基于WRF和统计方法的气温空间尺度分析     

于灵雪  张树文  刘廷祥  卜坤  杨久春 《地球信息科学学报》2013,(4):546-553

WRF模式作为一个中尺度气候模式,其分辨率从几米到几千公里,其自身的双向嵌套特征也为进行动力尺度下推提供了有力条件。本文利用WRF模式和传统的统计方法对研究区的气温进行尺度下推。首先,通过动力下推得到不同分辨率下的气温空间分布,并选取15个气象站点进行点对点验证,为了更明显观察不同尺度间的差异,对不同尺度的输出与ANUSPLIN插值结果进行比对,结果显示动力尺度下推中,分辨率越高模拟效果越好。其次,我们采用传统的统计下推方法,从27km下推到3km分辨率,并与WRF和ANUSPLIN插值在该尺度的结果进行对比分析,结果显示统计下推结果的趋势与动力下推的插值结果是一致的,但具有明显的马赛克效果,通过分析认为,这与统计方法的尺度下推只考虑高程信息的变化对气温的影响,而未考虑其他因素有关,如若在下推时加入更多的变量,如对温度有较大影响的坡度、坡向、土地覆被类型等因素,综合分析不同尺度之间的关系,会使下推结果有所改善。  相似文献   

Future projection of East China Sea temperature by dynamic downscaling of the IPCC_AR4 CCSM3 model result     

于晓林  王凡  唐晓晖 《中国海洋湖沼学报》2012,30(5):826-842

Future temperature distributions of the marginal Chinese seas are studied by dynamic downscaling of global CCSM3 IPCC_AR4 scenario runs.Different forcing fields from 2080-2099 Special Report on Emissions Scenarios(SRES) B1,A1,and A2 to 1980-1999 20C3M are averaged and superimposed on CORE2 and SODA2.2.4 data to force high-resolution regional future simulations using the Regional Ocean Modeling System(ROMS).Volume transport increments in downscaling simulation support the CCSM3 result that with a weakening subtropical gyre circulation,the Kuroshio Current in the East China Sea(ECS) is possibly strengthened under the global warming scheme.This mostly relates to local wind change,whereby the summer monsoon is strengthened and winter monsoon weakened.Future temperature fluxes and their seasonal variations are larger than in the CCSM3 result.Downscaling 100 years’ temperature increments are comparable to the CCSM3,with a minimum in B1 scenario of 1.2-2.0°C and a maximum in A2 scenario of 2.5-4.5°C.More detailed temperature distributions are shown in the downscaling simulation.Larger increments are in the Bohai Sea and middle Yellow Sea,and smaller increments near the southeast coast of China,west coast of Korea,and southern ECS.There is a reduction of advective heat north of Taiwan Island and west of Tsushima in summer,and along the southern part of the Yellow Sea warm current in winter.There is enhancement of advective heat in the northern Yellow Sea in winter,related to the delicate temperature increment distribution.At 50 meter depth,the Yellow Sea cold water mass is destroyed.Our simulations suggest that in the formation season of the cold water mass,regional temperature is higher in the future and the water remains at the bottom until next summer.In summer,the mixed layer is deeper,making it much easier for the strengthened surface heat flux to penetrate to the bottom of this water.  相似文献   

High resolution WRF climatic simulations for the Iberian Peninsula: Model validation     

《Physics and Chemistry of the Earth》2016

A high resolution atmospheric modelling study was done for a 20-year recent historical period. The dynamic downscaling approach adopted used the Max Planck Institute Earth System Model (MPI-ESM) to drive the WRF running in climate mode. Three online nested domains were used covering part of the North Atlantic and Europe, with a resolution 81 km, and reaching 9 km in the innermost domain which covers the Iberian Peninsula.This paper presents the validation of the WRF configuration, which is based on historic simulations between 1986 and 2005 and observational datasets of near surface temperature and precipitation for the same period. The validation was done in terms of comparison of probability distributions between model results and observations, as daily climatologies, spatially averaged inside subdomains obtained with cluster analysis of the observations, for each of the four seasons. In addition, Taylor diagrams are presented for each of the seasons and subdomains. This validation approach was repeated with the results of a new WRF simulation with the same parameterisations but forced by the ERA-Interim reanalysis. The capacity of the MPI-ESM driven WRF configuration to compare with observations and in a manner similar to the ERA-Interim driven WRF, ensures the capacity of the configuration for climate and climate change studies.Considering the difficulty to simulate extremes in long term simulations, the results showed a comfortable comparison of both models (forced by climate model and reanalysis results) with observations. This provides us confidence on the continuity of using the MPI-ESM driven WRF configuration for climate studies.  相似文献   

