Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro,Brazil

Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r ²), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r ²) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability.     

短时间序列气温要素空间插值方法精度的比较研究

利用中国气象局国家气象中心的全国1956年和1998年1月和7月平均气温数据,对综合法与ANUSPLIN软件插值精度进行了比较。结果表明:在具有30 a的月平均气温栅格数据库作为背景的前提下,采用综合法对短时间序列平均气温数据进行空间化处理,其方法简单、方便,误差相对较小,是一种较适合的方法。     

江西省降水空间插值方法适用性分析

文中基于数字高程模型,建立了多元线性回归插值模型(MLR)和PRISM空间插值方法,并与传统的反距离权重法(IDW)和普通克里金插值法(OK)进行比较.结果表明:1)江西省5月、7—10月降水量与海拔高度存在显著的相关性,与坡度、坡向无明显相关.2)从插值精度来看,3—9月PRISM和MLR空间插值精度明显优于IDW和OK,冬半年IDW和OK插值精度略高于MLR和PRISM;4种插值方法的年降水量插值精度排序为PRISM>MLR>OK>IDW;PRISM和MLR在高海拔地区的插值精度远高于IDW和OK.3)从插值效果来看,4种插值方法的降水空间分布具有一致性,MLR和PRISM优于IDW和OK.     

Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km². The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.     

不同校准方法检验雷达定量估测降水的效果对比

应用雷达低仰角基本反射率资料和地面加密自动站降水量资料,采用最优化方法,根据天津地区降水特点和不同降水类型,建立适用本地的雷达Z-I关系。经实际应用检验,积混降水类型Z-I关系具实用性。在天津本地化Z-I关系基础上,通过了对比分析6种不同校准方法在天津夏季降水估测中的检验效果。结果表明：Z-I关系校准法和最大集成法对降水的估测偏高,误差较大;最优插值法的估测精度最高,平均绝对误差和均方根误差最小;但计算不同校准方法与实况相关性表明,变分校准法的估测效果与雨量计降水量的相关性最好。同时,所有估测校准法对小雨量级的降水均出现了不同程度的偏高估测。     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

山地降水的空间分布特征研究综述

降水是影响自然环境基础的因子，是影响气候变化的重要指标，关于降水尤其是山地降水的空间特征研究对防灾减灾和开发利用水资源具有重要意义，本文对山地降水中地形的影响、降水空间分布研究方法以及国内外研究动态进行了综述，认为：对山地降水空间特征的研究必须结合数据本身的特点和空间特性选择合适的插值方法，必须把随机插值方法和确定性方法结合起来，要想进一步减小误差，需要更多的降水数据和更为精准的地图数据。     

Comparison of methods for estimation of flood hydrograph characteristics

This research has been carried out for investigation and comparison of the accuracy and reliability of different methods of unit hydrograph estimation, including geomorphologic (GIUH) and geomorphoclimatic (GCIUH) methods as well as methods by Nash (Nash-IUH), Rosso (Rosso-IUH) and the Soil Conservation Service (SCS); the methods simulated the rainfall-runoff process over the Manshad River basin located in central Iran. The first six equivalent rainfall-runoff events were selected, and a hydrograph of outlet runoff was calculated for each event. Compared were peak time, peak discharge, base time, W ₅₀ and W ₇₅ parameters (hydrograph widths at 50% and 75% of peak discharge) and the volume of outlet runoff simulated by the models; then determined was the model that most efficiently estimated the hydrograph of outlet flow. The comparison of calculated and observed hydrographs showed that the Nash model was more efficient in estimating peak discharge, peak time, outlet runoff volume and the shape of direct surface runoff (DSRO) hydrographs, though it could not precisely simulate base time and W ₅₀ and W ₇₅ parameters. The other methods were more accurate in simulating outlet runoff volume of the hydrographs. The Rosso-IUH and SCS models could estimate the base time parameter better than the others. GIUH performance was comparable to the Nash method and was relatively suitable. In spite of these results, the GIUH, GCIUH, Rosso-IUH and SCS models had weak performance for estimating other characteristics of outlet DSRO hydrographs.     

