Prediction of severe tropical cyclones over the Bay of Bengal during 2007–2010 using high-resolution mesoscale model

In this paper, the performance of a high-resolution mesoscale model for the prediction of severe tropical cyclones over the Bay of Bengal during 2007?C2010 (Sidr, Nargis, Aila, and Laila) is discussed. The advanced Weather Research Forecast (WRF) modeling system (ARW core) is used with a combination of Yonsei University PBL schemes, Kain-Fritsch cumulus parameterization, and Ferrier cloud microphysics schemes for the simulations. The initial and boundary conditions for the simulations are derived from global operational analysis and forecast products of the National Center for Environmental Prediction-Global Forecast System (NCEP-GFS) available at 1°lon/lat resolution. The simulation results of the extreme weather parameters such as heavy rainfall, strong wind and track of those four severe cyclones, are critically evaluated and discussed by comparing with the Joint Typhoon Warning Center (JTWC) estimated values. The simulations of the cyclones reveal that the cyclone track, intensity, and time of landfall are reasonably well simulated by the model. The mean track error at the time of landfall of the cyclone is 98?km, in which the minimum error was found to be for the cyclone Nargis (22?km) and maximum error for the cyclone Laila (304?km). The landfall time of all the cyclones is also fairly simulated by the model. The distribution and intensity of rainfall are well simulated by the model as well and were comparable with the TRMM estimates.     

Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India

Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.     

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan using WRF-ARW model

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan has been carried out using the Weather Research and Forecasting (WRF) model. This extraordinary rainfall event was localized over north-west Pakistan and recorded 274 mm of rainfall at Peshawar (34.02°N, 71.58°E), within a span of 24 h on that eventful day where monthly July normal rainfall is only 46.1 mm. The WRF model was run with the triple-nested domains of 27, 9, and 3 km horizontal resolution using Kain–Fritsch cumulus parameterization scheme having YSU planetary boundary layer. The model performance was evaluated by examining the different simulated parameters. The model-derived rainfall was compared with Pakistan Meteorological Department–observed rainfall. The model suggested that this flood producing heavy rainfall event over north-west region of Pakistan might be the result of an interaction of active monsoon flow with upper air westerly trough (mid-latitude). The north-west Pakistan was the meeting point of the southeasterly flow from the Bay of Bengal following monsoon trough and southwesterly flow from the Arabian Sea which helped to transport high magnitude of moisture. The vertical profile of the humidity showed that moisture content was reached up to upper troposphere during their mature stage (monsoon system usually did not extent up to that level) like a narrow vertical column where high amounts of rainfall were recorded. The other favourable conditions were strong vertical wind shear, low-level convergence and upper level divergence, and strong vorticity field which demarked the area of heavy rainfall. The WRF model might be able to simulate the flood producing rainfall event over north-west Pakistan and associated dynamical features reasonably well, though there were some spatial and temporal biases in the simulated rainfall pattern.     

Impact of cloud microphysics and cumulus parameterization on simulation of heavy rainfall event during 7–9 October 2007 over Bangladesh

In the present study, the Advanced Research WRF (ARW) version 3.2.1 has been used to simulate the heavy rainfall event that occurred between 7 and 9 October 2007 in the southern part of Bangladesh. Weather Research and Forecast (WRF–ARW version) modelling system with six different microphysics (MP) schemes and two different cumulus parameterization (CP) schemes in a nested configuration was chosen for simulating the event. The model domains consist of outer and inner domains having 9 and 3 km horizontal resolution, respectively with 28 vertical sigma levels. The impacts of cloud microphysical processes by means of precipitation, wind and reflectivity, kinematic and thermodynamic characteristics of the event have been studied. Sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather event. NCEP FNL data were used for the initial and boundary condition. The model ran for 72 h using initial data at 0000 UTC of 7 October 2007. The simulated rainfall shows that WSM6–KF combination gives better results for all combinations and after that Lin–KF combination. WSM3–KF has simulated, less area average rainfall out of all MP schemes that were coupled with KF scheme. The sharp peak of relative humidity up to 300 hPa has been simulated along the vertical line where maximum updraft has been found for all MPs coupled with KF and BMJ schemes. The simulated rain water and cloud water mixing ratio were maximum at the position where the vertical velocity and reflectivity has also been maximum. The production of rain water mixing ratio depends on MP schemes as well as CP schemes. Rainfall depends on rain water mixing ratio between 950 and 500 hPa. Rain water mixing ratio above 500 hPa level has no effect on surface rain.     

