Daily Rainfall Amounts Research Articles

Evaluation of regional climate model simulations of rainfall over the Upper Blue Nile basin

Climate change impact and adaptation studies can benefit from an enhanced understanding about the performance of individual as well as ensemble simulations of climate models. Studies that evaluate downscaled simulations of General Circulation Models (GCMs) by Regional Climate Models (RCMs) for African basins are noticeably missing. Recently, the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made multiple RCMs' outputs available for end users across the African continent. Before climate simulations receive applications in impact and adaptation studies, accuracy of the simulation results has to be evaluated. In this study, the rainfall accuracy of eight independent GCMs at a wide range of time scales over the Upper Blue Nile Basin (UBN) in Ethiopia is evaluated. The reference data for performance assessment was obtained from the rain gauge network of the National Meteorological Agency of Ethiopia (www.ethiomet.gov.et/). The models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error (RMSE), and Coefficient of Variation (CV). Plots of observed vs. simulated rainfall amounts are also used. Findings of this study indicate that the models have some limitations in simulating light (<1mm) daily rainfall and heavy (>10mm) daily rainfall amount. The annual rainfall bias of the models varies between −1.4% and −50% suggesting underestimation. In many aspects the MPI-ESM-LR climate model performed best in terms of bias and RMSE. However, it was found that the performance of the models differs subject to the performance measures used for evaluation. The use of the ensemble mean rainfall simulation did not improve representation of assessed rainfall characteristics in the basin. Based on the findings in this study, we suggest to use multi-models' simulations in order to capture different aspects of the Upper Blue Nile basin rainfall. Topography and rain rate dependent bias correction algorithms should be evaluated for the basin. This study is expected to be valuable for climate change impact and adaptation studies in the UBN basin.

A regional forecast model evaluation of statistical rainfall properties using the CPOL radar observations in different precipitation regimes over Darwin, Australia

This study evaluates the ability of the regional high‐resolution version of the Australian Community Climate and Earth‐System Simulator (ACCESS‐A) model (horizontal resolution of 12 km, 50 height levels with a 37.5 km top) to simulate statistical rainfall properties during three monsoon seasons using the Darwin C‐band polarimetric (CPOL) radar observation as a reference. The comparison consists of characteristics of precipitation, including the total‐domain accumulated daily rainfall, the daily rain intensity and frequency and the probability distribution function of hourly rain rate. These characteristics are then binned using five large‐scale atmospheric regimes that represent the main weather states in that region to assess the ability of the model to reproduce the variability of the statistical rainfall properties as a function of these defined regimes. The main results are that although the model reproduces the accumulated daily rainfall amount reasonably well, albeit tending to slightly overestimate it, the mean rain rate is too weak, compensated by too large a rain frequency of occurrence. In terms of intensity the model tends to perform worse in the dry regimes but improves in the wet regimes, especially during the ‘Deep Westerly’ regime, when most of the monsoonal precipitation is produced. Probability distribution functions of rain rates reveal that although there are small differences amongst regimes, marked discrepancies are found between model and observations. The ACCESS‐A overestimates light rain rates, underestimates moderate to heavy rain rates and produces spurious very heavy rain rates that are not observed at all by the radar. The evaluation of the diurnal cycle of rainfall properties also reveals a general underestimate of rain rates, compensated by an overestimated rain frequency over the whole cycle, resulting in an overestimated amplitude and a wrong timing of the diurnal cycle. In all regimes, the overestimation of total‐domain daily rainfall and rain frequency is found to be due to an overestimation of low rain rates produced by the ACCESS‐A convective scheme, while the underestimation of mean rain rate is due to an underestimate of high rain rates and high rain rates frequency by the ACCESS‐A convective scheme.

