Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10–fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.     

Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.     

Mapping near-surface air temperature,pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal reso- lution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km× 1 km. The approach comprises two steps： （1） fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; （2） apply a simple kriging procedure to the residual for trend surface correction. The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Predic- tion （NCEP） Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.     

Mapping average annual precipitation in Serbia (1961–1990) by using regression kriging

The appearence of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of meteorological elements over large areas in the applied climatology. The objective of this study is to use geostatistics to characterize the spatial structure and map the spatial variation of average values of precipitation for a 30-year period in Serbia. New, recently introduced, geostatistical algorithms facilitate utilization of auxiliary variables especially remote sensing data or freely available global datasets. The data from Advanced Spaceborn Thermal Emission and Reflection Radiometer global digital elevation model are incorporated as ancillary variables into spatial prediction of average annual precipitation using geostatistical method known as regression kriging. The R ² value of 0.842 proves high performance result of the prediction of the proposed method.     

基于ENVI-met模式的城市近地表气温模拟与分析-以南京市部分区域为例

城市不同下垫面与建筑物空间形态对近地表气温等微气候要素产生了重要影响。开展城市气温时空变化模拟与影响因素分析,对于城市热环境评价与城市规划具有重要意义。论文基于高空间分辨率Geoeye-1立体影像,在建筑物高度、下垫面覆盖类型信息提取的基础上,选择南京一中、光华东街、玄武湖、头陀岭4个区域,采用ENVI-met微气候模式,以城市基本气象站南京站的实时气象数据作为背景气象场,模拟不同区域近地表气温的时空分布特征,并利用区域自动气象站观测数据进行精度检验。结果表明:在时间变化上,ENVI-met模拟气温与实测值之间吻合程度较高;在空间分布上,南京一中与光华东街区域气温时空分布规律总体相似,但城市空间形态的差异使得局部区域气温变化不同,玄武湖区域气温由陆地中心向外围呈递减趋势,而头陀岭地形复杂多变,白天气温变化剧烈,夜间空间变化较小。     

Temperature scenarios for Norway: from regional to local scale

Scenarios with daily time resolution are frequently used in research on the impacts of climate change. These are traditionally developed by regional climate models (RCMs). The spatial resolution, however, is usually too coarse for local climate change analysis, especially in regions with complex topography, such as Norway. The RCM used, HIRHAM, is run with lateral boundary forcing provided from two global medium resolution models; the ECHAM4/OPYC3 from MPI and the HadAM3H from the Hadley centre. The first is run with IPCC SRES emission scenario B2, the latter is run with IPCC SRES emission scenarios A2 and B2. All three scenarios represent the future time period 2071–2100. Both models have a control run, representing the present climate (1961–1990). Daily temperature scenarios are interpolated from HIRHAM to Norwegian temperature stations. The at-site HIRHAM-temperatures, both for the control and scenario runs, are adjusted to be locally representative. Mean monthly values and standard deviations based on daily values of the adjusted HIRHAM-temperatures, as well as the cumulative distribution curve of daily seasonal temperatures, are conclusive with observations for the control period. Residual kriging are used on the adjusted daily HIRHAM-temperatures to obtain high spatial temperature scenarios. Mean seasonal temperature grids are obtained. By adjusting the control runs and scenarios and improving the spatial resolution of the scenarios, the absolute temperature values are representative at a local scale. The scenarios indicate larger warming in winter than in summer in the Scandinavian regions. A marked west–east and south–north gradient is projected for Norway, where the largest increase is in eastern and northern regions. The temperature of the coldest winter days is projected to increase more than the warmer temperatures.     

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as “field” or “global” significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ^° grid resolution. Monthly temperature climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.     

The thermodynamic influence of subgrid orography in a global climate model

Assessments of the impacts of climate change typically require information at scales of 10 km or less. Such a resolution in global climate simulations is unlikely for at least two decades. We have developed an alternative to explicit resolution that provides a framework for meeting the needs of climate change impact assessment much sooner. We have applied to a global climate model a physically based subgrid-scale treatment of the influence of orography on temperature, clouds, precipitation, and land surface hydrology. The treatment represents subgrid variations in surface elevation in terms of fractional area distributions of discrete elevation classes. For each class it calculates the height rise/descent of air parcels traveling through the grid cell, and applies the influence of the rise/descent to the temperature and humidity profiles of the elevation class. Cloud, radiative, and surface processes are calculated separately for each elevation class using the same physical parametrizations used by the model without the subgrid orography parametrization. The simulated climate fields for each elevation class can then be distributed in post-processing according to the spatial distribution of surface elevation within each grid cell. Parallel 10-year simulations with and without the subgrid treatment have been performed. The simulated temperature, precipitation and snow water are mapped to 2.5-minute (~5 km) resolution and compared with gridded analyses of station measurements. The simulation with the subgrid scheme produces a much more realistic distribution of snow water and significantly more realistic distributions of temperature and precipitation than the simulation without the subgrid scheme. Moreover, the 250-km grid cell means of most other fields are virtually unchanged by the subgrid scheme. This suggests that the tuning of the climate model without the subgrid scheme is also applicable to the model with the scheme.     

