WRF, HWRF, NCEP-HWRF etc. (vi) Dynamical and statistical models guidance A genesis potential parameter (GPP), for the NIO basin has been developed (Kotal and Bhattacharya, 2013a) as the product of four variables, namely vorticity at lower levels, middle tropospheric relative humidity, middle tropospheric instability, and the inverse of vertical wind shear. The GPP is used for predicting cyclogenesis at their early development stages (when T- number is 1.0, 1.5, 2.0). The grid point analysis and forecast of the genesis parameter up to seven days are generated on real time. Climatological inputs are taken from IMD s Cyclone eatlas [a tool for depicting tracks and statistics of CDs over NIO in graphical / map form - developed during 2008 in CD form (IMD, 2008) and later hosted in the web during 2012 at the URL: Monthly frequency of CDs over the NIO for a specified period from the year 1891 onwards could be generated using this tool in 2.5 x2.5 grid map and the same is utilised to determine the climatological probability for the grid corresponding to the region of the pre-existing low pressure area. (vii) Consensus based monitoring and forecast of cyclogenesis Depending upon the number of models predicting genesis and based on the past performance of these models, and prevailing synoptic climatological and conditions, the probability of cyclogenesis is estimated by the forecasters. Further the dynamical statistical guidance and synoptic and environmental conditions are also considered by the forecasters while taking the consensus decision. Thus, the official forecast is based on a consensus forecast determined from NWP, synoptic, environmental, statistical and dynamical-statistical inputs. It provides probability of cyclogenesis during next 72 hrs based on the observations at 0300 UTC of everyday and issued at 0600 UTC. This probabilistic forecast is issued in terms of nil, low, fair, moderate and high probability corresponding to 0, 1-25, 26-50, and % probability of occurrence. 19

20 4. DIFFICULT SITUATIONS 4.1 When the TC forms close to the coast During the year 2013, IMD introduced 120-hr forecast for TC track and intensity prediction. When a TC forms close to the coast and makes landfall within 5 days of formation, it leads to operational problems of issuing forecasts up to 120-hrs. For e.g., a Severe Cyclonic Storm (SCS, MWS knots) Helen formed as a depression over the West Central BOB on 19 th November 2013 /0000 UTC with centre near lat N and long E, intensified and crossed Andhra Pradesh coast close to south of Machilipatnam (near lat N and long E) between 0800 and 0900 UTC of 22 nd Nov 2013 and finally weakened into a well marked low pressure area over land on 23 rd November 2013 / 0000 UTC. Thus the lead period before the TC landfall was for 80 hrs only. In such cases disaster managers expect forecast of TC track, intensity and landfall from the pre-genesis stage itself which is a challenging task for the forecasters. 4.2 Formation of Meso vortices The NIO basin also experiences formation of meso vortices, also referred to as midget cyclones, which are not properly detected by satellite observations as well as synoptic scale analysis. However, if the system forms close to the coast, Radar observations provide the crucial inputs for detecting the cyclogenesis. An example of such a case, viz., formation and detection of Cyclonic Storm (MWS: knots) Ogni (28-30 October 2006) is presented below: The CS Ogni formed as a depression on 28 th October 2006/0300 UTC near 11.5 N/80.0 E about from the coast, moved northwards skirting the coast, intensified into a cyclonic storm and crossed coast near 15.7 N/80.3 E on 30 th /0700 UTC. During its entire life period, it was within the range of Doppler Weather Radar (DWR) facility at Chennai and could be monitored and tracked continuously even though forecast lead time was less and advance warning could not be made. As per the DWR analysis, the system skirted Indian coast for about 500 km in two days and caused extensive damage and flooding in the coastal areas of Tamil Nadu and Andhra Pradesh. Initially, it moved fairly fast (>20kmph) but eventually slowed down hours before and after crossing the coast. It was a small core system with a dimension of about 100 km (Hatwar et al, 2008). This demonstrates the need for DWR facility along the coastal belts of the NIO. The Indian coast line, as well as Bangladesh, are equipped with DWR facilities. Forecasting the formation of such mesovortices in coastal regions not equipped with DWR facility (Sri Lanka, Myanmar, Pakistan, Oman etc.) would be a challenging task of very high order. 