Anomalously wet and dry rainy seasons in Equatorial East Africa and associated differences in intra-seasonal characteristics

Differences between anomalously wet and dry rainy seasons over the period 1961–2001 in Equatorial East Africa (Kenya, northern Tanzania) are studied at local scale using a 53 rain-gauge network and analysing six intra-seasonal characteristics (ISCs): the onset and end dates, the wet spells number and length, daily mean rainfall amount and the dry spells length. As compared to anomalously dry Long Rains, anomalously wet ones record earlier onsets, more wet spells with higher daily mean rainfall amounts (above 14 mm/day). For the Short Rains, the end date is also widely modulated across the rain gauge network, and daily mean rainfall amounts above (below) 10 mm/day are more (less) frequent during anomalously wet (dry) seasons. The differences in the number, length and daily mean rainfall amount of wet spells between anomalously wet and anomalously dry rainy seasons is also analysed in an ensemble of 24 sea surface temperature-forced runs of the ECHAM4.5 Atmospheric General Circulation Model. For the Long Rains, results are inconsistent between runs which suggests a lack of reproducibility of the ISCs in the model. For the Short Rains, the ISCs distributions between anomalously wet and anomalously dry years are significantly different in most of the runs/locations. In particular, daily mean rainfall amounts above 8 mm/day are more (less) frequent in the model during anomalously wet (dry) years.

Spatiotemporal regional modeling of rainfall-induced slope failure in Hulu Kelang, Malaysia

This study demonstrated the use of transient rainfall infiltration and grid-based regional slope stability analysis model incorporated with spatial rainfall distribution model for regional mapping of rainfall-induced slope failures in a slope failure-prone area in Malaysia, namely Hulu Kelang. Infinite slope analysis was incorporated into the improved model to compute the pore water pressure and slope stability of the region. Digital terrain maps, spatial rainfall distributions, soil profiles, groundwater table, and soil properties were input into the model. The results showed that the slope failures in the study area can be grouped into shallow failures (<4 m) and sub-shallow failures (4–8 m). Most of the shallow slope failures (<4 m) were triggered by the northeast monsoon while the NE monsoon that characterized by more prolonged and intense rainfalls. Spatial temporal distributions of shallow and sub shallow slope failures predicted using the improved model showed reasonably good comparison with the historical slope failure regions. Monsoon events have affected slope stability by reduction in matric suction, complete loss of matric suction, and development of positive pore water pressure (PWP). Computation for the effects of rainfall infiltration on PWP response and consequent effects on slope stability is enhanced by the spatial rainfall distribution model. The pore water pressure response to monsoon rainfall is a function of rainfall duration, the rate of infiltration, groundwater depth, soil thickness, and slope inclination. The amount of the local daily rainfall is not the only factor affecting slope stability. The prolonged antecedent rainfall could have a role to play in building up the slope failure mechanism.

Comparison between two statistical downscaling methods for summer daily rainfall in Chongqing, China

ABSTRACTTwo circulation index‐based methods were applied to downscale summer daily rainfall at four meteorological stations in Chongqing, China. One is a regression method (CIM) and the other a hybrid circulation classification plus regression method (CPM). Both methods used the same three circulation indices and surface specific humidity as predictors. In the first method (CIM), the indices and humidity were used directly as predictors and only one model was developed at each station. In the second method (CPM), the indices were further used to define circulation patterns using the objective Lamb‐Jenkinson classification scheme, and one model was built using humidity as the sole predictor for each pattern. Logistic regression was used to define rainfall probability, and a gamma distribution was fit using observations to randomly generate daily rainfall amounts. The two downscaling methods were validated and compared. The results suggest that (1) both methods yield consistently reasonable results with respect to occurrence and amount of daily rainfall, although they perform poorly in reproducing interannual and interdecadal variability, (2) specific humidity should be used as a predictor. Both methods were forced by two future scenarios of a global climate model (GCM) to demonstrate the added value with two methods. The downscaled scenarios show a shift towards larger rainfall values, accompanied by more frequent dry days. This shift is mainly attributed to a change in specific humidity. Despite the similar performances of the two methods, CPM gives a higher frequency of dry days than CIM, whereas CIM produces stronger intensity of rainfall than CPM. This provides additional information about the uncertainty in the projections.

