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1.
Soil moisture (SM) can be retrieved from active microwave (AM), passive microwave (PM) and thermal infrared (TIR) observations, each having unique spatial and temporal coverages. A limitation of TIR‐based retrievals is a dependence on cloud‐free conditions, whereas microwave retrievals are almost all weather proof. A downside of SM retrievals from PM is the coarse spatial resolution. Although SM retrievals at coarse spatial resolution proved to be valuable for global‐scale and continental‐scale studies, their value for regional‐scale studies remains limited. To increase the use of SM retrievals from PM observations, an existing method to enhance their spatial resolution was applied. We present an intercomparison study over the Iberian Peninsula for three SM products on two different spatial sampling grids. The remotely sensed SM products were also compared with in situ observations from the Remedhus network. Variations between ground data and satellite‐based SM are observed; all three remotely sensed SM products show good agreement to the ground observations. The comparison shows that these ground observations and satellite data are consistent, based on the correlation coefficient (R) and root mean square error (RMSE). The remotely sensed products were intercompared after sampling at 25 × 25 km2 and after applying the smoothing filter‐based intensity modulation (SFIM) downscaling technique at 10 × 10 km2 grids. After the application of the SFIM technique, the SM retrievals from PM observations show better agreement with the other remotely sensed SM products for approximately 40% of the study area. For another 40% of the study area, we found a similar agreement between these product combinations, whereas in extreme environments, both arid and densely vegetated regions, the agreement decreases after the application of the SFIM technique. Agreement between retrievals of absolute SM content from PM and TIR observations is generally high (R = 0.77 for semi‐arid areas). This study enhances our understanding of the remotely sensed SM products for improvements of SM retrieval and merging strategies. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

2.
Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium‐Range Weather Forecasts 40‐year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10‐km grid resolution; stochastically generated data from weather generator; bias‐corrected dynamically downscaled; and bias‐corrected global reanalysis. The reanalysis products are considered as surrogates for large‐scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias‐corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

3.
Precipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for downscaling low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the downscaling algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by downscaling NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm downscaled daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the downscaling and the original NEXRAD precipitation maps. For three daily precipitation events, downscaled precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the downscaled map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on downscaling hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has potential to perform well at both daily and hourly precipitation fields, indicated from good regression performance.  相似文献   

4.
The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.  相似文献   

5.
The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be downscaled before being used. This study developed a statistical spatial downscaling scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The downscaled precipitation data were compared with ground observations and exhibited good agreement with r2 values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.  相似文献   

6.
With recent advances in downscaling methodologies, soil moisture (SM) estimation using microwave remote sensing has become feasible for local application. However, disaggregation of SM under all sky conditions remains challenging. This study suggests a new downscaling approach under all sky conditions based on support vector regression (SVR) using microwave and optical/infrared data and geolocation information. Optically derived estimates of land surface temperature and normalized difference vegetation index from MODerate Resolution Imaging Spectroradiometer land and atmosphere products were utilized to obtain a continuous spatio-temporal input datasets to disaggregate SM observation from Advanced SCATterometer in South Korea during 2015 growing season. SVR model was compared to synergistic downscaling approach (SDA), which is based on physical relationship between SM and hydrometeorological factors. Evaluation against in situ observations showed that the SVR model under all sky conditions (R: 0.57 to 0.81, ubRMSE: 0.0292 m3 m?3 to 0.0398 m3 m?3) outperformed coarse ASCAT SM (R: 0.55 to 0.77, ubRMSE: 0.0300 m3 m?3 to 0.0408 m3 m?3) and SDA model (mean R: 0.56 to 0.78, ubRMSE: 0.0324 m3 m?3 to 0.0436 m3 m?3) in terms of statistical results as well as sensitivity with precipitation. This study suggests that the spatial downscaling technique based on remote sensing has the potential to derive high resolution SM regardless of weather conditions without relying on data from other sources. It offers an insight for analyzing hydrological, climate, and agricultural conditions at regional to local scale.  相似文献   

