Use of oxygen-18 isotope to quantify flows in the upriver and middle reaches of the Heihe River,Northwestern China

In recharge areas, the Heihe River flow was separated into components of ice-snowmelt and precipitation according to 14 gauging stations and to monthly hydrograph using oxygen-18. As shown by the result of the two-component mixing model, on average, 19.8% of the runoff comes from ice-snowmelt. At three stations which are closer to glaciers and with headstream of ice-snow melted water, the ice-snowmelt runoff is larger than 28% of stream water. In addition, because most of the ice-snowmelt infiltrates the groundwater, which later discharges into the river at mountain outlets, the ice-snowmelt percentage in runoff is lower than average at these stations with the elevation higher than 3,600 m. According to monthly hydrograph, the lowest percentage of ice-snowmelt in runoff is in July (6.46%), whereas during November it is the largest (26.1%). In the middle basin, the fraction of groundwater in runoff had a marked increase from 23.57% near Zhangye City to 60.28% near Gaotai City, and then a dramatic drop to 13.61% near Zhengyixia Station because of agricultural flood irrigation in Zhangye Basin.     

Global monsoon: Dominant mode of annual variation in the tropics

This paper discusses the concept of global monsoon. We demonstrate that the primary climatological features of the tropical precipitation and low-level circulation can be represented by a three-parameter metrics: the annual mean and two major modes of annual variation, namely, a solstitial mode and an equinoctial asymmetric mode. Together, the two major modes of annual cycle account for 84% of the annual variance and they represent the global monsoon. The global monsoon precipitation domain can be delineated by a simple monsoon precipitation index (MPI), which is the local annual range of precipitation (MJJAS minus NDJFM in the Northern Hemisphere and NDJFM minus MJJAS in the Southern Hemisphere) normalized by the annual mean precipitation. The monsoon domain can be defined by annual range exceeding 300 mm and the MPI exceeding 50%.The three-parameter precipitation climatology metrics and global monsoon domain proposed in the present paper provides a valuable objective tool for gauging the climate models’ performance on simulation and prediction of the mean climate and annual cycle. The metrics are used to evaluate the precipitation climatology in three global reanalysis products (ERA40, NCEP2, and JRA25) in terms of their pattern correlation coefficients and root mean square errors with reference to observations. The ensemble mean of the three analysis datasets is considerably superior to any of the individual reanalysis data in representing annual mean, annual cycle, and the global monsoon domain. A major common deficiency is found over the Southeast Asia-Philippine Sea and southeast North America-Caribbean Sea where the east–west land–ocean thermal contrast and meridional hemispheric thermal contrast coexist. It is speculated that the weakness is caused by models’ unrealistic representation of Subtropical High and under-represented tropical storm activity, as well as by neglecting atmosphere–ocean interaction in the reanalysis. It is recommended that ensemble mean of reanalysis datasets be used for improving global precipitation climatology and water cycle budget. This paper also explains why the latitudinal asymmetry in the tropical circulation decreases with altitude.     

Intraseasonal variability in subtropical South America as depicted by precipitation data

Daily precipitation data from three stations in subtropical Argentina are used to describe intraseasonal variability (20–90 days) during the austral summer. This variability is compared locally and regionally with that present in outgoing longwave radiation (OLR) data, in order to evaluate the performance of this variable as a proxy for convection in the region. The influence of the intraseasonal activity of the South American Seesaw (SASS) leading convection pattern on precipitation is also explored. Results show that intraseasonal variability explains a significant portion of summer precipitation variance, with a clear maximum in the vicinity of the SASS subtropical center. Correlation analysis reveals that OLR can explain only a small portion of daily precipitation variability, implying that it does not constitute a proper proxy for precipitation on daily timescales. On intraseasonal timescales, though, OLR is able to reproduce the main features of precipitation variability. The dynamical conditions that promote the development of intraseasonal variability in the region are further analyzed for selected summers. Seasons associated with a strong intraseasonal signal in precipitation variability show distinctive wet/dry intraseasonal periods in daily raw data, and are associated with a well defined SASS-like spatial pattern of convection. During these summers, strong large-scale forcing (such as warm El Niño/Southern Oscillation (ENSO) events and/or tropical intraseasonal convective activity), and Rossby-wave-like circulation anomalies extending across the Pacific Ocean, are also observed.     

