Precipitation Data Series Research Articles

Subseasonal climate variability for North Carolina, United States

Subseasonal trends in climate variability for maximum temperature (Tmax), minimum temperature (Tmin) and precipitation were evaluated for 249 ground-based stations in North Carolina for 1950–2009. The magnitude and significance of the trends at all stations were determined using the non-parametric Theil–Sen Approach (TSA) and the Mann–Kendall (MK) test, respectively. The Sequential Mann–Kendall (SQMK) test was also applied to find the initiation of abrupt trend changes. The lag-1 serial correlation and double mass curve were employed to address the data independency and homogeneity. Using the MK trend test, statistically significant (confidence level≥95% in two-tailed test) decreasing (increasing) trends by 44% (45%) of stations were found in May (June). In general, trends were decreased in Tmax and increased in Tmin data series in subseasonal scale. Using the TSA method, the magnitude of lowest (highest) decreasing (increasing) trend in Tmax is −0.050°C/year (+0.052°C/year) in the monthly series for May (March) and for Tmin is −0.055°C/year (+0.075°C/year) in February (December). For the precipitation time series using the TSA method, it was found that the highest (lowest) magnitude of 1.00mm/year (−1.20mm/year) is in September (February). The overall trends in precipitation data series were not significant at the 95% confidence level except that 17% of stations were found to have significant (confidence level≥95% in two-tailed test) decreasing trends in February. The statistically significant trend test results were used to develop a spatial distribution of trends: May for Tmax, June for Tmin, and February for precipitation. A correlative analysis of significant temperature and precipitation trend results was examined with respect to large scale circulation modes (North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI)). A negative NAO index (positive-El Niño Southern Oscillation (ENSO) index) was found to be associated with the decreasing precipitation in February during 1960–1980 (2000–2009). The incremental trend in Tmin in the inter-seasonal (April–October) time scale can be associated with the positive NAO index during 1970–2000.

气候变化对鄱阳湖水位变化的影响分析

水位变化直接关系鄱阳湖流域防汛抗旱的科学决策、水资源的高效利用、水生态健康以及湖区经济的可持续发展。利用鄱阳湖各站点的平均日降水、气温和水位资料，建立了三层反向传播神经网络模型(BP- NNM)。基于统计降尺度方法把全球气候模式BCC-CSM1-1输出结果降解到流域尺度的气温和降水数据，输入到BP-NNM得到未来气候变化情景下鄱阳湖水位系列，对未来鄱阳湖的平均水位进行分析。研究结果表明：在RCP4.5情节下，鄱阳湖未来平均水位的多年均值变幅小于2.5%，但是平均水位的年际变化在各月份之间差异较大。The water level change directly relates to scientific decision-making of flood control and drought relief, efficient utilization of water resources, healthy of water ecology and sustainable development of economy in the Poyang Lake basin. Back Propagation Neural Networks Model (BP-NNM) was established based on observed daily temperature, precipitation and water level data series in this study region. The statistical downscale method was coupled with BCC-CSM1-1 global climate model to obtain basin scale air temperature and precipitation data series, and then as inputs of BP-NNM to simulate future water levels of the Poyang Lake. The future average water levels of the Poyang Lake were analyzed and discussed. The results show that the future annual mean water level change will less than 2.5% compared to the baseline period, but the inter-annual variability of water levels among months is large under RCP4.5 scenarios.

Water-Level Fluctuations of Urmia Lake: Relationship with the Long-Term Changes of Meteorological Variables (Solutions for Water-Crisis Management in Urmia Lake Basin)

Urmia Lake in northwest of Iran, through the recent years has been extremely faced with the water crisis. Climate variations and anthropogenic impacts could be two main affiliated factors in this regard. We considered the long term data series of precipitation, temperature and evaporation in monthly and yearly scales in order to compare to water-level values of Urmia Lake. The statistics approaches such as: standard deviation, trend analysis, T test, Pearson and Spearman correlations, liner regression are used to analyze all variables. The results released that the water-level of Urmia Lake along with the precipitation and temperature of the lake’s basin have experienced the periodic changes through 1961 to 2010, as there are some gradual dryness trends on the study area according to precipitation and temperature variations. Urmia Lake periodic water-level fluctuations show more significant correlation to temperature than the precipitation. Whiles, the water-level’s decreasing behavior especially through 1998 to 2010 is more harsh and different than the rate that is considered for precipitation’s decrease and temperature’s increase. Thus, there could be some anthropogenic factors in the basin which produced some supplementary causes to shrink Urmia Lake. Extracting the double precipitation over the basin through introducing and categorizing of atmospheric synoptic systems in order to cloud seeding operation could be one of urgent and innovative solutions to mitigate water crisis in the basin.

Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.

Seasonal and annual precipitation time series trend analysis in North Carolina, United States

The present study performs the spatial and temporal trend analysis of the annual and seasonal time-series of a set of uniformly distributed 249 stations precipitation data across the state of North Carolina, United States over the period of 1950–2009. The Mann–Kendall (MK) test, the Theil–Sen approach (TSA) and the Sequential Mann–Kendall (SQMK) test were applied to quantify the significance of trend, magnitude of trend, and the trend shift, respectively. Regional (mountain, piedmont and coastal) precipitation trends were also analyzed using the above-mentioned tests. Prior to the application of statistical tests, the pre-whitening technique was used to eliminate the effect of autocorrelation of precipitation data series. The application of the above-mentioned procedures has shown very notable statewide increasing trend for winter and decreasing trend for fall precipitation. Statewide mixed (increasing/decreasing) trend has been detected in annual, spring, and summer precipitation time series. Significant trends (confidence level≥95%) were detected only in 8, 7, 4 and 10 nos. of stations (out of 249 stations) in winter, spring, summer, and fall, respectively. Magnitude of the highest increasing (decreasing) precipitation trend was found about 4mm/season (−4.50mm/season) in fall (summer) season. Annual precipitation trend magnitude varied between −5.50mm/year and 9mm/year. Regional trend analysis found increasing precipitation in mountain and coastal regions in general except during the winter. Piedmont region was found to have increasing trends in summer and fall, but decreasing trend in winter, spring and on an annual basis. The SQMK test on “trend shift analysis” identified a significant shift during 1960−70 in most parts of the state. Finally, the comparison between winter (summer) precipitations with the North Atlantic Oscillation (Southern Oscillation) indices concluded that the variability and trend of precipitation can be explained by the Oscillation indices for North Carolina.

Homogeneidade espacial da precipitação pluvial na bacia hidrográfica do Rio Vacacaí, RS

O objetivo, neste trabalho, foi determinar o número mínimo de anos com observações necessários para a obtenção de valores médios dos totais mensais de precipitação pluvial consistentes e a área de abrangência das estações pluviométricas, localizadas na bacia hidrográfica do Rio Vacacaí, estado do Rio Grande do Sul. Foram utilizadas as séries históricas de dados dos totais mensais de 13 estações pluviométricas localizadas na área e no entorno da referida bacia hidrográfica. O número mínimo de anos foi calculado pela relação entre o número de anos com observações e a estabilidade dos coeficientes de determinação. A abrangência das estações foi determinada pela relação dos coeficientes de determinação dos totais mensais de precipitação pluvial entre cada estação e a estação base, no caso Santa Maria, e as distâncias lineares entre a estação base e essas estações. A partir dessas equações, foram determinadas as distâncias entre estações em que há 80%, 85%, 90%, 95% e 99% de concordância dos valores. Constatou-se que, para ter uma razoável precisão nos totais mensais de precipitação pluvial na área da bacia hidrográfica do Rio Vacacaí, são necessários em torno de 30 a 40 anos de observação e que, nos meses de dezembro, janeiro e fevereiro, a abrangência das estações pluviométricas é menor do que 1,0km e nos outros meses uma concordância de 99% é obtida para distâncias de 1,3 a 2,2km, de 95% para distâncias de 6,3 a 11,1km e de 90% para distâncias de 12,5 a 22,2km.

Variation analysis of precipitation during past 286 years in Beijing area, China, using non‐parametric test and wavelet analysis

AbstractNon‐parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%‐significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85‐year, 35‐year and 21‐year periodic events are found to be the main periodic series of long‐term precipitation data, and they are all statistically significant. Moreover, the results of non‐parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32‐year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35‐year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.

