Rainfall Accumulation Research Articles

Distribution of Accuracy of TRMM Daily Rainfall in Makassar Strait

This research aims to evaluate rainfall estimates of satellite products in regions that have high variations of rainfall pattern. The surrounding area of Makassar Strait have chosen because of its distinctive rainfall pattern between the eastern and western parts of the Makassar Strait. For this purpose, spatial distribution of Pearson’s coefficient correlation and Root Mean Square Error (RMSE) is used to evaluate accuracy of rainfall in the eastern part of Kalimantan Island and the western part of Sulawesi Island. Moreover, we also used the contingency table to complete the parameter accuracy of the TRMM rainfall estimates. The results show that the performance of TRMM rainfall estimates varies depending on space and time. Overall, the coefficient correlation between TRMM and rain observed from no correlation was -0.06 and 0.78 from strong correlation. The best correlation is on the eastern coast of South West Sulawesi located in line with the Java Sea. While, no variation in the correlation was related to flatland such as Kalimantan Island. On the other hand, in the mountain region, the correlation of TRMM rainfall estimates and observed rainfall tend to decrease. The RMSE distribution in this region depends on the accumulation of daily rainfall. RMSE tends to be high where there are higher fluctuations of fluctuating rainfall in a location. From contingency indicators, we found that the TRMM rainfall estimates were overestimate. Generally, the absence of rainfall during the dry season contributes to improving TRMM rainfall estimates by raising accuracy (ACC) in the contingency table.

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

Abstract. This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weighted mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.

Impact De La Variabilité Pluviométrique Sur La Saison Culturale Dans La Zone De Production Cotonnière En Côte d’Ivoire

In Côte d’Ivoire, agriculture is mostly rain-fed. As a result, changes and variations of climate have considerable impacts on crops production including cotton production. This paper focuses on analyzing the effects of rainfall variations on the cropping season (useful rain season) of cotton in cotton production area of Côte d’Ivoire. A set of stationarity tests was applied to the Nicholson index using rainfall data of the period 1950-2000. Then, from a frequency analyses, the variability of rainfall and characteristic parameters of the cropping season was evaluated in terms of risks. The result shows a general downward trend of rainfall in the cotton growing area with years of breaks between 1964 and 1975. Moreover, spatial evolution of the cropping season parameters is a function of latitude. After the years of breaks, the beginning and the end of the cotton cropping season, which became respectively later and earlier, indicated that the length of useful rain season became shorter. The deficits of seasonal rainfall accumulations vary up to 60%. It is therefore necessary to update the crop calendar by taking into account variability of parameters of the useful rain season.

Critical scales to explain urban hydrological response: an application in Cranbrook, London

Abstract. Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

The potential role of land cover on secular changes of the hydroclimate of Peninsular Florida

The seasonal cycle of Peninsular Florida rainfall is a robust feature that has monsoon like seasonality. We objectively define the onset, demise, and length of the wet seasons over the time period of 1948–2006 in Peninsular Florida based on daily rainfall from the Climate Prediction Center of the National Centers for Environmental Prediction. This definition defines onset (demise) of the wet season of Peninsular Florida as the first day the daily accumulated precipitation anomaly reaches a minimum (maximum) for the year at a given grid point. A discernible linear trend of later onset, earlier demise, and a resulting shorter length of the wet season in urban areas relative to the rural areas is observed. The wet season accumulation of rainfall, however, does not reveal a proportional change in the linear trend implying a perceptible contrast of comparatively higher (lower) linear trend of the daily rain rate of the wet season over urban (rural) areas of Peninsular Florida.