5. EXPERIMENTAL AND RESEARCH EFFORTS A Forecast Demonstration Project on BOB TCs is being undertaken by IMD for the period 15-October to 30 November every year since 2008 under which intense observation periods are declared and special observations are taken from the period of cyclogenesis (Mohapatra et al, 2013). Singh and Singh (2011) have shown using scatterometer winds that there is a steep rise in the surface vorticity anomaly in the genesis area three days before the formation of depression. A study on utilisation of Indian Remote Sensing satellite (IRS-P4) and INSAT satellite derived SST, Cloud Motion Vectors (CMVs) and Water Vapour Wind Vectors (WVWVs), Cloud Liquid Water (CLW) and Vorticity fields for understanding of formation and development of TCs during May 2001 has been undertaken by Mahajan et al (2012). Balachandran (2013) has analysed the genesis of TC Khaimuk (2008) from the energetics point of view and showed that barotropic energy conversion from the horizontal shear of the mean zonal flow to eddy kinetic energy was the dominant factor in the formation of vortex and genesis of TC Khaimuk. 20

21 Pattanaik et al (2013) have attempted extended range prediction using ECMWF and NCEP coupled model outputs of dynamical variables 5-11 days before cyclogenesis during the cyclone season of Even though the results are encouraging, more cases have to be examined to consider the scheme for operational forecasting. Mohapatra and Adhikary (2011) have examined the role of MJO on cyclogenesis over NIO and have shown that the MJO phases 3 and 4 are favourable for cyclogenesis even if the amplitude is less than one. Hence monitoring of MJO helps in medium to extended range guidance for cyclogenesis. However, the study also indicates that only 60% of cyclogenesis over the NIO is associated with favourable MJO phases. Research efforts are on for seasonal prediction of cyclonic activity over the NIO basin Pattanaik et al (2014), Balachandran and Geetha (2009). Real time experimental predictions are attempted from the year 2011 onwards and the performance of the statistical model is quite encouraging for taking up seasonal prediction operationally (Balachandran and Geetha (2014). 6. CONCLUSIONS Significant increase in satellite and ocean observations, installation of Doppler Weather Radars along the coast line and research results have improved the day-to-day monitoring of TC genesis by synoptic analysis during the recent years. But cyclone forecasting all over the world basins has greatly benefited from the NWP models during the last two decades. Significant improvement in accuracy and reliability of NWP products has been driven by sophisticated numerical techniques, advanced data assimilation algorithms, new observing systems, the phenomenal increase in satellite observations, improved modelling techniques, physical parameterizations, and advancements in computing power allowing for higher spatial resolution and longer forecast lead time. NWP models are able to predict the cyclogenesis successfully 5-7 days in advance. In this context, the new insight in this area is the dynamical-statistical technique, which can add skill to dynamical forecasts. Real time grid point analysis and forecasting of the GPP is found to be very successful to locate the most potential zone for cyclogenesis and to understand the potential for intensification of a low pressure system into a cyclone at the early stages of development (T-number 1.0, 1.5, 2.0). Grid point analysis and forecasts of GPP is able to predict the formation and location of a system before 168 hours of its formation. Tropical Cyclone Formation