Changes of characteristics of daily precipitation and runoff in the High Tatra Mountains, Slovakia over the last fifty years

Abstract The article presents the results of the analysis of time series of daily precipitation and runoff at selected places in the highest part of the Western Carpathians. It was focused on both wet and dry periods in precipitation and runoff data series. The precipitation data were analysed for a period 1961-2010. They revealed a significant increase of the number of days with daily precipitation 40-60 mm. Trend analysis for 67 analysed flow characteristics did not show statistically significant changes over the studied period. The focus was given particularly to the characteristics of the maximum and minimum flows, i.e. 3- and 7- day minimum flows, 1-, 3- and 7-day maximum flows. We found an increase of flows classified as small floods in two of three mountain catchments in the study area in the last decade (2001-2010). It may be linked to the above increased number of days with daily precipitation reaching 40-60 mm.

Dust Events in the Western Parts of Iran and the Relationship with Drought Expansion over the Dust-Source Areas in Iraq and Syria

The most famous deserts exist in subtropical regions which is the direct outcome of insufficient precipitation and high temperatures. The Middle East deserts are subjected often to dust, which reduces horizontal visibility to 5 km, and sometimes even to less than 100 m. The severe and prolong drought recently afflicting the west Asia region has been suggested to be instrumental in producing an increased output of dust into the atmosphere from the region. Regarding the increasing of dust events over the west of Iran with the external origin in the recent decade (from 2000 to present), so the main dust-source areas over Iraq and Syria have been detected using the dust-source map of the southwest of Asia, satellite images and soil type maps. We considered the relationship between the increasing of dust events in the western of Iran and drought expansion over the main dust-production areas during the recent decade. Dust frequency data series, and drought variables which include the VHI (vegetation health index), precipitation and temperature data series in long-term and monthly scales have been monitored and compared. And then we used the correlation analysis that indicated the significant proximity between the dust events and droughts/dryness in a yearly scale and also during the warm season (May to Aug). Meantime the derived results from the T-student test for the aforementioned data series confirm the fact that the droughts are parallel to the increasing of dust events from 1996 to 2011 (especially in the recent decade). We found that the recent droughts in the external dust source areas had the remarkable potential to increase the dust events in the west of Iran.

气候变化对渭河流域径流的影响

本文采用GCMs和水文模型耦合的方式，基于SWAT在渭河流域构建分布式水文模型，并采用SUFI-2算法进行参数敏感性分析、参数率定、模型验证以及不确定性分析，从而对渭河流域1961~2008年的径流过程进行模拟。然后将GCMs降尺度生成不同情景下降水、最高、最低气温日序列输入SWAT模型，模拟流域未来径流量，从而分析未来不同气候变化条件下流域径流可能的变化。研究结果表明：2046~2065和2081~2100时期不同情景下流域多年平均径流量分别为80.4与104.3亿m3，较基准期增加12.4%和45%。未来两个时期，枯季流量较基准期更低，而洪峰流量则将较基准期更高，即流域内极端事件(干旱与洪水)在未来两个时期有加剧趋势。不同情景下渭河流域径流深空间变化较为一致，即上游部分子流域和北洛河上游地区径流量较基准期有所减少，而流域中下游地区径流量均呈一定的增加趋势。 In this study, Soil and Water Assessment Tool (SWAT) was selected to set up a hydrological model in the Wei River basin (WRB), calibrated and validated with Sequential Uncertainty Fitting program (SUFI-2) based on river discharge, then future daily precipitation, maximum and minimum air temperature data series at each station, generated by the Statistical Downscaling Method (SDSM), were inputted to drive the SWAT model for analyzing the spatiotemporal characteristics of runoff during the future periods (2046-2065 and 2081-2100) under three climate scenarios including CSIRO, INM and MRI. Two emission scenarios (SRES A2 and SRES B1) were also included. The results show that average values of mean annual runoff in the periods of 2046-2065 and 2081-2100 were 80.4 × 108 m3 and 104.3 × 108 m3, which were greater than runoff in the base period by 12.4% and 45%, respectively. In both of the future periods, low flows would be much lower, while high flows tend to be much higher than that in the base period. In other words, there would be more extreme events (droughts and floods) in the future. For the spatial distribution of runoff over the WRB, it showed consistency for runoff changes under most combined scenarios, with runoff decreased at some areas of upstream and the upstream of Beiluo River, while increased at mid-lower stream of the WRB.

