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.     

重庆山地区域气象要素空间插值方法对比

利用重庆地区1999年和2018年气象数据,分别采用薄盘光滑样条、协同克里金、普通克里金、反距离加权4种方法,从年和月两种尺度对气温、降水、太阳总辐射三个要素进行空间插值;采取交叉验证方法,用MAE、MRE、RMSE评估插值精度,确定各要素最优插值方法。结果表明：气温和太阳总辐射最优插值方法为薄盘光滑样条,降水为反距离加权;插值精度上气温、太阳总辐射高值月份优于低值月份,降水则相反,但三个要素均表现出年尺度优于月尺度。MRE检验表明,插值精度为气温>太阳总辐射>降水,1999年年尺度插值精度分别为1.86%、4.60%、6.87%,月尺度插值精度分别为2.79%、5.82%、17.42%;2018年太阳总辐射年、月尺度插值精度分别为3.03%、4.88%,区域站加密后气温、降水年尺度插值精度分别为2.03%、11.20%,月尺度对应插值精度分别为3.20%、23.14%。     

气象要素插值的空间化精度提高方法研究

为了提高气象要素空间化的精度,本文提出通过预先对气象数据进行处理,然后再进行空间化,以比较直接插值与原始数据处理之后再插值的精度的变化。文中采用数据为全国743个常规气象站40 a(1961—2000年)整编气象资料及2005年的常规气象资料;插值方法有反距离加权法(IDW)、克立格法(Kriging)和样条函数法(Spline);数据预处理方法采用距平处理。结果发现:使用IDW、Kriging和spline对平均温度距平进行插值精度比较,发现IDW方法最优;温度距平精度的提高比降水和相对湿度要好;降水距平误差呈现由东向西递增的趋势。由此可见,对气象要素做距平处理可以有效提高插值精度。     

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。     

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.     

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     

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.     

成飞飞行区域短时强降水时空分布特征及强降水指数分析

成飞飞行空域包含高原、盆地、山区等多种地形,局地气候显著,短时强降水频发。该文使用国家气象信息中心2017—2021年多资料融合逐小时降水数据、国家自动站探空观测数据。统计分析发现,盆地周围沿山地区为盆地短时强降水高发区;101～102°E,31～32°N区域为高原短时强降水高发区。利用百分位法得到高原地区强对流指数阈值：CAPE值≥1930.5 J·kg^-1,BCAPE值≥1974.7 J·kg^-1,抬升指数≥2.6℃,大气可降水量≥86.1 mm,K指数≥37.2℃,SI指数≤-0.9℃。盆地地区强对流指数阈值：CAPE值≥2230.6 J·kg^-1,BCAPE值≥2264.4 J·kg^-1,抬升指数≥1.8℃,大气可降水量≥93.0 mm,K指数≥40.8℃,SI指数≤-1.8℃。建立短时强降水不同下垫面强对流指数阈值,为今后短时强降雨客观预报提供新的思路和方向。     

吐鲁番地区暖季降水时空分布特征

以吐鲁番5个国家气象站近55 a(1960—2014年)与26个区域气象站近3 a(2013—2015年)逐小时降水资料为基础,利用Pearson相关分析、气候倾向率、Mann-Kendall突变分析、Morlet小波分析等方法,分析了吐鲁番地区暖季降水时空分布特征,并就地形对吐鲁番降水的影响进行了量化研究。结果表明:在新疆趋暖趋湿的气候背景下,吐鲁番盆地平原区和山区存在截然不同的降水时空变化特征,吐鲁番地区降水高度集中在暖季,且暖季山区降水集中度和稳定性更好;暖季盆地内存在频率55%的夜雨区和昼雨区,盆地西南坡地和腹地平原区为夜雨区,盆地北部天山山区降水则集中在午后,海拔高度大约每增加(减少)300 m,降水集中时段提前(延后)1 h。研究还表明,吐鲁番降水与地形关系密切,海拔高度是影响吐鲁番降水的决定性因素,其暖季降水量、降水时数均与海拔高度呈显著正相关,降水量增加的主要原因是降水时数随海拔高度的递增;降水量随海拔高度的变化呈二次曲线型,其最大降水高度为1900 m;在最大降水高度以下,降水量由盆地腹地的平原区向山区递增,降水垂直变率平均为6.2 mm/100 m,其中1500~1900 m高度是降水量与降水垂直变率最大的区域,降水垂直变率达20 mm/100 m。     

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.     

PMP估算中大气可降水量计算方法的探讨

干旱区水汽变化影响区域水资源系统的结构和演变,基于2020年1月—2022年12月中昆仑山北坡地区4个地基GPS遥感大气可降水量资料(GPS-PWV)、2个探空站观测资料和108个地面气象观测站逐时水汽压资料,利用一元线性拟合方法建立了适用于中昆仑山北坡地区的大气水汽含量(W-PWV)和地面水汽压计算模型(W-e)并对计算结果进行评估,分析了中昆仑山北坡地区东段、中段、西段W-PWV的时空分布特征及降水开始时刻与W-PWV峰值的关系。结果表明：(1) W-PWV年平均高值区位于研究区西段,中段次之,东段沙漠南缘W-PWV最低。海拔高度大于1 500 m测站W-PWV随高度升高逐渐减少。夏季地面气象观测站平均W-PWV是春、秋季的2倍左右;(2) 研究区W-PWV月变化具有单峰型特征,其中海拔高度1 300~1 500 m测站的W-PWV在7月和8月达到峰值,其余测站的W-PWV在8月达到峰值,海拔低于2 000 m和高于2 000 m测站W-PWV分别在夜间和白天维持较高值;(3) 水汽含量模型计算的测站W-PWV与降水开始时刻有较好的对应关系,降水前各站W-PWV均存在不同程度跃变过程,降水过程前1~2 h内W-PWV峰值达到测站W-PWV月平均值的1.5倍以上。