Comparison of methods used in the measurement of precipitation and evaporation

Summary The measurement of evaporation and precipitation continues to be most problematic, particularly in terms of the accuracy of the instruments used and their standardization world-wide. This paper analyzes daily data from three rain gauges and two evaporimeters over a five-year period and correlates observed differences with the prevailing meteorological conditions. It was determined that ground-level gauges and large evaporation tanks provide the most acceptable and representative measurements from instruments currently in use. However, new developments are necessary for improved accuracy and world-wide adoption.With 2 Figures     

Monthly precipitation mapping of the Iberian Peninsula using spatial interpolation tools implemented in a Geographic Information System

Summary In this study, spatial interpolation techniques have been applied to develop an objective climatic cartography of precipitation in the Iberian Peninsula (583,551 km²). The resulting maps have a 200 m spatial resolution and a monthly temporal resolution. Multiple regression, combined with a residual correction method, has been used to interpolate the observed data collected from the meteorological stations. This method is attractive as it takes into account geographic information (independent variables) to interpolate the climatic data (dependent variable). Several models have been developed using different independent variables, applying several interpolation techniques and grouping the observed data into different subsets (drainage basin models) or into a single set (global model). Each map is provided with its associated accuracy, which is obtained through a simple regression between independent observed data and predicted values. This validation has shown that the most accurate results are obtained when using the global model with multiple regression mixed with the splines interpolation of the residuals. In this optimum case, the average R ² (mean of all the months) is 0.85. The entire process has been implemented in a GIS (Geographic Information System) which has greatly facilitated the filtering, querying, mapping and distributing of the final cartography.     

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.     

流域面雨量的一种估算方法—网格插值法

针对近年来渭河防汛的严峻形势,为搞好防汛气象服务,采用面雨量的3种估算方法,对2003年渭河大洪灾中4次洪峰时期渭河流域面雨量的实际估算和对比分析,结果表明网格插值法的估算精度优于等值线法和算术平均法,运用网格插值法估算渭河流域面雨量是可行的。     

多种空间检验方法在不同分辨率模式降水预报评估中的应用

利用单一的客观评估方法并不能有效揭示预报误差来源。利用逐小时5 km格点融合降水产品,本研究使用了多种客观评估方法综合评估了南京大学2016年夏季汛期试验4 km与12 km WRF模式。整体上,两种分辨率都能成功地预报主雨带,4 km WRF在午后对流及复杂地形预报上更优。比较了各类客观评估方法,邻域法显示4 km WRF预报准确性更高,但对于强降水(≥13 mm·(6 h)^-1),两种模式预报的空间误差都较大。尺度分离法显示,对于小尺度系统,4 km WRF能较好再现对流但存在较大位置误差,而12 km WRF则漏报。MODE法(Method for Object-based Diagnostic Evaluation)显示4 km WRF在对象强度预报上更接近观测,但强度和范围偏大,导致华南偏强,而范围偏小造成江淮偏弱,12 km WRF低估主要是漏报。不同评估方法能清晰展示4 km WRF和12 km WRF预报误差的差异,为后续模式改进提供了重要参考。     

近50a江苏梅雨量异常的时空分布及其环流特征

利用1961-2009年江苏66站逐日降水资料和NCEP／NCAR逐日再分析资料,采用EOF分解、小波分析、合成分析等方法,探讨近50a江苏省梅雨量异常的时空变化和环流特征。结果表明,江苏梅雨量存在全区一致型、南北（以32°N为界）反位相型、江淮之间与淮北及沿江苏南反位相型三种主要空间型;江苏梅雨量异常均存在显著年际变化和年代际变化特征,并存在准2a、准4a和准6a的振荡周期;近50a江苏梅雨量呈一致的增长趋势,且淮北地区和苏南地区的正趋势明显,通过置信水平为95％的检验。此外,江苏丰梅年份,南亚高压中心偏东,强度偏弱,面积偏小;副热带高压西伸脊点偏东,588dagmp线北界偏北,面积偏大;东北冷涡活动频繁,引导干冷空气南下,与强盛的西南暖湿气流相互作用,冷暖气流在江苏交绥,梅雨量偏多。枯梅年环流形势则与之相反。     

近25a重庆地区小时降水时空分布特征分析

利用重庆34个自动站1991—2015年逐小时降水资料,分别从降水比率、强降水占比、强降水频次、强降水事件、极大强降水及极端强降水阈值等方面分析了重庆时空分布特征。结果表明:(1)降水比率、强降水占比、强降水频次、强降水事件、极大强降水及极端强降水阈值在空间分布上具有一致性,高值区主要分布在东南部与西部,低值区主要位于东北部与中部。(2)降水比率、强降水占比、强降水频次及极大强降水在年变化上表现出波动起伏特征,且降水比率相对变化幅度较小,后三者表现出同相位的变化特点。在月变化上,降水比率呈双峰特征,后三者一致呈单峰特征。在日变化上,强降水高频次主要出现在03—05时,低频次主要是13—15时。(3)在强降水事件持续性上,强降水事件持续时间及其降水开始至最强降水时间的空间分布一致:高值区主要集中在东北部与东南部,而低值区主要分布在中部与西部。总体上看,持续时间越长,产生最强降水的时间越延后,且持续时间长的强降水事件主要产生在23时至次日04时。(4)第99、99.5、99.9百分位阈值与广义极值(GEV)分布函数5、10、20、50、100 a重现期阈值及极大强降水观测值在空间分布上与强降水具有一致性。