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This paper analyzes rainfall data to characterize the rainy season and define rice and maize crop calendars for current and future conditions in Madagascar. The daily rainfall data are taken from observational climate records and climate model simulations from the CMIP6 under the SSP245 and SSP585 scenarios. Rainy season characteristics are calibrated to fit rice and maize crop growth stages. The comparison between the past (1950–2018) and the future (2030–2100) highlights changes in the onset and cessation dates, which happen later and earlier, respectively. This causes the reduction of the rainy season duration, which affects the rice and maize crop calendars, especially its sowing or seeding periods. The worst (best) case is mainly observed in the southeast (southwest). On the one hand, the southwestern region may need to adapt to grow rice and maize crops with short or medium crop cycles in the future. In the Highland or Central land, the length of the sowing or seeding period increases. On the other hand, the North and East face a significant reduction in the length of the sowing or seeding period. Rice endures more than maize. Growing rice crops twice a year may not be possible in the future. But rather, we observe minor changes in the West. Our analysis suggests the imperative necessity to advise smallholder farmers to rely on short crop cycle varieties of rice and maize crops. Predominantly, the harvesting period is postponed. It is recommended to carefully consider our results for the definition of food policies. [ABSTRACT FROM AUTHOR]

A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

The frequency of flash floods resulting from heavy rainfall over West Africa has increased in recent years with serious socio‐economic consequences. Therefore, the need to utilize numerical weather prediction models to forecast heavy rainfall events reliably is also rising at many operational meteorological centres in West Africa. This paper evaluates the performance of the Consortium for Small‐scale Modelling (COSMO) model of the German Meteorological Services (DWD) in predicting rainfall over West Africa for high‐impact rainfall events that occurred between 19 and 26 August 2017. The paper aims to investigate the synoptic forcings modulating daily rainfall variability during that period. Results show that COSMO simulates adequately the spatio‐temporal variability of rainfall distribution over West Africa, though with inherent biases. COSMO displays a decreasing skill in producing spatial rainfall distribution as rainfall amounts tend to 30 mm and above. Additionally, areas of heavy rainfall, mostly about 100–300 km southwest of the core of the Africa Easterly Jet (AEJ), often coincide with areas of decreasing mean sea level pressure of at least 0.6 hPa and areas of increasing convective available potential energy of at least 500 J/kg. Although not in all cases, the trough of the Africa Easterly Wave (AEW) is always located to the east of these areas. We show that not every storm, especially east of the prime meridian, is associated with an AEW trough. COSMO is able to reproduce the atmospheric dynamics modulating the daily rainfall variability, in addition to capturing the daily propagation of the AEW trough, and the core of the AEJ. However, the reproducibility skill of the model in predicting atmospheric dynamics may not transform into the predictive skill of the model in producing rainfall. Nevertheless, operational forecasters may be able to determine likely areas of heavy rainfall by estimating the position of the AEJ core based on the position of areas of the least falling pressure from COSMO. Finally, the incorporation of the fractions skill score metric based on the neighbourhood approach could also assist operational forecasters to decide at which scale a severe weather alert can be issued. [ABSTRACT FROM AUTHOR]

ABSTRACT Making high-resolution observations of heavy rainfall from cumulonimbus clouds involves choosing a good location where convection occurs frequently and with some degree of predictability so that aircraft do not have to chase clouds, and radars and other ground-based instruments can be optimally placed. It also requires significant resources, international collaboration and a good deal of serendipity. This paper describes several observational campaigns that were designed to understand relatively small-scale convective clouds that produce heavy precipitation, and presents results that have been found over the years. It complements the companion papers in this special issue by giving an observational perspective. It is by no means comprehensive and is limited to cumulonimbus clouds. The Convective Storm Initiation Project ( CSIP) was designed to understand the phenomena responsible for the initiation of convection in the maritime environment of southern England. There was a similar objective in the Convective Orographically-induced Precipitation Study ( COPS), but for the complex terrain of the Black Forest mountains, Germany, and the Vosges Mountains, France. The recent Convective Precipitation Experiment ( COPE) was designed to understand the entire life cycle of convective clouds that formed along sea-breeze convergence lines in the southwestern peninsula of England. Its particular focus was on the dynamics and microphysics of the convective clouds. Examples of observations from these and other field campaigns designed to study the initiation of convection, cumulonimbus clouds and convective precipitation are presented, with a view to demonstrating how they contribute to a physical understanding of the phenomena and the processes involved. [ABSTRACT FROM AUTHOR]

The paper compares the final run of Integrated Multi-Satellite Retrieval of Global Precipitation Mission (IMERG) products with India Meteorological Department (IMD) gridded data over the Indian land mass during the southwest monsoon period (June-September) in the period 2014-2017. Spatiotemporal variations of the IMERG are evaluated with IMD rainfall by employing different statistical techniques, and the capability of the IMERG is examined using the categorical skill metrics. A region in central India is the focus in order to study the southwest monsoon's static and dynamic characteristics, such as rainfall distribution and monsoon activity, using the IMERG in conjunction with IMD rainfall data sets. The integrated condensation rate (ICR) was estimated using the specific humidity profiles, and it correlated with the IMERG and IMD rainfall. The IMERG is found to be a potential source for adequately reflecting the ground gauge-gridded data of categorical rainfall amounts, from very light rain (trace-2.4 mm) to very heavy rain (about 115.6-204.4 mm). However, the IMERG does not reflect satisfactorily the extreme heavy rain events (≥ 204.5 mm·day-1) during the study period. The significant correlation between IMERG/IMD rainfall and the ICR suggests that improved adjustments methods are required for better results when depicting accurate extreme heavy rainfall events by the IMERG. [ABSTRACT FROM AUTHOR]

The objectives of the paper are to determine the best‐fitted probability distribution functions for annual rainfall in Turkey and to provide more accurate estimates of rainfall quantiles under different return periods. To achieve these aims, 10 popular and widely used probability distributions: gamma, generalized extreme value, Gumbel, normal, logistic, log‐logistic, two‐parameter log‐normal, three‐parameter log‐normal, Pearson type III and Weibull, are considered for modelling annual rainfall data covering the period 1975–2014 from 155 gauge stations uniformly distributed across Turkey. Unlike previous studies, the performances of the candidate probability distributions were evaluated by various model selection techniques: Akaike information criterion (AIC), Bayesian information criterion (BIC), Anderson–Darling (AD) criterion, the Cramér–von Mises test (CvM) and the Kolmogorov–Smirnov test (KS). According to the ranking‐based scoring evaluation, the gamma, log‐logistic, two‐parameter log‐normal, normal and Weibull distributions, which show the best fit for 78% of the data series, were mainly found to be the most suitable distributions. T year (25, 50 and 100) rainfall quantiles were then estimated using the best‐fitted distributions. Finally, the universal Kriging method, which is an effective interpolation method, was used to map the estimated rainfall quantiles. [ABSTRACT FROM AUTHOR]

