Rainfall Fields Research Articles

Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

The goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007–2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from “observational data.” The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48h, without a consistently best or worst NWP model.

On the Structure and Dynamics of Indian Monsoon Depressions

ERA-Interim reanalysis data from the past 35 years have been used with a newly developed feature tracking algorithm to identify Indian monsoon depressions originating in or near the Bay of Bengal. These were then rotated, centralized, and combined to give a fully three-dimensional 106-depression composite structure—a considerably larger sample than any previous detailed study on monsoon depressions and their structure. Many known features of depression structure are confirmed, particularly the existence of a maximum to the southwest of the center in rainfall and other fields and a westward axial tilt in others. Additionally, the depressions are found to have significant asymmetry owing to the presence of the Himalayas, a bimodal midtropospheric potential vorticity core, a separation into thermally cold (~−1.5 K) and neutral (~0 K) cores near the surface with distinct properties, and the center has very large CAPE and very small CIN. Variability as a function of background state has also been explored, with land–coast–sea, diurnal, ENSO, active–break, and Indian Ocean dipole contrasts considered. Depressions are found to be markedly stronger during the active phase of the monsoon, as well as during La Niña. Depressions on land are shown to be more intense and more tightly constrained to the central axis. A detailed schematic diagram of a vertical cross section through a composite depression is also presented, showing its inherent asymmetric structure.

What if the 25 October 2011 event that struck Cinque Terre (Liguria) had happened in Genoa, Italy? Flooding scenarios, hazard mapping and damage estimation

Abstract. During the autumn of 2011 two catastrophic, very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods. The first occurred in October and the second at the beginning of November. Both the events were characterized by very high rainfall intensities (> 100 mm h−1) that persisted on a small portion of territory causing local huge rainfall accumulations (> 400 mm 6 h−1). Two main considerations were made in order to set up this work. The first consideration is that various studies demonstrated that the two events had a similar genesis and similar triggering elements. The second very evident and coarse concern is that two main elements are needed to have a flash flood: a very intense and localized rainfall event and a catchment (or a group of catchments) to be affected. Starting from these assumptions we did the exercise of mixing the two flash flood ingredients by putting the rainfall field of the first event on the main catchment struck by the second event, which has its mouth in the biggest city of the Liguria Region: Genoa. A complete framework was set up to quantitatively carry out a “what if” experiment with the aim of evaluating the possible damages associated with this event. A probabilistic rainfall downscaling model was used to generate possible rainfall scenarios maintaining the main characteristics of the observed rainfall fields while a hydrological model transformed these rainfall scenarios in streamflow scenarios. A subset of streamflow scenarios is then used as input to a 2-D hydraulic model to estimate the hazard maps, and finally a proper methodology is applied for damage estimation. This leads to the estimation of the potential economic losses and of the risk level for the people that stay in the affected area. The results are interesting, surprising and in a way worrying: a rare but not impossible event (it occurred about 50 km away from Genoa) would have caused huge damages estimated between 120 and EUR 230 million for the affected part of the city of Genoa, Italy, and more than 17 000 potentially affected people.

A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25° daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas.

Dynamic rainfall monitoring using microwave links

In this work, we propose a sparsity-exploiting dynamic rainfall monitoring methodology using rain-induced attenuation measurements from microwave links. To estimate rainfall field intensity dynamically from a limited number of non-linear measurements, we exploit physical properties of the rainfall such as spatial sparsity and non-negativity along with the dynamics of rainfall intensity. We develop a dynamic state estimation algorithm, where the aforementioned spatial properties are utilized as prior information. To exploit spatial sparsity, we use a basis function to tailor the sparse representation of the rainfall intensity. The basis is selected based on some criteria for sparse reconstruction such as orthonormality and mutual coherence. The tuning parameter that controls the sparsity in the spatial rainfall distribution is dynamically updated at every correction step. The developed methodology is applied to dynamically monitor the rainfall field intensity in an area with a specified spatial resolution using less number of simulated non-linear measurements than pixels. The proposed methodology can be generalized for any dynamic field reconstruction, where the limited number of non-linear measurements are field intensities integrated over a linear path.

