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1.
The importance of satellite datasets as alternative sources of precipitation information has been argued in numerous studies. Future developments in satellite precipitation algorithms as well as utilization of satellite data in operational applications rely on a more in‐depth understanding of satellite errors and biases across different spatial and temporal scales. This paper investigates the capability of satellite precipitation data sets with respect to detecting heavy precipitation rates over different temporal accumulations. In this study, the performance of Tropical Rainfall Measuring Mission real time (TRMM‐RT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and CPC MORPHing (CMORPH) is compared against radar‐based gauge‐adjusted Stage IV data. The results show that none of the high temporal resolution (3‐h) datasets are ideal for detecting heavy precipitation rates. In fact, the detection skill of all products drops as the precipitation thresholds (i.e. 75 and 90 percentiles) increase. At higher temporal accumulations (6, 12 and 24 h), the detection skill improves for all precipitation products, with CMORPH showing a better detection skill compared to all other products. On the other hand, all precipitation products exhibit high false alarm ratios above the heavy precipitation thresholds, although TRMM‐RT lead to a relatively smaller level of false alarms. These results indicate that further efforts are necessary to improve the precipitation algorithms so that they can capture heavy precipitation rates more reliably. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

2.
《水文科学杂志》2012,57(2):296-310
ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).  相似文献   

3.
Correctly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi‐tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, n = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, n = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were <0 using GHCN weather station records (8 of 11) and CFSR reanalysis data (9 of 11). Autocalibration of individual SWAT models for each of the 11 basins against each of the available weather datasets yielded NSE values > 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations <10 km from the basin centroid. In addition to demonstrating the need to evaluate available weather inputs, this research reinforces the value of CFSR data as a means to supplement ground records and consistently determine a baseline for hydrologic model performance. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

4.
Forest ecohydrological feedbacks complicate the threshold behaviour of stormflow response to precipitation or wetting conditions on a long-term scale (e.g. several years). In this study, the threshold behaviours in an evergreen-deciduous mixed forested headwater catchment in southern China were examined during 2009–2015, when damaged vegetation was recovering after the great 2008 Chinese ice and snowstorm. The non-uniqueness of the thresholds and the slow and rapid responses of stormflow at the outlet of the catchment in different hydro-climate datasets with different maximum values of gross precipitation (P) and sums of precipitation and antecedent soil moisture index (P + ASI) were assessed. The thresholds of P and P + ASI required to trigger stormflows (i.e. ‘generation thresholds’) and the transition from slow to rapid responses of stormflow (i.e. ‘rise thresholds’) were compared both seasonally and annually. The results indicated significant differences in the analysed datasets, highlighting the need to compare thresholds with care to avoid misinterpretation. Seasonal variations in threshold behaviours in the catchment suggested that vegetation canopy interception contributed to higher rise thresholds, and wetter conditions resulted in higher runoff sensitivity to precipitation during the growing and rainy seasons. Furthermore, the generation thresholds were higher in the dormant season, possibly due to drier soil moisture conditions in the near-channel areas. During the vegetation recovery period, the annual generation thresholds increased, however the rise thresholds did not exhibit a similar trend. The rapid stormflow response above the threshold decreased, possibly due to transpiration and interception of the recovered vegetation. However, the slow stormflow response to small rainfall events below the thresholds was higher in wetter years but lower in drier years, suggesting that the total water input dominated the stormflow response during small rainfall events. In conclusion, the seasonal and annual variations in threshold behaviours highlight that vegetation recovery and hydro-climatic conditions had a notable impact on the stormflow response.  相似文献   

5.
Future extreme precipitation (EP, daily rainfall amount over certain thresholds) is projected to increase with global climate change; however, its effect on groundwater recharge has not been fully explored. This study specifically investigates the spatiotemporal dynamics of groundwater recharge and the effects of extreme precipitation (daily rainfall amount over the 95th percentile, which is tagged by ranking the percentiles in each season for a base period) on groundwater recharge from 1950 to 2010 over the Northern High Plains (NHP) Aquifer using the Soil Water Balance Model. The results show that groundwater recharge significantly (p < 0.05) increased in the eastern NHP from 1950 to 2010, where the highest annual average groundwater recharge occurs compared to the central and the western NHP. In the eastern NHP, 45.1% of the annual precipitation fell as EP, which contributed 56.8% of the annual total groundwater recharge. In the western NHP, 30.9% of the annual precipitation fell as extreme precipitation, which contributed 62.5% of the annual total groundwater recharge. In addition, recharge by extreme precipitation mainly occurred in late spring and early summer, before the maximum evapotranspiration rate, which usually occurs in mid‐summer until late fall. A dry site in the western NHP and a wet site in the eastern NHP were analysed to indicate how recharge responds to EP with different precipitation regimes. The maximum daily recharge at the dry site exceeded the wet site when there was EP. When precipitation fell as non‐extreme rainfall, most recharge was less than 5 mm at both the dry and wet sites, and the maximum recharge at the dry site became lower than the wet site. This study shows that extreme precipitation plays a significant role in determining groundwater recharge. © 2016 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.  相似文献   

