Search Results

You are looking at 31 - 40 of 1,237 items for :

  • Forecasting techniques x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All
Marc Schleiss

-based quantitative precipitation forecast techniques: Eulerian persistence, Lagrangian persistence, and a neural network. They defined predictability as the lead time for which the cross correlation between observations and forecasts falls below a certain threshold. By successively smoothing the radar images, they were able to show that large-scale features are characterized by longer Lagrangian persistence, that is, that predictability increases with decreasing resolution. Using a large number of radar

Full access
Simon R. Osborne and Graham P. Weedon

1. Introduction Representation of drought in land surface models (LSMs) is important for accurate weather forecasts, assessing water resources, and projecting the possible impacts of droughts under climate change. LSMs have been used to investigate both the changing frequency of drought under climate change and the potential for future droughts to have increased societal impacts ( Prudhomme et al. 2011 , 2014 ; Dai 2013 ). The trends of changing drought frequency and intensity are dependent

Restricted access
Thomas M. Smith, Samuel S. P. Shen, and Ralph R. Ferraro

1. Introduction Accurate short- to medium-range climate forecasting, from weeks to seasons, is valuable for planning and preparing for situations that could have large economic or health impacts. Precipitation forecasting is particularly important because it is critical to agriculture, municipal water supplies and control, and disaster relief support. However, predicting precipitation tends to be more difficult than predicting temperature (see, e.g., Barnston and Smith 1996 ), and much effort

Full access
James D. Brown and Dong-Jun Seo

1. Introduction Forecasts of hydrometeorological and hydrologic variables often contain large uncertainties ( Beven and Binley 1992 ; Anderson and Bates 2001 ; Gupta et al. 2005 ; NRC 2006 ; Ajami et al. 2007 ). Ensemble techniques are widely used in meteorology and, increasingly, in hydrology to quantify these uncertainties ( Stensrud et al. 1999 ; Jolliffe and Stephenson 2003 ; Brown and Heuvelink 2005 ; Olsson and Lindström 2008 ). For example, the National Weather Service (NWS) River

Full access
Marc Berenguer, Carles Corral, Rafael Sánchez-Diezma, and Daniel Sempere-Torres

into the first group of the classification were recently developed ( Germann and Zawadzki 2002 ; Seed 2003 ). These techniques were tested using radar data, and, from the point of view of the forecasted precipitation fields, they turn out to improve the results obtained with Lagrangian persistence (which consists of simply advecting the most recently measured radar scan according to an estimation of the motion field). The two techniques developed by Germann and Zawadzki (2002) and Seed (2003

Full access
Jon Olav Skøien, Konrad Bogner, Peter Salamon, and Fredrik Wetterhall

Abstract

Different post-processing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the Ensemble Model Output Statistics method to post-process the ensemble output from a continental scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the post-processing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. This was the case both for the local and global calibration methods. As the post-processing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the post-processing.

Restricted access
Elisa Brussolo, Jost von Hardenberg, and Nicola Rebora

resolution, and sensitivity to initial conditions of atmospheric models. At large scales the different sources of uncertainty in meteorological forecasts can be captured by dynamical ensemble forecasting techniques ( Epstein 1969 ; Leith 1974 ; Toth and Kalnay 1993 ; Palmer 1993 ; Molteni et al. 1996 ; Toth and Kalnay 1997 ), in which the probability of occurrence of different meteorological scenarios is estimated from the relative frequency of different forecast ensemble members. Existing

Full access
Elisa Brussolo, Jost von Hardenberg, Luca Ferraris, Nicola Rebora, and Antonello Provenzale

precipitation ( Hendrick and Comer 1970 ; Zawadski 1973 ). To cope with these “representativeness errors,” Tustison et al. (2001) suggested the use of stochastic models capable of representing the statistical properties of precipitation at multiple scales. In particular, rainfall downscaling techniques ( Droegemeier et al. 2000 ; Ferraris et al. 2003 ) allow us to derive, from a single precipitation forecast with limited spatial and temporal resolution, higher-resolution stochastic ensembles of

Full access
Thomas C. Pagano, Andrew W. Wood, Maria-Helena Ramos, Hannah L. Cloke, Florian Pappenberger, Martyn P. Clark, Michael Cranston, Dmitri Kavetski, Thibault Mathevet, Soroosh Sorooshian, and Jan S. Verkade

verify forecast accuracy ( Stokstad 1999 ). The scientific community recently completed a decade-long initiative on prediction in ungauged basins, and although initiatives such as these contributed much new understanding and many innovative techniques ( Hrachowitz et al. 2013 ), real-time forecasting received less attention and remains a major challenge ( Randrianasolo et al. 2011 ). Remote sensing data sources such as satellites may be able to provide information about river width and water slope

Full access
Akhil Sanjay Potdar, Pierre-Emmanuel Kirstetter, Devon Woods, and Manabendra Saharia

Abstract

In the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multi-dimensional statistical modeling approach. Amongst different machine learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created which can be deployed in the future for flash flood forecasting. The results confirm that although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of sub-basin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins which could most benefit from distributed hydrologic modeling.

Restricted access