Spatial Rainfall Patterns Research Articles

Comparing the Spatial Patterns of Rainfall and Atmospheric Moisture among Tropical Cyclones Having a Track Similar to Hurricane Irene (2011)

Irene was the most destructive tropical cyclone (TC) of the 2011 Atlantic hurricane season due to flooding from rainfall. This study used a Geographic Information System to identify TCs with similar tracks and examine the spatial attributes of their rainfall patterns. Storm-total rainfall was calculated from the Unified Precipitation Dataset for 11 post-1948 storms and statistics corresponding to the top 10% of rainfall values left of track were computed. Irene-type tracks occur every 6.6 years. Floyd (1999) produced the highest rainfall overall and was the closest analog to Irene, yet Irene produced more rainfall in the northeastern U.S. where higher values of precipitable water existed. Areas of high rainfall expanded as five TCs moved north due to synoptic-scale forcing during extratropical transition. However, Irene and three other TCs did not exhibit this pattern. The amount of moisture in the environment surrounding the TC, rather than storm speed or intensity, exhibited the strongest correlations with rainfall totals and their spatial distribution. These results demonstrate the high variability that exists in the production of rainfall among TCs experiencing similar steering flow, and show that advection of moisture from the tropics is key to higher rainfall totals in the mid-latitudes.

Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran’s heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord G i statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman’s correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d-1). A strong relationship between GPM rainfall and error was found from heavy (~35 mm d-1) to violent rain (160 mm d-1). The output of this study provides reference regarding the performance of GPM data for respective hydrology studies in this region.

Spatial variation of different rain systems during El Niño and La Niña periods over India and adjoining ocean

The spatial patterns of rainfall and rain systems during El Nino and La Nina episodes are distinctly different due to the longitudinal variations in the Walker circulation ascent/decent branches over India and adjoining Oceans. In order to examine these differences, 16 years (1998–2013) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data have been utilized. TRMM-PR delineated precipitating systems (convective, stratiform and shallow) show distinctly different spatial structures over India and adjoining Oceans during El Nino and La Nina episodes. During the El Nino episode, the occurrence of deep systems is high over north of 20°N latitude, and shallow systems are plentiful over south of 20°N latitude. On the other hand, during the La Nina episodes, the occurrence of shallow systems is in excess over Pakistan, central India, northwest Arabian Sea, southwest Indian Ocean and northern Bay of Bengal while the deep systems are abundant over west coast of India, Ganges basin, eastern Indian Ocean and Arabian Sea. The excess convective rain pixels observed during El Nino years are from deep and deep and wide convective core systems due to increase in the CAPE, nevertheless the broad stratiform rain systems are prevalent during La Nina years due to the high convergence of moisture flux and mid-tropospheric upward motion. Though the convective occurrence is more, their intensity is weaker during El Nino years than during La Nina years, indicating the intense nature of convective storms during La Nina episodes.

Variability and teleconnections of South and East Asian summer monsoons in present and future projections of CMIP5 climate models

Coupled Model Inter-comparison Project Phase 5 (CMIP5) model outputs of the South and East Asian summer monsoon variability and their tele-connections are investigated using historical simulations (1861-2005) and future projections under the RCP4.5 scenario (2006-2100). Detailed analyses are performed using nine models having better representation of the recent monsoon teleconnections for the interactive Asian monsoon sub-systems. However, these models underestimate rainfall mainly over South Asia and Korea-Japan sector, the regions of heavy rainfall, along with a bias in location of rainfall maxima. Indeed, the simulation biases, underestimations of monsoon variability and teleconnections suggest further improvements for better representation of Asian monsoon in the climate models. Interestingly, the performance of Australian Community Climate and Earth System Simulator version 1.0 (ACCESS1.0) in simulating the annual cycle, spatial pattern of rainfall and multi-decadal variations of summer monsoon rainfall over South and East Asia appears to more realistic. In spite of large spread among the CMIP5 models, historical simulations as well as future projections of summer monsoon rainfall indicate multi-decadal variability. These rainfall variations, displaying certain epochs of more rainfall over South Asia than over East Asia and vice versa, suggest an oscillatory behaviour. Teleconnections between South and East Asian monsoon rainfall also exhibit a multi-decadal variation with alternate epochs of strengthening and weakening relationship. Furthermore, large-scale circulation features such as South Asian monsoon trough and north Pacific subtropical high depict zonal oscillatory behaviour with east-west-east shifts. Periods with eastward or westward extension of the Mascarene High, intensification and expansion of the upper tropospheric South Asian High are also projected by the CMIP5 models.