Combining satellite derived phenology with climate data for climate change impact assessment

The projected influence of climate change on the timing and volume of phytomass production is expected to affect a number of ecosystem services. In order to develop coherent and locally effective adaptation and mitigation strategies, spatially explicit information on the observed changes is needed. Long-term variations of the vegetative growing season in different environmental zones of Europe for 1982–2006 have been derived by analysing time series of GIMMS NDVI data. The associations of phenologically homogenous spatial clusters to time series of temperature and precipitation data were evaluated. North-east Europe showed a trend to an earlier and longer growing season, particularly in the northern Baltic areas. Despite the earlier greening up large areas of Europe exhibited rather stable season length indicating the shift of the entire growing season to an earlier period. The northern Mediterranean displayed a growing season shift towards later dates while some agglomerations of earlier and shorter growing season were also seen. The correlation of phenological time series with climate data shows a cause-and-effect relationship over the semi natural areas consistent with results in literature. Managed ecosystems however appear to have heterogeneous change pattern with less or no correlation to climatic trends. Over these areas climatic trends seemed to overlap in a complex manner with more pronounced effects of local biophysical conditions and/or land management practices. Our results underline the importance of satellite derived phenological observations to explain local nonconformities to climatic trends for climate change impact assessment.

Long-Term Trend Analysis of Seasonal Precipitation for Beijing, China

A comprehensive precipitation trend and periodic analysis at the seasonal scale on a 286–year data series (1724–2009) for Beijing are presented using linear regression, 11-year moving averages, Mann-Kendall test and continuous Morlet wavelet analysis. We found that in the past 300 years precipitation has increased except during winter. There were strong increasing trends after the 1780s in both summer and annual precipitation data series and the trend was significant for a longterm period. The abrupt points of summer and annual data series of precipitation are 1764 and 1768 respectively, after that, the trend changed from decreasing to increasing. It shows different periodic traits in four seasons respectively: 30–170 years, 80–95 years, 75–95 years and 55–65 years are considered to be the strongest period in spring, summer, autumn and winter. One hundred and fifty-three years, 85 years, 83 years and 59 years are the first order main periods in spring, summer, autumn and winter, respectively. The trend and period of annual precipitation are mainly impacted by rainfall in summe. According to the first main period of 85 years in both summer and annual precipitation data series, Beijing will experience a time period of less precipitation in 2009–2030.

Prediction of asphaltene precipitation by the extended Flory–Huggins model using the modified Esmaeilzadeh–Roshanfekr equation of state

Asphaltene precipitation is caused by a number of factors, such as the variation of pressure and temperature, the change in composition and the mixing of oil with diluting solvents. The deposition of asphaltene precipitation is one of the main problems with the oil industries, appearing in the well bore, the well tubing and the refining processes. This causes an increase in the operating costs and imposes the costs of cleaning and washing well tubing as well. Therefore, it would be economically beneficial to know under what conditions and to what amount the asphaltene precipitates.In this paper, a model is presented based on the Flory–Huggins theory of polymeric solutions. Because the interaction parameter term plays a key role in the asphaltene precipitation, a correlation is proposed to account for the effect of the solvent ratio in addition to molecular weight. Several adjustable parameters in terms of the interaction parameter are determined in this work using a series of experimental precipitation data from a crude oil sample of a field located in the southwest of Iran (oil sample 1), and applying a robust optimization method (the differential evolution). Regarding the influence of the solubility parameter on the accuracy of the final results, a comparison is made between the m-ER, PR and the SRK EOSs. Finally, the obtained results from the comparison between the asphaltene precipitation amounts of various solvents and the existing experimental values for another group of data from oil sample 1, and two other oil samples verify the accuracy of the presented model.

A data portal for regional climatic trend analysis in a Peruvian High Andes region

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

To know what we cannot know: Global mapping of minimal detectable absolute trends in annual precipitation