7.
Assessment of the suitability of satellite soil moisture products at large scales is urgently needed for numerous climatic and hydrological researches, particularly in arid mountainous watersheds where soil moisture plays a key role in landatmosphere exchanges. This study presents evaluation of the SMOS(L2) and SMAP(L2_P_E and L2_P) products against ground-based observations from the Upstream of the Heihe River Watershed in situ Soil Moisture Network(UHRWSMN) and the Ecological and Hydrological Wireless Sensor Network(EHWSN) over arid high mountainous watersheds, Northwest China.Results show that all the three products are reliable in catching the temporal trend of the in situ observations at both point and watershed scales in the study area. Due to the uncertainty in brightness temperature and the underestimation of effective temperature, the SMOS L2 product and both the SMAP L2 products show "dry bias" in the high, cold mountainous area. Because of the more accurate brightness temperature observations viewing at a constant angle and more suitable estimations of single scattering albedo and optical depth, both the SMAP L2 products performed significantly better than the SMOS product.Moreover, comparing with station density of in situ network, station representation is much more important in the evaluation of the satellite soil moisture products. Based on our analysis, we propose the following suggestions for improvement of the SMOS and SMAP product suitability in the mountainous areas: further optimization of effective temperature; revision of the retrieval algorithm of the SMOS mission to reduce the topographic impacts; and, careful selection of in situ observation stations for better representation of in situ network in future evaluations. All these improvements would lead to better applicability of the SMOS and SMAP products for soil moisture estimation to the high elevation and topographically complex mountainous areas in arid regions.  相似文献   

8.
This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

9.
Estimating reference evapotranspiration using numerical weather modelling   总被引：3，自引：0，他引：3  
Evapotranspiration is an important hydrological process and its estimation usually needs measurements of many weather variables such as atmospheric pressure, wind speed, air temperature, net radiation and relative humidity. Those weather variables are not easily obtainable from in situ measurements in practical water resources projects. This study explored a potential application of downscaled global reanalysis weather data using mesoscale modelling system 5 (MM5). The MM5 is able to downscale the global data down to much finer resolutions in space and time for use in hydrological investigations. In this study, the ERA‐40 reanalysis data are downscaled to the Brue catchment in southwest England. The results are compared with the observation data. Among the studied weather variables, atmospheric pressure could be derived very accurately with less than 0·2% error. On the other hand, the error in wind speed is about 200–400%. The errors in other weather variables are air temperature (<10%), relative humidity (5–21%) and net radiation (4–23%). The downscaling process generally improves the data quality (except wind speed) and provides higher data resolution in comparison with the original reanalysis data. The evapotranspiration values estimated from the downscaled data are significantly overestimated across all the seasons (27–46%) based on the FAO Penman–Monteith equation. The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period). There are clear patterns among some weather variables and they could be used to correct the biases in the downscaled data from either short‐term in situ measurements or through regionalization from surrounding weather stations. Artificial intelligence tools could be used to map the downscaled data directly into evapotranspiration or even river runoff if rainfall data are available. This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encouraged. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

10.
We have studied, for the first time, variations in absolute surface geostrophic currents (SGC) using satellite data only. The proposed approach combines 18 years’ altimetry data, which provide reliable measurements of absolute sea level (ASL), with a gravity field and steady-state ocean circulation explorer geoid model to obtain dynamic topography, and achieves unprecedented precision and accuracy. Our proposal overcomes the main limitations of existing approaches based solely on altimetry data (which suffer from lack of an independent reference for derivation of ASL maps), and approximations based on in-situ data (which are characterized by a sparse and inhomogeneous coverage in time and space). Features of annual variations of SGC are also addressed. As a result of our study we provide new absolute SGC climatology in the form of a 52-week data set of surface current fields, gridded at quarter degree longitude and latitude resolution and resolving spatial scales as short as 140 km. For presentation, this data set is averaged monthly and the results, presented as monthly climatology, are compared with climatology based on in-situ observations from drifter data.  相似文献   

11.
One of the basic limitations to the use of geomorphological maps is their coarse resolution relative to the needs of pure and applied geomorphological research. In response to this, attempts have been made to ‘downscale’ geomorphological information to finer spatial resolutions. However, the potential of statistical downscaling in geomorphology has been insufficiently examined. We downscaled four different periglacial features (wind deflation, palsa mire, earth hummock and sorted solifluction sheet) from a 100 ha grid to a 1 ha grid resolution utilizing two different techniques: point sampling (PSA) and direct (DA) approaches. We assessed the predictive accuracy of the models with the area under the curve (AUC) of a receiver operating characteristic plot using independent evaluation data. The PSA technique yielded encouraging results with a mean accuracy of 0·81, whereas the performance of DA was poorer. The predictive performance of the palsa mire and solifluction sheet models was excellent (AUC values from 0·89 to 0·96), whereas the AUC values of deflation and earth hummock models were lower (AUC = 0·57–0·81). The application of a point sampling approach as used here provides an efficient method to translate geomorphological information to finer resolution. However, further testing of the downscaling approaches is required before they can be applied to real‐world situations. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