A 10-Yr Global Land Surface Reanalysis Interim Dataset (CRA-Interim/Land): Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models, and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions. In this paper, we report the development of a 10-yr China Meteorological Administration (CMA) global Land surface ReAnalysis Interim dataset (CRA-Interim/Land; 2007–2016, 6-h intervals, approximately 34-km horizontal resolution). The dataset was produced and evaluated by using the Global Land Data Assimilation System (GLDAS) and NCEP Climate Forecast System Reanalysis (CFSR) global land surface reanalysis datasets, as well as in situ observations in China. The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land, GLDAS, and CFSR climatology are highly consistent, while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets. Compared with ground observations in China, CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0-10-cm soil layer and has higher correlations and slightly lower root mean square errors (RMSE) for the 10-40-cm soil layer. However, CRA-Interim/Land shows negative biases in 10-40-cm soil moisture in Northeast China and north of central China. For ground temperature and the soil temperature in different layers, CRA-Interim/Land behaves better than the CFSR, especially in East and central China. CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters. Therefore, this dataset is potentially a critical supplement to the CRA-Interim. Further evaluation of the CRA-Interim/Land, assimilation of near-surface atmospheric forcing variables, and extension of the current dataset to 40 yr (1979–2018) are in progress.     

Analysis of rainfall seasonality from observations and climate models

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere–ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring a distinct wet and dry season, it is directly related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. Models participating to the Coupled Model Intercomparison Project platform, Phase 5, consistently overestimate the RE over tropical Latin America and underestimate it in West Africa, western Mexico and East Asia. It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions, too large during the wet months and too small in the months preceding and following the wet season; negative biases are instead due, in most cases, to an excess of rainfall during the premonsoonal months.     

陆面过程模式BCC_AVIM中地表覆盖数据现状

地表覆盖是陆面和气候模式中的一个重要基础数据。以陆面过程模式BCC_AVIM为例,介绍模式中的地表覆盖数据变量、数据分辨率、不同类型数据的来源,重点比较分类方法差异巨大且类型众多的植被覆盖。综述比较了国际和国内常用的几套全球地表覆盖数据的来源、分类系统和分类方法以及空间分辨率,根据陆面过程模式的地表覆盖数据需求,确定不同全球土地覆盖数据在模式中的应用方法,讨论分析了全球地表覆盖产品在模式应用中存在的差距,提出不同遥感数据产品之间一致性较差的可能解决方案,探讨遥感数据产品在模式中应用的可能方式,以期更好地发挥全球地表覆盖数据产品的作用。     

Assessment of interpolated ERA-40 reanalysis temperature and precipitation against observations of the Balkan Peninsula

Due to their ready availability and temporal and spatial consistency, reanalysis data are widely used within the climate community. Nevertheless, higher spatial resolutions are often required and statistical interpolation techniques are applied to increase the data resolution. This work aims to derive a set of high spatial resolution data through three-dimensional interpolation of daily temperature and precipitation. Thin plate spline interpolation has been chosen and used to interpolate ERA-40 temperature and precipitation from a coarse grid (110 km) into a finer one of 1-km spatial resolution. The study evaluates the method by comparing the simulated variables with available in situ meteorological measurements. The chosen stations are distributed over the study region and, most importantly, contain information from a range of altitudes. The results indicate that accounting for the topography in the interpolation process improves the comparisons, with the biggest improvements being evident in the most mountainous areas. The method is found to be better in estimating temperature than precipitation fields. Moreover, the method performs better for maximum temperature in high altitudes and for minimum temperature in low altitudes.     