Sensitivity of simulated cyclone Gonu intensity and track to variety of parameterizations: Advanced hurricane WRF model application

Domain configuration and several physical parameterization settings such as planetary boundary layer, cumulus convection, and ocean–atmosphere surface flux parameterizations can play significant roles in numerical prediction of tropical cyclones. The present study focuses to improve the prediction of the TC Gonu by investigating the sensitivity of simulations to mentioned configurations with the Advanced Hurricane WRF model. The experiments for domain design sensitivity with 27 km resolution has been shown moving the domains towards the east improve the results, due to better account for the large-scale process. The fixed and movable nests on a 9-km grid were considered separately within the coarse domain and their results showed that despite salient improvement in simulated intensity, an accuracy reduction in simulated track was observed. Increasing horizontal resolution to 3 km incredibly reduced the simulated intensity accuracy when compared to 27 km resolution. Thereafter, different initial conditions were experimented and the results have shown that the cyclone of 1000 hPa sea level pressure is the best simulation initial condition in predicting the track and intensity for cyclone Gonu. The sensitivity of simulations to ocean–atmosphere surface-flux parameterizations on a 9-km grid showed the combination of ‘Donelan scheme’ for momentum exchanges along with ‘Large and Pond scheme’ for heat and moisture exchanges provide the best prediction for cyclone Gonu intensity. The combination of YSU and MYJ PBL scheme with KF convection for prediction of track and the combination of YSU PBL scheme with KF convection for prediction of intensity are found to have better performance than the other combinations. These 22 sensitivity experiments also implicitly lead us to the conclusion that each particular forecast aspect of TC (e.g., track, intensity, etc.) will require its own special design.     

Interannual variability of summer monsoon rainfall over Myanmar

Observed summer (May–October) rainfall in Myanmar for the period 1981–2010 was used to investigate the interannual variability of summer monsoon rainfall over Myanmar. Empirical orthogonal function, the sequential Mann-Kendall test, power spectrum analysis, and singular value decomposition (SVD) were deployed in the study. Results from spectral analysis showed that the variability of rainfall over Myanmar exhibits a 2- to 6-year cycle. An abrupt change in rainfall over the country was noted in 1992. There was a notable increasing rainfall trend from 1989. After the sudden change, the mean rainfall increased by 36.1 mm, compared with the mean rainfall before the sudden change, and was associated with a rise in temperature of about 0.2 °C. An increase in heavy rainfall days was observed from the early 1990s to 2010. IOD and ENSO play an important role in the interannual variability of the summer rainfall over Myanmar. The covariability between rainfall over Myanmar and Indian Ocean SST generally suggests that a positive IOD mode is associated with suppressed rainfall in the central and northern parts of Myanmar. During a negative IOD mode, nearly the whole Myanmar experiences enhanced rainfall, which is associated with devastating socioeconomic impacts. The covariability between the rainfall over Myanmar and the sea surface temperature in the Pacific Ocean in the first and second SVD modes was dominated by warming in the east and central Pacific—an El Niño-like pattern—resulting in dry conditions in central Myanmar.     