Fresh water resources, human societies, and ecosystems are expected to be strongly impacted by climate change, with precipitation trends being one of the most important elements that will be closely monitored. However, the natural variability of precipitation data can often mask existing trends such that the results appear as statistically insignificant. Information on the limitations of trend detection is important for risk assessment and for decision making related to adaption strategies under inherent uncertainties. This paper reports on an effort to quantify and map minimal detectable absolute trends in annual precipitation data series on a global scale. Monte Carlo simulations were conducted to generate realizations of trended precipitation data for different precipitation means and coefficients of variance, and the Mann–Kendall method was applied for detecting the trend significance. Global Precipitation Climatology Centre (GPCC) VASClimO data was used to compute the mean and coefficient of variance of annual precipitation over land and to map minimal detectable absolute trends. It was found that relatively high magnitude trends (positive or negative) have a low chance of being detected as a result of high natural variance of the precipitation data. The largest undetectable trends were found for the tropics. Arid and semiarid regions also present high relative values in terms of percent change from the mean annual precipitation. Although the present analysis is based on several simplified assumptions, the goal was to point out an inherent problem of potentially undetectable high absolute trends that must be considered in analyzing precipitation data series and assessing risks in adaption strategies to climate change.

Seasonal and Annual Impacts of Climate Change on Watershed Response Using an Ensemble of Global Climate Models

Climate change impacts watershed hydrology and contributes to alteration of hydrologic regimes in streams. However, global climate models (GCMs) operate at spatial and temporal scales that are too large to capture important watershed-scale hydrologic shifts. A method of disaggregating monthly ensemble GCM data into temperature and precipitation data series for daily, watershed-specific hydrologic simulations with SWAT was developed and assessed in the Soldier Creek watershed in northeast Kansas. A stochastic weather generator (WINDS) was employed to produce a baseline scenario (no changes from late 20th century conditions) and two scenarios based on ensemble means of 15 GCMs representing future conditions (A2 storyline) referred to as the 2050 and 2100 scenarios. Future hydrologic regimes exhibited non-linear annual and monthly responses in hydrologic budget components, such as surface runoff, baseflow, and soil moisture, to temperature and precipitation changes. For the 2050 scenario, the combination of higher temperatures along with decreased annual precipitation and increased spring precipitation resulted in higher surface runoff, baseflow, and streamflow in May and June with a longer drought season later in summer. The significant decrease in streamflow, runoff, and baseflow for the 2100 scenario reflected an increase in monthly and annual temperature rather than a direct result of precipitation decline. The 2100 scenario also produced a reduction in low-flow duration, an increase in the number of drought occurrences, and a decrease in flood frequency and duration. In retrospect, use of the stochastic weather generator to temporally downscale monthly GCM results while incorporating site-specific climate variability (such as occurs with convective storms often missed in coarse-resolution GCM data) produced more meaningful analysis of hydrological impacts, which is critical to predicting and understanding the impacts of climate change. Although this method allowed simulation of future-climate shifts based on GCM-simulated monthly shifts, it could not simulate potential shifts in climate patterns within a month, such as changes in transitional probabilities that govern the intensity and distribution of storms with months. In future work, translation of regional climate model responses into WINDS stochastic parameter adjustments will allow more accurate and efficient simulation. The severity of the increased drought and decreased flood responses simulated in this study would not be anticipated by review of precipitation trends alone nor by analysis of annual hydrologic responses alone. Similarly, many critical hydrologic responses reflected interactions between climate variables (e.g., precipitation and temperature) at sub-annual temporal scales, which highlights the need to consider climatic interactions in future studies of climate change impacts.

A test for network‐wide trends in rainfall extremes

AbstractTemporal trends in meteorological extremes are often examined by first reducing daily data to annual index values, such as the 95th or 99th percentiles. Here, we report how this idea can be elaborated to provide an efficient test for trends at a network of stations. The initial step is to make separate estimates of tail probabilities of precipitation amounts for each combination of station and year by fitting a generalised Pareto distribution (GPD) to data above a user‐defined threshold. The resulting time series of annual percentile estimates are subsequently fed into a multivariate Mann‐Kendall (MK) test for monotonic trends. We performed extensive simulations using artificially generated precipitation data and noted that the power of tests for temporal trends was substantially enhanced when ordinary percentiles were substituted for GPD percentiles. Furthermore, we found that the trend detection was robust to misspecification of the extreme value distribution. An advantage of the MK test is that it can accommodate non‐linear trends, and it can also take into account the dependencies between stations in a network. To illustrate our approach, we used long time series of precipitation data from a network of stations in The Netherlands. Copyright © 2010 Royal Meteorological Society

New Techniques for the Detection and Adjustment of Shifts in Daily Precipitation Data Series