A method is presented for deriving probabilistic medium‐range (1‐to‐2‐week) weather pattern forecasts for India. This method uses an existing set of 30 objectively derived daily weather patterns, which provide climatological representations for unique states in the large‐scale circulation over India. Weather pattern forecast probabilities are based on the number of ensemble members objectively assigned to each weather pattern. Two summer monsoon case studies illustrate the best use of the forecasting tool within medium‐range guidance, such as highlighting the most likely weather pattern transitions and relating these to the likelihood of weather impacts. Forecast skill is evident out to at least 10–15 days. Winter dry period weather patterns have the highest forecast skill, closely followed by retreating monsoon weather patterns. In contrast, monsoon onset and break monsoon weather patterns have the lowest forecast skill. Finally, a prototype weather pattern forecast climatology application is presented for use in highlighting when extreme rainfall is more likely than normal. This application is based on weather pattern empirical probabilities of threshold exceedances using a high‐resolution regional reanalysis. The transitional pre‐ and post‐monsoon seasons have the greatest variability in rainfall across all possible weather patterns, with a slight dip in variability during the main summer monsoon season. In contrast, very little variability across weather patterns is evident during the relatively dry winter months. This highlights the times of year when a climatology‐based weather pattern forecasting approach may have its greatest benefits over that of a basic daily climatology.Probabilistic weather pattern forecasts over India can highlight the most likely weather pattern transitions and can be related to the likelihood of weather impacts. For example, a weather pattern forecast climatology application is presented for highlighting when extreme rainfall is more likely than normal. Weather pattern forecast skill is evident out to at least 10 days. Winter dry period and retreating monsoon patterns have the highest forecast skill. Monsoon onset and break monsoon patterns have the lowest forecast skill. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper describes an ensemble-based first guess support tool for severe weather, which has evolved over time to support changing requirements from the UK National Severe Weather Warning Service ( NSWWS). This warning tool post-processes data from the regional component of the Met Office Global and Regional Ensemble Prediction System ( MOGREPS), and is known as MOGREPS-W (' W' standing for 'warnings'). The original system produced area-based probabilistic first guess warnings for severe and extreme weather, providing forecasters with an objective basis for assessing risk and making probability statements. The NSWWS underwent significant changes in spring 2011, removing area boundaries for warnings and focusing more on a risk-based approach. Warnings now include details of both likelihood and impact, whereby the higher the likelihood and impact, the greater the risk of disruption. This paper describes these changes to the NSWWS along with the corresponding changes to MOGREPS-W, using case studies from both the original and new systems. Calibration of the original MOGREPS-W system improves forecast accuracy of severe wind gust and rainfall warnings by reducing under-forecasting. In addition, verification of forecasts from different groups of areas of different sizes shows that larger areas have better forecast accuracy than smaller areas. © 2013 British Crown copyright, the Met Office. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

A large stretch of the east coast of Australia experienced unprecedented rainfall and flooding over a two‐week period in early 2022. It is difficult to reliably estimate the likelihood of such a rare event from the relatively short observational record, so an alternative is to use data from an ensemble prediction system (e.g., a seasonal or decadal forecast system) to obtain a much larger sample of simulated weather events. This so‐called 'UNSEEN' method has been successfully applied in several scientific studies, but those studies typically rely on a single prediction system. In this study, we use data from the Decadal Climate Prediction Project to explore the model uncertainty associated with the UNSEEN method by assessing 10 different hindcast ensembles. Using the 15‐day rainfall total averaged over the river catchments impacted by the 2022 east coast event, we find that the models produce a wide range of likelihood estimates. Even after excluding a number of models that fail basic fidelity tests, estimates of the event return period ranged from 320 to 1814 years. The vast majority of models suggested the event is rarer than a standard extreme value assessment of the observational record (297 years). Such large model uncertainty suggests that multi‐model analysis should become part of the standard UNSEEN procedure. [ABSTRACT FROM AUTHOR]

ABSTRACT A general circulation model (GCM) is an alternative way for predicting Indian summer monsoon rainfall (ISMR) over the existing empirical/statistical models in recent time. However, the inherent biases present in the GCM affect its performance. Therefore, there is a high requirement for bias correction of the GCM. Few studies on bias correction of GCMs are available in the context of ISMR. A comparative study is reported in this paper on the six different bias correction methods by applying on the hindcast (May start, June-July-August-September) of the climate forecast system (CFS) model from the National Centers for Environmental Prediction (NCEP) for 27 years (1982-2008). Among the six methods discussed in this paper, three methods did not use any statistical transformation (Mean Bias-remove technique (U), Multiplicative shift technique (M) and Standardized-reconstruction technique (Z)) and the remaining three methods used statistical transformation (Regression technique (R), Quantile Mapping Method (Q), Principal Component Regression (PCR)). Finally, it was found that the Standardized-reconstruction technique (Z) and Quantile Mapping Method (Q) are more skilful than the others and both are equally skilful in simulating ISMR. Bias-corrected rainfall in four extreme years, out of which 1988 and 1994 are characterized by excess rainfall and 1987 and 2002 are characterized by deficit, are also examined here. Results indicate that both methods efficiently capture the extreme rainfall cases. [ABSTRACT FROM AUTHOR]