A preliminary evaluation of the necessity of using a cumulus parameterization scheme in high-resolution simulations of Typhoon Haiyan (2013)

The Weather Research and Forecasting model was used to test the sensitivity of Typhoon Haiyan (2013) to the use of a cumulus parameterization scheme, specifically the revised Kain–Fritsch (rKF) scheme, at high horizontal resolutions with grid spacing varying from 9 to 2 km. The rKF scheme simulated the typhoon in best agreement with the observation compared with other schemes, but some fundamental drawbacks relating the rKF scheme, e.g., neglecting the momentum adjustment and being less applicable to high-resolution modeling than multi-scaled schemes, could influence the results and were discussed. Initial results showed that the typhoon track simulations benefited little from the use of the rKF scheme or a fine resolution, partially because of the similar large-scale steering flows induced by the analyzed boundary conditions used in each simulation. The influences of using the rKF scheme on typhoon intensity, size, structure, and precipitation were dependent on the grid spacing, and the most apparent changes occurred near a grid length of 4 km. At 9–4-km grid spacings, using the rKF scheme produced typhoons much stronger with more rainfall and surface latent heat flux than did using no cumulus parameterization scheme. At 3- or 2-km grid spacing, using the rKF scheme caused little changes on typhoon intensity, and the changes in precipitation and surface latent heat flux were relatively small. These results suggested that the grid spacing of 2 km for simulations using no cumulus parameterization scheme or the grid spacing of 4 km for simulations using the rKF scheme facilitated reproducing the observed Typhoon Haiyan.     

Trends in extreme rainfall over ecologically sensitive Western Ghats and coastal regions of Karnataka: an observational assessment

Rainfall is one of the pivotal climatic variables, which influence spatio-temporal patterns of water availability. In this study, we have attempted to understand the interannual long-term trend analysis of the daily rainfall events of ≥?2.5 mm and rainfall events of extreme threshold, over the Western Ghats and coastal region of Karnataka. High spatial resolution (0.25°?×?0.25°) daily gridded rainfall data set of Indian Meteorological Department was used for this study. Thirty-eight grid points in the study area was selected to analyze the daily precipitation for 113 years (1901–2013). Grid points were divided into two zones: low land (exposed to the sea and low elevated area/coastal region) and high land (interior from the sea and high elevated area/Western Ghats). The indices were selected from the list of climate change indices recommended by ETCCDI and are based on annual rainfall total (RR), yearly 1-day maximum rainfall, consecutive wet days (≥?2.5 mm), Simple Daily Intensity Index (SDII), annual frequency of very heavy rainfall (≥?100 mm), frequency of very heavy rainfall (≥?65–100 mm), moderate rainfall (≥?2.5–65 mm), frequency of medium rainfall (≥?40–65 mm), and frequency of low rainfall (≥?20–40 mm). Mann-Kendall test was applied to the nine rainfall indices, and Theil-Sen estimator perceived the nature and the magnitude of slope in rainfall indices. The results show contrasting trends in the extreme rainfall indices in low land and high land regions. The changes in daily rainfall events in the low land region primarily indicate statistically significant positive trends in the annual total rainfall, yearly 1-day maximum rainfall, SDII, frequency of very heavy rainfall, and heavy rainfall as well as medium rainfall events. Furthermore, the overall annual rainfall strongly correlated with all the rainfall indices in both regions, especially with indices that represent heavy rainfall events which is responsible for the total increase of rainfall.     

Evaluation of real-time flash flood forecasts for Haiti during the passage of Hurricane Tomas, November 4–6, 2010

The January 2010 earthquake that devastated Haiti left its population ever more vulnerable to rainfall-induced flash floods. A flash flood guidance system has been implemented to provide real-time information on the potential of small (~70 km²) basins for flash flooding throughout Haiti. This system has components for satellite rainfall ingest and adjustment on the basis of rain gauge information, dynamic soil water deficit estimation, ingest of operational mesoscale model quantitative precipitation forecasts, and estimation of the times of channel flow at bankfull. The result of the system integration is the estimation of the flash flood guidance (FFG) for a given basin and for a given duration. FFG is the amount of rain of a given duration over a small basin that causes minor flooding in the outlet of the basin. Amounts predicted or nowcasted that are higher than the FFG indicate basins with potential for flash flooding. In preparation for Hurricane Tomas’ landfall in early November 2010, the FFG system was used to generate 36-h forecasts of flash flood occurrence based on rainfall forecasts of the nested high-resolution North American Model of the National Centers for Environmental Prediction. Assessment of the forecast flood maps and forecast precipitation indicates the utility and value of the forecasts in understanding the spatial distribution of the expected flooding for mitigation and disaster management. It also highlights the need for explicit uncertainty characterization of forecast risk products due to large uncertainties in quantitative precipitation forecasts on hydrologic basin scales.     