A 12‐year radar‐based climatology of daily and sub‐daily extreme precipitation over the Swiss Alps

The characterization of the alpine extreme precipitation is the basis to study the projected changes in frequency and intensity of heavy rainfall and is needed to improve the resilience of communities to high‐impact weather. Climatological features of extreme daily and sub‐daily precipitation are documented here for the Swiss Alps and surrounding regions at a high spatial resolution (1 km2). The basis is 12 years of data from rain gauges and CombiPrecip, a rainfall field produced by locally adjusting the radar precipitation map to the values measured by rain gauges. The agreement between rain gauges and CombiPrecip concerning both the timing and the magnitude of the extreme events is quantified by cross‐validation; overall, it increases with diminishing the severity of the extremes and increasing accumulation time. If the extremes represent on average the 10 most intense rainfall accumulations per year, in general 50–65% of rain gauges extremes are extremes also for CombiPrecip, 40–50% of CombiPrecip extremes are not extremes according to rain gauges, and CombiPrecip extremes are till 7% lower than rain gauges extremes. The maps presented in this paper show that both daily and sub‐daily extremes are more intense along the alpine slopes compared to the crest of the Alps in all seasons, with the Lago Maggiore region showing the largest values. The fraction of yearly rainfall due to extremes is generally smaller in the Alps than in flat terrain. Extreme 1‐hr precipitation is more clustered in time in the inner Alps, but is less frequent, and exhibits a strong diurnal cycle in summer. The paper also shows that sub‐daily and daily extremes occur essentially over the same 24‐hr period.

Threshold contour production of rainfall intensity that induces landslides in susceptible regions of northern Turkey

This study is aimed at developing rainfall intensity contour lines to illustrate upper and lower rainfall intensity limits that trigger landslides. The first phase began by developing precipitation empirical thresholds as follows: (i) the relationship between intensity (I) and accumulative rainfall (E) and date (I-date and E-date, respectively); (ii) the antecedent rainfall up to 3, 5, 10, 15, and 30 days prior to landslide occurrence; (iii) the relationship between the I and the duration (D); and (iv) the relationship between the cumulative rainfall event and the D (ID and ED, respectively). The data recorded by two rain gauges (Rize and Rize–Pazar) in the province of Rize in Northwest Turkey were used to analyze 24 previous landslide events during the period 1985 to 2006. The second phase began by developing surface interpolation maps after deriving threshold and assumption of normality, thereby producing contours of the rainfall intensity data of the rain gauges. All thresholds were verified by no-rainfall events, and the ID threshold successfully distinguished 97 and 95% (in Rize and Rize–Pazar, respectively) of false alarm days with the highest accuracy among the thresholds (antecedent rainfall days, I-date, and E-date thresholds). Thus, a descriptive analysis was conducted using the rainfall data of selected rain gauges to test the normality of rainfall between the selected rain gauges in the study area. The linear correlation coefficient was found at 0.75, and other tests verified the normality with optimistic results. A universal or collaborative kriging interpolation (UK) in a geographic information systems’ environment was used to estimate the spatial distribution of rainfall intensity because rainfall is controlled robustly by topography and other factors. This distribution corresponds to the landslide day event and hole-effect mathematical models that are used to forecast un-sampled values in the study area. Topographic elevation, slope, and normalized difference vegetation index (NDVI) maps were integrated into the landslide day events by using rainfall intensity. The UK map was validated by a cross-validation procedure using root-mean-square error. The highest interpolated surface map accuracy was reached without using an external drift (using rainfall intensity values only) and when using an NDVI as the external drift. The optimal model was selected by comparing the measured and predicted values at approximately 78% accuracy. Ultimately, the rainfall intensity contour lines were used to illustrate the upper and lower rainfall intensity limits that trigger landslides. This study used well-documented spatio-temporal data of landslide events and introduced a primary threshold that can be validated for any future event.

Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

Abstract. Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

Analogy of multiday sequences of atmospheric circulation favoring large rainfall accumulation over the French Alps

We propose in this article an analogy approach for characterizing the similarity between daily atmospheric state sequences—in our case large‐scale geopotential height fields. The similarity is measured using two indices—the persistence and the singularity. The persistence of a sequence is defined through the average distance between its consecutive states. Its singularity is the average distance between its states and their closest analogues. We apply these indices to geopotential heights over Western Europe in view of characterizing the sequences yielding record rainfall accumulations over several days in the Northern French Alps, more specifically in the Isère River catchment at Grenoble. We show that these indices remarkably stratify the heaviness of rainfall sequences in the region. We find that the less singular and the more persistent the atmospheric state sequence, the wetter the rainfall sequence. Although the persistence of atmospheric states leading to extremes was expected, their low singularity might be a feature of the Northern French Alps, which usually experiences roughly westerly winds triggering orographically enhanced precipitation. Relying on some choices that may be improvable, our study opens the door to future research on the characterization of the atmospheric state sequences favoring regional climate extremes based on analogy.