This paper investigates the use of the Flash Flood Guidance FFG method and a method of modelbased threshold runoff computation to improve the accuracy of flash flood forecasts at ungauged locations. The methodology proposed in this paper requires running a lumped hydrological model to derive flood frequencies at the outlet of the ungauged basin under consideration, and then to derive the threshold runoff from these modelbased discharges. The study examines the potential of this method to account for the hydrological models uncertainty and for biases originated by lack of model calibration, which is the typical condition in ungauged basins.Experiments to validate this approach involve the implementation of a semidistributed continuous rainfallrunoff model and the operation of the FFG method over four basins located in the centraleastern Italian Alps and ranging in size from 75.2 to 213.7 km2. The model is calibrated on two larger basins and the model parameters are transposed to the other two basins to simulate operations in ungauged basins. The FFG method is applied by using the 2 year discharge as the threshold runoff. The threshold runoff is derived both by using local discharge statistics and the modelbased approach advocated here. Examination of the results obtained by this comparison shows that the use of modelbased threshold leads to improvements in both gauged and ungauged situations. Overall, the Critical Success Index CSI increases by 12 for gauged basins and by 31 for ungauged basins by using the modelbased threshold with respect to use of local data. As expected, the increase of CSI is more remarkable for ungauged basins, due to lack of local model calibration and the greater likelihood of occurrence of a simulation bias in model application over these basins. This shows that the method of threshold runoff computation provides an inherent bias correction to reduce systematic errors in model applications to ungauged and gauged basins. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT Africa has a paucity of long term reliable meteorological ground station data and reanalysis products are used to provide the climate estimations that are important for climate change projections. In this paper monthly observed precipitation records in the Logone catchment of the Lake Chad Basin are used to evaluate the performance of two global reanalysis products: the Climate Forecasting System Reanalysis ( CFSR) and ERA Interim datasets. The two reanalysis products reproduced the monthly, annual and decadal cycle of precipitation and variability relatively accurately albeit with some discrepancies. The catchment rainfall gradient was also well captured by the two products. There are good correlations between the reanalysis and rain gauge datasets although significant deviations exist, especially for CFSR. Both reanalysis products overestimated rainfall in 68% of the rain gauge stations. ERA Interim produced the lowest bias and mean absolute error with average values of 2% and 6.5 mm month−1 respectively compared to 15% and 34 mm month−1 for the CFSR. However, both reanalysis products systematically underestimated annual rainfall in the catchment during the period 1997-2002 for ERA Interim and 1998-2000 for CFSR. This research demonstrates that evaluating reanalysis products in remote areas like the Logone catchment enables users to identify artefacts inherent in reanalysis datasets. This will facilitate improvements in certain aspects of the reanalysis forecast model physics and parameterization to improve reanalysis dataset quality. This study concludes that the application of each reanalysis product in the catchment will depend on the purpose for which it is to be used and the spatial scale required. [ABSTRACT FROM AUTHOR]

ABSTRACT On the night of 2 January 2014, an aircraft on approach to Cork airport was required to abort a landing attempt due to sea salt aerosol ( SSA) accretion on the windscreen. The salt reduced forward-facing visibility to dangerously low levels and prohibited a safe approach by the aircraft. A landing was eventually effected on the 3rd attempt after a light rain shower cleared enough of the contaminant to allow the pilot to view the runway lights and markings. This was the first time such an incident was recorded by Ireland's Air Accident Investigation Unit. This paper elaborates the meteorological conditions present at the time the SSA became a visibility hazard. The atmospheric boundary layer was thermodynamically unstable and had a relatively dry airmass and the surface and upper level winds were high. SSA formation mechanisms are described in terms of the relationship between SSA concentration enhancement and meteorological variables. A brief comparison between the meteorological conditions surrounding this and another notable event documented by the US naval service is made. The potential for SSA to be a significant hazard to aviation and the recommendations for further research are considered. [ABSTRACT FROM AUTHOR]

A compact analytical expression of the Fraction Skill Score (FSS) is derived for a case with a single displaced rainband in a rectangular domain. The rainband is oriented parallel to the border and displaced perpendicularly to its orientation. An analytical solution is used to determine some of the properties of the FSS which might also be applicable in other cases. The solution is independent of the length of the rainband (it is valid also for a displaced rainy grid point). The position of the borders perpendicular to the rainband orientation does not influence the FSS value. The position of the other two borders does however influence the FSS value in a complex way; moving a border closer to the rainbands can either increase or decrease the FSS value depending on the location of the borders. FSS is shown to be a monotonically increasing function of the neighbourhood size (regardless of the position of the borders). If the FSS value for a displacement that is half the neighbourhood size is used to define a 'useful' FSS value then the usefulness criterion is somewhat different than presented in the original FSS paper (there is no dependence on the frequency of the observations/forecasts). 'Useful' FSS values are always >1/2 but depend on the position of the borders and the size of the displacement. [ABSTRACT FROM AUTHOR]

ABSTRACT Heavy rain events or rainstorms of various scales often occur in Hong Kong from April to September. In extremes cases, they lead to serious landslides and flash floods resulting in loss of life, and damage to property and infrastructure. With climate change, extreme rain events are projected to be more frequent and more intense in this region. A system to gauge and label the severity of rainstorms would be useful for identifying and communicating extreme conditions in warning and disaster prevention operations. In parallel, a better understanding of the characteristics of rainstorms would also be important for impact assessments and engineering design purposes. This paper formulates a practical framework to index the magnitude and severity of rainstorms in Hong Kong. An index termed as the Severity Index combining the magnitude of different rainstorm attributes is devised for quantifying the overall severity of rainstorms. This index is flexible to allow tuning for specific applications. [ABSTRACT FROM AUTHOR]

Subseasonal‐to‐seasonal (S2S) prediction has gained momentum in the recent past as a need for predictions between the weather forecasting timescale and seasonal timescale exists. The availability of S2S databases makes prediction and predictability studies possible over all the regions of the globe. Most S2S studies are, however, relevant to the northern hemisphere. In this review, the S2S literature relevant to the southern hemisphere (SH) are presented. Predictive skill, sources of predictability, and the application of S2S predictions are discussed. Indications from the subseasonal predictability studies for the SH regions suggest that predictive skill is limited to 2 weeks in general, particularly for temperature and rainfall, which are the variables most frequently investigated. However, temperature has enhanced skill compared to rainfall. More S2S prediction studies that include the quantification of the sources of predictability and the identification of windows of opportunity need to be conducted for the SH, particularly for the southern African region. The African continent is vulnerable to weather‐ and climate‐related disasters, and S2S forecasts can assist in alleviating the risk of such disasters. [ABSTRACT FROM AUTHOR]