6.
This study investigates the impact of the spatio-temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)-Hydro system to simulate hydrological response during two catastrophic flood events over the Eastern Black Sea (EBS) and the Mediterranean (MED) regions of Turkey. Three time-variant and high spatial resolution external SST products (GHRSST, Medspiration and NCEP-SST) and one coarse-resolution and time-invariant SST product (ERA5- and GFS-SST for EBS and MED regions, respectively) already embedded in the initial and the boundary conditions datasets of WRF model are used in deriving near-surface atmospheric variables through WRF. After the proper event-based calibration is performed to the WRF-Hydro system using hourly and daily streamflow data in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST-triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher cross-correlations (GHRSST and Medspiration) revealed significant improvement in flood hydrographs for both regions. The GHRSST dataset shows a substantial improvement in NSE (~70%), RMSE reduction up to 20%, and an increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region. The use of both GHRSST and Medspiration SST data characterized with high spatio-temporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m3/s by reducing the overestimation seen in invariable SST (GFS) in the MED region. Improved precipitation simulation skills of the WRF model with the detailed SST representation show that the hydrographs of GHRSST and Medspiration simulations show better performance compared to the simulated hydrographs by observed precipitation.  相似文献   

7.
Satellite‐based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global high‐resolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi‐distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN‐CDR and NCEP‐CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN‐CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP‐CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN‐CDR‐driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP‐CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN‐CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP‐CFSR performs just the reverse, compared with PERSIANN‐CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

8.
The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations.  相似文献   

9.
The temporal consistency of the moisture fields (precipitation, evaporation and total precipitable water) from five global reanalyses is examined over Antarctica and the Southern Ocean during 1989?C2009. This concern is important given that (1) global reanalyses are known to be prone to inhomogeneities and artificial trends caused by changes in the observing system, and (2) the period of study has seen a dramatic increase in the volume of satellite observations available for data assimilation. In particular, the study aims to determine whether the recent reanalyses are suitable for investigating changes in Antarctic surface mass balance. The datasets investigated consist of NCEP-2, JRA-25, ERA-Interim, MERRA and CFSR. Strong evidence of spurious changes is found in NCEP-2, JRA-25, MERRA and CFSR, although the magnitude, spatial patterns and timing of these artifacts vary between the reanalyses. MERRA exhibits a jump in Antarctic precipitation-minus-evaporation (P?CE) and in Southern Ocean precipitation in the late 1990s. This jump is related to the introduction of sounding radiances from the Advanced Microwave Sounding Unit (AMSU). The impact of AMSU is also discernible, albeit less pronounced, in CFSR data. It is shown that ERA-Interim likely provides the most realistic depiction of the interannual variability and overall change in Antarctic P?CE since 1989. We conclude that the presence of spurious changes is not a solved problem in recent global reanalyses. Caution should continue to be exercised when using these datasets for trend analyses in general, particularly in high southern latitudes.  相似文献   

10.
ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.  相似文献   

11.
ABSTRACT

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.  相似文献   

12.
以中国气象局逐小时地面降水数据集为参考基准,采用8种统计评价指标综合评估对比了美国NASA研发的全球降水计划（GPM）多卫星降水联合反演IMERG（Integrated Multi-satellitE Retrievals for GPM）卫星降水产品的三个不同版本的Final数据,分析了三套卫星降水在中国大陆地区多时空尺度下的反演精度,探讨了IMERG最新版本V5数据的改进情况及反演中仍然存在的问题.结果表明：IMERG数据能够准确地捕捉到中国大陆地区的降水区域特征,但是在中国西北部地面站点稀疏地区误差较大,精度较低,难以精确估测该地区的实际降水值.最新版本V5数据精度整体上优于先前的V3和V4数据,V5与地面观测数据的相关系数为0.75,均方根误差为7.03 mm/d,较V3、V4有明显提高,改善了V3、V4在中国西北部出现的降水低估问题;但是V5在冬季表现较差且没有解决前期版本存在的高估问题,整体上相对实际降水仍处于高估状态;同时V5在对高雨强事件的捕捉监测能力方面还存在一定的不足,因此建议在强降雨事件监测中需谨慎使用卫星降水IMERG数据集.目前V5系统中的校正算法还存在部分缺陷：为消除全球降水系统性低估问题,目前的校正算法整体性抬升了卫星降水值,从而导致卫星降水反演在中国地区高雨强事件下出现高误报以及高估问题,进而影响到IMERG数据回推以及后续再生数据的精度.  相似文献   

