首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 250 毫秒
1.
Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T max and T min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.  相似文献   

2.
Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T max and T min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948?C2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001?C2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T max and T min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO2 concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.  相似文献   

3.

This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011–2100) maximum temperature (T max) and minimum temperature (Tmin) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T max, T min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011–2100, 2011–2040, 2041–2070, 2070–2100) using Mann-Kendall non-parametric test and Sen’s slope estimator, respectively. The temperature data series for the observed duration (1981–2000) has been found to show increasing trends in T max and T min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011–2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T max and T min have been seen to have decreasing trends in some months.

  相似文献   

4.
ARPEGE general circulation model simulations were dynamically downscaled by The Weather Research and Forecasting Model (WRF) for the study of climate change and its impact on grapevine growth in Burgundy region in France by the mid twenty-first century. Two time periods were selected: 1970–1979 and 2031–2040. The WRF model driven by ERA-INTERIM reanalysis data was validated against in situ surface temperature observations. The daily maximum and minimum surface temperature (Tmax and Tmin) were simulated by the WRF model at 8?×?8?km horizontal resolution. The averaged daily Tmax for each month during 1970–1979 have good agreement with observations, the averaged daily Tmin have a warm bias about 1–2?K. The daily Tmax and Tmin for each month (domain averaged) during 2031–2040 show a general increase. The largest increment (~3?K) was found in summer. The smallest increments (<1?K) were found in spring and fall. The spatial distribution of temperature increment shows a strong meridional gradient, high in south in summer, reversing in winter. The resulting potential warming rate in summer is equivalent to 4.7?K/century under the IPCC A2 emission scenario. The dynamically downscaled Tmax and Tmin were used to simulate the grape (Pinot noir grape variety) flowering and véraison dates. For 2031–2040, the projected dates are 8 and 12?days earlier than those during 1970–1979, respectively. The simulated hot days increase more than 50% in the two principal grapevine regions. They show strong impact on Pinot noir development.  相似文献   

5.
This study analyzes mid-21st century projections of daily surface air minimum (Tmin) and maximum (Tmax) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2°C or higher increases in Tmin and Tmax for all seasons. However, there are much greater (>3°C) increases in Tmax during summer at higher elevations and in Tmin during winter at lower elevations. Tmax increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. Tmin increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.  相似文献   

6.
The absence of continuous long term meteorological dataset has led to limited knowledge of glaciers’ response to climate change over Himalayas. This study presents an open source long term temperature dataset Climatic Research Unit (CRU) available since 1901 to study trend analysis of temperature (Tmax, Tmin and Tmean) for Gangotri basin in Himalayas. The study first establishes close agreement between CRU time series data and observed temperature dataset available from National Institute of Hydrology (NIH), Roorkee for a period of 11 years from 2005 to 2015 using standard anomaly, Wilcoxon Signed-Rank (WSR) and correlation tests. The close agreement of CRU with NIH data validate the use of CRU time series to study variation in meteorological parameter for hilly terrain of Himalayas. The second part includes application of different statistical tests such as Mann-Kendall (MK), Sen’s slope and CUSUM technique on CRU data to detect existence of any possible trends and identification of change points in Tmax, Tmin and Tmean on long term scale. On annual scale, significant increasing trends for Tmean and Tmin were observed with no significant trend for Tmax. On seasonal and monthly scale, Tmax showed significant decreasing trend for monsoon season and increasing trend for winters while Tmin show significant increasing trend for all months (except May) and seasons. CUSUM technique identified 8 change points from 3 annual time series with 2 for Tmean (1974 and 1999), 3 each for Tmax (1941, 1975 and 1999) and Tmin (1941, 1965 and 1999) respectively. Overall, significant increase in Tmin with no significant trend for Tmax has been identified over the study area.  相似文献   

7.
Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (Tmin and Tmax), 90th percentile of maximum temperature (Tmax90), 10th percentile of minimum temperature (Tmin10), number of frost days (Tnfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter Tmax90 and autumn Tmin10. Finally, MSLP is the best predictor for spring Tmin while Z500 is the best predictor for spring Tmax90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.  相似文献   

8.
In this study, the trends of the annual, seasonal and monthly maximum (T max) and minimum (T min) air temperatures time series were investigated for 20 stations in the western half of Iran during 1966?C2005. Three statistical tests including Mann?CKendall, Sen??s slope estimator and linear regression were used for the analysis. The annual T max and T min series showed a positive trend in 85% of the stations and a negative trend in 15% of the stations in the study region. The highest increase of T max and T min values were obtained over Kermanshah and Ahwaz at the rates of (+)0.597°C/decade and (+)0.911°C/decade, respectively. On the seasonal scale, the strongest increasing trends were identified in T max and T min data in summer. The highest numbers of stations with positive significant trends occurred in the monthly T max and T min series in August. In contrast, the lowest numbers of stations with significant positive trends were observed between November and March. Overall, the results showed similar increasing trends for the study variables, although T min generally increased at a higher rate than T max in the study period.  相似文献   