Coupling Groundwater, Vegetation, and Atmospheric Processes: A Comparison of Two Integrated Models

Abstract This study compares two modeling platforms, ParFlow.WRF (PF.WRF) and the Terrestrial Systems Modeling Platform (TerrSysMP), with a common 3D integrated surface–groundwater model to examine the variability in simulated soil–vegetation–atmosphere interactions. Idealized and hindcast simulations over the North Rhine–Westphalia region in western Germany for clear-sky conditions and strong convective precipitation using both modeling platforms are presented. Idealized simulations highlight the strong variability introduced by the difference in land surface parameterizations (e.g., ground evaporation and canopy transpiration) and atmospheric boundary layer (ABL) schemes on the simulated land–atmosphere interactions. Results of the idealized simulations also suggest a different range of sensitivity in the two models of land surface and atmospheric parameterizations to water-table depth fluctuations. For hindcast simulations, both modeling platforms simulate net radiation and cumulative precipitation close to observed station data, while larger differences emerge between spatial patterns of soil moisture and convective rainfall due to the difference in the physical parameterization of the land surface and atmospheric component. This produces a different feedback by the hydrological model in the two platforms in terms of discharge over different catchments in the study area. Finally, an analysis of land surface and ABL heat and moisture budgets using the mixing diagram approach reveals different sensitivities of diurnal atmospheric processes to the groundwater parameterizations in both modeling platforms.

Evaluation of present-day rainfall simulations over West Africa in CORDEX regional climate models

The objective of this study is to evaluate the ability of seven CORDEX regional climate models (RCMs), driven by ERA-Interim reanalysis dataset to simulate the observed rainfall characteristics over West Africa during the period of 1990–2008. The seasonal climatology, annual rainfall cycles, interannual variability, 850 hPa specific humidity, and wind fields of the RCMs outputs were assessed over a number of spatial scales covering three climatically homogenous subregions (Guinea Coast, Savannah, and Sahel) and the entire West Africa domain. The ability of the RCMs to simulate the response to El Nino and La Nina events were further assessed. Results indicate that the RCMs captured the spatial pattern of rainfall and the three distinctive phases of the West African monsoon reasonably. It is worth noting that RCA and CRCM5 failed to distinctively reproduce the monsoon jump while CCLM, HIRHAM, and REMO largely overestimated the amount of the pre- and the post-monsoon rainfall. The analysis also showed significant biases in individual models depending on the subregion and season under consideration. These biases appear to be linked to the model’s failure to resolve convective processes and topography accurately. The majority of the RCMs used were consistent with the ground observation in capturing the dry (wet) conditions associated with the El Nino (La Nina) events. Statistical analysis conclusively revealed that the RCMs performance varies over the subregions and seasons, implying that no single model is best at all time. In general, REGCM3 was found to be the most outstanding of all the RCMs and is therefore recommended for use in rainfall assessment over West Africa.

Geo-statistical modeling of mean annual rainfall over the Iran using ECMWF database

In the present study, the main aim was the spatial evaluation annual rainfall of Iran based on the European Centre for Medium Range Weather Forecast (ECMWF) database. An attempt, using geo-statistical modeling by ordinary least squares (OLS) and geographically weighted regression (GWR) procedures, was also made. The results represented that the GWR model with higher S2, lower residuals without spatial autocorrelation effect and lower RMSE is an optimized geo-statistical model for rainfall modeling of Iran based on ECMWF gridded database. This model can explain spatio-temporal rainfall distribution in Iran in a diversified topographical and geographical background. This model revealed that two high mountain ranges of Zagros and Alborz in west and north, respectively, strikingly affect the temporal and spatial patterns of rainfall. Therefore, the statistical correlation matrix revealed that Iranian rainfall data is dominantly depended on geographical latitude and topographical altitude/slope with 0.56 and 0.32 correlation coefficients, respectively.