It is widely known that precipitation is a key variable of the hydrological cycle that is strongly affected by recent climate changes. Therefore, there is a growing interest in research activities focused on alteration of rainfall regime, as it conditions the planning of countermeasures against flood and landslide hazards. The available literature about precipitation tendencies over Italian peninsula offers a limited number of studies about recent changes of extreme events and precipitation intensity. This work aims at adding a contribution to fill this research gap, investigating the changes in rainfall regime observed over the 2002–2021 period in the Campania region (southern Italy). To pursue this aim, a dataset including daily precipitation records collected at 107 stations was analysed both through 11 indices developed by the Expert Team on Climate Change Detection and Indices and through the Standardized Precipitation Index in order to detect signals of changes in extreme events and to assess tendencies towards drier or wetter conditions. The Theil‐Sen method and the Mann–Kendall non‐parametric test were employed to evaluate the trends and their statistical significance. The main results emerging from this work are (i) an increasing tendency in precipitation intensity and in the frequency of occurrence of heavy rainfall events in autumn, mainly in the northern part of the region and in the mountainous areas, (ii) an upward trend of the duration of the longest wet spell in the coastal areas and (iii) an increasing trend of dry spells in spring and in summer in the Gulf of Salerno. [ABSTRACT FROM AUTHOR]

ABSTRACT India gets the maximum amount of rainfall during the months of June to September ( JJAS) which is known as the summer monsoon season. The erratic nature of Indian summer monsoon rainfall ( ISMR), in terms of both rainfall amount and distribution, is highly responsible for the interannual variability in agricultural production as well as occurrence of floods and droughts. Accurate seasonal predictions of ISMR are required for appropriate hydrological planning and disaster management systems. Studies have revealed that probabilistic prediction, based on the products of General Circulation Models ( GCMs), can be generated in a parametric as well as non-parametric manner. The present paper discusses the enhancement of probabilistic prediction by improving the potential predictable signal obtained from these GCMs. A Singular- Value- Decomposition based multiple linear regression method ( SVD-MLR) has been applied to improve the signal and a simple average of all GCMs ( EM) has been used as the benchmark to examine the skill of the SVD-MLR method. The potential of the proposed method has been assessed through Brier Skill Score ( BSS) and Rank Probability Skill Score ( RPSS). A rigorous analysis has finally revealed that SVD-MLR method has better skill than EM in predicting the typical nature of observed monsoon rainfall in extreme years. [ABSTRACT FROM AUTHOR]

Stochastic Multi-states First-order Markov Chain (SMFOMC) models have been used to describe occurrence of daily rainfall. This paper describes optimization of SMFOMC parameters through the generation of synthetic daily rainfall sequences. Three SMFOMC parameters were the number of states (NS), the preserved proportion in the last state (PPL) and the state divider (SD). The multi-objective differential evolution (MODE) was used to find the Pareto-optimal line (POL) of two conflicting objectives; (1) minimization of total monthly absolute total relative error (TMATRE), and, (2) minimization of NS. Three probability distributions functions (PDFs) for generating daily rainfall amounts in the last Markov Chain state were compared. They were: (1) the shifted exponential distribution (SE), (2) the exponential distribution (E), and, (3) the two-parameter gamma distribution (G-2). The optimal SMFOMC parameters were applied to generate the daily rainfall sequences of 44 rainfall stations located in five regions of Thailand. Reliability of the optimal SMFOMC parameters for each PDF was measured by TMATRE and coefficient of determination ( R2). Performance of PDFs was analysed by a ranking method. Results showed that the three PDFs were mostly found to be fitted well with the synthetic daily rainfall sequences. However, highest error was found in case of monthly average minimum daily rainfall values. Out of the three PDFs, the SE demonstrated the lowest performance, while G-2 performed the best. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

On the 8 January 2005 the city of Carlisle in northwest England was severely flooded following 2 days of almost continuous rain over the nearby hills. Orographic enhancement of the rain through the seeder–feeder mechanism led to the very high rainfall totals. This paper shows the impact of running the Met Office Unified Model UM with a grid spacing of 4 and 1 km compared to the 12 km available at the time of the event. These forecasts, and forecasts from the Nimrod nowcasting system, were fed into the Probability Distributed Model PDM to predict river flow at the outlets of two catchments important for flood warning. The results show the benefit of increased resolution in the UM, the benefit of coupling the highresolution rainfall forecasts to the PDM and the improvement in timeliness of flood warning that might have been possible. Copyright © 2008 Royal Meteorological Society, © Crown Copyright 2008. [ABSTRACT FROM AUTHOR]

The generation of flow forecasts using rainfall inputs to hydrological models has been developed over many years. Unfortunately, errors in input data to models may vary considerably depending upon the different sources of data such as raingauges, radar and high resolution Numerical Weather Prediction NWP models. This has hampered the operational use of radar for quantitativeflow forecasting. The manner with which radar rainfall input and model parametric uncertainty influence the character of the flow simulation uncertainty in hydrological models has been investigated by several authors. In this paper an approach to this problem based upon a stochastic hydrological model is considered. The errors in the input data, although they may be constrained, do propagate through the model to the flow predictions. Previous work on this error propagation through a fully distributed model is described, and a similar analysis for a stochastic hydrological model implemented in a mixed rural and urban area in northwest England carried out. Results are compared with those previously published for an American catchment. A possible approach to selecting flow forecast ensemble members is proposed. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine how current techniques for flashflood monitoring and forecasting can meet the needs of the population at risk to evaluate the flood severity and anticipate its danger. To this end, the social response time for different social actions in the course of two well studied flash flood events which occurred in France and Italy is identified. The event management activities are broadly characterized into three types according to their main objective information, organisation and protection. The activities are also classified into three other types according to the scale and nature of the human group involved individuals, communities and institutions. The conclusions reached relate to 1 the characterisation of the social responses according to watershed scale and to the information available, and 2 to the appropriateness of the existing surveillance and forecasting tools to support the social responses. Results suggest that representing the dynamics of the social response with just one number representing the average time for warning a population is an oversimplification. It appears that the social response time exhibits a parallel with the hydrological response time, by diminishing in time with decreasing size of the relevant watershed. A second result is that the human groups have different capabilities of anticipation, apparently based on the nature of information they use. Comparing watershed response times and social response times shows clearly that at scales of less than 100 km2, a number of actions were taken with response times comparable to the catchment response time. The implications for adapting the warning processes to social scales individual or organisational scales are considerable. At small scales, and for the implied anticipation times, the reliable and highresolution description of the actual rainfall field becomes the major source of information for decisionmaking processes such as deciding between evacuations or advising to stay home. This points to the need to improve the accuracy and quality control of real time radar rainfall data, especially for extreme flash floodgenerating storms. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Providing flood forecasts in flashy catchments poses significant challenges to the hydrologist. This is particularly the case when the prediction of high intensity rainfall at small spatial scales is difficult. Radar rainfall nowcasts, such as those provided by the Met Office Nimrod system, provide short range predictions at these spatial scales, and can be used as an input to hydrological models for the prediction of flood flows. Such short term forecasts are, however, considerably uncertain, and this uncertainty will influence the reliability of hydrological forecasts used in flood warning and forecasting. In this paper the value and benefit of the use of radar rainfall nowcasts in three small catchments in central Scotland is assessed through the evaluation of a large sample of forecasts. Both the reliability of the catchment rainfall predictions and the forecast flows are assessed. Whilst it is demonstrated that the rainfall predictions provided by Nimrod are uncertain and at times biased, it is also shown that there is considerable benefit in their use for flood forecasting when compared to the alternative of using no future prediction of rainfall. To deal with the uncertainty in the forecast, a method is shown that can help the hydrologist and forecaster to understand the structure of the uncertainties, allowing them to use the guidance provided by the forecasts more effectively in the provision of flood warnings. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The need to evaluate global climate data has increased in recent times. In this study, we evaluate the ability of global precipitation products to monitor drought during three wet seasons (Belg/March–May, Kiremt/June–September and Autumn/September–November) and associated rainfall regions in Ethiopia. We employed statistical methods to quantify and evaluate precipitation products based on probability of drought detection (POD), the extent of false alarms (FAR) and the critical success index (CSI) to see the overall performance of the studied precipitation products. The majority of the studied precipitation datasets were relatively better in capturing the Autumn drought in southern Ethiopia, and 18 out of 21 precipitation products captured accurately more than 50% of observed droughts. The CSI scores for this season are also above 0.5 for 14 precipitation products. On the other hand, 15 and 14 precipitation products accurately captured more than 50% of the seasonal drought in Kiremt and Belg rainfall seasons in north‐eastern Ethiopia. In contrast, most precipitation products do not clearly represent the drought conditions of the Kiremt season in north‐western Ethiopia. Only 8 of the 21 precipitation products accurately captured more than 50% of the observed drought in this region, and only 6 precipitation products had a CSI score greater than 0.5. The results can facilitate the selection of precipitation products for drought monitoring purposes, for use in specific wet seasons and regions of Ethiopia. [ABSTRACT FROM AUTHOR]

