Search Results

You are looking at 131 - 140 of 1,221 items for :

  • Forecasting techniques x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All
Alyssa M. Stansfield, Kevin A. Reed, Colin M. Zarzycki, Paul A. Ullrich, and Daniel R. Chavas

) provides the 6-hourly observed TC track data for the same time period as the model simulations, 1985–2014 ( Fig. 3 , top). Additionally, 6-hourly 10-m wind, sea level pressure, and geopotential height data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; Copernicus Climate Change Service 2017 ) with 31-km horizontal grid spacing for 1985–2014 are used for TC track ( Fig. 3 , bottom), size, and precipitation analyses. Since reanalysis is

Free access
Jefferson S. Wong, Xuebin Zhang, Shervan Gharari, Rajesh R. Shrestha, Howard S. Wheater, and James S. Famiglietti

-Interim (WFDEI) was developed to provide datasets of subdaily (3-hourly) and daily meteorological data, with global coverage at 0.5° spatial resolution (~50 km) from 1979 to 2012 ( Weedon et al. 2014 ). WFDEI has been updated to provide datasets up to 2016. Using the same methodology as WATCH ( Weedon et al. 2011 ), WFDEI was constructed based on the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis product ( Dee et al. 2011 ), combined with the Climatic Research Unit (CRU

Open access
F. Chen, W. T. Crow, L. Ciabatta, P. Filippucci, G. Panegrossi, A. C. Marra, S. Puca, and C. Massari

1. Introduction Satellite-based precipitation estimates (SPE) are increasingly being applied to important environmental applications such as numerical weather prediction, flood forecasting, and agricultural drought monitoring. A potential SPE of interest is the H23 gridded precipitation product generated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). The H

Restricted access
Yanan Meng, Jianhua Sun, Yuanchun Zhang, and Shenming Fu

-stationary satellite data and CMORPH (the Climate Prediction Center morphing technique) precipitation data. Feng et al. (2019) obtained the characteristics of MCSs in the United States using satellite, precipitation, and radar data, and pointed out that long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains. Some studies have examined the variations in the cloud parameters, precipitation, and synoptic circulations of MCSs and have reported the relationships among those

Restricted access
Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

1. Introduction Accurate representations of the land–atmosphere (LA) coupling processes are critical for weather forecasts and climate predictions ( Seneviratne et al. 2006 , 2010 ; Santanello et al. 2018 ). A lack of quantitative understanding of the nature and characteristics of LA coupling remains (e.g., Betts 2004 ; Ek and Holtslag 2004 ; Guillod et al. 2014 ; Santanello et al. 2018 ), owing to the multivariate and multiscale interactive processes between the land surface, planetary

Restricted access
Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser

variations of soil moisture is essential for climate predictability on seasonal to annual time scales ( van den Hurk et al. 2012 ; Sospedra-Alfonso and Merryfield, 2018 ), flood and drought forecasts ( Sheffield et al. 2014 ; Wanders et al. 2014 ), and climate impact studies ( Seneviratne et al. 2010 ). Soil moisture can be estimated in three ways: in situ measurements, satellite remote sensing, and model-based simulations. Each of these techniques has its own specific properties and limitations. In

Restricted access
Jiali Ju, Heng Dai, Chuanhao Wu, Bill X. Hu, Ming Ye, Xingyuan Chen, Dongwei Gui, Haifan Liu, and Jin Zhang

first term on the right-hand side is the partial variance contributed by θ i and the second term represents the partial variance caused by the model inputs except θ i . The first-order sensitivity index is thus defined as S i = V θ i ⁡ [ E θ ~ i ⁡ ( Δ | θ i ) ] / V ⁡ ( Δ ) . This index measures the percentage of output uncertainty contributed by θ i and estimates its relative importance compared to other uncertain inputs. This variance decomposition technique has been recursively applied by

Restricted access
Huihui Zhang, Hugo A. Loáiciga, Da Ha, and Qingyun Du

modeling and exploring the associations between impact factors ( Zhang and Wang 2008 ). It is very efficient in forecasting. The genetic algorithm (GAs) is a metaheuristic technique inspired by natural evolution. The GA was introduced by Holland (1975) . It has been widely used to optimize neural networks ( Mohsen et al. 2007 ). BP applies the Levenberg–Marquardt optimization algorithm (LM). The GA improves the performance of the LM ( Zheng et al. 2019 ) by finding a suboptimal solution from a global

Free access
Sungmin O, Emanuel Dutra, and Rene Orth

aforementioned DSST studies. For the first time, we extend the scope of such model evaluation by considering a diverse set of state-of-the-art models. Three different models with widely varying complexities are employed, namely, physically based, conceptual, and empirical models: the Hydrology-Tiled European Centre for Medium Range Weather Forecasting (ECMWF) Scheme for Surface Exchanges over Land (HTESSEL; Balsamo et al. 2009 ), the Simple Water Balance Model (SWBM; Koster and Mahanama 2012 ; Orth and

Open access
Emily A. Slinskey, Paul C. Loikith, Duane E. Waliser, Bin Guan, and Andrew Martin

of AR frequency, physical characteristics, and impacts across the CONUS summarized over the seven NCA regions. AR detection is based on IVT magnitude thresholds, as well as a number of geometric and directional criteria following the technique described in Guan and Waliser (2015) and updated in Guan et al. (2018) . Seasonal climatologies of AR frequency across the CONUS reveal ARs in the Northwest and Southwest are most common in the winter and autumn ( Figs. 2a,d ). Although considerably less

Restricted access