A spatio-temporal evaluation of the WRF physical parameterisations for numerical rainfall simulation in semi-humid and semi-arid catchments of Northern China

Mesoscale Numerical Weather Prediction systems can provide rainfall products at high resolutions in space and time, playing an increasingly more important role in water management and flood forecasting. The Weather Research and Forecasting (WRF) model is one of the most popular mesoscale systems and has been extensively used in research and practice. However, for hydrologists, an unsolved question must be addressed before each model application in a different target area. That is, how are the most appropriate combinations of physical parameterisations from the vast WRF library selected to provide the best downscaled rainfall? In this study, the WRF model was applied with 12 designed parameterisation schemes with different combinations of physical parameterisations, including microphysics, radiation, planetary boundary layer (PBL), land-surface model (LSM) and cumulus parameterisations. The selected study areas are two semi-humid and semi-arid catchments located in the Daqinghe River basin, Northern China. The performance of WRF with different parameterisation schemes is tested for simulating eight typical 24-h storm events with different evenness in space and time. In addition to the cumulative rainfall amount, the spatial and temporal patterns of the simulated rainfall are evaluated based on a two-dimensional composed verification statistic. Among the 12 parameterisation schemes, Scheme 4 outperforms the other schemes with the best average performance in simulating rainfall totals and temporal patterns; in contrast, Scheme 6 is generally a good choice for simulations of spatial rainfall distributions. Regarding the individual parameterisations, Single-Moment 6 (WSM6), Yonsei University (YSU), Kain-Fritsch (KF) and Grell-Devenyi (GD) are better choices for microphysics, planetary boundary layers (PBL) and cumulus parameterisations, respectively, in the study area. These findings provide helpful information for WRF rainfall downscaling in semi-humid and semi-arid areas. The methodologies to design and test the combination schemes of parameterisations can also be regarded as a reference for generating ensembles in numerical rainfall predictions using the WRF model.

On the dust load and rainfall relationship in South Asia: an analysis from CMIP5

This study is aimed at examining the consistency of the relationship between load of dust and rainfall simulated by different climate models and its implication for the Indian summer monsoon system. Monthly mean outputs of 12 climate models, obtained from the archive of the Coupled Model Intercomparison Project phase 5 (CMIP5) for the period 1951–2004, are analyzed to investigate the relationship between dust and rainfall. Comparative analysis of the model simulated precipitation with the India Meteorological Department (IMD) gridded rainfall, CRU TS3.21 and GPCP version 2.2 data sets show significant differences between the spatial patterns of JJAS rainfall as well as annual cycle of rainfall simulated by various models and observations. Similarly, significant inter-model differences are also noted in the simulation of load of dust, nevertheless it is further noted that most of the CMIP5 models are able to capture the major dust sources across the study region. Although the scatter plot analysis and the lead–lag pattern correlation between the dust load and the rainfall show strong relationship between the dust load over distant sources and the rainfall in the South Asian region in individual models, the temporal scale of this association indicates large differences amongst the models. Our results caution that it would be pre-mature to draw any robust conclusions on the time scale of the relationship between dust and the rainfall in the South Asian region based on either CMIP5 results or limited number of previous studies. Hence, we would like to emphasize upon the fact that any conclusions drawn on the relationship between the dust load and the South Asian rainfall using model simulation is highly dependent on the degree of complexity incorporated in those models such as the representation of aerosol life cycle, their interaction with clouds, precipitation and other components of the climate system.

Sensitivity of simulated South America climate to the land surface schemes in RegCM4

This work evaluates the impact of two land surface parameterizations on the simulated climate and its variability over South America (SA). Two numerical experiments using RegCM4 coupled with the Biosphere–Atmosphere Transfer Scheme (RegBATS) and the Community Land Model version 3.5 (RegCLM) land surface schemes are compared. For the period 1979–2008, RegCM4 simulations used 50 km horizontal grid spacing and the ERA-Interim reanalysis as initial and boundary conditions. For the period studied, both simulations represent the main observed spatial patterns of rainfall, air temperature and low level circulation over SA. However, with regard to the precipitation intensity, RegCLM values are closer to the observations than RegBATS (it is wetter in general) over most of SA. RegCLM also produces smaller biases for air temperature. Over the Amazon basin, the amplitudes of the annual cycles of the soil moisture, evapotranspiration and sensible heat flux are higher in RegBATS than in RegCLM. This indicates that RegBATS provides large amounts of water vapor to the atmosphere and has more available energy to increase the boundary layer thickness and cause it to reach the level of free convection (higher sensible heat flux values) resulting in higher precipitation rates and a large wet bias. RegCLM is closer to the observations than RegBATS, presenting smaller wet and warm biases over the Amazon basin. On an interannual scale, the magnitudes of the anomalies of the precipitation and air temperature simulated by RegCLM are closer to the observations. In general, RegBATS simulates higher magnitude for the interannual variability signal.