New precipitation and temperature grids for northern Patagonia: Advances in relation to global climate grids

Climate data of mean monthly temperature and total monthly precipitation compiled from different sources in northern Patagonia were interpolated to 20-km resolution grids over the period 1997–2010. This northern Patagonian climate grid (NPCG) improves upon previous gridded products in terms of its spatial resolution and number of contributing stations, since it incorporates 218 and 114 precipitation and temperature records, respectively. A geostatistical method using surface elevation from a Digital Elevation Model (DEM) as the ancillary variable was used to interpolate station data into even spaced points. The maps provided by NPCG are consistent with the broad spatial and temporal patterns of the northern Patagonian climate, showing a comprehensive representation of the latitudinal and altitudinal gradients in temperature and precipitation, as well as their related patterns of seasonality and continentality. We compared the performance of NPCG and various other datasets available to the climate community for northern Patagonia. The grids used for the comparison included those of the Global Precipitation Climatology Project, ERAInterim, Climate Research Unit (University of East Anglia), and University of Delaware. Based on three statistics that quantitatively assess the spatial coherence of gridded data against available observations (bias, MAE, and RMSE), NPCG outperforms other global grids. NPCG represents a useful tool for understanding climate variability in northern Patagonia and a valuable input for regional models of hydrological and ecological processes. Its resolution is optimal for validating data from the general circulation models and working with raster data derived from remote sensing, such as vegetation indices.     

Recent progress in studies on land cover change and its regional climatic effects over China during historical times

The recent progresses on the reconstruction of historical land cover and the studies on regional climatic effects to temperature,precipitation,and the East Asian Monsoon across China were reviewed.Findings show that the land cover in China has been significantly modified by human activities over the last several thousands years,mainly through cropland expansion and forest clearance.The cropland over traditional Chinese agricultural areas increased from 5.32×105 km2 in the mid-17th century to 8.27×105 km2 in...     

Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation

We present seasonal precipitation reconstructions for European land areas (30°W to 40°E/30–71°N; given on a 0.5°×0.5° resolved grid) covering the period 1500–1900 together with gridded reanalysis from 1901 to 2000 (Mitchell and Jones 2005). Principal component regression techniques were applied to develop this dataset. A large variety of long instrumental precipitation series, precipitation indices based on documentary evidence and natural proxies (tree-ring chronologies, ice cores, corals and a speleothem) that are sensitive to precipitation signals were used as predictors. Transfer functions were derived over the 1901–1983 calibration period and applied to 1500–1900 in order to reconstruct the large-scale precipitation fields over Europe. The performance (quality estimation based on unresolved variance within the calibration period) of the reconstructions varies over centuries, seasons and space. Highest reconstructive skill was found for winter over central Europe and the Iberian Peninsula. Precipitation variability over the last half millennium reveals both large interannual and decadal fluctuations. Applying running correlations, we found major non-stationarities in the relation between large-scale circulation and regional precipitation. For several periods during the last 500 years, we identified key atmospheric modes for southern Spain/northern Morocco and central Europe as representations of two precipitation regimes. Using scaled composite analysis, we show that precipitation extremes over central Europe and southern Spain are linked to distinct pressure patterns. Due to its high spatial and temporal resolution, this dataset allows detailed studies of regional precipitation variability for all seasons, impact studies on different time and space scales, comparisons with high-resolution climate models as well as analysis of connections with regional temperature reconstructions. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle

In 1989, the need for reliable gridded land surface precipitation data sets, in view of the large uncertainties in the assessment of the global energy and water cycle, has led to the establishment of the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst on invitation of the WMO. The GPCC has calculated a precipitation climatology for the global land areas for the target period 1951–2000 by objective analysis of climatological normals of about 67,200 rain gauge stations from its data base. GPCC's new precipitation climatology is compared to several other station-based precipitation climatologies as well as to precipitation climatologies derived from the GPCP V2.2 data set and from ECMWF's model reanalyses ERA-40 and ERA-Interim. Finally, how GPCC's best estimate for terrestrial mean precipitation derived from the precipitation climatology of 786 mm per year (equivalent to a water transport of 117,000 km³) is fitting into the global water cycle context is discussed.     