Adjustment to rainfall measurement undercatch with a tipping-bucket rain gauge using ground-level manual gauges

Wind-caused undercatch is an important factor among types of systematic errors in point measurements of rainfall. The daily amount of rainfall measured by a MR3H tipping-bucket rain gauge (TBR-MR3H), which is elevated above the ground surface, was considerably underestimated on average by 46 % when compared with the corresponding measurements done by a pair of ground-level manual gauges (M-Rs). The undercatch was also confirmed by the measurements using a set of microlysimeters (MLs). The daily rainfall totals measured by the manual rain gauge were used as a reference in adjusting the rainfall undercatch with the tipping-bucket rain gauge. A simplified equation, developed based on the relationship between logarithmic wind profile and its effect on the catch ratio (CR) of the two gauges, was used to calculate the correction factor on a daily basis. The effect of wind speed depends on the intensity of rainfall. Parameters were optimized, and the proposed equation was validated. The calculated and measured daily rainfall amounts were in good agreement with a correlation coefficient of 0.99, and overall deviation of 0.04.

Circulation weather types and spatial variability of daily precipitation in the Iberian Peninsula

The relationships between atmospheric circulation patterns and daily Iberian rainfall are here explored at high spatial resolution (0.2°) using the Jenkinson and Collison automated classification scheme with 26 Weather Types (WTs). The WTs were computed by means of the daily EMULATE Mean Sea Level Pressure dataset (EMSLP) while the high resolution precipitation database corresponds to the recent Iberia02 daily gridded precipitation dataset over the 1950-2003 period. Six monthly indexes relating the WTs and precipitation were analyzed: their Frequency, the Mean Precipitation, the Percentage Contribution, the Area of Influence, the Precipitation Intensity and Efficiency. Except for the Frequency of the WTs, all other indexes were evaluated studying their spatial distribution over the Iberian Peninsula, focusing on a WT and a month at time. A small number of WTs (7) was found to capture a high percentage (~70%) of monthly Iberian precipitation. The Westerly WT is the most influent one, followed by the Cyclonic, the Northwesterly and the Southwesterly WTs. Westerly flows, however, do not affect the Mediterranean fringe or the Cantabrian coast, which are dominated by the Easterly and Northerly WTs, respectively. Rainfall along the Mediterranean coastline and the Ebro basin depends on a variety of WTs, but their effects are confined to narrow areas and short temporal intervals, suggesting that local factors such as convective processes, orography and the proximity to a warm water body could play a major role in precipitation processes. We show that the use of daily gridded precipitation dataset holds the advantage of measuring the daily rainfall amount due to each WT directly instead to relying on the predicted values of the regression model as done in previous works.

Space‐time structure of extreme precipitation in Europe over the last century

ABSTRACTWe investigate the space‐time structure of extreme precipitation in Europe over the last century, using daily rainfall data from the European Climate Assessment &amp; Dataset (ECA&amp;D) archive. The database includes 267 stations with records longer than 100 years. In the winter season (October to March), for each station, two classes of daily rainfall amount values are selected that, respectively, exceed the 90th and 95th percentile of daily rainfall amount over all the 100 years. For each class, and at each location, an annual time series of the frequency of exceedance and of the total precipitation, defined respectively as the number of days the rainfall threshold (90th and 95th percentiles) is exceeded and total precipitation on days when the percentile is exceeded, are developed. Space‐time structure of the frequency and total precipitation time series at the different locations are then pursued using multivariate time and frequency domain methods. The identified key trends and organized spectral modes are linked to well‐known climate indices, as North Atlantic Oscillation (NAO) and El Nino Southern Oscillation (ENSO). The spectra of the leading principal component of frequency of exceedance and of total precipitation have a peak with a 5‐year period that is significant at the 5% level. These are also significantly correlated with ENSO series with this period. The spectrum of total rainfall is significant at the 10% level with a period of ∼8 years. This appears to be significantly correlated to the NAO index at this period. Thus, a decomposition of both secular trends and quasi‐periodic behaviour in extreme daily rainfall is provided.