Hurricane wind risk in Louisiana

A statistical procedure for estimating the risk of strong winds from hurricanes, known as the Hurricane Risk Calculator, is demonstrated and applied to several major cities in Louisiana. The procedure provides an estimate of wind risk over different length periods and can be applied to any location experiencing this hazard. Results show that an area 100 km around the city of New Orleans can expect to see hurricane winds blowing at 49 ms^?1 (44.3–53.7) [90 % confidence interval (CI)] or stronger, on average, once every 20 years. In comparison, for the same time period, the capital city of Baton Rouge and the surrounding area can expect to see hurricane winds of 43 ms^?1 (38.2–47.8) (90 % CI) or stronger. Hurricane track direction is also analyzed at the cities of interest. For Morgan City, Lafayette, Lake Charles, and Alexandria, tropical cyclones with winds at least 18 ms^?1 travel from the southeast to northwest. New Orleans and Baton Rouge tropical cyclones have a greater tendency to turn toward the east while within 100 km of the city, historically giving them a southwesterly approach. Tropical cyclones within 350 km off the south-central Louisiana coast occur most often in September, and the most extreme of these events are becoming stronger through time as shown with quantile regression.     

Rainfall over Oman and its teleconnection with El Niño Southern Oscillation

The Sultanate of Oman is located in the south-eastern part of the Arabian Peninsula and covers the larger part of the southern coasts of the Arabian Peninsula in both arid and semi-arid environments except for the southern part which is swept by the monsoon affecting the Arabian Sea during the period from June to September. The summer rainfall over Oman shows year-to-year variability, and this is caused by oceanic and atmospheric influences. In the present study, we tried to explore the influence of El Niño on the rainfall over Oman using different data sets. The empirical orthogonal function (EOF) technique employed to the zonal wind at 850 hPa for the 30-year period shows that the second and third modes of EOF are showing high variability over the Oman regions. The corresponding PCs were subjected to FFT analysis, and it showed a peak about 5–6 years. In addition to this, the zonal wind over the Oman regions is correlated with the global zonal wind and found a significant correlation (1 % significant level). It has already been proved that the wind and rainfall during summer monsoon is in phase. Moreover, the spectral analysis of rainfall at Masirah station and the Niño3.4 index show the similar mode of variability indicating a direct relationship. The correlation between rainfall and the Niño3.4 index is also showing a positive significant value, and therefore, it can be concluded that the El Niño in the Pacific favours rainfall over the Oman region.     

The simulation of heavy rainfall episode over Mumbai: impact of horizontal resolutions and cumulus parameterization schemes

In this study, the simulation of an extreme weather event like heavy rainfall over Mumbai (India) on July 26, 2005 has been attempted with different horizontal resolutions using the Advanced Research Weather Research Forecast model version 2.0.1 developed at the National Center for Atmospheric Research (NCAR), USA. The study uses the Betts–Miller–Janjic (BMJ) and the Grell–Devenyi ensemble (GDE) cumulus parameterization schemes in single and nested domain configurations. The model performance was evaluated by examining the different predicted parameters like upper and lower level circulations, moisture, temperature, and rainfall. The large-scale circulation features, moisture, and temperature were compared with the National Centers for Environmental Prediction analyses. The rainfall prediction was assessed quantitatively by comparing rainfall from the Tropical Rainfall Measuring Mission products and the observed station values reported in the Indian Daily Weather Reports from India Meteorological Department (IMD). The quantitative validation of the simulated rainfall was done by calculating the categorical skill scores like frequency bias, threat scores (TS), and equitable threat scores (ETS). It is found that in all simulations, both in single and nested domains, the GDE scheme has outperformed the BMJ scheme for the simulation of rainfall for this specific event.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