The need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate‐change‐induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3‐month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4‐month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi‐objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite‐gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia. [ABSTRACT FROM AUTHOR]

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

An analysis of artificial rainbow observations in ZhaoSu region from 2017 to 2019 shows that rainbows mainly occur between 16:00 and 22:00 (LST) from April to September. The analysis based on the meteorological observation in the same period shows that precipitation, temperature, wind force and cloud variety contribute to rainbow formation. Approximately 90% of rainbows appear one hour after rainfall, and 100% of rainbows occur when the Beaufort wind scale is less than level 8 following Beaufort wind scale (less than 20.9 m/s), the temperature is greater than 8°C, and the cloud amount is greater than 40%, respectively. A rainbow probabilistic forecast model is constructed based on five meteorological factors. The forecast ability of the model is independently assessed by comparing rainbow forecasts and its observation in 2020. The Brier score is 0.20, indicating that the objective model is effective for rainbow forecasts. [ABSTRACT FROM AUTHOR]

For the study region (southern Africa), regional and remote large‐scale climate forcing play vital roles in the seasonal rainfall variability. Thus, uncovering the nature of the relationship between large‐scale circulations and homogeneous regions of rainfall anomalies will enhance the predictability of seasonal rainfall at specific domains in southern Africa. Considering both land and adjacent oceans in southern Africa, six austral summer homogeneous regions of rainfall anomalies were classified using the rotated principal component analysis. The analysis of the physical processes associated with the modulation of the distinct rainfall variability patterns reveals that generally, regional variations in cyclonic/anticyclonic circulations and convergence/divergence in the adjacent oceans (and landmasses) modulate the regional convergence of moisture fluxes in southern Africa. Some classified rainfall variability patterns feature homogeneous landmasses that are contiguous with the adjacent ocean, revealing land and adjacent oceans that respond coherently to the large‐scale circulation anomalies associated with the time development of the rainfall variability pattern. Further, remote climate drivers were found to be distinctively related to the regionalized rainfall anomalies, implying that the respective homogeneous rainfall regions respond differently to the large‐scale forcing induced by the (remote) climate drivers over southern Africa. Specifically, among the climate drivers that influence the hydroclimate of southern Africa, variations in the Southern Annular Mode (SAM) and the El Niño Southern Oscillation are relatively more associated with regionalized summer rainfall anomalies in southern Africa. Above‐average Nino 3.4 index (i.e., El Niño) negatively correlates with regionalized summer rainfall anomalies over large parts of southern Africa. The positive phase of the SAM positively correlates with rainfall anomaly in the homogeneous summer rainfall region comprising the subtropical parts of southern Africa and the homogeneous summer rainfall region comprising the western equatorial parts of the study region. [ABSTRACT FROM AUTHOR]

The regional climate of the Arabian Gulf (AG) and its variability are examined based on a 40‐year (1980–2019), 5‐km regional reanalysis of the Arabian Peninsula (AP reanalysis). The AP reanalysis fields were first validated against the available observations over the AG, suggesting that this high‐resolution reanalysis well reproduces the spatio‐temporal features of the AG atmospheric circulations. The validated AP reanalysis fields were then analysed to examine the climatic characteristics over the AG including the monthly mean, maximum and minimum temperatures, and the seasonal variations in winds, relative humidity and rainfall over the AG. The AG climate is mostly dry between May and October, and experiences moderate rainfall between December and January. The higher (lower) pressure difference between the northwest and southeast AG during summer (winter) generates the northwesterly Shamal winds over the north (central) AG. The mean Shamal winds are relatively stronger (weaker) and prolonged (shorter) during summer (winter); however, the short lived Shamal jet events in winter can be occasionally stronger than summer. In terms of interannual variability, the Shamal winds are stronger and more persistent in summer during El Niño years and in winter during La Niña years. These differences are mainly associated with changes in temperature gradients between the eastern AG and northwestern AP. [ABSTRACT FROM AUTHOR]