12.
A three dimensional structure of mesoscale circulation in the Black Sea is simulated using the Proudman Oceanographic Laboratory Coastal Ocean Modelling System. A number of sensitivity tests reveal the response of the model to changes in the horizontal resolution, time steps, and diffusion coefficients. Three numerical grids are examined with x-fine (3.2 km), fine (6.7 km) and coarse (25 km) resolution. It is found that the coarse grid significantly overestimates the energy of the currents and is not adequate even for the study of basin-scale circulation. The x-fine grid, on the other hand, does not give significant advantages compared to the fine grid, and the latter is used for the bulk of simulations. The most adequate parameters are chosen from the sensitivity study and used to model both the basin-scale circulation and day-to-day variability of mesoscale currents for the months of May and June of 2000. The model is forced with actual wind data every 6 h and monthly climatic data for evaporation, precipitation, heat fluxes and river run-off. The results of the fine grid model are compared favourably against the satellite imagery. The model adequately reproduces the general circulation and many mesoscale features including cyclonic and anticyclonic eddies, jets and filaments in different parts of the Black Sea. The model gives a realistic geographical distribution and parameters of mesoscale currents, such as size, shape and evolution of the eddies.  相似文献   

13.
利用降尺度方法对CMIP5全球气候模式进行空间降尺度并以此研究鄱阳湖流域未来气候时空变化趋势,能够为流域生态环境保护提供数据、技术和理论上的支持.通过简化原始网络结构,在网络首部添加插值层,采用反卷积算法作为上采样算法对传统U-Net网络进行改进,建立基于深度学习的气候模式空间降尺度模型(DLDM).以1965-200...  相似文献   

14.
In situ soil moisture data from the Bibeschbach experimental catchment in Luxembourg are used to evaluate relative surface soil moisture observed with the MetOp‐A Advanced Scatterometer (ASCAT). Filtered and bias‐corrected surface soil wetness indices (SWIs) derived from coarse‐resolution (25 km) C‐band scatterometer observations are shown to be highly correlated (r = 0.86) with catchment‐averaged soil moisture measured in the field. The combination of ASCAT and ENVISAT Advanced Synthetic Aperture Radar (ASAR) data sets yields high‐resolution (1 km) relative surface soil moisture that is equally well correlated with in situ measurements. It is concluded that for soil moisture monitoring applications at a catchment scale, the two soil moisture products are equivalent. The best correlation between the SWI derived from ASCAT and ASCAT‐ASAR with in situ soil moisture observations at ca. 5 cm depth is obtained with a characteristic time length parameter T equal to 288 h. These results suggest that satellite‐derived surface soil wetness may serve as proxy for soil storage that enables the monitoring of abrupt switches in river system dynamics to appear when an effective field capacity is exceeded and rapid subsurface stormflow is initiated. In catchments where soil moisture is the main controlling factor of rapid subsurface flow, MetOp ASCAT–derived SWI has the potential to monitor how a river system approaches a critical threshold. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

15.
This work deals with analysis of hydrographic observations and results of numerical simulations. The data base includes acoustic Doppler current profilers (ADCP) observations, continuous measurements on data stations and satellite data originating from the medium resolution imaging spectrometer (MERIS) onboard the European Space Agency (ESA) satellite ENVISAT with a spatial resolution of 300 m. Numerical simulations use nested models with horizontal resolutions ranging from 1 km in the German Bight to 200 m in the East Frisian Wadden Sea coupled with a suspended matter transport model. Modern satellite observations have now a comparable horizontal resolution with high-resolution numerical model of the entire area of the East Frisian Wadden Sea allowing to describe and validate new and so far unknown patterns of sediment distribution. The two data sets are consistent and reveal an oscillatory behaviour of sediment pools to the north of the back-barrier basins and clear propagation patterns of tidally driven suspended particulate matter outflow into the North Sea. The good agreement between observations and simulations is convincing evidence that the model simulates the basic dynamics and sediment transport processes, which motivates its further use in hindcasting, as well as in the initial steps towards forecasting circulation and sediment dynamics in the coastal zone.  相似文献   