Analysis and Simulation of Propagule Dispersal and Salinity Intrusion from Storm Surge on the Movement of a Marsh–Mangrove Ecotone in South Florida

Coastal mangrove–freshwater marsh ecotones of the Everglades represent transitions between marine salt-tolerant halophytic and freshwater salt-intolerant glycophytic communities. It is hypothesized here that a self-reinforcing feedback, termed a “vegetation switch,” between vegetation and soil salinity, helps maintain the sharp mangrove–marsh ecotone. A general theoretical implication of the switch mechanism is that the ecotone will be stable to small disturbances but vulnerable to rapid regime shifts from large disturbances, such as storm surges, which could cause large spatial displacements of the ecotone. We develop a simulation model to describe the vegetation switch mechanism. The model couples vegetation dynamics and hydrologic processes. The key factors in the model are the amount of salt-water intrusion into the freshwater wetland and the passive transport of mangrove (e.g., Rhizophora mangle) viviparous seeds or propagules. Results from the model simulations indicate that a regime shift from freshwater marsh to mangroves is sensitive to the duration of soil salinization through storm surge overwash and to the density of mangrove propagules or seedlings transported into the marsh. We parameterized our model with empirical hydrologic data collected from the period 2000–2010 at one mangrove–marsh ecotone location in southwestern Florida to forecast possible long-term effects of Hurricane Wilma (24 October 2005). The model indicated that the effects of that storm surge were too weak to trigger a regime shift at the sites we studied, 50 km south of the Hurricane Wilma eyewall, but simulations with more severe artificial disturbances were capable of causing substantial regime shifts.     

Real-time nowcast of a cloudburst and a thunderstorm event with assimilation of Doppler weather radar data

Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model

The Advanced Research WRF (ARW) model is used to simulate Very Severe Cyclonic Storms (VSCS) Hudhud (7–13 October, 2014), Phailin (8–14 October, 2013) and Lehar (24–29 November, 2013) to investigate the sensitivity to microphysical schemes on the skill of forecasting track and intensity of the tropical cyclones for high-resolution (9 and 3 km) 120-hr model integration. For cloud resolving grid scale (<5 km) cloud microphysics plays an important role. The performance of the Goddard, Thompson, LIN and NSSL schemes are evaluated and compared with observations and a CONTROL forecast. This study is aimed to investigate the sensitivity to microphysics on the track and intensity with explicitly resolved convection scheme. It shows that the Goddard one-moment bulk liquid-ice microphysical scheme provided the highest skill on the track whereas for intensity both Thompson and Goddard microphysical schemes perform better. The Thompson scheme indicates the highest skill in intensity at 48, 96 and 120 hr, whereas at 24 and 72 hr, the Goddard scheme provides the highest skill in intensity. It is known that higher resolution domain produces better intensity and structure of the cyclones and it is desirable to resolve the convection with sufficiently high resolution and with the use of explicit cloud physics. This study suggests that the Goddard cumulus ensemble microphysical scheme is suitable for high resolution ARW simulation for TC’s track and intensity over the BoB. Although the present study is based on only three cyclones, it could be useful for planning real-time predictions using ARW modelling system.     