Classification of rainfall radar images using the scattering transform

The classification of rainfall fields has mainly focused on the split between convective and stratiform rainfall fields. In the present case study, the wavelet-based scattering transform is used to classify rainfall events observed by a weather radar. This very recent method has, to the best of the authors’ knowledge, not yet been applied for such a purpose. This method considers the spatial properties of rainfall radar images. This case study regroups 34 rainfall periods recorded over the Nantes region (western France) during 23days in both 2009 and 2012. These periods display different characteristics in terms of duration and type of rainfall field. A reference configuration of the scattering transform has been evaluated and compared to various configurations in order to approximate the application conditions most appropriate to this case study. This evaluation is performed by a leave-one-out cross validation. A global accuracy of 93.5% of well classified images is obtained in the reference conditions which is an encouraging result. The temporal sampling of the rainfall fields is an important aspect of the classification process.

A radar-based regional extreme rainfall analysis to derive the thresholds for a novel automatic alert system in Switzerland

Abstract. This paper presents a regional extreme rainfall analysis based on 10 years of radar data for the 159 regions adopted for official natural hazard warnings in Switzerland. Moreover, a nowcasting tool aimed at issuing heavy precipitation regional alerts is introduced. The two topics are closely related, since the extreme rainfall analysis provides the thresholds used by the nowcasting system for the alerts. Warm and cold seasons' monthly maxima of several statistical quantities describing regional rainfall are fitted to a generalized extreme value distribution in order to derive the precipitation amounts corresponding to sub-annual return periods for durations of 1, 3, 6, 12, 24 and 48 h. It is shown that regional return levels exhibit a large spatial variability in Switzerland, and that their spatial distribution strongly depends on the duration of the aggregation period: for accumulations of 3 h and shorter, the largest return levels are found over the northerly alpine slopes, whereas for longer durations the southern Alps exhibit the largest values. The inner alpine chain shows the lowest values, in agreement with previous rainfall climatologies. The nowcasting system presented here is aimed to issue heavy rainfall alerts for a large variety of end users, who are interested in different precipitation characteristics and regions, such as, for example, small urban areas, remote alpine catchments or administrative districts. The alerts are issued not only if the rainfall measured in the immediate past or forecast in the near future exceeds some predefined thresholds but also as soon as the sum of past and forecast precipitation is larger than threshold values. This precipitation total, in fact, has primary importance in applications for which antecedent rainfall is as important as predicted one, such as urban floods early warning systems. The rainfall fields, the statistical quantity representing regional rainfall and the frequency of alerts issued in case of continuous threshold exceedance are some of the configurable parameters of the tool. The analysis of the urban flood which occurred in the city of Schaffhausen in May 2013 suggests that this alert tool might have complementary skill with respect to radar-based thunderstorm nowcasting systems for storms which do not show a clear convective signature.

Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

Summary The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon ‘Talas’ of 2011 over the two catchments, which are Futatsuno (356.1 km 2 ) and Nanairo (182.1 km 2 ) dam catchments of Shingu river basin (2360 km 2 ), which is located in the Kii peninsula, Japan.

Statistical and geostatistical analysis related to geographical parameters for spatial and temporal representation of rainfall in semi-arid environments: the case of Algeria

The economic challenges related to the fields of agriculture and industry led us to adopt the best suited method to represent the rain on the spatial and temporal plan especially in areas characterized by heterogeneous rainfall distribution additionally to drought periods. The methods of analysis and estimation of rainfall, using a number of tools (statistics, geostatistics and digital mapping), provide the opportunity to represent the average inter-yearly rainfall fields in the eastern high plateaus region of Algeria. In this study, an approach was proposed for yearly rainfall characterization. Data series for the period 1986–2007 were collected from 65 rain-gauging stations. This approach is based on two combined methods (geostatistic and multiple linear regression) including direct relationship between rainfall and geographical parameters (longitude, latitude and altitude). Statistical analysis indicates that the annual rainfall values ranges from 127 to 752.2 mm and that their distribution is Platykurtic. Results show that yearly rainfall structure obeys mainly a north/south gradient, and latitude is the most influential geographical parameter with a coefficient of 261.25 contrary to the longitude (17.06) and altitude (0.04) which have a non-significant effect on precipitation. In addition, other factors such as vegetation, temperature and air mass movement affect negatively the rainfall structure. Moreover, the map of rainfall indicates that the rain bands ranging from 300 to 400 mm dominate 58 % of the total study area whereas rain bands greater than 400 mm occupy 37 % of the total study area.