Stochastic Modelling of the Daily Rainfall Frequency and Amount

Daily rainfall is an important origin of water supply to the dry land in cultivated areas of Pakistan where the inhabitants primarily rely on farming. Stochastic rainfall models are concerned with generation of the occurrence and amount of daily rainfall. To generate the sequence of rainfall occurrence, first-order Markov chain model was employed by using the method of transitional probability matrices, while rainfall amount was generated by applying gamma distribution. The parameters of model were estimated from the archives of daily rainfall records for the period 1981–2010. The shape and scale parameters were estimated by method of moments and hence it became possible to find the parametric values at the study areas and then generate synthetic sequences according to the gamma distribution. The essential parameters for the stochastic generation include the means, variance or standard deviation and conditional and unconditional probabilities of wet and dry days.

Increases in thunderstorm activity and relationships with air pollution in southeast China

This study analyzes 15 years of Tropical Rainfall Measuring Mission (TRMM) satellite data, together with surface observations of thunderstorms and visibility, to study trends and relationships between aerosols and thunderstorms in southeast China. TRMM data used are from the lightning imaging sensor (LIS) and the precipitation radar (PR). Surface data are human-observed thunderstorm occurrence and visibility for the period of 1990–2012 at 70 plain stations and 4 mountain stations. Thunderstorm and lightning activities, as well as PR echo top heights, have all increased significantly over the region during the period under study, while regional mean visibility has decreased greatly at the plain stations. The daily rainfall amount during thunderstorm days has increased significantly, but rainfall without thunderstorms has no trend during this period. In comparison, the four mountain weather stations at elevations greater than 1100 m showed little trend in the number of thunderstorm days during the period of 1990–2012. The ratio of the number of thunderstorm days between plain and mountain stations has increased significantly. The distinct trends seen between plain and mountain stations may originate from large differences in aerosol concentration between the plain and mountain regions. The accumulation of pollution aerosols in the plain region likely invigorates thunderstorms, whereas a lesser, or no, impact on intense convection is found over high-altitude regions.

The Influence of Precipitation and Consecutive Dry Days on Burned Areas in Yunnan Province, Southwestern China

Precipitation is among the more limiting meteorological factors affecting the occurrence and extent of forest fire. We examined the correlation between burned area of individual wildfires and the rainfall amounts occurring on the day of the burn and the number of consecutive dry days for a range of limiting daily rainfall amounts (0–6mm) used to define a “dry” day. Daily threshold rainfall levels that most significantly affected area burned were determined for each ecoregion in Yunnan province, a major fire-prone area, in southwestern China. Results showed that the burned area of a wildfire decreased exponentially with increasing rainfall amounts on the day of burning. Burned area was also positively correlated to the number of consecutive dry days prior to burning. The threshold rainfall value providing the highest correlation between burned area and the number of consecutive dry days prior to a burn varied between ecoregions. Consecutive dry days with rainfall less than the specified threshold predominantly affected large fires (&gt;100 ha) rather than more frequently occurring small fires. These results will help forest managers evaluate regionalfire danger indices for forest fire prevention, particularly for catastrophic forest wildfires causing significant economic losses and threats to human life and environment.