The sensitivity of different cumulus physical parameterization schemes for simulating extreme winter precipitation events over the Arabian Peninsula (AP) is investigated using a high‐resolution weather research and forecasting (WRF) model. For winters in 2001–2016, the following three parameterization schemes are examined: (i) Kain–Fritsch (KF), (ii) Betts–Miller–Janjić (BMJ), and (iii) Grell–Freitas (GF). The simulation results suggest that the AP extreme winter rainfall events are best simulated using the KF, followed by the BMJ, in terms of spatial distribution and intensity. The spatial pattern correlation coefficient between the model‐simulated and observed rainfall is highest with KF (0.94), followed by BMJ (0.91) and GF (0.76). These results are attributed to a better representation of the moisture transport associated with upper‐tropospheric cyclonic circulation and potential vorticity intrusions. By contrast, the GF scheme fails to simulate moisture convergence and updrafts, leading to an unrealistic representation of cloud hydrometeors and an improper organization of convection and associated extreme rainfall intensities. Meanwhile, the KF and BMJ also successfully simulate the dynamics and thermodynamics of extreme rainfall events that are usually driven by synoptic forcing. The study results suggest that the choice of cumulus parameterization schemes in the WRF model is critical for reliable simulation of extreme rainfall in the hyperarid AP region. [ABSTRACT FROM AUTHOR]

The present study was designed to develop a disease predictive model based upon meteorological variables, that is, minimum and maximum temperatures, rainfall, and relative humidity to predict lentil wilt severity. Correlation and regression analyses were performed to determine the relationship of meteorological variables with disease severity. A significant correlation was found between all meteorological variables and lentil wilt severity. Maximum temperature showed negative correlation, while minimum temperature, rainfall, and relative humidity exhibited positive correlation with lentil wilt severity. Environmental variables and disease severity data of 2 years (2017–2018) were used to develop a disease predictive model using a stepwise multiple regression analysis. Maximum and minimum temperatures, rainfall, and relative humidity significantly contributed to disease development and explained 94.39% variability in disease severity. This model, based on 2 years data, was then validated with 5 years (2012–2016) meteorological variables and disease severity data set. Homogeneity of regression line in the multiple regression equations of 2 years (2017–2018) model and 5 years (2012–2016) model indicated that they validated each other. Lentil wilt severity was high at maximum (17–25°C) and minimum temperature (9.5–15.1°C), rainfall (4.5–6.5 mm), and relative humidity (55%–85%), respectively. The lentil wilt disease predictive model developed for four lentil varieties, namely M‐85, NL‐2, Mansehra‐89, and NARC‐08‐1, during the present investigation will be useful to predict disease severity before epidemic occurrence and the time of fungicide application. Hence, the model is helpful to reduce the use of fungicides, lessen environmental pollution, and help limit the cost of production of lentil growers. [ABSTRACT FROM AUTHOR]

This study evaluates the predictive performance of the North American Multi-model Ensemble (NMME) over Central Africa (CA) using the historical rainfall data. The African Rainfall Climatology Version 2 (ARC2) is used as a substitute for reference observational data to examine the capability of 11 NMME and their NMME ensemble mean (MME) in simulating rainfall. Using the Kling- Gupta efficiency (KGE), Taylor skill score (TSS), and Heidke skill score, the predictive evaluation of the models is performed from lead 0 to lead 5 of each season. The results show that the NMME models satisfactorily reproduce the bimodal and unimodal structure of rainfall in CA at the lead 0 level of different seasons: December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON). The pattern correlation coefficient (PCC) shows values of NMME and MME greater than ~0.69 and TSS > 0.60 for all four seasons. The MME presents a maximum in DJF 0.~96 between 0 and 1 month lead time. With the same time scale, just over 85% of the NMME have a KGE between 0 and 0.42. It follows that as the forecast lead time increases, the PCC and TSS of each model become small, with PCC 0:~12 in JJA and DJF, TSS<0.21 in JJA at lead 5. The NMME models exhibit an important rainfall bias and the calculated scores show the quality of the forecast decreases with increasing lead time; this may justify a constraint on the models to keep the good quality of the long-term seasonal forecast in CA. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one by P. Kumar et al. on forecasts of moonsoon rainfall, A. Kumar et al. on the use of multi-model ensembles in order to account for forecast uncertainty, and Ha et al. on the multi-scale variability of the East Asian moonsoon.

The insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate-related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate. [ABSTRACT FROM AUTHOR]

The performance of the forecasts of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecasting System (IFS) with a horizontal resolution of 0.125° is assessed through comparison with station raingauge data in the warm season (May-October) in the period 2017-2018 over southwestern China. The mean state of rainfall amount, frequency and intensity, as well as the diurnal cycle of precipitation, are involved in the evaluation. The IFS can capture well the spatial distributions of rainfall amount, frequency and intensity in the gauge data, but the rainfall frequency is generally larger and the intensity is weaker in the IFS. The rainfall events of the IFS usually start and peak much earlier, and the afternoon rainfall peaks are overestimated. The discrepancy between the gauge data and the IFS is larger over the western part of southwestern China. The results indicate that the IFS forecasts show considerable uncertainty when moving to the sub-daily scale, especially over areas with complex topography. When the IFS forecasts are applied operationally, the deviations (as revealed in this study) should be paid more attention. [ABSTRACT FROM AUTHOR]

Rain monitoring through signal attenuation has been a significant concern for the scientific community over the last decade. The lowest L-, S- and C-bands in the microwave spectrum are full of potential for environmental monitoring devices. It has not been extensively proven whether signal attenuation can be used for rain estimation throughout the microwave spectrum in the region of 1-6 GHz. The study derives a power-law rain estimation model by validating the one year data for the amount of rain and signal attenuation at the lowest limit of the S-band. This is achieved by a prototype accurate experimental setup in a laboratory environment at the campus of the University of Ioannina, northwest Greece. A comparison with global rain attenuation models as well as measurements near this frequency band are also presented and discussed. [ABSTRACT FROM AUTHOR]

Dual‐frequency radar (DFR) could improve the accuracy of estimating the microphysical properties of rain profiles. Unfortunately, factors that cause inaccuracies in retrieved rain profiles include uncertainties in raindrop size distribution (DSD) parameterizations, DFR retrieval methods and radar measurement errors. The primary objective of this study was to assess the uncertainties in retrieving rain profiles to offer insight into application considerations for ground‐based DFRs in China. The uncertainties caused by DSD models are assessed by comparing attenuation coefficients and rain rates from DFR algorithms with those directly derived from DSD spectra from disdrometers. Then, based on a DSD model, the impacts of retrieval methods, radar range resolution, measurement error and errors in temperature on DFR retrievals are explored by comparing the rain profiles obtained using DFR algorithms with those directly output from the Weather Research and Forecasting model. Overall, the impact of measurement error on the DFR retrievals is relatively significant for both the forward retrieval method and the iterative backward retrieval method and should be eliminated in practical applications. At an individual uncertainty level, the impact of DSD parameterizations on the retrievals from the dual‐frequency technique is less than ±10% bias when the shape factor μ of the gamma distribution ranges from 2 to 4. The retrieved rain profiles from the forward retrieval method are relatively sensitive to radar range resolution and the retrieval performance can be improved with increasing the range resolution, while the iterative backward retrieval method exhibits stable retrieval performance. The impact of temperature errors on the retrieved rain profiles is negligible. [ABSTRACT FROM AUTHOR]