Water-soluble organic and nitrogen levels in cloud and rainwater in a background marine environment under influence of different air masses

Chemical characterization was performed on cloud and rainwater samples collected as part of the Rain In Cumulus over the Ocean Experiment (RICO). This experiment took place at a mountaintop site (East Peak) in Puerto Rico from December 2004 to March 2007 in order to determine water-soluble organic and nitrogen fractions in a marine background environment. For cloud water, similar average concentrations of 1.0 (±0.3) mg/L were found for total organic carbon (TOC) and total nitrogen (TN) and an average concentration of 0.8 (±0.2) mg/L was found for dissolved organic carbon (DOC). In rainwater, these concentrations were lower, ranging from 0.3 to 0.5 (±0.1) mg/L. Changes in the concentrations of these species were observed in periods under the influence of anthropogenic, African dust, and volcanic ash air masses. In these periods the concentrations of TOC, DOC, and TN were 2 to 4 times higher than in periods under the influence of trade winds. The insoluble organic material arriving during African dust events showed total carbon (TC) concentrations on averaging 1.5 mg/L for cloud water. The TC was composed mainly of organic carbon with polar compounds from low to high molecular weight (MW). The polar compounds with high MW were probably associated with pollution (e.g., fossil fuel combustion) from other regions. Crustal species (Al and Fe) dominated particles associated with dust episodes, confirming the soil origin. Our results suggested that a fraction (40–80%) of TOC and (<100%) of TN in Puerto Rican cloud/rainwater could be originated from long-range transport of dust, ash and/or pollution.     

The remarkable wide range spatial scaling of TRMM precipitation

The advent of space borne precipitation radar has opened up the possibility of studying the variability of global precipitation over huge ranges of scale while avoiding many of the calibration and sparse network problems which plague ground based rain gage and radar networks. We studied 1176 consecutive orbits of attenuation-corrected near surface reflectivity measurements from the TRMM satellite PR instrument. We find that for well-measured statistical moments (orders 0 < q < 2) corresponding to radar reflectivities with dBZ < 57 and probabilities > 10^− 6, that the residuals with respect to a pure scaling (power law) variability are remarkably low: ± 6.4% over the range 20,000 km down to 4.3 km. We argue that higher order moments are biased due to inadequately corrected attenuation effects. When a stochastic three — parameter universal multifractal cascade model is used to model both the reflectivity and the minimum detectable signal of the radar (which was about twice the mean), we find that we can explain the same statistics to within ± 4.6% over the same range. The effective outer scale of the variability was found to be 32,000 ± 2000 km. The fact that this is somewhat larger than the planetary scale (20,000 km) is a consequence of the residual variability of precipitation at the planetary scales. With the help of numerical simulations we were able to estimate the three fundamental parameters as α ≈ 1.5, C₁ = 0.63 ± 0.02 and H = 0.00 ± 0.01 (the multifractal index, the codimension of the mean and the nonconservation parameter respectively). There was no error estimate on α since although α = 1.5 was roughly the optimum value, this conclusion depended on assumptions about the instrument at both low and high reflectivities. The value H = 0 means that the reflectivity can be modeled as a pure multiplicative process, i.e. that the reflectivity is conserved from scale to scale. We show that by extending the model down to the inner “relaxation scale” where the turbulence and rain decouple (in light rain, typically about 40 cm), that even without an explicit threshold, the model gives quite reasonable predictions about the frequency of occurrence of perceptible precipitation rates.While our basic findings (the scaling, outer scale) are almost exactly as predicted twenty years ago on the basis on ground based radar and the theory of anisotropic (stratified) cascades, they are incompatible with classical turbulence approaches which require at least two isotropic turbulence regimes separated by a meso-scale “gap”. They are also incompatible with classical meteorological phenomenology which identifies morphology with mechanism and breaks up the observed range 4 km–20 000 km into several subranges each dominated by different mechanisms. Finally, since the model specifies the variability over huge ranges, it shows promise for resolving long standing problems in rain measurement from both (typically sparse) rain gage networks and radars.     