Duration and seasonality of hourly extreme rainfall in the central eastern China

Compared with daily rainfall amount, hourly rainfall rate represents rainfall intensity and the rainfall process more accurately, and thus is more suitable for studies of extreme rainfall events. The distribution functions of annual maximum hourly rainfall amount at 321 stations in China are quantified by the Generalized Extreme Value (GEV) distribution, and the threshold values of hourly rainfall intensity for 5-yr return period are estimated. The spatial distributions of the threshold exhibit significant regional differences, with low values in northwestern China and high values in northern China, the mid and lower reaches of the Yangtze River valley, the coastal areas of southern China, and the Sichuan basin. The duration and seasonality of the extreme precipitation with 5-yr return periods are further analyzed. The average duration of extreme precipitation events exceeds 12 h in the coastal regions, Yangtze River valley, and eastern slope of the Tibetan Plateau. The duration in northern China is relatively short. The extreme precipitation events develop more rapidly in mountain regions with large elevation differences than those in the plain areas. There are records of extreme precipitation in as early as April in southern China while extreme rainfall in northern China will not occur until late June. At most stations in China, the latest extreme precipitation happens in August-September. The extreme rainfall later than October can be found only at a small portion of stations in the coastal regions, the southern end of the Asian continent, and the southern part of southwestern China.

Estimating Recurrence Intervals of Extreme Rainfall Through a Probabilistic Modeling Approach for Different Urban Cities of Pakistan

Extreme rainfall events give rise to severe flooding in Pakistan and their increasing magnitude have an extremely detrimental impact on the urban infrastructure. This work is an attempt to estimate the recurrence intervals of extreme rainfalls for the period 1981–2010 using extreme value theory through probabilistic modeling for different urban cities (Skurdu, Muzaffarabad, Quetta, Peshawar, Lahore and Karachi) of Pakistan. We use four models [generalized extreme value (GEV), Frechet, Weibull and Gumbel maximum] based on the extremal type theorem, among which the GEV distribution appears to be the best fitted model. Calculations reveal that among the considered urban cities, Muzaffarabad and Skurdu have the highest (129 mm) and lowest amount (41mm) of daily rainfall for the 5-year recurrence interval, respectively. The estimated 5-year recurrence interval for Lahore is 100 mm, while for Quetta, Peshawar and Karachi it is more than 50 mm. The aim of this paper is to present a contemporary statistical view of this problem.

Fire Risk Assessment in the Roburnia Commercial Forestry Plantation, Mpumalanga Province, South Africa

The aim of this study was to undertake fire risk assessment at Roburnia Plantation in order to determine factors that contribute to fire risk, risk level, and extent of fire damage. The study was conducted at the Roburnia Plantation, Mpumalanga south region, South Africa. Both epistemological and scientific research designs were used in this study in order to assess the level of fire risk at Roburnia Plantation. The quantitative approach was used to collect data on the factors that influence the occurrence of fire risk, while the qualitative approach was used to collect data on perceptions on fire risk level and prevention measures. A questionnaire was used to collect the primary data, while the secondary data was collected from online databases and through literature reviews. Climatic and fire danger index (FDI) data sets were gathered from the study area, which was downloaded from the Vital Weather System. Data on the number of hectares burnt, number of fires and fire extinguishing costs were downloaded from the Disaster Management System (DMS) at the Roburnia Plantation. Purposive sampling method was followed in order to collect data and information from experienced sampled people. Data on fire risk assessment and perceptions on fire prevention measures were collected through interviewing foresters (5) and fire fighters (3) managers at Roburnia Plantation. Officials dealing with commercial forest management were also interviewed at the Department of Agriculture, Fisheries and Forestry (7), Working on Fire in Mpumalanga and Limpopo (1), and NCT Forestry Cooperation Limited (2). A transect line (1km) was also followed in order to collect fuelload data per square meter, measured in every 100m along the transect line. The amount of fuelload was measured using an electronic kitchen measuring scale. The collected data sets were captured in the Ms Excel 2007 and then analysed using descriptive and inferential statistics. Regression analysis, analysis of variance (anova: single factor) and t-test were applied. The LvFDI model and risk equations were also employed in order to analyse the extent of fire risk at Roburnia Plantation. Results of this study show that rainfall and relative humidity have overriding influence on the rate of FDI. This is due to the fact that high moisture content in the fuel has the potential to stop fire, whilst low moisture content in the fuel influence efficient burning. The FDI values are low during summer, mainly due to high amount of moisture on the fuel, but becomes high during winter, mainly due to low moisture content in the fuel. This study found a positive regression between the availability of rainfall and the rate of FDI at Roburnia Plantation (R2 = 0.1). The average daily FDI was high when the average daily amount of rainfall was at 0mm, but decreases when the amount of rainfall increases above that level. There was also a positive regression between the availability of humidity and the rate of FDI at Roburnia Plantation (R2 = 0.7). The FDI was high (above 45) when the relative humidity was low (less than 30%), but decreases when the relative humidity increases from more than 30%. Analysis of this study found a weak regression between temperature and the rate of FDI at Roburnia Plantation (R2 = 0.000). This suggests that increase in temperature has insignificant effect on the rate of FDI. This is due to the fact that very dangerous FDI values were predicted even when the temperature was less than 1 degree Celsius. This study also found a weak regression between the velocity of wind and rate of FDI at Roburnia Plantation (R2 = 0.005). This is due to the fact that very dangerous FDI values were predicted even when the wind velocity was low, at 0 km/h. The application of Wisner and UNISDR risk equations shows that the Roburnia Plantation is highly exposed to fire risks, followed by harsh weather condition and least by tree diseases, pests and pathogens. This study also found that more than 70% of fires at Roburnia Plantation are caused by arsons. Species that are mostly affected by fires are the Gums and Pines. The mean (± s.d.) number of fires, hectares burnt and costs for extinguishing fires at Roburnia Plantation between 2007 and 2011 were 165.40 ± 56.19, 126.45 ± 110.81 and R471,903.01 ± R256,330.32 respectively. The findings of this study therefore implies that management at Roburnia Plantation need to effectively implement fire prevention plans in order to reduce the number of fires, hectares burnt and costs incurred due to extinguishing fires.