Using high‐resolution satellite precipitation for flood frequency analysis: case study over the Connecticut River Basin

This study evaluates the feasibility of using satellite precipitation datasets in flood frequency analysis based on the accuracy of different return period flows derived using a hydrological model driven with satellite and ground‐based reference rainfall fields over the Connecticut River Basin. Four quasi‐global satellite products (TRMM‐3B42V7, TRMM‐3B42RT, CMORPH, and PERSIANN) at 3‐h/0.25° resolution and the National Weather Service (Stage IV) gauge‐adjusted radar rainfall dataset (representing the reference rainfall) are integrated in this study, with the Coupled Routing and Excess Storage distributed hydrological model to simulate annual peak flows during warm season (May–November) months. The log‐Pearson type III frequency distribution applied to an 11‐year record of annual peak flow data is used to derive different return period flows. Evaluation against the Stage IV‐driven simulations shows that the TRMM‐3B42V7 product has the highest correlation and lowest bias in terms of the derived annual maxima flows compared to the other satellite products. In terms of the different return period flood frequency curves, the various satellite product‐based results well‐represent the variability across the different basins depicted in the reference precipitation‐driven simulations. With the increasing record length of high‐resolution satellite products, results from this paper can motivate future studies over basins lacking adequate ground‐based records to support flood frequency analyses.

Rapid Screening of Operational Freshwater Availability Using Global Models

Freshwater shortage already affects large parts of the world, and is expected to increase rapidly over the coming decades as a result of increased water demands and the impacts of climate change. Global-scale water risk or stress maps are available online, but these lack quantitative information on local freshwater availability, rendering them unsuitable for water risk assessment from an operational perspective, i.e. when comparing water availability to a specific quantified water demand (in m3s−1 rather than generic risk indicators). Therefore, our main goal was to develop a rapid screening method to estimate current and future operational freshwater availability using global-scale models. Operational Freshwater Availability (OFWA) was computed using the PCR-GLOBWB global hydrology and water resources model, coupled to a global MODFLOW groundwater model. PCR-GLOBWB was forced with rainfall and temperature fields from the IPSL-CM5A-LR climate model under the RCP6.0 climate scenario, with water demands based on the SSP2 socio-economic scenario. Unique to our study are the downscaling of the coupled PCR-GLOBWB-MODFLOW model to 90 m resolution and the provision of quantitative estimates on long term trends in operational freshwater availability. Our results showed a high, i.e. operationally relevant, accuracy for operational surface water availability, while the uncertainty about operational groundwater availability remained high due to limited availability of subsurface data. With this method, we developed a modelling capacity for rapidly generating scenario-based water availability projections with operational relevance in a rigorous, systematic way, such that it enables like-for-like comparisons. Further refinement is required for accurate estimates of operational groundwater availability.

Forecasting of Humidity of Some Selected Stations from the Northern Part of Bangladesh: An Application of SARIMA Model

Community risk from natural hazards and climate change depends largely on physical and climatic settings of an area, socio-economic condition of a community and the magnitude, duration and consecutiveness of the hazard or change itself. Impacts of climate change can be characterized by increasing temperatures, rainfall, humidity changes and climate related extreme events such as floods, cyclone, droughts, sea level rise, salinity and soil erosion etc. Humidity affects crops through evaporation, transpiration and condensation. Crop agriculture is highly influenced by climatic change and majority of population is dependent on agricultural crop in Bangladesh. Any unfavorable change in future climate could have a devastating impact on agriculture and the economy of the country. It is needed to know the socio-economic settings of the rural community, their agricultural practices, anticipated changes in climatic parameters and the link between the climatic variables and crop growth and productivity. Time Series analysis and forecasting has become a major tools in different applications in meteorological phenomena, such as rainfall, humidity, temperature, draught etc. and environmental management fields. Among the most effective approaches for analyzing time series data is the Autoregressive Integrated Moving Average (ARIMA) model introduced by Box and Jenkins. In this study, we used Box-Jenkins methodology to build seasonal ARIMA model for monthly Humidity data taken from Bogra, Dinajpur, Rajshahi and Rangpur stations over the period January, 2001 to October, 2014. In this study, ARIMA (2,0,2)(2,1,2)12, ARIMA (0,1,2)(1,1,1)12, ARIMA (1,0,2)(2,1,1)12 and ARIMA (1,0,2)(2,1,2)12 model respectively are found to be suitable models for Dinajpur, Rajshahi, Bogra and Rangpur stations respectively and these models are used to forecasting the monthly humidity for the upcoming two years to help decision makers to establish priorities in terms of water demand management.