Projecting South Asian summer precipitation in CMIP3 models: A comparison of the simulations with and without black carbon

Considering the importance of black carbon (BC), this study began by comparing the 20th century simulation of South Asian summer climate in IPCC CMIP3, based on the scenario of models with and without BC. Generally, the multi-model mean of the models that include BC reproduced the observed climate relatively better than those that did not. Then, the 21st century South Asian summer precipitation was projected based on the IPCC CMIP3 projection simulations. The projected precipitation in the present approach exhibited a considerable difference from the multimodel ensemble mean (MME) of IPCC AR4 projection simulations, and also from the MME of the models that ignore the effect of BC. In particular, the present projection exhibited a dry anomaly over the central Indian Peninsula, sandwiched between wet conditions on the southern and northern sides of Pakistan and India, rather than homogeneous wet conditions as seen in the MME of IPCC AR4. Thus, the spatial pattern of South Asian summer rainfall in the future may be more complicated than previously thought.

Impact of climate change on the precipitation pattern of district Sargodha, Pakistan

PurposeSpatio-temporal variations in precipitation pattern of district Sargodha is one of the most significant researchable questions because of the massive reliance on rainfall for agricultural practice in the study area. The pattern of current rainfall in the study area is unexpectedly changed. The purpose of the present study is to examine the changing precipitation pattern and to link it with climate change.Design/methodology/approachThe study was conducted by using rainfall data of the past 30 years collected from 8 meteorological stations around the study area. The averages of rainfall on monthly basis were temporally arranged, and the fluctuation trends were studied using GIS and statistics. The temporal data of rainfall were compared and contrasted with the precipitation normals of the study area from 1981to 2010. The rainfall deviation in the present study was calculated. The spatial pattern of rainfall was plotted by interpolating the eight points of Punjab around the study area for the first two decades, whereas the past decade was analysed by incorporating five more points of Tehsils in the existing eight. The spatial and statistical representation of data were examined by compare and contrast with the previous findings.FindingsThe rainfall in the study area showed remarkable changes in magnitude and spatiality. The rainfall in the district is on the rise, whereas the spatial pattern of rainfall is becoming more complex and anomalous in character. This paper provides convincing evidence about the impact of climate change on the magnitude and spatial patterns of precipitation in the study area.Practical implicationsIt will be helpful for understanding the shifts in the rainfall pattern in future as well as for the preparation of response to the issue of climate change and its impacts.Originality/valueThe current manuscript, for the very first time, provided detailed insights about the precipitation pattern shifting during the last 30 years in district Sargodha, Punjab, Pakistan. Furthermore, agricultural sector would likely get severally affected because of seasonal changes in climatic factors like rainfall and have strong food security implications. The current findings will be useful to manage the climate change-related issues in Pakistan and helpful for the policy makers to design a coping strategy for climate change impacts.

Climate Change in Chile: An Analysis of State-of-the-Art Observations, Satellite-Derived Estimates and Climate Model Simulations

Although there is a reasonably large body of work focusing on South American climate, few studies focus on just Chile and even fewer consider climate processes operating over longer timescales, such as those at which climate change becomes apparent. This paper provides an overview of Chilean present-day and future climate, needed to plan for potential impacts of climate change. Firstly, present-day climate conditions are assessed using a number of observational rainfall and temperature datasets. All available GCMs are then examined, to firstly assess their ability to simulate climate during the end of the 20th century and secondly to examine their projections during the 21st century. The results of the present day analysis show a showing general agreement in spatial and temporal patterns of rainfall and temperature, between the datasets. When assessing the models’ ability to simulate observed rainfall and temperature, the results suggest that although the majority captures the spatial and temporal patterns, there are significant differences between models. When assessing future projections, the results suggest that over the next ~30 years, most GCMs show either no change in rainfall or a small reduction; however there is a lack of agreement regarding the sign of change, suggesting high uncertainty. For temperature, most models agree on a warming trend. This is also true for the longer-term, with most GCMs suggesting a small rainfall decrease by 2100 but a large temperature increase. Lastly, it is suggested that this temperature increase is due to an increase in minimum temperatures, which may have implications for certain frost-sensitive crops.