Hydroclimatic teleconnections between global sea surface temperature (SST) anomaly fields and monthly rainfall over east and west Japan (divided along 138° E longitude) are identified for summer (June–August) and winter (December–February) using the concept of global climate pattern (GCP). The analysis indicates that the hydroclimatic teleconnections over both regions vary at both intra‐ and inter‐seasonal time scales. In addition, the teleconnections over the two regions have differing origins. The teleconnection features associated with rainfall anomalies over west Japan have origins in the tropical Pacific and Indian oceans, whereas those over east Japan are associated with high‐latitude SST anomalies. The early summer (winter) rainfall over west Japan is linked to the El Niño Modoki (La Niña Modoki) phenomena, whereas the early summer and winter rainfall anomalies over east Japan are associated with the SST anomaly over the eastern subtropical Pacific and South Pacific oceans, respectively. Having identified the teleconnections, prediction model approaches—a machine‐learning approach, namely support vector regression (SVR), and a hybrid graphical modelling/C‐Vine copula (GM‐Copula)—were developed to forecast the rainfall over both east and west Japan. The predictors were derived from the monthly SST anomalies at different lags (1–6 months), and whereas the hidden, nonlinear relationship was well captured by the SVR approach, the complex association was decidedly better captured by the GM‐Copula approach. Hence, it is recommended for forecasting the rainfall over east and west Japan. [ABSTRACT FROM AUTHOR]

The goal of this study was to improve the accuracy of model forecasting, such that forecasters could use model products to make more efficient daily weather predictions. Historical data of the 12 hr following a given time for various meteorological factors from the control forecasts of the European Centre for Medium‐Range Weather Forecasting (ECMWF) between 20 ° and 40 ° N latitude and 110 °–130 ° E longitude were used to verify the performance of the proposed method. Eight major meteorological factors were selected via correlation analysis between control forecast meteorological factors and real‐time rainfall. The samples were divided into four types using the K‐means clustered method. Each type was respectively modelled by long short‐term memory (LSTM) in order to correct rainfall forecasts for eastern China. The eight major meteorological factors were used as the model input, and the differences between real‐time rainfall data and model‐forecast rainfall were used as the model output. The corrected results revealed that the root mean square error decreased by 0.65, and the threat scores of light rainfall and rainstorms were improved. [ABSTRACT FROM AUTHOR]

Hydrological disasters are recurrent over the northeast Indian region (NEI) due to heavy downpours within a short span of time. Therefore, the characteristics of rainfall extremes during the southwest monsoon over the NEI, their evolution and dissipation features, are discussed using rainfall data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and the Climate Prediction Center for 16 years from 2000 to 2015. Circulation features during the lead–lag of extreme events are also examined using the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis products. Thirty‐two extreme events such that rainfall of the day exceeds the 90th percentile were identified. During the extreme events, heavy rainfall is located mainly in the NEI without much spread in the aerial domain. The highest number of extreme rain events is found in July, followed by June and August. Trend analysis of the rainfall indicates a significant decrease in low rainfall events and this decreasing trend is compensated by the significant increasing trend in very heavy (>95 percentile) to extremely heavy (>99 percentile) rainfall events. The average life of the extreme rainfall events is found to be about 3 days during which the NEI receives more than 100 mm/day; however, on individual examination, this ranges from 2 to 5 days. A strengthening of the southerly component of lower level wind from the Bay of Bengal and an updraft due to convergence at 850 hPa over the region favours the rainfall peak. The sea level pressure anomaly gradient and vertical velocity show a sudden development of conditions favourable for a heavy downpour over the northeast region. [ABSTRACT FROM AUTHOR]

A fully automated quasi‐real‐time method is presented to disaggregate hourly to sub‐hourly precipitation information operationally in a blended radar–rain gauge product. The method proposes a fully automated solution to disaggregate precipitation in regions characterized by measurement errors or partial absence of auxiliary information on the temporal precipitation evolution. The solution relies on a combination of low‐pass filtered radar information and stochastically generated noise fields. A comprehensive validation of the new method is provided demonstrating higher skill compared to a uniform disaggregation in time. The method is now an integral part of CombiPrecip, the official operational code of MeteoSwiss for radar–rain gauge merging. [ABSTRACT FROM AUTHOR]

Results from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs. [ABSTRACT FROM AUTHOR]

Rainfall estimation using the weather research and forecasting (WRF) model is sensitive to physical parameterizations and downscaling configurations. Concerned with the correlations between physical parameterizations and dynamical downscaling, these significant issues were considered simultaneously in this study, and WRF‐based ensembles were integrated and used to estimate eight representative rainfall events. The results revealed that both rainfall estimates and intensities were sensitive to downscaling configurations. Specifically, light rainfall events that were homogeneous over space and time were estimated well using a 5 km horizontal resolution and were overestimated with a 1 km resolution where the planetary boundary layer (PBL) schemes were probably the source of positive errors in light rain conditions. However, when the rainfall was intense and displayed spatiotemporal heterogeneity, the rainfall peaks and volume were only well estimated with a 1 km resolution where cumulus (CU) schemes were the dominant schemes, demonstrating that higher resolutions could better reproduce rainfall patterns for wetter cases. At a 10 km horizontal resolution, the simulations did not display much accuracy, and different physics schemes did not make a substantial difference. Therefore, the rainfall characteristics cannot be ignored. More importantly, the contributions of microphysics, CU and PBL ensembles were quantified, and the sensitivities of the rainfall estimates to three downscaling configurations were studied. [ABSTRACT FROM AUTHOR]