13.
The long‐term and large‐scale soil moisture (SM) record is important for understanding land atmosphere interactions and their impacts on the weather, climate, and regional ecosystem. SM products are one of the parameters used in some Earth system models, but these records require evaluation before use. The water resources on the Qinghai–Tibet Plateau (QTP) are important to the water security of billions of people in Asia. Therefore, it is necessary to know the SM conditions on the QTP. In this study, the evaluation metrics of multilayer (0–10, 10–40, and 40–100 cm) SM in different reanalysis datasets of the European Centre for Medium‐Range Weather Forecasts interim reanalysis (ERA‐Interim [ERA]), National Centers for Environmental Prediction Climate Forecast System and the Climate Forecast System version 2 (CFSv2), and China Meteorological Administration Land Data Assimilation System (CLDAS) are compared with in situ observations at 5 observation sites, which represent alpine meadow, alpine swamp meadow, alpine grassy meadow, alpine desert steppe, and alpine steppe environments during the thawing season from January 1, 2011, to December 31, 2013, on the QTP. The ERA SM remains constant at approximately 0.2 m3?m?3 at all observation sites during the entire thawing season. The CLDAS and CFSv2 SM products show similar patterns with those of the in situ SM observations during the thawing season. The CLDAS SM product performs better than the CFSv2 and ERA for all vegetation types except the alpine swamp meadow. The results indicate that the soil texture and land cover types play a more important role than the precipitation to increase the biases of the CLDAS SM product on the QTP.  相似文献   

14.
The study focuses on the spatial and temporal variations of intense/extreme rainfall events over Gujarat State (India) during the period 1970–2014. Average monsoon rainfall for the state shows a significant increasing trend, with an increase of 48 mm/decade. Some of the stations in the Saurashtra region show a statistically significant increasing trend but none of the stations in the state show a decreasing trend. The increasing trend in monsoon rainfall is very significant for the past three decades, with an increase of 167 mm/decade. Instead of fixed absolute threshold values, relative threshold values of rainfall corresponding to the 95th, 98th, 99th and 99.5th percentiles for each station have been proposed to represent heavy, very heavy, intense and extreme rainfall, which varied between 70–120, 105–160, 130–210 and 165–280 mm, respectively. Significant increasing trends are observed for the frequency of heavy and very heavy rainfall events over the state.  相似文献   

15.
This study examines the short-range forecast accuracy of the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) as applied to the July 2006 episode of the Indian summer monsoon (ISM) and the model's sensitivity to the choice of different cumulus parameterization schemes (CPSs), namely Betts-Miller, Grell (GR) and Kain-Fritsch (KF). The results showed that MM5 day 1 (0–24 h prediction) and day 2 (24–48 h prediction) forecasts using all three CPSs overpredicted monsoon rainfall over the Indian landmass, with the larger overprediction seen in the day 2 forecasts. Among the CPSs, the rainfall distribution over the Indian landmass was better simulated in forecasts using the KF scheme. The KF scheme showed better skill in predicting the area of rainfall for most of the rainfall thresholds. The root mean square error (RMSE) in day 1 and day 2 rainfall forecasts using different CPSs showed that rainfall simulated using the KF scheme agreed better with the observed rainfall. As compared to other CPSs, simulation using the GR scheme showed larger RMSE in wind speed prediction at 850 and 200 hPa over the Indian landmass. MM5 24-h temperature forecasts at 850 hPa with all the CPSs showed a warm bias of the order of 1 K over the Indian landmass and the bias doubled in 48-h model forecasts. The mean error in temperature prediction at 850 hPa over the Indian region using the KF scheme was comparatively smaller for all the forecast intervals. The model with all the CPSs overpredicted humidity at 850 hPa. The improved prediction by MM5 with the KF scheme is well complemented by the smaller error shown by the KF scheme in vertical distribution of heat and mean moist static energy in the lower troposphere. In this study, the KF scheme which explicitly resolve the downdrafts in the cloud column tended to produce more realistic precipitation forecasts as compared to other schemes which did not explicitly incorporate downdraft effects. This is an important result especially given that the area covered by monsoon-precipitating systems is largely from stratiform-type clouds which are associated with strong downdrafts in the lower levels. This result is useful for improving the treatment of cumulus convection in numerical models over the ISM region.  相似文献   