A Multiplicative Cascade Model for High‐Resolution Space‐Time Downscaling of Rainfall

AbstractDistributions of rainfall with the time and space resolutions of minutes and kilometers, respectively, are often needed to drive the hydrological models used in a range of engineering, environmental, and urban design applications. The work described here is the first step in constructing a model capable of downscaling rainfall to scales of minutes and kilometers from time and space resolutions of several hours and a hundred kilometers. A multiplicative random cascade model known as the Short‐Term Ensemble Prediction System is run with parameters from the radar observations at Melbourne (Australia). The orographic effects are added through multiplicative correction factor after the model is run. In the first set of model calculations, 112 significant rain events over Melbourne are simulated 100 times. Because of the stochastic nature of the cascade model, the simulations represent 100 possible realizations of the same rain event. The cascade model produces realistic spatial and temporal patterns of rainfall at 6 min and 1 km resolution (the resolution of the radar data), the statistical properties of which are in close agreement with observation. In the second set of calculations, the cascade model is run continuously for all days from January 2008 to August 2015 and the rainfall accumulations are compared at 12 locations in the greater Melbourne area. The statistical properties of the observations lie with envelope of the 100 ensemble members. The model successfully reproduces the frequency distribution of the 6 min rainfall intensities, storm durations, interarrival times, and autocorrelation function.

Sensitivity of Extreme Rainfall to Atmospheric Moisture Content in the Arid/Semiarid Southwestern United States: Implications for Probable Maximum Precipitation Estimates

AbstractSensitivity of extreme rainfall to atmospheric moisture content for the 19 August 2014 storm in Arizona is investigated based on numerical experiments using the Weather Research and Forecasting model. Analyses are designed to develop an improved understanding of the roles of atmospheric moisture content and complex terrain in controlling spatial and temporal variability of extreme rainfall in the arid/semiarid southwestern United States. The control simulation identifies complex interactions of low‐level moisture transport and orographic lift as key elements in producing extreme rainfall for the first storm episode. Two sensitivity experiments are performed by increasing the relative humidity by 10% and 20% in both initial and boundary conditions used for the control simulation. Changes in atmospheric moisture content modify the storm structure and evolution, instability of the storm environment, and interactions of synoptic flow with complex terrain. The two storm episodes within this event exhibit contrasting responses to the increase of moisture content, with rainfall accumulation and maximum convective available potential energy increased for the second storm episode as opposed to a more complex relationship for the first storm episode. A moisture maximization simulation was also designed to mimic the framework of moisture maximization in probable maximum precipitation estimates. Our results highlight the nonlinear relationship between extreme rainfall and atmospheric moisture content in complex environments where small‐scale convection plays a dominant role. Limitations of probable maximum precipitation estimates based physical models are also discussed.

Statistical comparison of satellite-retrieved precipitation products with rain gauge observations over Bangladesh