9.
Daily minimum and maximum air temperatures recorded in Naples (1872–1982) and in surrounding areas have been analysed in order to set up a statistical model for investigating climatic changes of extreme air temperature. We have analysed on various time-scales the mean values of minimum air temperature lower than the 10th percentile (Tmin10) and the mean values of the maximum air temperature greater than the 90th percentile (Tmax90). The results have shown for the city: (i) a significant secular trend both for yearly Tmin10 and Tmax90, mostly due to the process of urbanization, that is also responsible for (ii) the ascertained change in the character of the annual cycle, (iii) a reasonable ability to forecast winter Tmin10 and summer Tmax90 in statistical terms using a markovian model, and (iv) a significant 11-yr cycle with an amplitude of 0.5 °C directly related to solar activity which has never been succesfully determined before.  相似文献   

10.
Observations show that the surface diurnal temperature range (DTR) has decreased since 1950s over most global land areas due to a smaller warming in maximum temperatures (T max) than in minimum temperatures (T min). This paper analyzes the trends and variability in T max, T min, and DTR over land in observations and 48 simulations from 12 global coupled atmosphere-ocean general circulation models for the later half of the 20th century. It uses the modeled changes in surface downward solar and longwave radiation to interpret the modeled temperature changes. When anthropogenic and natural forcings are included, the models generally reproduce observed major features of the warming of T max and T min and the reduction of DTR. As expected the greenhouse gases enhanced surface downward longwave radiation (DLW) explains most of the warming of T max and T min while decreased surface downward shortwave radiation (DSW) due to increasing aerosols and water vapor contributes most to the decreases in DTR in the models. When only natural forcings are used, none of the observed trends are simulated. The simulated DTR decreases are much smaller than the observed (mainly due to the small simulated T min trend) but still outside the range of natural internal variability estimated from the models. The much larger observed decrease in DTR suggests the possibility of additional regional effects of anthropogenic forcing that the models can not realistically simulate, likely connected to changes in cloud cover, precipitation, and soil moisture. The small magnitude of the simulated DTR trends may be attributed to the lack of an increasing trend in cloud cover and deficiencies in charactering aerosols and important surface and boundary-layer processes in the models.  相似文献   

11.
Summary Summer-season (May–September) daily maximum temperature (T max) and daily minimum temperature (T min) observations and three types of heat spells obtained from these temperature observations at seven weather stations located in southern Quebec (Canada) for the 60-year period from 1941 to 2000 are studied to assess temporal changes in their characteristics (i.e. frequency of occurrence, seasonal hot days and extremal durations of heat spells). Type-A and Type-B heat spells are obtained respectively from T max and T min observations and Type-C heat spells from simultaneous joint observations of T max and T min using suitable thresholds and spells of duration ≥1-day and ≥3-day. The results of this investigation show that the majority of the selected percentiles (i.e. 5P, 10P, 25P, 50P, 75P, 80P, 90P, 92P, 95P, and 98P) of T max observations show a negative time-trend with statistically significant decreases (at 10% level) in some of the higher percentiles and in the maximal values at four out of seven stations. Almost all of the selected percentiles (same as for the T max) and the maximal and minimal values of T min observations show a positive trend, with statistically significant increases for all seven stations. Examination of frequencies of occurrence of heat spells, seasonal hot days and annual extremes of heat spell durations indicate that many of these characteristics of heat spells have undergone statistically significant changes over time at some of the stations for Type-A and Type-B heat spells as compared to Type-C heat spells. The Type-C heat spells are generally small in number and are found to be relatively temporally stable. More severe Type-C heat spells, i.e. the ones having T max and T min values simultaneously above very high thresholds and with duration ≥3-day have been rarely observed in southern Quebec.  相似文献   

12.
This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T 2) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T 2 were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T 2 were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.  相似文献   

13.
The usefulness of two remotely sensed variables, land surface temperature (LST) and cloud cover (CC), as predictors for the gridding of daily maximum and minimum 2 m temperature (T min/T max) was assessed. Four similar gridding methods were compared, each of which applied regression kriging to capture the spatial variation explained by the predictors used; however, both methods differed in the interpolation steps performed and predictor combinations used. The robustness of the gridding methods was tested for daily observations in January and July in the period 2009–2011 and in two different regions: the Central European region (CER) and the Iberian Peninsula (IP). Moreover, the uncertainty estimate provided by each method was evaluated using cross-validation. The regression analyses for both regions demonstrated the high predictive skills of LST for T min and T max on daily and monthly timescales (and lower predictive skills of CC). The application of LST as a predictor considerably improved the gridding performance over the IP region in July; however, there was only a slight improvement over the CER region. CC reduced the loss of spatial variability in the interpolated daily T min/T max values over the IP region. The interpolation skill was mainly controlled by the station density, but also depended on the complexity of the terrain. LST was shown to be of particular value for very low station densities (1 station per 50,000 km2). Analyses with artificially decreasing station densities showed that even in the case of very low station densities, LST allows the determination of useful regression functions.  相似文献   

