Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.     

Statistical downscaling for daily precipitation in Korea using combined PRISM,RCM, and quantile mapping: Part 1, methodology and evaluation in historical simulation

In this study, we present the Parameter-elevation Relationships on Independent Slopes Model (PRISM)-based Dynamic downscaling Error correction (PRIDE) model, which is suitable for complex topographies, such as the Korean peninsula. The PRIDE model is constructed by combining the PRISM module, the Regional Climate Model (RCM) anomaly, and quantile mapping (QM) to produce high-resolution (1 km) grid data at a daily time scale. The results show that the systematic bias of the RCM was significantly reduced by simply substituting the climatological observational seasonal cycle at a daily timescale for each grid point obtained from the PRISM. QM was then applied to correct additional systematic bias by constructing the transfer functions under the cumulative density function framework between the model and observation using six types of transfer functions. K-fold cross-validation of the PRIDE model shows that the number of modeled precipitation days is approximately 90~121% of the number of observed precipitation days for the five daily precipitation classes, indicating that the PRIDE model reasonably estimates the observational frequency of daily precipitation under a quantile framework. The relative Mean Absolute Error (MAE) is also discussed in the framework of the intensity of daily precipitation.     

Variability of daily precipitation over Nigeria

This study identified coherent daily precipitation regions in Nigeria by analyzing the spatial and temporal homogeneity of daily precipitation; investigating the dependence of wet day amount (WDAMT) and percentage of wet day (PWD) on latitude, longitude, elevation and distance from the ocean and finally regionalizing the daily precipitation stations. Non-parametric spatial homogeneity test was carried out on daily precipitation over 23 stations in Nigeria between 1992 and 2000 while the temporal analysis was done from 1971 to 2000. Regression analysis was used to determine the dependence of WDAMT and PWD on latitude, longitude, elevation and distance from the ocean. Principal component and cluster analyses were conducted to regionalize the precipitation stations. Seven homogeneous groups of stations were identified. Elevation explains 19.9 and 4.8 % of the variance in WDAMT and PWD, respectively, while latitude explains 76.2 % of variance in PWD. Eight principal components that explain 63.1 % of the variance in the daily precipitation data were retained for cluster analysis. Precipitation in the six daily precipitation regions that emerged from the cluster analysis is influenced by the Inter-tropical Convergence Zone, latitude, distance from ocean and southwesterlies while the northern region alone is influenced by the African Easterly Wave. In addition, precipitation in all the regions is influenced by topography. Low to medium spatial coherence exists in the precipitation regions. The spatial variations of PWD and WDAMT have implications for agricultural productivity and water resources in different parts of the country.     

Downscaling daily precipitation time series using a combined circulation- and regression-based approach

The aim of this paper is to introduce a new conditional statistical model for generating daily precipitation time series. The generated daily precipitation can thus be used for climate change impact studies, e.g., crop production, rainfall–runoff, and other water-related processes. It is a stochastic model that links local rainfall events to a continuous atmospheric predictor, moisture flux, in addition to classified atmospheric circulation patterns. The coupled moisture flux is proved to be capable of capturing continuous property of climate system and providing extra information to determine rainfall probability and rainfall amount. The application was made to simultaneously downscale daily precipitation at multiple sites within the Rhine River basin. The results show that the model can well reproduce statistical properties of daily precipitation time series. Especially for extreme rainfall events, the model is thought to better reflect rainfall variability compared to the pure CP-based downscaling approach.     

定量测量降水量的回波结构自适应算法研究

为提高临近预报中对流性降水估测的准确性,分析了统计法估算降水的局限性。依据降水物理机制和降水回波结构,研究雷达反射率因子定量测量降水量(QPE-Qualitative Precipitation Estimate)的自适应算法,旨在减少由于雨滴谱变化导致的Z-I关系不稳定所引起的降水量测量误差。同时考虑组合反射率因子RC和垂直液态水含量VIL两个参量对降水的影响,建立适于城市区域的动态自适应的Z-I关系,改进对强降水严重低估的问题。     

北京地区夏季极端降水变化特征及城市化影响

利用1981-2016年京津冀地区174个国家站逐日降水资料,采用百分位方法和线性倾向估计方法对京津冀地区极端降水的时空分布特征及演变趋势进行了分析。结果表明：（1）对于京津冀地区极端降水空间分布,不同百分位降水阈值表现为一致的分布特征,年平均极端降水量、平均极端降水强度与百分位极端降水阈值分布大体一致,而年平均极端降水日数的分布则与其相反。（2）年平均极端降水量在103.6~259.1 mm之间,年平均极端降水日数在3.0~4.0 d之间,平均极端降水强度在大雨到暴雨之间,极端降水量对总降水量贡献达28%以上。（3）极端降水总站次和极端降水日数年变化趋势一致,7月、8月和10月是极端降水较活跃月份。（4）在36 a期间,年平均极端降水量、年平均极端降水日数、平均极端降水强度以及极端降水量对总降水量贡献的变化趋势分布情况基本一致,呈减少趋势的站点均相对较多,年平均极端降水量增减幅度较大,年平均极端降水日数变化在1 d·（10 a）^-1以内,平均极端降水强度和极端降水量对总降水量贡献减少趋势相对明显。