Variations in the sequences of daily rainfall across Nigeria

The study compared the sequences of daily rainfall over coastal southern and semi-arid northern Nigeria. Daily rainfall occurrences for 41 years (1971–2011) over four meteorological stations in Lagos, Rivers, Borno, and Katsina were analyzed using frequency analysis and Markov chain model. Findings indicate that the coastal area had a predominance of 2–4-day wet spells while the semi-arid area showed a wet spell distribution that is geometric in nature with 1-day spell predominance. The dry spell behavior was nearly the opposite of the wet spell occurrence. The coastal region showed a dry spell of 1–4-day spell predominance while the semi-arid region showed a predominance of higher dry spells of 2–6 days. Accumulation of the amount of rainfall in each spell also showed that much rainfall from the coastal area was obtained from rains of spells of 3 days and above while the semi-arid had more of its rain from spells of 1–3 days. The mean annual rainfall was 1423.75 mm (Lagos), 2173.56 mm (Rivers), 517.50 mm (Katsina), and 578.34 mm (Borno). The wettest month was June (274.08 mm) in Lagos, September (378.18 mm) in Rivers, and August in Katsina and Borno (172.98 and 184.81 mm, respectively). The driest months were January for Lagos and Rivers (15.77 and 18.96 mm, respectively) and November–February for Katsina and Borno (0–0.06 mm). This showed that the coastal areas had nearly three times the volume of rain in the semi-arid area. The study further showed that onset of rain for the coastal area was March/April while the cessation of rainy season was October/November. On the other hand, the onset of rainy season in the semi-arid area was May/June and cessation of rainy season was September. Findings portend drier days for the semi-arid area due to dry spell persistence and hence, the consequent challenges of providing artificial water supply for agriculture and other purposes especially from October to May.     

Anomalous atmospheric events leading to Kyushu’s flash floods,July 11–14, 2012

During July 11–14, 2012, deadly floods and landslides triggered by a series of unprecedented heavy rains hit Kyushu, Japan, causing at least 32 deaths and around 400,000 evacuations. We focus on synoptic anomalies identified after inspecting rainfall patterns and documenting the conditions associated with this tragic event using data combined from the Global Rainfall Map in Near Real Time data, the NCEP/NCAR Reanalysis dataset, and the global forecast system. Rainfall maps indicated that there were many heavy rains in Kyushu in these days and this disaster was associated with the pattern of forecasts and standardized anomalies. A weather trough with positive height anomalies appeared, the center of which moved to the north of Japan over this period, which might cause wind anomalies and whereby lots of water vapor were transported to Kyushu area with up to 90 m s^?1, and high values of precipitable water formed with up to 60 mm. These results suggest that a larger-scale pattern is conducive for heavy rainfall and the anomalies put the pattern in context as to the potential for an extreme rainfall event, which can provide insights and methods for predicting extreme events’ or something similar.     

About the variability in thunderstorm and rainfall activity over India and its association with El Niño and La Niña

Thunderstorms are of much importance in tropics, as this region is considered to have central role in the convective overturn of the atmosphere and play an important role in rainfall activity. It is well known that El Niño and La Niña are well associated with significant climate anomalies at many places around the globe. Therefore, an attempt is made in this study to analyze variability in thunderstorm days and rainfall activity over Indian region and its association with El Niño and La Niña using data of thunderstorm day’s for 64 stations well distributed all over India for the period 1981–2005 (25 years). It is seen that thunderstorm activity is higher and much variable during pre-monsoon (MAM) and southwest monsoon (JJAS) than the rest of the year. Positive correlation coefficients (CCs) are seen between thunderstorms and rainfall except for the month of June during which the onset of the southwest monsoon sets over the country. CCs during winter months are highly correlated. Composite anomalies in thunderstorms during El Niño and La Niña years suggest that ENSO conditions altered the patterns of thunderstorm activity over the country. Positive anomalies are seen during pre-monsoon (MAM) and southwest monsoon months (JAS) during La Niña years. Opposite features are seen in southwest monsoon during El Niño periods, but El Niño favors thunderstorm activity during pre-monsoon months. There is a clear contrast between the role of ENSO during southwest monsoon and post-monsoon on thunderstorm activity over the country. Time series of thunderstorms and precipitation show strong association with similarities in their year-to-year variation over the country.