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Zeyu Xue and Paul Ullrich

dynamical downscaling relative to the original CMIP5/6 data. d. LOCA statistical downscaling data LOCA is a statistical downscaling technique that uses historical analogs to add fine-scale details to global climate model simulations. The LOCA dataset includes 28 downscaled CMIP5 models from 1950 to 2005 at a resolution of 0.0625° ( Pierce et al. 2014 ). Bias correction is applied in LOCA based on the Livneh observationally based gridded product ( Livneh et al. 2015 ). e. Watershed Boundary Dataset

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Hanqing Chen, Bin Yong, Weiqing Qi, Hao Wu, Liliang Ren, and Yang Hong

1. Introduction Accurate estimation of precipitation is essential for climate analysis, hydrological simulation, drought monitoring, flood forecasting, landslide warning, and related emergency management ( Kidd and Levizzani 2011 ; Maggioni et al. 2016 ). At present, high-quality precipitation estimation mainly depends on rain gauge networks and ground-based radars although satellite technology and satellite-based retrievals have made great progress in recent years. The widely used satellite

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Liang Chen, Trent W. Ford, and Priyanka Yadav

fully coupled atmosphere and land components in accordance with the Atmospheric Model Intercomparison Project (AMIP) protocol. Considering the model uncertainties, the second experiment is a Control simulation with atmospheric nudging to effectively drive the model states toward observations ( Wehrli et al. 2019 ). Following the approach in reference ( Wehrli et al. 2019 , 2018 ), we relax the horizontal winds toward the 6-hourly European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis

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Hanh Nguyen, Jason A. Otkin, Matthew C. Wheeler, Pandora Hope, Blair Trewin, and Christa Pudmenzky

the ET fraction, and r E T ¯ and σ ( r ET ) are its climatological mean and standard deviation, respectively, for that time of the year computed over the period 1975–2018. Note that 2019 was not included in the climatological mean because at the time of computation the year was not complete. Therefore, we fixed the climatology to 1975–2018. Further details on the AWRA-L land surface model and the ESI computational technique can be found in Nguyen et al. (2019) . Areas that lack enough in situ

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Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, and Patrick C. McGuire

mapping. The results showed that this procedure remarkably reduces the differences between the discharge estimates from low- and high-resolution models in most of the 5992 outlets, and thereby globally. Moreover, the method remains robust and independent of resolution when discarding observation sites that represent up to 1 × 10 3 km 3 yr −1 , or even excluding more, if the discarded sites are not those that require strong bias correction. Comparing the bias correction techniques, CDF mapping is

Open access
Peter J. Shellito, Sujay V. Kumar, Joseph A. Santanello Jr., Patricia Lawston-Parker, John D. Bolten, Michael H. Cosh, David D. Bosch, Chandra D. Holifield Collins, Stan Livingston, John Prueger, Mark Seyfried, and Patrick J. Starks

radiation is partitioned between sensible and latent heating. These fluxes, in turn, affect transpiration rates, the carbon cycle, weather and climate forecasts, and drought and flood assessments, all of which have humanitarian and environmental impacts. One way to enhance the utility of a hydrologic LSM is to pass it observational information through data assimilation (DA) using a formal framework such as the ensemble Kalman filter (EnKF; Kumar et al. 2014a ; Reichle et al. 2002 ). At the simplest

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Dashan Wang, Xianwei Wang, Lin Liu, Dagang Wang, and Zhenzhong Zeng

heterogeneity of precipitation from 2008 to 2015 ( Fig. 1a ). The CMPA was derived from merging dense rain gauge network observations (~30 000 automatic weather stations) from the China Meteorology Administration (CMA) with the Climate Prediction Center morphing technique (CMORPH) satellite-based quantitative precipitation estimates ( Joyce et al. 2004 ). It is in 0.1°/hourly resolution and starts from January 2008 to present over mainland China ( Shen et al. 2014 ). This product shows good performance over

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Zhongkun Hong, Zhongying Han, Xueying Li, Di Long, Guoqiang Tang, and Jianhua Wang

America ( He et al. 2016 ). In addition, the random forest–based merging procedure (RF-MEP) that combines gridded precipitation products performs well over Chile for 2000–16 ( Baez-Villanueva et al. 2020 ). A recurrent neural network (RNN) model for simulating the hydrological response from various sources of rainfall was used to merge multiple precipitation sources for flash flood forecasting in Taiwan in China, indicating the potential of neural networks in merging multisource information ( Chiang

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Sharon E. Nicholson, Douglas Klotter, and Adam T. Hartman

African gauge data, using “smart” interpolation techniques that take the spatial correlation structure into account. The CHIRPS2 data have low bias and better gauge coverage over Africa compared to other similar products ( Dezfuli et al. 2017 ). PERSIANN-CDR ( Ashouri et al. 2015 ) is also based on geostationary thermal IR brightness temperature, with a neural network approach applied to produce the precipitation estimates. The product is calibrated using NCEP/NCAR precipitation forecasts. It is bias

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Linlin Wang, Zhiqiu Gao, Zaitao Pan, Xiaofeng Guo, and Elie Bou-Zeid

using the transfer-function technique with the cospectral models of Kaimal et al. (1972) (see Moore 1986 for algorithm). The planar fit coordinate is then applied by following the recommendation of Wilczak et al. (2001) . The above corrections are implemented by using the EdiRe software, developed by the University of Edinburgh (see http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/ ). Following Foken and Wichura (1996) and Foken et al. (2004) , a stationarity test is made for the

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