A Geostatistical Framework for Quantifying the Temporal Evolution and Predictability of Rainfall Fields

Abstract A geostatistical framework for quantifying the temporal evolution and predictability of rainfall fields for time lags between 5 min and 3 h is proposed. The method is based on the computation of experimental space–time variogram maps of radar reflectivity fields. Two new metrics for quantifying temporal innovation and predictability based on minimum semivariance values at different time lags are proposed. The method is applied to high-resolution composite radar reflectivity maps over the United States to study the evolution of 25 convective and 25 stratiform events during the warm season of 2014. Results show that the temporal innovation can be modeled as the sum of two exponential functions of time lag, with approximately 50% of the total innovation occurring over the first 60 min. The median predictable scales for convective events are on the order of 1.6 km at 5 min, 5 km at 15 min, and 12.7 km at 1 h. Furthermore, the optimal time lag for predicting future innovation, taking into account measurement uncertainty and natural variability, appears to be between 30 and 60 min.

Metrics to identify meaningful downscaling skill in WRF simulations of intense rainfall events

Dynamical downscaling attempts to provide regional detail to climate change projections that subsequently can be used as input to climate change impact models. However, unlike forecasts by numerical weather prediction models, downscaled projections cannot be tested for skill because the future of interest is decades away. Nevertheless, models can be tested in terms of how well they simulate current weather or climate, thus giving an indication of skill in representing the process of interest. Here, six configurations using different combinations of three microphysics and two planetary boundary layer schemes are assessed on their skill to simulate desired characteristics in daily rainfall fields from three two week simulations in southeast Australia; ‘desired’ meaning desirable in relation to the intended application. Of different metrics and analysis assessed, a metric based on variography analysis, summarising characteristics about spatial variability and dissimilarity, is shown to provide the most informative guidance relative to the desirable characteristics.

An Examination of Precipitation Using CSU–CHILL Dual-Wavelength, Dual-Polarization Radar Observations

AbstractThe CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X bands with coaxial beams. One of the capabilities of this radar system is the possibility of developing and/or validating algorithms across dual wavelengths and dual polarizations. This paper presents one such instance, showing how the rainfall field can be estimated either from the S- and X-band reflectivities or from the differential propagation phase at X band. To do so, the paper first presents a dual-wavelength attenuation correction method that uses the reflectivity measured at S band, as the constraint for the correction of the reflectivity measured at X band, and it describes how Mie scattering regions at X band may be detected from the retrieved path-integrated attenuation field. Then, the paper describes how the resulting specific attenuation field relates to rainfall and specific phase at X band, which can be obtained from dual-polarization data at a single wavelength as well, and shows examples. Finally, the paper looks at the relation between attenuation and the differential phase as a function of elevation angle for a few cases, which may be related to the drop size distribution and mean diameter, as well as temperature.

What Really Happens at the End of the Rainbow? – Paying the Price for Reducing Uncertainty (Using Reverse Hydrology Models)

Modelling of environmental processes is subject to a high degree of uncertainty due to the incorporation of random errors and a lack of knowledge about how processes operate at the scale of interest. Use of uncertain data when identifying and calibrating a model can lead to disinformative data being included in the procedure, resulting in uncertain parameter estimation and ambiguity in the outcomes. Rainfall-runoff modelling where a single rain-gauge is often assumed to be representative of the potentially highly variable (in both space and time) rainfall field is a good example. The noisy pattern of rainfall inputs is transformed by the catchment into streamflow. The streamflow pattern is dependent on the spatio-temporal pattern of rainfall and of the dominant processes operating within the catchment. Inverse modelling of the catchment dynamics, that is, inferring catchment rainfall from streamflow, provides a possible means of improving the estimated rainfall input because all rain falling on the catchment becomes streamflow, and thus, providing improved forecasts of the streamflow output. A combination of inverse modelling, time series analysis, spatial analysis and spectral analysis may also help to provide an insight into the complex processes operating within the catchment system. This paper applies a novel method for inferring true catchment rainfall from streamflow highlighting that the streamflow is better estimated using inferred rainfall than observed rainfall (from a single gauge) because a single gauge only gives a partial description of the rainfall field. However reducing uncertainty in this way comes at a price, in this case, the reduction in time-resolution of the inferred rainfall series.