Modes of Variability of Annual and Seasonal Rainfall in Mexico

The aim of this study is to identify temporal and spatial variability patterns of annual and seasonal rainfall in Mexico. A set of 769 weather stations located in Mexico was examined. The country was divided into 12 homogeneous rainfall regions via principal component analysis. A Pettitt test was conducted to perform a homogeneity analysis for detecting abrupt changes in mean rainfall levels, and a Mann-Kendall test was conducted to examine the presence of monotonically increasing/decreasing patterns in the data. In total, 14.4% of the annual series was deemed nonstationary. Fourteen percent of the samples were nonstationary in the winter and summer, and 9% were nonstationary in the spring and autumn. According to the results, the nonstationarity of some seasonal rainfall series may be associated with the presence of atmospheric phenomena (e.g., El Nino/Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation). A rainfall frequency analysis was performed for the nonstationary annual series, and significant differences in the return levels x^T can be expected for the scenarios analyzed. The identification of areas that are more susceptible to changes in rainfall levels will improve water resource management plans in the country, and it is expected that these plans will take into account nonstationary theory.

CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India

Performances of the state-of-the-art CMIP5 models in reproducing the spatial rainfall patterns over seven homogeneous rainfall zones of India viz. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI) have been assessed using different conventional performance metrics namely spatial correlation (R), index of agreement (d-index), Nash–Sutcliffe efficiency (NSE), Ratio of RMSE to the standard deviation of the observations (RSR) and mean bias (MB). The results based on these indices revealed that majority of the models are unable to reproduce finer-scaled spatial patterns over most of the zones. Thereafter, four bias correction methods i.e. Scaling, Standardized Reconstruction, Empirical Quantile Mapping and Gamma Quantile Mapping have been applied on GCM simulations to enhance the skills of the GCM projections. It has been found that scaling method compared to other three methods shown its better skill in capturing mean spatial patterns. Multi-model ensemble (MME) comprising 25 numbers of better performing bias corrected (Scaled) GCMs, have been considered for developing future rainfall patterns over seven zones. Models’ spread from ensemble mean (uncertainty) has been found to be larger in RCP 8.5 than RCP4.5 ensemble. In general, future rainfall projections from RCP 4.5 and RCP 8.5 revealed an increasing rainfall over seven zones during 2020s, 2050s, and 2080s. The maximum increase has been found over southwestern part of NWI (12–30%), northwestern part of WPI (3–30%), southeastern part of NEI (5–18%) and northern and eastern part of SPI (6–24%). However, the contiguous region comprising by the southeastern part of NCI and northeastern part of EPI, may experience slight decreasing rainfall (about 3%) during 2020s whereas the western part of NMI may also receive around 3% reduction in rainfall during both 2050s and 2080s.

Multivariate analysis applied to monthly rainfall over Rio de Janeiro state, Brazil

Spatial and temporal patterns of rainfall were identified over the state of Rio de Janeiro, southeast Brazil. The proximity to the coast and the complex topography create great diversity of rainfall over space and time. The dataset consisted of time series (1967–2013) of monthly rainfall over 100 meteorological stations. Clustering analysis made it possible to divide the stations into six groups (G1, G2, G3, G4, G5 and G6) with similar rainfall spatio-temporal patterns. A linear regression model was applied to a time series and a reference. The reference series was calculated from the average rainfall within a group, using nearby stations with higher correlation (Pearson). Based on t-test (p < 0.05) all stations had a linear spatiotemporal trend. According to the clustering analysis, the first group (G1) contains stations located over the coastal lowlands and also over the ocean facing area of Serra do Mar (Sea ridge), a 1500 km long mountain range over the coastal Southeastern Brazil. The second group (G2) contains stations over all the state, from Serra da Mantiqueira (Mantiqueira Mountains) and Costa Verde (Green coast), to the south, up to stations in the Northern parts of the state. Group 3 (G3) contains stations in the highlands over the state (Serrana region), while group 4 (G4) has stations over the northern areas and the continent-facing side of Serra do Mar. The last two groups were formed with stations around Paraiba River (G5) and the metropolitan area of the city of Rio de Janeiro (G6). The driest months in all regions were June, July and August, while November, December and January were the rainiest months. Sharp transitions occurred when considering monthly accumulated rainfall: from January to February, and from February to March, likely associated with episodes of “veranicos”, i.e., periods of 4–15 days of duration with no rainfall.