ABSTRACTIn this investigation, six satellite-derived precipitation products namely Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction Centre (CPC) Morphing Technique (CMORPH), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) final run both non gauge-calibrated (IMERG) and gauge-calibrated (IMERG-GC), and Global Satellite Mapping of Precipitation (GSMaP) for GPM both non gauge-calibrated (GSMaP) and gauge-calibrated (GSMaP-GC) are evaluated over Bangladesh, using ground-based rain gauge observations as reference over a 3 years period from January 2014 to December 2016. Nine widely used categorical and volumetric statistical matrices such as bias, probability of detection, volumetric hit index, false alarm ratio, volumetric false alarm ratio, critical success index, volumetric critical success index, miss index, and volumetric miss index are employed to exploit the performance of the precipitation products in detecting extremes above different quantile thresholds (i.e. 50%, 75%, and 90% quantiles) for various temporal window (i.e. 3 h, 6 h, 12 h, and 24 h). The bias values show that none of the satellite rainfall data sets are ideal for detecting extreme rainfall accumulations. In fact, all products lose their detection skills consistently as the extreme precipitation thresholds (50%, 75%, and 90% quantiles) increase. The results indicate that PERSIANN shows the worst performance over the study region. Overall, GSMaP-GC performs better than the other precipitation products. However, the FAR values of GSMaP are also higher over monsoon and post-monsoon months. The categorical and volumetric scores reveal that the detection skill increases remarkably for all rainfall data sets throughout the year with the increase of extreme quantile thresholds. At higher temporal accumulations, the detection capability of the products also improves considerably, and this improvement is more significant during monsoon period. The performance is relatively poor for all precipitation data sets over the cold months. In general, all six satellite precipitation products are doing well in detecting the occurrence of rainfall but are not so good in estimating the amount of rainfall.

Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin, Canada

AbstractGauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.

Evaluation of the ECMWF Sub-seasonal to Seasonal Precipitation Forecasts during the Peak of West Africa Monsoon in Nigeria

Motivated by the increasing needs for reliable seasonal climate forecasts for enhanced living and protection of property, this study evaluates the predictive skill of the European Center for Medium-range Weather Forecast’s Sub-seasonal to Seasonal (ECMWF-S2S) precipitation forecasts during the peak of West Africa Monsoon in Nigeria. It investigates the ability of the ECMWF-S2S model to reproduce the atmospheric dynamics that influence the monsoon variability in West-Africa. Rain gauge values of 46 meteorological stations and 10-member ensemble of ECMWF-S2S forecasts from the Ensemble Prediction System (EPS) version of the ECMWF were subjected to quantitative statistical analyses. Results show that the model has weak capability in predicting wind strength at 700mb level to depict the African Easterly Jet (AEJ). However, irrespective of the ENSO phases, ECMWF-S2S model is capable of adequately and reliably predicting the latitudinal positions of the Inter-Tropical Discontinuity (ITD), mean sea level pressure component of the thermal lows and sea surface temperature (SST) anomalies over the Pacific and Atlantic Oceans. On inter-annual time-scales, results also show that ECMWF-S2S model performs best over the Savannah in forecasting of rainfall anomalies (synchronization = 75%) and over the Sahel in the prediction of rainfall accumulation. The model may however not be able to forecast extreme precipitation reliably because the disagreement between the model’s ensemble members increases as higher rainfall accumulation values are attained. The implication here is that reproducibility of the atmospheric dynamics by the model is a better measure of rainfall structural pattern than the actual quantitative rainfall forecasts especially in areas South of 10oN. The study therefore suggests considering some climate driving mechanisms as predictability sources for the ECMWF-S2S model to enable the atmospheric dynamics to be better represented in the model.

Satellite-Link Attenuation Measurement Technique for Estimating Rainfall Accumulation

A technique using satellite-link signal attenuation measurements for estimating rainfall accumulation along the link path is evaluated. Power law relationships between attenuation rate $A$ and rainfall rate $R$ are used to estimate $R$ and rainfall accumulation with a satellite link operating at Ku-band (12.3 GHz). Polarimetric radar measurements obtained from a National Weather Service Weather Surveillance Radar—1988 Doppler system near State College, Pennsylvania, are utilized to provide a comparison of rainfall accumulation estimates. A tipping-bucket rain gauge, colocated with the satellite receiver, is also used for comparison. A method based on bit error ratio measurements for the satellite link is used to identify periods of rain during which the rainfall rate is estimated from signal attenuation measurements. The effective rain height used in converting the attenuation rate along the link path into the rainfall rate is estimated from polarimetric radar observations. The Ku-band link is not very sensitive to light rain below 1.5 mm/h. Rainfall accumulation estimates obtained for 11 different days using satellite link attenuation show good comparisons with radar (within 19%) for accumulations greater than 6 mm and not so good (within 43%) for accumulations below 3 mm. The results presented in this paper show that using satellite-link attenuation measurements to estimate rainfall accumulations is a promising technique that requires further testing and refinement.