Assessment of future flood intensification in Central Vietnam using a super-high-resolution climate model output

This paper presents an assessment of the changes in future floods. The ranked area-average heavy daily rainfall amounts simulated by a super-high-resolution (20 km mesh) global climate model output are corrected with consideration of the effects of the topography on heavy rainfall patterns and used as a basis to model design storm hyetographs. The rainfall data are then used as the input for a nearly calibration-free parameter rainfall–runoff model to simulate floods in the future climate (2075–2099) at the Upper Thu Bon River basin in Central Vietnam. The results show that although the future mean annual rainfall will not be considerably different compared to the present-day climate (1979–2003), extreme rainfall is projected to increase vigorously, leading to a similar order of intensification of future floods. It is very likely that the flood peak with a 25-year recurrence will increase approximately 42% relative to the present-day climate. The occurrence of floods with a 10-year recurrence may exceed those with a 25-year recurrence in the present-day climate. The projection results also exhibit insignificant uncertainties caused by an artificial neural network-based bias correction model. Additionally, the presented bias correction model shows advantages over a simple climatology scaling method.

Non-Homogeneous Hidden Markov Model for Daily Rainfall Amount in Peninsular Malaysia

The non-homogeneous hidden Markov model (NHMM) generates the rainfall observation depends on few weather states which serve as a link between the large scale atmospheric measures. The daily rainfall at 20 stations from Peninsular Malaysia for 33 years sequences is analyzed using NHMM during the northeast monsoon season. A NHMM with six hidden states are identified. The atmospheric variable was obtained from NCEP Reanalysis Data as predictor. The gridded atmospheric fields are summarized through the principle component analysis (PCA) technique. PCA is applied to sea level pressure (SLP) to identify their principal spatial patterns co-varying with rainfall. The NHMM can accurately simulate the observed daily mean rainfall, correlations between stations for daily rainfall amounts and the quantile-quantile plots. It can be concluded that the NHMM is a useful method to simulate the daily rainfall amounts that may be used to prepare strategies and planning for the unpredicted disaster such as flood and drought.