Simulation of streamflow with statistically downscaled daily rainfall using a hybrid of wavelet and GAMLSS models     

Md. Mamunur Rashid  Simon Beecham 《水文科学杂志》2013,58(11):1327-1339

ABSTRACT

A rainfall–streamflow model is proposed, in which a downscaled rainfall series and its wavelet-based decomposed sub-series at optimum lags were used as covariates in GAMLSS (Generalized Additive Model in Location, Scale and Shape). GAMLSS is applied in climate change impact assessment using CMIP5 general climate model to simulate daily streamflow in three sub-catchments of the Onkaparinga catchment, South Australia. The Spearman correlation and Nash-Sutcliffe efficiency between the observed and median simulated streamflow values were high and comparable for both the calibration and validation periods for each sub-catchment. We show that the GAMLSS has the capability to capture non-stationarity in the rainfall–streamflow process. It was also observed that the use of wavelet-based decomposed rainfall sub-series with optimum lags as covariates in the GAMLSS model captures the underlying physics of the rainfall–streamflow process. The development and application of an empirical rainfall–streamflow model that can be used to assess the impact of catchment-scale climate change on streamflow is demonstrated.  相似文献   

基于Landsat数据的2种地表温度空间降尺度算法比较研究     

范鹏宇  丁凤  张鑫 《亚热带资源与环境学报》2013,(2):20-27

以2003年5月29日福州市LandsmETM＋影像为数据源,对2种地表温度空间降尺度算法——EM算法和HUTS算法进行实验、比较与分析,EM算法是利用高空间分辨率的地表比辐射率对低空间分辨率的亮度温度影像进行调节,从而达到提高热红外影像空间分辨率的目的;HUTS算法则是引入了归一化差异植被指数NDVI和地表反照率d,通过在低空间分辨率拟合二者与地表温度LST之间的关系,然后根据其尺度不变性,将该关系应用到高空间分辨率的影像上,从而达到提高热红外影像空间分辨率的目的．研究结果表明：1）2种算法所得结果影像都能在有效提高空间分辨率的同时较好地保持了原始地表温度影像的空间分布特征,达到了较好的降尺度效果;2）以RMSE为定量评价指标,HUTS算法要略优于EM算法,其中,EM算法的RMSE为1．2494,而HUTS算法仅为0．9869．  相似文献   

Potential impact of climate change on flooding in the Upper Great Miami River Watershed,Ohio, USA: a simulation-based approach     

Shuang-Ye Wu 《水文科学杂志》2013,58(8):1251-1263

Abstract

This study aims to assess the potential impact of climate change on flood risk for the city of Dayton, which lies at the outlet of the Upper Great Miami River Watershed, Ohio, USA. First the probability mapping method was used to downscale annual precipitation output from 14 global climate models (GCMs). We then built a statistical model based on regression and frequency analysis of random variables to simulate annual mean and peak streamflow from precipitation input. The model performed well in simulating quantile values for annual mean and peak streamflow for the 20th century. The correlation coefficients between simulated and observed quantile values for these variables exceed 0.99. Applying this model with the downscaled precipitation output from 14 GCMs, we project that the future 100-year flood for the study area is most likely to increase by 10–20%, with a mean increase of 13% from all 14 models. 79% of the models project increase in annual peak flow.

Citation Wu, S.-Y. (2010) Potential impact of climate change on flooding in the Upper Great Miami River Watershed, Ohio, USA: a simulation-based approach. Hydrol. Sci. J. 55(8), 1251–1263.  相似文献   

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices     

Deepti Joshi  Andre St-Hilaire  Taha B. M. J. Ouarda  Anik Daigle  Nathalie Thiemonge 《水文科学杂志》2013,58(11):1996-2010

ABSTRACT

This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.