Flooding threshold rainfall events in Bermuda

Recently, much media attention has been drawn to the relatively frequent ‘nuisance’ flooding of urban low‐lying and poor drainage areas in Bermuda. This flooding results in traffic disruptions and loss of business, with only minor property damage. More significant flooding with property inundations is relatively rare. Efforts were made to reduce the incidence of flooding in low‐lying areas of Pembroke Parish through the construction of a canal in the 1800s. However, recent urbanisation and inadequate maintenance have reduced both its drainage capacity and efficiency. There is currently no formal warning system in place for flooding in Bermuda. The results of this study may serve as the context for the development of such a system. Recent heavy rain and flooding in January 2017 further motivates an examination of the rainfall record for trends and variability in daily rainfall accumulations. A conservative catalogue of flood reports can be constructed from anecdotal reports of flooding for the last decade. The flood catalogue events are related to daily rainfall amounts to develop a threshold daily rainfall for flood events, determined to be on average 39.3mm of rain. This threshold was used as a basis for examination of the historical record of daily rainfall for the period 1949–2016, in order to assess return frequencies of flood‐inducing rainfall events. A return period of approximately 2 months was calculated for the threshold daily rainfall accumulation. Although there are increasing trends apparent in the daily rainfall accumulations and counts of days on which rain was recorded (‘rain days’) since 1949, they are not statistically significant.

Measuring Hydrometeors Using a Precipitation Microphysical Characteristics Sensor: Sampling Effect of Different Bin Sizes on Drop Size Distribution Parameters

In order to improve the measurement of precipitation microphysical characteristics sensor (PMCS), the sampling process of raindrops by PMCS based on a particle-by-particle Monte-Carlo model was simulated to discuss the effect of different bin sizes on DSD measurement, and the optimum sampling bin sizes for PMCS were proposed based on the simulation results. The simulation results of five sampling schemes of bin sizes in four rain-rate categories show that the raw capture DSD has a significant fluctuation variation influenced by the capture probability, whereas the appropriate sampling bin size and width can reduce the impact of variation of raindrop number on DSD shape. A field measurement of a PMCS, an OTT PARSIVEL disdrometer, and a tipping bucket rain Gauge shows that the rain-rate and rainfall accumulations have good consistencies between PMCS, OTT, and Gauge; the DSD obtained by PMCS and OTT has a good agreement; the probability of N0, μ, and Λ shows that there is a good agreement between the Gamma parameters of PMCS and OTT; the fitted μ-Λ and Z-R relationship measured by PMCS is close to that measured by OTT, which validates the performance of PMCS on rain-rate, rainfall accumulation, and DSD related parameters.

Deployment and Performance of an X-Band Dual-Polarization Radar during the Southern China Monsoon Rainfall Experiment

An X-band dual-polarization radar (XPRAD) was deployed in Guangdong province as part of the Southern China Monsoon Rainfall Experiment (SCMREX) during the storm season in 2016. This paper presents a comprehensive assessment of XPRAD observations during SCMREX with emphasis on data processing and rainfall products. The differential phase-based attenuation correction and radar calibration using self-consistency of dual-polarization observables are presented. It is found that the standard deviation of the Z d r bias is less than 0.2 dB based on ‘light rain at low angle’ and ‘dry aggregate snow’ observations. Cross-comparison with two standard S-band China New Generation Weather Radars (CINRAD) shows that the bias of Z h has a mean value less than 1.5 dBZ and a standard deviation less than 0.5 dBZ. In addition, fifteen rainfall events that occurred during the intensive observing period (IOP) are analyzed to demonstrate the rainfall estimation performance of XPRAD. In particular, rainfall accumulations at 1-, 2- and 3-h scales derived using R( K d p ) and R( Z h , Z d r ) relations are evaluated using national level rain gauge data and CINRAD-based rainfall estimation. The results show that both R( K d p )- and R( Z h , Z d r )-based products agree well with the rain gauge observations and CINRAD estimation. The difference between R ( K d p ) and R ( Z h , Z d r ) is not significant, although R ( K d p ) shows slightly better performance than R ( Z h , Z d r ) .