Regionalisation of precipitation for the Iberian Peninsula and climate change

Temporal variability of precipitation over the Iberian Peninsula (IP) has high spatial gradients. Therefore, statistics of the temporal behaviour of precipitation and derived quantities over the IP must be estimated taking into account these spatial gradients. Some statistics can be displayed over a map. However there are statistics, such as Probability Density Functions at each location of the IP, that are impossible to display in a map. Because of this, it is mandatory to reduce the number of degrees of freedom which, in this case, consists of a reduction of the time series representative of the IP domain. In this work, we present a spatial partition of the IP region into areas of similar precipitation. For that, an observed dataset of daily-total precipitation for the years between 1951 and 2003 was used. The land-only high resolution data was obtained on a regular grid with 0.2° resolution in the IP domain. This data was subjected to a k-means Cluster Analysis in order to divide the IP into K regions. The clustering was performed using the squared Euclidean distance. Four clusters of IP grid points, defining 4 IP regions, were identified. The grid points in each region share the same time-varying behaviour which is different from region to region. The annual precipitation discriminates the following regions: (1) north Iberia, (2) a large region extending from the centre to the Mediterranean shores of the IP, (3) a large region ranging from the centre to the western and southwestern shores of the Iberia, and (4) northwest Iberia. The regions obtained for the four seasons of the year are similar. These results are consistent with the thermodynamic characteristics described in the available literature. These Iberian regions were used to assess climate change of seasonal precipitation from the multi-model ensemble of the fifteen simulations provided by the European project ENSEMBLES. Probability Density Functions of annual- and seasonal-total precipitation, consecutive dry days, and total precipitation above the 95th percentile, averaged in each region were estimated for a reference climate (1961–1960), a near-future climate (2021–2050), and a distant-future climate (2069–2098). Climate change projections are based on comparisons of these functions between each future climate and the reference climate.Finally, we emphasize that: (i) the methodology used here, based on Cluster Analysis, can be used to regionalise other areas of the world, and (ii) the identified regions of the IP can be used to represent the Iberian precipitation by four time series that can be subjected to further analysis, whose results can be presented in a concise manner.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily,Italy

The availability of good and reliable rainfall data is fundamental for most hydrological analyses and for the design and management of water resources systems. However, in practice, precipitation records often suffer from missing data values mainly due to malfunctioning of raingauge for specific time periods. This is an important issue in practical hydrology because it affects the continuity of rainfall data and ultimately influences the results of hydrologic studies which use rainfall as input. Many methods to estimate missing rainfall data have been proposed in literature and, among these, most are based on spatial interpolation algorithms.     

石家庄地区一次秋季冷锋云系垂直微物理结构的观测研究

利用2008年10月4～5日石家庄一次降水性层状云系的飞机探测资料,结合实时天气、卫星、雷达等资料,分析了降水过程的天气背景条件、降水初期云中的液态含水量、云粒子平均直径和粒子谱等要素的垂直分布与变化特征,以及云系的垂直微物理结构。结合云中冰雪晶二维粒子图像的增长和地面降水特征,初步探讨了该层状云系降水形成的物理机制。...     

基于数值预报产品的面雨量分析与检验

通过分析检验2007～2009年5～10月T213、德国、日本数值模式在雅砻江流域1～4区的面平均雨量——即面雨量预报发现,3个模式面雨量有无TS评分的差异较小,随着面雨量量级的加大各模式的差异变大。多模式集成预报总体上优于单模式,小雨级、中雨级优势更明显。二滩电站入库流量与面雨量集成预报值的相关系数在全流域明显低于与实测值的相关系数,但预报值与实测值的波动趋势几乎是一致的,仍对预报有较好的参考价值。