16.
A number of watersheds are selected from the Hydro‐Climate Data Network over southeastern United States to examine possible changes in hydrological time series, e.g. precipitation, introduced by changing climate. Possible changes in monthly precipitation are examined by three different methods to detect second order stationarity, abrupt changes in the variance and smooth changes in quantiles of the time series. An analysis of second order stationarity shows that precipitation in eight of the 56 watersheds display nonstationary behaviour. Change‐point analyses reveal that changes in the long‐term variance of monthly precipitation are only detected for a few sites. As a complementary analysis tool, quantile regression aims to detect potential changes of different percentiles of the monthly precipitation over time. Several sites show diverging trends in the quantiles, which implies that the range and thus variance of the data, is increasing. As distinct change‐points are not identified, this suggests that the effect is small and cumulative. Results are analysed in detail, and possible explanations are provided. This type of thorough analysis provides a basis for understanding the possible redistribution of water cycle. It also provides implications for water resources management and hydrological engineering facility design and planning. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

17.
A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.  相似文献   

18.
The multisensor precipitation estimates (MPE) data, available in hourly temporal and 4 km × 4 km spatial resolution, are produced by the National Weather Service and mosaicked as a national product known as Stage IV. The MPE products have a significant advantage over rain gauge measurements due to their ability to capture spatial variability of rainfall. However, the advantages are limited by complications related to the indirect nature of remotely sensed precipitation estimates. Previous studies confirm that efforts are required to determine the accuracy of MPE and their associated uncertainties for future use in hydrological and climate studies. So far, various approaches and extensive research have been undertaken to develop an uncertainty model. In this paper, an ensemble generator is presented for MPE products that can be used to evaluate the uncertainty of rainfall estimates. Two different elliptical copula families, namely, Gaussian and t‐copula are used for simulations. The results indicate that using t‐copula may have significant advantages over the well‐known Gaussian copula particularly with respect to extremes. Overall, the model in which t‐copula was used for simulation successfully generated rainfall ensembles with similar characteristics to those of the ground reference measurements. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

19.
Jia  Zuo  Ren  Fumin  Zhang  Dalin  Ding  Chenchen  Yang  Mingjen  Feng  Tian  Chen  Boyu  Yang  Hui 《中国科学:地球科学(英文版)》2020,63(1):27-36
Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast(LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones(LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.  相似文献   

20.
The availability of in situ measurements of precipitation in remote locations is limited. As a result, the use of satellite measurements of precipitation is attractive for water resources management. Combined precipitation products that rely partially or entirely on satellite measurements are becoming increasingly available. However, these products have several weaknesses, for example their failure to capture certain types of precipitation, limited accuracy and limited spatial and temporal resolution. This paper evaluates the usefulness of several commonly used precipitation products over data scarce, complex mountainous terrain from a water resources perspective. Spatially averaged precipitation time series were generated or obtained for 16 sub-basins of the Paute river basin in the Ecuadorian Andes and 13 sub-basins of the Baker river basin in Chilean Patagonia. Precipitation time series were generated using the European Centre for Medium Weather Range Forecasting (ECMWF) 40 year reanalysis (ERA-40) and the subsequent ERA-interim products, and the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset 1 (NCEP R1) hindcast products, as well as precipitation estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The Tropical Rainfall Measurement Mission (TRMM) 3B42 is also used for the Ecuadorian Andes. These datasets were compared to both spatially averaged gauged precipitation and river discharge. In general, the time series of the remotely sensed and hindcast products show a low correlation with locally observed precipitation data. Large biases are also observed between the different products. Hydrological verification based on river flows reveals that water balance errors can be extremely high for all evaluated products, including interpolated local data, in basins smaller than 1000 km2. The observations are consistent over the two study regions despite very different climatic settings and hydrological processes, which is encouraging for extrapolation to other mountainous regions.  相似文献   

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