Satellite-derived estimates of potential carbon sequestration through afforestation of agricultural lands in the United States

Afforestation of marginal agricultural lands represents a promising option for carbon sequestration in terrestrial ecosystems. An ecosystem carbon model was used to generate new national maps of annual net primary production (NPP), one each for continuous land covers of ‘forest’, ‘crop’, and ‘rangeland’ over the entire U. S. continental area. Direct inputs of satellite “greenness” data from the Advanced Very High Resolution Radiometer (AVHRR) sensor into the NASA-CASA carbon model at 8-km spatial resolution were used to estimate spatial variability in monthly NPP and potential biomass accumulation rates in a uniquely detailed manner. The model predictions of regrowth forest production lead to a conservative national projection of 0.3 Pg C as potential carbon stored each year on relatively low-production crop or rangeland areas. On a regional level, the top five states for total crop afforestation potential were: Texas, Minnesota, Iowa, Illinois, and Missouri, whereas the top five states for total rangeland afforestation potential are: Texas, California, Montana, New Mexico, and Colorado. Afforestation at this level of intensity has the capacity to offset at least one-fifth of annual fossil fuel emission of carbon in the United States. These projected afforestation carbon gains also match or exceed recent estimates of the annual sink for atmospheric CO₂ in currently forested area of the country.     

Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM

Urban-rural difference of land cover is the key determinant of urban heat island (UHI). In order to evaluate the impact of land cover data on the simulation of UHI, a comparative study between up-to-date CORINE land cover (CLC) and Urban Atlas (UA) with fine resolution (100 and 10 m) and old US Geological Survey (USGS) data with coarse resolution (30 s) was conducted using the Weather Research and Forecasting model (WRF) coupled with bulk approach of Noah-LSM for Berlin. The comparison between old data and new data partly reveals the effect of urbanization on UHI and the historical evolution of UHI, while the comparison between different resolution data reveals the impact of resolution of land cover on the simulation of UHI. Given the high heterogeneity of urban surface and the fine-resolution land cover data, the mosaic approach was implemented in this study to calculate the sub-grid variability in land cover compositions. Results showed that the simulations using UA and CLC data perform better than that using USGS data for both air and land surface temperatures. USGS-based simulation underestimates the temperature, especially in rural areas. The longitudinal variations of both temperature and land surface temperature show good agreement with urban fraction for all the three simulations. To better study the comprehensive characteristic of UHI over Berlin, the UHI curves (UHIC) are developed for all the three simulations based on the relationship between temperature and urban fraction. CLC- and UA-based simulations show smoother UHICs than USGS-based simulation. The simulation with old USGS data obviously underestimates the extent of UHI, while the up-to-date CLC and UA data better reflect the real urbanization and simulate the spatial distribution of UHI more accurately. However, the intensity of UHI simulated by CLC and UA data is not higher than that simulated by USGS data. The simulated air temperature is not dominated by the land cover as much as the land surface temperature, as air temperature is also affected by air advection.     

过去近50年全球变暖纬向平均模

Zonal mean annual temperature trends were estimated using four reanalysis and three analysis grid datasets. The trends over land and for the entire globe were estimated from 1958-2001 and 1979-2007, respectively. Estimates of temperature trends over land from Climate Research Unit (CRU) analysis data indicate more intense warming moving northward, at a rate of about 3.5ºC per century at 65ºN, then declining further to the north. CRU estimates indicated dramatic warming over the latitudes of the Antarctic Peninsula, with a localized cooling trend at 45ºS. A global estimate was conducted by comparing estimates of the reanalysis datasets. Temperature distribution trends of the reanalysis data were similar to those generated by land observations but with large bias in the Polar Regions. The bias could be reduced by comparing these estimates with those from the analysis data at high latitudes. Extreme warming trends were estimated at rates of 2.9ºC-3.5ºC per century in the Arctic and 3.2ºC-4.7ºC per century in the Antarctic for 1958-2001. Surface warming was even more intense in the Northern Hemisphere for 1979-2007, with extreme arctic warming rates ranging from 8.5ºC-8.9ºC per century, as estimated by the analysis and reanalysis datasets. Trends over Antarctica for this period were contradictory, as Japan Meteorological Agency (JMA) reanalysis (JRA-25) indicated a cooling trend at about -7ºC per century, while other reanalysis datasets showed sharp warming over the continent.     

Creation of high-resolution climatic grid datasets for the territory of Georgia

Created are the grid datasets of monthly mean and annual mean temperature as well of monthly, seasonal, and annual values of the total precipitation with the resolution of 25 km for the period of 1936–2011. The obtained datasets characterize the real picture of the spatial distribution of temperature and precipitation on the territory of Georgia; therefore, they are used for working out geoinformative maps of temperature and precipitation variations. Revealed are the areas and centers with different intensity of warming and cooling. It is found that the annual temperature and total annual precipitation averaged for the territory do not vary considerably under conditions of the global warming.