Modelling rainfall, an important element of the hydrological cycle, is of crucial importance in hydrology, water resources engineering, irrigation scheduling and environmental issues. In the current study, the monthly rainfall amounts were simulated through the least squares support vector machine (LSSVM), the model tree (MT) and the geostatistical kriging approaches using geographical inputs. So, the latitude, longitude, altitude and the periodicity component were introduced as model inputs for simulating monthly rainfall values. Long‐term rainfall records from 73 weather stations covering different climatic contexts of Iran were used for developing and evaluating the proposed methodology. Two modelling scenarios, namely the at station and pooled scenarios, were carried out for assessing the capability of the applied models in modelling rainfall values. First, two different heuristic methods, the LSSVM and MT methods, were compared with each other. The results obtained revealed that the LSSVM model produced more accurate results than the MT for the at station scenario. Both methods gave better results in arid/semi‐arid regions than humid stations. In the pooled scenario, the rainfall data of humid and arid training stations were pooled and used for calibration and then tested at each station. In this scenario, the LSSVM was superior to the MT in modelling long‐term monthly rainfall. The LSSVM and MT models were then compared with the geostatistical kriging method in both scenarios. It was observed that the kriging method generally performed better than the heuristic methods in spatial modelling of rainfall. [ABSTRACT FROM AUTHOR]

Rainfall prediction is a very challenging task due to its dependence on many meteorological parameters. Because of the complex nature of rainfall, the uncertainty associated with its predictability continues to be an issue in rainfall forecasting. The Hurst exponent is considered as a measure of persistence and it is believed that if a time series has persistence (as reflected by a Hurst exponent value greater than 0.5) it is also predictable. However, very limited studies have been carried out to demonstrate this hypothesis. This study, through experimental work on hypothetical data as well as real data, demonstrates that the Hurst exponent can be taken as an indicator for predictability provided that the exponent values at "smaller levels" of the time series are also significantly greater than 0.5 together with the Hurst exponent of the overall time series. It is also shown that it is better to predict the "similarity" aspect associated with a time series (and derive the predicted rainfall) than to predict the rainfall directly. [ABSTRACT FROM AUTHOR]

The rainfall distribution associated with landfalling tropical cyclones (TCs) along the South China coast are examined using radar data from Hong Kong Observatory (HKO). This preliminary study relates the landfall direction, and possible rotations/transitions of TC rainfall asymmetries before and after the landfall. Three types of landfalling TCs on the South China coast are categorized: from the east, normal to the coast and from the southwest. For those from the east, the rainfall maximum rotates anticyclonically from the southwest before later landfall to the northwest. TCs making landfall perpendicular to the coast have a rainfall maximum remaining in the southwest quadrant throughout the landfall process. For TCs making landfall from the southwest, the rainfall maximum rotates cyclonically from the southeast before later landfall to the northeast. The relative importance of the impact of land properties and vertical wind shear on these rainfall asymmetries is investigated by using a numerical model. Sensitivity tests show that the rotations/transitions of landfalling TC rainfall asymmetry appear to be insensitive to land properties (e.g. moisture availability, surface friction and topography), but result primarily from changes in the orientations of the vertical wind shear in the environment. However, the surface friction of the landmass appears to contribute to the enhancement of rainfall intensity. The rainfall distribution associated with landfalling tropical cyclones (TCs) along the South China coast are examined using radar data. Three types of landfalling TCs are categorized based on the landfall direction, and possible rotations/transitions of TC rainfall asymmetries before and after the landfall. The rotations/transitions of landfalling TC rainfall asymmetry appear to be insensitive to land properties (e.g. moisture availability, surface friction and topography), but result primarily from the orientations of the vertical wind shear in the environment. [ABSTRACT FROM AUTHOR]

ABSTRACT: The northeast monsoon (NEM) over south India plays a vital role in producing light and moderate rainfall events during October through December. Excess NEM rainfall is mainly due to the intensified tropical cyclones and easterly waves from the Pacific east–west troughs over the Bay of Bengal. The main heat source and sink regions of this monsoon season are the western Pacific high and the Siberian high, respectively. Relative variations in the areal extent and intensity of these source and sink regions influence the NEM season by excess or deficient rainfall. Hence, the aim of the present study was to observe the impact of the Siberian high and the western Pacific high on the NEM rainfall in excess and deficient years. For this purpose, dynamic and thermodynamic parameters, the zonal and meridional winds, the velocity potential, the sea surface temperature and the relative humidity of the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data were used for the period 1951–2014. Analysis of these variables revealed the importance of the source and sink regions for the enhancement/reduction of NEM rainfall during excess/deficient years. [ABSTRACT FROM AUTHOR]

ABSTRACT: Typical weather in Ireland provides conditions favourable for sustaining grass growth throughout most of the year. This affords grass based farming a significant economic advantage due to the low input costs associated with grass production. To optimize the productivity of grass based systems, farmers must manage the resource over short time scales. While research has been conducted into developing predictive grass growth models for Ireland to support on‐farm decision making, short term weather forecasts have not yet been incorporated into these models. To assess their potential for use in predictive grass growth models, deterministic forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) were verified for lead times up to 10 days using observations from 25 Irish weather stations. Forecasts of air temperature variables were generally precise at all lead times, particularly up to 7 days. Verification of ECMWF soil temperature forecasts is limited, but here they were shown to be accurate at all depths and most precise at greater depths such as 50 cm. Rainfall forecasts performed well up to approximately 5 days. Seven bias correction techniques were assessed to minimize systematic biases in the forecasts. Based on the root mean squared error values, no large improvement was identified for rainfall forecasts on equivalent ECMWF forecasts, but the optimum bias corrections improved air and soil temperature forecasts greatly. Overall, the results demonstrated that forecasts predict observations accurately up to approximately a week in advance and therefore could prove valuable in grass growth prediction at farm level in Ireland. [ABSTRACT FROM AUTHOR]

ABSTRACT Future changes in extreme rainfall arising from climate change may have a significant influence on flood and water erosion control and management strategies to a great extent. The maximum daily rainfall time series were projected for 2020-2049 using six general climate models and two scenarios through artificial neural networks for 22 stations across the north of Iran. The results indicate a reduction of between −3.0 and −0.2% in maximum rainfall for the selected stations and five out of six of the general climate models. The changes in the frequency and magnitude of extreme rainfall were then investigated by fitting a generalized extreme value distribution to the historical (from 1981 to 2010) and projected maximum rainfall. The location parameter of the generalized extreme value distribution fitted to the projected maximum rainfall does not show a significant change while the scale and shape parameters exhibit significant changes compared to the historical period. Estimating the 2, 50 and 100 year return periods showed that the maximum rainfall will have a reduction in the probability of large amounts across the region compared with the base period while the number of extraordinary extreme events may show growth. As a region vulnerable to flash floods and water erosion due to rainfall characteristics and land use change from forest to agriculture, the results may send an alarm to define long term and effective strategies for future flood control management in the region. [ABSTRACT FROM AUTHOR]