14.
Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development. Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda, Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada.  相似文献   

15.
Assessing disease risk has become an important component in the development of climate change adaptation strategies. Here, the infection ability of leaf blast (Magnaporthe oryzae) was modeled based on the epidemiological parameters of minimum (T min), optimum (T opt), and maximum (T max) temperatures for sporulation and lesion development. An infection ability response curve was used to assess the impact of rising temperature on the disease. The simulated spatial pattern of the infection ability index (IAI) corresponded with observed leaf blast occurrence in Indo-Gangetic plains (IGP). The IAI for leaf blast is projected to increase during the winter season (December–March) in 2020 (2010–2039) and 2050 (2040–2069) climate scenarios due to temperature rise, particularly in lower latitudes. However, during monsoon season (July–October), the IAI is projected to remain unchanged or even reduce across the IGP. The results show that the response curve may be successfully used to assess the impact of climate change on leaf blast in rice. The model could be further extended with a crop model to assess yield loss.  相似文献   

16.
Surface temperatures are projected to increase 3–4°C over much of Africa by the end of the 21st century. Precipitation projections are less certain, but the most plausible scenario given by the Intergovernmental Panel on Climate Change (IPCC) is that the Sahel and East Africa will experience modest increases (~5%) in precipitation by the end of the 21st century. Evapotranspiration (Ea) is an important component of the water, energy, and biogeochemical cycles that impact several climate properties, processes, and feedbacks. The interaction of Ea with climate change drivers remains relatively unexplored in Africa. In this paper, we examine the trends in Ea, precipitation (P), daily maximum temperature (Tmax), and daily minimum temperature (Tmin) on a seasonal basis using a 31?year time series of variable infiltration capacity (VIC) land surface model (LSM) Ea. The VIC model captured the magnitude, variability, and structure of observed runoff better than other LSMs and a hybrid model included in the analysis. In addition, we examine the inter-correlations of Ea, P, Tmax, and Tmin to determine relationships and potential feedbacks. Unlike many IPCC climate change simulations, the historical analysis reveals substantial drying over much of the Sahel and East Africa during the primary growing season. In the western Sahel, large increases in daily maximum temperature appear linked to Ea declines, despite modest rainfall recovery. The decline in Ea and latent heating in this region could lead to increased sensible heating and surface temperature, thus establishing a possible positive feedback between Ea and surface temperature.  相似文献   

17.
Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.  相似文献   

18.
Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area.  相似文献   

19.
Extreme normalised residuals, defined as departures from the average values, of 65 daily maximum, T max, and minimum, T min, temperature series recorded in Catalonia (NE Spain) during 1950–2004 are analysed. Similarly to the sampling strategies applied to long dry spells, the partial duration series (PDS) offer some advantages in comparison with the annual extreme series. Instead of using a common percentile threshold for all temperature series, PDS are chosen according to the mean excess plot procedure. Series of extreme residuals are modelled, in terms of the L-moments formulation, by the generalised Pareto distribution. Extreme residuals of T max and T min are estimated for return periods ranging from 2 to 50 years and their spatial distribution is represented for selected return periods of 2, 5, 10, 25 and 50 years. Two daily extreme temperatures events, a hot episode (in August) and a cold episode (in February), are simulated taking into account the average T max (T min) for a day in August (February), their standard deviations and the extremes for a 50-year return period. Both simulations are compared with outstanding real episodes recorded on August 13th 2003 and February 11th 1956. Additionally, a spatial regionalisation of Catalonia in several clusters, in terms of the extreme residuals for return periods from 2 to 50 years, is done. A principal component analysis is applied to the extreme residual curves characterising every temperature series and, using as variables the principal components, the regionalisation is obtained by applying the average linkage clustering algorithm. Finally, each cluster is characterised by its average extreme residual curve for return periods ranging from 2 to 50 years at 1-year interval.  相似文献   

20.
The seasonal trend of vertical temperature profiles within and above an urban canopy has been investigated. We measured air temperatures and wind velocities along a 29-m tower in a residential area of Tokyo, Japan continually for 14 months. The height of the daily maximum temperature ZTmax varied with the season; ZTmax was at the roof level in winter but near the ground in summer. The seasonal change of ZTmax is likely due to the change of height at which solar energy is absorbed. At the time of the maximum temperature, the atmosphere above the canopy is always unstable whereas the air within the canopy is unstable in summer but stable in winter.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号