     

近20年中国气象灾害损失的初步分析

采用全国气象部门收集的县（区）域行政单元灾情普查资料,结合全国气象站点降水观测资料,分析了1984—2008年中国暴雨及其引发的洪涝灾害的时空演变特点及灾害损失情况,揭示了气候变化及人类活动双重作用下中国暴雨洪涝灾害变化趋势和演变特点,以及暴雨洪涝灾害影响的时空差异性。结果表明：近25 a来中国暴雨日数总体上稍有增加,暴雨强度和暴雨天数的空间分布均表现为南方高于北方,东部高于西部的特点,20世纪90年代中后期为中国暴雨高发期。研究时段内,中国暴雨洪涝灾害造成的直接经济损失呈增加趋势,但直接经济损失占当年GDP的比例则呈下降趋势,平均每年经济损失约为573亿元人民币,损失较高的地区主要集中在中国南方地区,县域年平均损失超过2 000万元的县约占15%,其中有34个县超过亿元。受灾人口呈增加趋势,但因灾死亡人口呈下降趋势;暴雨洪涝灾害对农作物受灾面积和绝收面积的影响均呈微弱上升趋势,年平均作物受灾面积近9.00×10⁶ hm²,作物绝收面积为1.27×10⁶ hm²。

     

Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set

最近,NASA发布了一套基于CMIP5 21个耦合模式输出的高分辨率降尺度逐日数据集,简称NEX-GDDP。本文评估了NEX-GDDP对中国极端降水的模拟性能。研究发现:(1)相比CMIP5直接输出结果,NEX-GDDP能够更好刻画中国极端降水的空间分布;(2)未来中国极端降水事件明显增多、强度增强,NEX-GDDP在区域尺度上给出了更多的气候变化信息;(3)NEXGDDP预估的中国未来极端降水变化的不确定性范围相比CMIP5直接输出结果明显减少,使得预估结果更加可靠.     

Atmospheric circulation and daily precipitation in Wales

Summary Daily precipitation affinity areas are established for Wales by means of S-mode Principal Components Analysis (PCA) and Cluster Analysis, using data for the autumn-winter (September-January) period, 1982–87, subset according to surface wind direction. Relatively strong PCA simple structures were found for five wind directions clockwise from south to north, using the OBLIMIN rotation for the first four (south to northwest) and the VARIMAX rotation for the last (north). Whilst interpretation of many of the most significant Prinicipal Components (PCs) was difficult in strictly meteorological or climatological terms, the precipitation areas produced by clustering based on the most important PCs, yielded a continuum of change in areal organisation with progressively changing wind direction. Only the far southwest and northwest of the country appear in every area map, although superficially, northeastern, English border and southeastern areas were present on most. The derived areas bear only small overall resemblance to currently used forecasting areas.With 9 Figures     

Nonstationary frequency analysis of extreme daily precipitation amounts in Southeastern Canada using a peaks-over-threshold approach

Theoretical and Applied Climatology - In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary...     

Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea

The ability of the CLImate GENerator (CLIGEN) weather generator to reproduce daily precipitation characteristics for Korea was assessed on the basis of 55-year long historical daily precipitation records from eight weather stations (Seoul, Incheon, Daegu, Ulsan, Gwangju, Busan, Kangneung, and Jeonju) representing different parts of the Korean peninsula. The basic statistics of daily precipitation (mean, standard deviation, skewness of daily precipitation, number of rainy days, and the lengths of wet/dry period), probability distribution characteristics of daily precipitation (percentiles and maximum value), and the spatial covariance statistic generated by CLIGEN were compared with those derived from the observed weather series. Significance tests were conducted on the difference between the historical and generated statistics with the 1% significance level. The results show that CLIGEN simulates most of the daily precipitation characteristics satisfactorily with a tendency to slightly underestimate the mean and variability of daily precipitation. Especially, the number of rainy days is perfectly reproduced with mean relative error of 0.4% across all the stations. It is also found that the spatial covariance statistic from eight different stations is well reproduced by CLIGEN with respect to the leading EOF mode of summer season daily precipitation.     

Preserving spatial linear correlations between neighboring stations in simulating daily precipitation using extended Markov models

Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.     

Improved confidence in climate change projections of precipitation evaluated using daily statistics from the PRUDENCE ensemble

An ensemble of regional climate modelling simulations from the European framework project PRUDENCE are compared across European sub-regions with observed daily precipitation from the European Climate Assessment dataset by characterising precipitation in terms of probability density functions (PDFs). Models that robustly describe the observations for the control period (1961–1990) in given regions as well as across regions are identified, based on the overlap of normalised PDFs, and then validated, using a method based on bootstrapping with replacement. We also compare the difference between the scenario period (2071–2100) and the control period precipitation using all available models. By using a metric quantifying the deviation over the entire PDF, we find a clearly marked increase in the contribution to the total precipitation from the more intensive events and a clearly marked decrease for days with light precipitation in the scenario period. This change is tested to be robust and found in all models and in all sub-regions. We find a detectable increase that scales with increased warming, making the increase in the PDF difference a relative indicator of climate change level. Furthermore, the crossover point separating decreasing from increasing contributions to the normalised precipitation spectrum when climate changes does not show any significant change which is in accordance with expectations assuming a simple analytical fit to the precipitation spectrum.     

Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models

Probability density functions for daily precipitation data are used as a validation tool comparing station measurements to seven transient regional climate model runs, with a horizontal resolution of 25 km and driven by the SRES A1B scenario forcing, within the ENSEMBLES project. The validation is performed for the control period 1961–1990 for eight predefined European subregions, and a ninth region enclosing all eight subregions, with different climate characteristics. Models that best match the observations are then used for making climate change projections of precipitation distributions during the twenty-first century for each subregion separately. We find, compared to the control period, a distinct decrease in the contribution to the total precipitation for days with moderate precipitation and a distinct increase for days with more intense precipitation. This change in contribution to the total precipitation is found to amplify with time during all of the twenty-first century with an average rate of 1.1% K^?1. Furthermore, the crossover point separating the decreasing from the increasing contributions does not show any significant change with time for any specific subregion. These results are a confirmation and a specification of the results from a previous study using the same station measurements but with a regional climate model ensemble within the PRUDENCE project.     

Changes in daily precipitation under enhanced greenhouse conditions

 An increase in global average precipitation of about 10% is simulated by two global climate models with mixed layer oceans in response to an equilibrium doubling of carbon dioxide. The UKHI model was developed in the United Kingdom at the Hadley Centre for Climate Prediction and Research and the CSIRO9 model was developed in Australia by the CSIRO Division of Atmospheric Research. Regional changes in daily precipitation simulated by these models have been compared. Both models simulate fewer wet days in middle latitudes, and more wet days in high latitudes. At middle and low latitudes, there is a shift in the precipitation type toward more intense convective events, and fewer moderate non-convective events. At high latitudes, the precipitation type remains non-convective and all events simply get heavier, resulting in fewer light events and more moderate and heavy events. The probability of heavy daily precipitation increases by more than 50% in many locations. Extreme events with a probability of 1% or less were considered in terms of return periods (the average period between events of the same magnitude). For a given return period of at least 1 y, precipitation intensity in Europe, USA, Australia and India increases by 10 to 25%. For a given precipitation intensity, the average return period becomes shorter by a factor of 2 to 5. Given that larger changes in frequency occur for heavier simulated events, changes may be even greater for more-extreme events not resolved by models. Received: 1 July 1996 / Accepted: 21 March 1997     

锦州地区逐日降水概率分布特征

利用锦州地区的逐日降水量观测资料对逐日降水量的概率分布进行了统计分析,采用最大似然估计法得到Gamma函数分布的形状参数α和尺度参数β,通过Gamma概率分布模拟观测站点逐日降水的概率分布。结果表明：锦州地区逐日降水频率整体趋势先上升后下降,基本呈对称式分布,降水概率有一定的振荡,个别日会出现远超相邻日期的降水频率,7月21日降水频率最高,在不计微量降水的情况下,最低逐日降水概率有多个日期为0。各季降水频率偏低是造成义县地区干旱的原因之一;北镇夏季平均降水频率最低,但其夏季平均降水量却为锦州地区最高,说明北镇可能易出现较大量级降水或易出现极端降水天气。清明期间降水频率在50%以上、高考期间降水频率在80%以上,符合大众日常对特殊日期降水情况的认知;逐日降水频率可以为公众气象服务提供新的思路。凌海、北镇更容易出现极端降水天气;锦州地区日降水出现小雨天气概率最高,暴雨以上降水概率较低,锦州地区各站极少出现大暴雨以上量级降水,对锦州降水量级预报,尤其是暴雨或大暴雨以上降水量级的预报起到一定的指示作用。     

New estimations of precipitation and surface sublimation in East Antarctica from snow accumulation measurements

Surface mass balance (SMB) distribution and its temporal and spatial variability is an essential input parameter in mass balance studies. Different methods were used, compared and integrated (stake farms, ice cores, snow radar, surface morphology, remote sensing) at eight sites along a transect from Terra Nova Bay (TNB) to Dome C (DC) (East Antarctica), to provide detailed information on the SMB. Spatial variability measurements show that the measured maximum snow accumulation (SA) in a 15 km area is well correlated to firn temperature. Wind-driven sublimation processes, controlled by the surface slope in the wind direction, have a huge impact (up to 85% of snow precipitation) on SMB and are significant in terms of past, present and future SMB evaluations. The snow redistribution process is local and has a strong impact on the annual variability of accumulation. The spatial variability of SMB at the kilometre scale is one order of magnitude higher than its temporal variability (20–30%) at the centennial time scale. This high spatial variability is due to wind-driven sublimation. Compared with our SMB calculations, previous compilations generally over-estimate SMB, up to 65% in some areas.     

Multisite stochastic simulation of daily precipitation from copula modeling with a gamma marginal distribution

Theoretical and Applied Climatology - Multisite stochastic simulations of daily precipitation have been widely employed in hydrologic analyses for climate change assessment and agricultural model...