16.
The Basque coastal waters (South Bay of Biscay) are directly influenced by the Adour River freshwater plume. The Adour outflow leads to important variations of suspended matter concentrations and turbidity, which in turn may affect biological productivity and water quality. This study aims at both developing specific algorithms and testing the efficiency of atmospherically corrected MODIS-Aqua 250-m surface reflectance product (MYD09) to map total suspended matter concentrations and turbidity within the Adour coastal region. First, regional empirical algorithms based on in-situ data were tested to retrieve the concentration of total suspended matter and turbidity from the remote sensing reflectance. Then, the respective sensitivity of MODIS surface reflectance bands 1 and 2 for water quality application was investigated as well as the quality of atmospheric corrections. Finally, selected algorithms were applied to the MYD09 product. The resulting 250-m resolution maps were then compared to 1000-m maps produced by IFREMER and comparisons between satellite measurements and in-situ sampling points were performed. Results show that MODIS-Aqua band 1 (620–670 nm) is appropriate for predicting turbidity and total suspended matter concentrations using polynomial regression models, whilst band 2 is unadapted. Comparison between total suspended matter concentration 250-m resolution maps and mineral suspended matter 1000-m maps (generated by IFREMER) produced consistent results. A high correlation was obtained between turbidity measured in-situ and turbidity retrieved from MODIS-Aqua satellite data.  相似文献   

17.
Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.  相似文献   

18.
The National Airborne Field Experiment 2006 (NAFE’06) was conducted during a three week period of November 2006 in the Murrumbidgee River catchment, located in southeastern Australia. One objective of NAFE’06 was to explore the suitability of the area for SMOS (Soil Moisture and Ocean Salinity) calibration/validation and develop downscaling and assimilation techniques for when SMOS does come on line. Airborne L-band brightness temperature was mapped at 1 km resolution 11 times (every 1–3 days) over a 40 by 55 km area in the Yanco region and 3 times over a 40 by 50 km area that includes Kyeamba Creek catchment. Moreover, multi-resolution, multi-angle and multi-spectral airborne data including surface temperature, surface reflectance (green, read and near infrared), lidar data and aerial photos were acquired over selected areas to develop downscaling algorithms and test multi-angle and multi-spectral retrieval approaches. The near-surface soil moisture was measured extensively on the ground in eight sampling areas concurrently with aircraft flights, and the soil moisture profile was continuously monitored at 41 sites. Preliminary analyses indicate that (i) the uncertainty of a single ground measurement was typically less than 5% vol. (ii) the spatial variability of ground measurements at 1 km resolution was up to 10% vol. and (iii) the validation of 1 km resolution L-band data is facilitated by selecting pixels with a spatial soil moisture variability lower than the point-scale uncertainty. The sensitivity of passive microwave and thermal data is also compared at 1 km resolution to illustrate the multi-spectral synergy for soil moisture monitoring at improved accuracy and resolution. The data described in this paper are available at www.nafe.unimelb.edu.au.  相似文献   

19.
Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

20.
 Mapping the mesoscale surface velocity stream function by combining estimates of surface height from satellite altimetry and surface currents from sequential infrared (sea-surface temperature) imagery using optimal interpolation is described. Surface currents are computed from infrared images by the method of maximum cross-correlations (MCC) and are combined with altimeter sea-level anomaly data from the TOPEX/Poseidon and ERS satellites. The analysis method was applied to 6 years of data from the East Australian Current region. The covariance of velocity and sea-level data is consistent with the statistical assumptions of homogeneous, isotropic turbulence, with typical length scales of order 220 km and time scales of 10 days in this region. Augmenting the analysis of altimeter data with MCC velocity observations improves the resolution of the surface currents, especially near the Australian coast, and demonstrates that the two data sources provide consistent and complementary observations of the surface mesoscale circulation. The volume of MCC data is comparable to that from a satellite altimeter, but with a more variable distribution of spatial and temporal resolution. In concert with altimetry, satellite radiometer velocimetry represents a technique useful for retrospective analysis of currents from high-resolution satellite radiometer data-sets. Received: 3 July 2001 / Accepted: 16 November 2001  相似文献   

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