Theoretical framework to estimate spatial rainfall averages conditional on river discharges and point rainfall measurements from a single location: an application to western Greece

Abstract. We focus on the special case of catchments covered by a single rain gauge and develop a theoretical framework to obtain estimates of spatial rainfall averages conditional on rainfall measurements from a single location, and the flow conditions at the catchment outlet. In doing so we use (a) statistical tools to identify and correct inconsistencies between daily rainfall occurrence and amount and the flow conditions at the outlet of the basin; (b) concepts from multifractal theory to relate the fraction of wet intervals in point rainfall measurements and that in spatial rainfall averages, while accounting for the shape and size of the catchment, the size, lifetime and advection velocity of rainfall-generating features and the location of the rain gauge inside the basin; and (c) semi-theoretical arguments to assure consistency between rainfall and runoff volumes at an inter-annual level, implicitly accounting for spatial heterogeneities of rainfall caused by orographic influences. In an application study, using point rainfall records from the Glafkos river basin in western Greece, we find the suggested approach to demonstrate significant skill in resolving rainfall–runoff incompatibilities at a daily level, while reproducing the statistics of spatial rainfall averages at both monthly and annual time scales, independent of the location of the rain gauge and the magnitude of the observed deviations between point rainfall measurements and spatial rainfall averages. The developed scheme should serve as an important tool for the effective calibration of rainfall–runoff models in basins covered by a single rain gauge and, also, improve hydrologic impact assessment at a river basin level under changing climatic conditions.

Prediction of a Mei-yu Frontal Cyclone and the Associated Torrential Rainfall over Southern Taiwan During 2008 Mei-yu Season

A torrential rain episode (≧ 200 mm day^(-1)) was observed over coastal regions of southern Taiwan (south of 23.0°N) on 5 June 2008 under the influence of a meso-scale frontal cyclone (MFC) formed along the western flank of a Mei-yu frontal zone. Through the incorporation of additional observations over the ocean collected during Southwest Monsoon Experiment (SoWMEX)/Terrain-influenced Monsoon Rainfall Experiment (TiMREX), the location of a Mei-yu frontal boundary, defined by the wind shift, is better identified over the open ocean by the data assimilation of dropsonde observations. Meanwhile, the low-level cyclonic vorticity and horizontal convergence are increased along the Mei-yu frontal zone, and an increment maximum center of cyclonic vorticity locates over the north of South China Sea (SCS), where the MFC subsequently forms.For the development and maintenance of a MFC over the northern SCS, the additional data assimilation, using the combination of the dropsonde and the radar radial velocity, has significantly improved the prediction of an MFC with a best track over the open ocean. Therefore, the daily rainfall distribution and amount over southern Taiwan are reproduced and significantly improved, particularly using dropsonde observations combined with the radar radial velocity.

A conditional disaggregation algorithm for generating fine time-scale rainfall data in a warmer climate

Summary This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1 h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems.

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Three statistical downscaling methods (conditional resampling statistical downscaling model: CR-SDSM, the generalised linear model for daily climate time series: GLIMCLIM, and the non-homogeneous hidden Markov model: NHMM) for multi-site daily rainfall were evaluated and compared in the North China Plain (NCP). The comparison focused on a range of statistics important for hydrological studies including rainfall amount, extreme rainfall, intra-annual variability, and spatial coherency. The results showed that no single model performed well over all statistics/timescales, suggesting that the user should chose appropriate methods after assessing their advantages and limitations when applying downscaling methods for particular purposes. Specifically, the CR-SDSM provided relatively robust results for annual/monthly statistics and extreme characteristics, but exhibited weakness for some daily statistics, such as daily rainfall amount, dry-spell length, and annual wet/dry days. GLIMCLIM performed well for annual dry/wet days, dry/wet spell length, and spatial coherency, but slightly overestimated the daily rainfall. Additionally, NHMM performed better for daily rainfall and annual wet/dry days, but slightly underestimated dry/wet spell length and overestimated the daily extremes. The results of this study could be applied when investigating climate change impact on hydrology and water availability for the NCP, which suffers from intense water shortages due to climate change and human activities in recent years.