Probability Guidance Product of RAMMB, NOAA, phase and amplitude forecasts of Madden-Julien Oscillation (MJO) by NCEP and other meteorological agencies are the other inputs useful for prediction of cyclogenesis. ECMWF Ensemble Prediction System, a unique tropical cyclogenesis model has demonstrated its skill 7-10 days in advance for tropical cyclogenesis in the North Indian Ocean. CFAN's tropical cyclone forecast products include a sophisticated analysis of the ECMWF Variable Ensemble Prediction System for 1-15 day and monthly forecast products that are integrated into a multi-model analysis with forecasts from other global and regional models. Although with the advancement of NWP techniques have given a thrust in prediction of cyclogenesis in terms of location of formation over Sea, probability, forecast lead time and accuracy and reliability, still prediction of cyclogenesis is not a solved problem. Further improvement should be possible through enhancement of data over Sea and improvement of NWP technique along with data assimilation system and also by adding skill to the NWP outputs by sophisticated dynamical-statistical techniques. 21

22 References Balachandran S., 2013, Barotropic energetics analysis of tropical cyclone Khai-Muk, Mausam, 64, 1, Balachandran S., and Geetha B., 2009, Statistical prediction of seasonal cyclonic activity over North Indian Ocean, Mausam, 63, Balachandran S., and Geetha B., 2014, Experimental statistical prediction of seasonal cyclonic activity over North Indian Ocean, Book of Abstracts of National Workshop on Enhanced and Unique cyclonic activity over North Indian Ocean, July 2014, New Delhi, India. Bhatia R.C. and Sharma A.K., 2013, Recent advances in observational support from space-based systems for tropical cyclones, Mausam, 64, Hatwar H.R, V Subrahmanyam, M Mohapatra, SK Roy Bhowmik, BK Bandyopadhay, Ch Singh, K Srivastava, 2008, A report on the cyclonic storm OGNI 2006, IMD Met Monograph No: Cyclone Warning Division 2/2008. Kotal, S.D., Kundu, P.K. and Roy Bhowmik S.K., Analysis of Cyclogenesis parameter for developing and non-developing low pressure systems over the Indian Sea. Natural hazards (Springer), 50: Kotal, S.D. and Bhattacharya S.K., 2013a, Tropical cyclone Genesis Potential Parameter (GPP) and it s application over the north Indian Sea, 64, Kotal, S.D., Sumit Kumar Bhattacharya, S. K. Roy Bhowmik, Y. V. Rama Rao and Arun Sharma 2013b, NWP Report on Cyclonic Storms over the North Indian Ocean during 2013 ( Mahajan P.N., Khaladkar R.M., Narkhedkar S.G., Sathy nair, Amita prabhu and Mahakur M., 2012, Investigation of features of May, 2001 tropical cyclone over the Arabian Sea through IRS-P4 and other satellite data, Mausam, 63, Mohapatra, M. and S Adhikary, 2010, Seasonal prediction of cyclonic disturbances over the Bay of Bengal during summer monsoon season: Identification of potential predictors, Mausam, 61, Mohapatra M. and Adhikary S., 2011, Modulation of cyclonic disturbances over the north Indian Ocean by Madden-Julian oscillation, Mausam, 62, Mohapatra M., Sikka D.R., Bandyopadhyay B.K and Tyagi A., 2013, Outcomes and challenges of Forecast Demonstration Project (FDP) on landfalling cyclones over the Bay of Bengal, Mausam, 64, Pattanaik D.R., Mohapatra M., Mukhopadhyay B. and Tyagi A., A preliminary study about the prospects of extended range forecast of tropical cyclogenesis over the north Indian Ocean during 2010 post-monsoon season, 2013, 64, Pattanaik D.R., Sreejith O.P., Pai D.S. and MaduriMusale, 2014 Seasonal Forecast of Tropical Cyclogenesis over Bay of Bengal During Post-Monsoon Season, Proceedings of National Workshop on Enhanced and Unique cyclonic activity over North Indian Ocean, July 2014, New Delhi, India. Roy Bhowmik S.K. (2003) An evaluation of cyclone genesis parameter over the Bay of Bengal using model analysis. Mausam 54: Raghavan S., 2013, Observational aspects including weather radar for tropical cyclone monitoring, Mausam, 64, Singh O.P. and Singh Harvir, 2011, Use of scatterometer based surface vorticity fields in forecasting genesis of tropical cyclones over the north Indian Ocean, Mausam, 62, 1,