A space-time geostatistical approach for ensemble rainfall nowcasting

Nowcasting systems are essential to prevent extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy, location and time. Uncertainties associated with the precipitation events have an impact on the hydrological forecasts, especially when it concerns localized flash flood events. Radar monitoring can help to detect the space-time evolution of rain fields, but nowcasting techniques are needed to go beyond the observation and provide scenarios of rainfall for the next hours of the event. In this study, we investigate a space-time geostatistical framework to generate multiple scenarios of future rainfall. The rainfall ensemble is generated based on space-time properties of precipitation fields given by radar measurements and rainfall data from rain gauges. The aim of this study is to investigate the potential of a framework that applies a geostatistical conditional simulation method to generate an ensemble nowcasting of rainfall fields. The Var region (south eastern France) and 14 events are used to validate the approach. Results show that the proposed method can be a solution to combine information from radar fields and rain gauges to generate nowcasting rainfall fields adapted for flash flood alert.

Stochastic rainfall fields time-series for probabilistic flood hazard assessment

Probabilistic flood hazard assessment are usually carried out through juxtaposed reach-wise hydraulic simulations, using as input “representative” hydrographs for the studied return periods - at least by their peak discharge. However, reach-wise approaches have drawbacks, especially in the presence of natural or man-made singularities. An approach based on continuous simulation is developed to better assess flood hazard at the scale of the catchment and of the flood regime. A stochastic rainfall fields generator yields continuous times series, thus keeping the variability of the rainfall fields. Catchment-wise rainfall-runoff modelling, completed when necessary by a hydraulic model, allows to reproduce the individual and combined response of each feature to a heterogeneous rainfall event. The current CPU performances allow to process long rainfall time-series, but the codes have to be adapted to deal with unusually long input and output files. Local flood quantiles are then derived from discharge time-series, and flooding probability can be derived from local inundation frequency. This approach can be used in all contexts, urban floods or catchment-scale management; the modules have just to be chosen accordingly. This approach offers many perspectives, and in particular to better estimate local expected annual damages using damages time-series and multivariate damage curves.

Development of Tracking Technique for the Short Term Rainfall Field Forecasting

Recently, rapid change awareness of spatial and temporal variability of rainfall that change is an attempt to prevent disasters such as flash floods due to heavy rainfall. Rainfall is one of the important input factors of hydrological models. But rainfall events such as heavy rains and a typhoon that occurs frequency due to global climate change are difficult to forecast. Weather radar rainfall data has been recognized for making a valuable contribution to short term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground based rainfall gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatial and temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showed a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model. In other words, the hydrological model in conjunction with high-resolution flash flood forecasting system is expected to be available.

Southern Caribbean Hurricane Case Study: Observations and WRF Simulation

The meteorological conditions of three hurricanes passing through the southern Caribbean are analyzed using Hurdat, atmosphere - ocean reanalysis, satellite rainfall and SST fields, and buoy observations. The cases are Ivan 2004, Emily 2005 and Dean 2007, each traced to an African easterly wave moving zonally beneath an atmospheric ridge over the North Atlantic. SST were > 29 o C west of the Antilles Islands due to +40 W/m 2 surplus net heat flux and weak upper ocean currents. The analysis results suggested that the warm sea temperatures enhanced moisture fluxes, rain rates and convective heating, which affected these hurricanes significantly. WRF model simulations at 3 km resolution are evaluated for ability to forecast intensification up to 48 h lead time. In Emily (2005), the simulation results showed that upper winds disrupt the outflow. As the result, the model recovers the vortex too slowly. Overall, the WRF forecast decrease in central pressure was close to observed in Ivan (2004), but less than half of the observed in Dean (2007). Although the WRF model simulations gave evidence of initialization and intensification errors in comparison with NHC observations, the WRF 3 km resolution domain is essential for improving hurricane wind simulations. Nevertheless, significant improvements still depended on the initialization and data assimilation.