Abstract This study integrates a Box–Cox power transformation procedure into a common trend two-phase regression-model-based test (the extended version of the penalized maximal F test, or “PMFred,” algorithm) for detecting changepoints to make the test applicable to non-Gaussian data series, such as nonzero daily precipitation amounts or wind speeds. The detection-power aspects of the transformed method (transPMFred) are assessed by a simulation study that shows that this new algorithm is much better than the corresponding untransformed method for non-Gaussian data; the transformation procedure can increase the hit rate by up to ∼70%. Examples of application of this new transPMFred algorithm to detect shifts in real daily precipitation series are provided using nonzero daily precipitation series recorded at a few stations across Canada that represent very different precipitation regimes. The detected changepoints are in good agreement with documented times of changes for all of the example series. This study clarifies that it is essential for homogenization of daily precipitation data series to test the nonzero precipitation amount series and the frequency series of precipitation occurrence (or nonoccurrence), separately. The new transPMFred can be used to test the series of nonzero daily precipitation (which are non Gaussian and positive), and the existing PMFred algorithm can be used to test the frequency series. A software package for using the transPMFred algorithm to detect shifts in nonzero daily precipitation amounts has been developed and made freely available online, along with a quantile-matching (QM) algorithm for adjusting shifts in nonzero daily precipitation series, which is applicable to all positive data. In addition, a similar QM algorithm has also been developed for adjusting Gaussian data such as temperatures. It is noticed that frequency discontinuities are often inevitable because of changes in the measuring precision of precipitation, and that they could complicate the detection of shifts in nonzero daily precipitation data series and void any attempt to homogenize the series. In this case, one must account for all frequency discontinuities before attempting to adjust the measured amounts. This study also proposes approaches to account for detected frequency discontinuities, for example, to fill in the missed measurements of small precipitation or the missed reports of trace precipitation. It stresses the importance of testing the homogeneity of the frequency series of reported zero precipitation and of various small precipitation events, along with testing the series of daily precipitation amounts that are larger than a small threshold value, varying the threshold over a set of small values that reflect changes in measuring precision over time.

In-cloud alteration of desert-dust matrix and its possible impact on health: a test in southeastern Anatolia, Turkey

Transport of desert dust is often accepted as a migration of clay minerals having various size fractions. Smaller fractions are capable of achieving transcontinental or transoceanic transport via prevailing wind systems as well as going deep into respiratory system through inhalation. Dust and cloud interactions assisted by the action of bacterial excretion products can lead to alterations of desert-dust clay fraction within the cloud droplet, which result in the formation of reduced iron and some other essential micronutrient elements within the dust veil, provided there is sufficient solar-light intensity. Here we present on a daily basis a 6-month time series of air-quality measurements, precipitation data and number of patients applying hospitals having respiratory problems. The number of patients correlates with the air-quality deterioration associated with dust pulses, excepted during the precipitation-free season. We therefore tentatively assign this correlation to the effects of in-cloud bacterial interaction with dust, rather than to dust itself.

Assessing trends in extreme precipitation events intensity and magnitude using non‐stationary peaks‐over‐threshold analysis: a case study in northeast Spain from 1930 to 2006

AbstractMost applications of the extreme value (EV) theory have assumed stationarity, i.e. the statistical properties of the process do not change over time. However, there is evidence suggesting that the occurrence of extreme events is not stationary but changes naturally, as it has been found for many other climate variables. Of paramount importance for hazard analysis is whether the observed precipitation time series exhibit long‐term trends or cycles; such information is also relevant in climate change studies. In this study, the theory of non‐stationary extreme value (NSEV) analysis was applied to data series of daily precipitation using the peaks‐over‐threshold (POT) approach. A Poisson/generalized Pareto (P/GP) model, in which the model parameters were allowed to vary linearly with time, was fitted to the resulting series of precipitation event's intensity and magnitude. A log‐likelihood ratio test was applied to determine the existence of trends in the model parameters. The method was applied to a case study in northeast Spain, comprising a set of 64 daily rainfall series from 1930 to 2006. Statistical significance was achieved in less than 5% of the stations using a linear non‐stationary model at the annual scale, indicating that there is no evidence of a generalized trend in extreme precipitation in the study area. At the seasonal scale, however, a significant number of stations along the Mediterranean (Catalonia region) showed a significant decrease of extreme rainfall intensity in winter, while experiencing an increase in spring. Copyright © 2010 Royal Meteorological Society