Experimental Identification on Thresholds for Early Warning of Rainfall-induced Failure on Fractured Slopes after Giant Earthquake

Prevention and mitigation of rainfall induced geological hazards after the Ms=8 Wenchuan earthquake on May 12th, 2008 were significant for rebuild of earthquake hit regions. After the Wenchuan earthquake, there were tens of thousands of fractured slopes which were broken and loosened by the ground shaking, they were very susceptible to heavy rainfall and change forms into potential debris flows. In order to carry out this disaster reduction and prediction effectively in Longmenshan region, careful real-time monitoring and pre-warning of mountain hazards in both regional and site-specific scales is reasonable as alternatives in Wenchuan earthquake regions. For pre-warning the failure of fractured slopes induced by rainfall, the threshold value or the critical value of the precipitation of hazards should be proposed. However, the identification of critical criterion and parameters to pre-warning is the most difficult issue in mountainous hazards monitoring and pre-warning system especially in the elusive and massive fractured slopes widespread in Wenchuan earthquake regions. In this study, a natural coseismic fractured landslide in the Taziping village, Hongkou County, Dujianyan City, was selected to conduct the field experimental test, in order to identify the threshold parameters and critical criterion of the fractured slopes of Taziping. After the field experimental test, the correlation of rainfall intensity, rainfall duration and accumulative rainfall was investigated. The field experimental test was capable of identifying the threshold factors for failure of rainfall-induced fractured slopes after the giant earthquake.

Malaria early warning tool: linking inter-annual climate and malaria variability in northern Guadalcanal, Solomon Islands

BackgroundMalaria control remains a significant challenge in the Solomon Islands. Despite progress made by local malaria control agencies over the past decade, case rates remain high in some areas of the country. Studies from around the world have confirmed important links between climate and malaria transmission. This study focuses on understanding the links between malaria and climate in Guadalcanal, Solomon Islands, with a view towards developing a climate-based monitoring and early warning for periods of enhanced malaria transmission.MethodsClimate records were sourced from the Solomon Islands meteorological service (SIMS) and historical malaria case records were sourced from the National Vector-Borne Disease Control Programme (NVBDCP). A declining trend in malaria cases over the last decade associated with improved malaria control was adjusted for. A stepwise regression was performed between climate variables and climate-associated malaria transmission (CMT) at different lag intervals to determine where significant relationships existed. The suitability of these results for use in a three-tiered categorical warning system was then assessed using a Mann–Whitney U test.ResultsOf the climate variables considered, only rainfall had a consistently significant relationship with malaria in North Guadalcanal. Optimal lag intervals were determined for prediction using R2 skill scores. A highly significant negative correlation (R = − 0.86, R2 = 0.74, p < 0.05, n = 14) was found between October and December rainfall at Honiara and CMT in northern Guadalcanal for the subsequent January–June. This indicates that drier October–December periods are followed by higher malaria transmission periods in January–June. Cross-validation emphasized the suitability of this relationship for forecasting purposes {text{R}}^{2}{_{text{LOOCV}}} = 0. 6 3 as did Mann–Whitney U test results showing that rainfall below or above specific thresholds was significantly associated with above or below normal malaria transmission, respectively.ConclusionThis study demonstrated that rainfall provides the best predictor of malaria transmission in North Guadalcanal. This relationship is thought to be underpinned by the unique hydrological conditions in northern Guadalcanal which allow sandbars to form across the mouths of estuaries which act to develop or increase stagnant brackish marshes in low rainfall periods. These are ideal habitats for the main mosquito vector, Anopheles farauti. High rainfall accumulations result in the flushing of these habitats, reducing their viability. The results of this study are now being used as the basis of a malaria early warning system which has been jointly implemented by the SIMS, NVBDCP and the Australian Bureau of Meteorology.