Spatial rainfall patterns associated with Indian northeast monsoon derived from high resolution rainfall estimates of Chennai DWR

The Indian northeast monsoon (NEM) season benefits the southeastern parts of peninsular India during the period October-November-December (OND). In this study, which is a first of this type for the Indian region, certain new and salient features of the NEM rainfall (RF) have been derived utilising the very high resolution (333 m × 333 m) radar estimated rainfall (RERF) data generated by the Doppler Weather Radar (DWR) at Chennai for the 12 year period (2002-13), over a circular area of 100 km radius spreading over both land and ocean. More than 2.8 lakhs of grid point data per day have been processed. Rain gauge measured rainfall (RGRF) data of 34 inland stations has also been used. Monthwise spatial distributions of RERF for October, November and December and for the entire season OND have been generated. It is shown through rigorous analysis that RERF is heavier closer to the coast and for a given longitude over land, southern latitudes receive 10-15% more RF than the northern latitudes. Decrease of RF eastwards into Bay of Bengal (BoB) is gradual whereas westwards over inland it is sharp and almost linear. By and large, the climatological features of NEM derived from historical analysis of RGRF data are well-captured by the analysis based on RERF data. A few new features of monthly and seasonal RF have also been identified. For the 34 stations, 12 year data set for OND, the mean RERF and RGRF values are 629.8 mm and 627.4 mm respectively yielding a difference of just 2.4 mm but with a substantial mean absolute deviation of 69.2 mm. RERF during pre-NEM days of Oct contributed to 10% of the seasonal OND total. RERF in the area of study, during days of cyclonic disturbances (CD days) is nearly twice over outer oceanic areas of BoB than over land. It has been shown that during the onset to withdrawal period of NEM, RERF is heavier over areas close to the coast (75 cm) than oceanic areas (68 cm) within the 100 km radius of the DWR. High RF zones approximately extending 25-30 km westwards into land and around 30-40 km eastwards over the ocean have been delineated. Spatial distributions of RERF during the various phases of NEM, viz., dry, weak, normal, active and vigorous as identified from the RGRF data have been generated, critically analysed and results drawn. In the case of vigorous, active and vigorous (AV) NEM days excluding CD days, a relatively high daily RERF patch of 5-6 cm located approximately 5-10 km west of the coast inland and in the SW sector of Chennai DWR has been identified. During post-NEM withdrawal days of December, oceanic areas of eastern sector are shown to receive highest RF compared to land areas, a feature consistent with the withdrawal pattern of NEM. The instrumental limitations and artifacts of radars contributing to errors in RERF have been discussed.

RAINFALL ANALYSIS OF THE KELANTAN BIG YELLOW FLOOD 2014

In December 2014, Kelantan was hit by the worst flood ever recorded. Did the rainfall exceed historical records, how rare are they and what causes them? This paper answers these questions. Estimation of the return periods uses the GEV distribution model and stations with more than 25 years records. Spatial distribution plots of the cumulated rainfall depths were constructed using IDW interpolation method. Four major outcomes are: 1) Spatial rainfall patterns show high amounts of rainfall accumulated by phases (Phase 1- daily rainfall up to 300 mm; Phase 2- daily rainfall up to 500 mm); 2) record breaking rainfall events occurred at 9 stations significantly at Gunung Gagau (1598.9 mm compared to 976.5 mm 7-day cumulated rainfall). Many stations upstream of the river basin experienced ARIs near and over 100 years and several experienced more than 200 years; and 4) Enhanced rainfall were experienced due to the combined effect of the monsoon season, Madden Julian Oscillation and temperature below anomalies at the Siberian High

Response of rainfall to land surface properties under weak wind shear conditions

Responses of rainfall trends to land surface properties (roughness length and sensible heat flux) under weak wind shear conditions were investigated by numerical simulation and idealized experiments. The results show that total amount, spatial pattern and intensity of rainfall were highly affected by the difference in sensible heat flux rather than the difference in roughness length. The initiation time for occurrence of rainfall became more delayed and the rainfall intensified as the given sensible heat flux decreased. A smaller sensible heat flux and a larger roughness length increased the convective available potential energy before the rainfall occurrence, resulting in stronger initial convection. The initiation processes affected the resulting convective structure, such that initial latent heat release occurred and remained downstream, leading to a widely spread convective structure above the cold pool. Spread and connected convection magnified the upright structure, thereby causing release of much more latent heat for ice water species and thus stronger rainfall intensity.