Search Results

You are looking at 31 - 40 of 3,109 items for :

  • Forecasting techniques x
  • Journal of Climate x
  • All content x
Clear All
Mengqian Lu, Upmanu Lall, Jaya Kawale, Stefan Liess, and Vipin Kumar

al. 2013 ; Landman et al. 2012 ; Schepen et al. 2012 ; Charles et al. 2013 ; Jiang et al. 2013 ; Lee et al. 2013 ; Singh et al. 2013 ; Kang et al. 2014 ; Nicholson 2014 ) and weather forecasts, through either quantitative precipitation forecasts (QPFs) or numerical weather prediction (NWP) (e.g., Lin et al. 2005 ), typically up to 7 days, and radar-based nowcasting ( Ligda 1953 ; Golding 1998 ; Wilson et al. 1998 ; Foresti et al. 2015 ; Olsson et al. 2014 ; Sokol et al. 2013 ; Dai

Full access
Fengpeng Sun, Alex Hall, Marla Schwartz, Daniel B. Walton, and Neil Berg

in seasonality and timing of snowfall and snowmelt are expected. Understanding which areas of mountain ranges are most vulnerable to climate change is critical for regional and local climate assessments and water management planning. To address the limitations of coarse-resolution GCMs, dynamical and statistical techniques are commonly used to downscale GCM projections. Dynamical downscaling employs a regional climate model with much higher spatial resolution than GCMs, while relying on GCMs for

Full access
Mariza Costa-Cabral, John S. Rath, William B. Mills, Sujoy B. Roy, Peter D. Bromirski, and Cristina Milesi

1. Introduction Establishing a statistical association between local precipitation and large-scale climate patterns has potential value for several applications in water resources planning over different time scales, among which are 1) seasonal forecasts of total precipitation, which can provide support for planning reservoir operations for water supply and flood control, over time frames of months; 2) forecasting the risk of intense precipitation and wet antecedent conditions that may lead to

Full access
Oliver Watt-Meyer and Paul J. Kushner

behaviors can be seen in nature (see Fig. 1 of Watt-Meyer and Kushner 2015 , hereafter WK15 ). However, to our knowledge, a quantitative decomposition of the variability of the upward wave activity flux into standing and traveling components in reanalysis data has not been made. This study makes such a decomposition by using a statistical technique that was developed by WK15 in order to decompose general wave variability into standing and traveling components. This method is an improvement on

Full access
Shan Li, Shaoqing Zhang, Zhengyu Liu, Xiaosong Yang, Anthony Rosati, Jean-Christophe Golaz, and Ming Zhao

estimation are conducted in a single-column-based model (SCM) following Betts and Miller (1986) , where such interaction is usually overlooked. Emanuel and Živković-Rothman (1999) tried to estimate cumulus convection parameters by minimizing model forecast errors using a variational approach based on an SCM. Golaz et al. (2007) utilized an ensemble parameter estimation technique to calibrate a single-column cloud parameterization by attempting to match predicted fields to reference large

Full access
G. Conti, A. Navarra, and J. Tribbia

still be successfully predicted using an initial value problem, but for longer time scales the role of the ocean, and therefore of the lower boundary conditions, becomes prominent ( Shukla 1985 ). The feasibility of seasonal forecasting using multiple general circulation model (GCM) simulations by prescribing the global sea surface temperature (SST) was then shown by Stern and Miyakoda (1995) , following a large number of results indicating the large control exercised by SST on atmospheric seasonal

Full access
Fraser C. Lott and Peter A. Stott

.g., above-average temperature or lower-decile rainfall) against its modeled or forecast probability, usually in a given season. Typically this is binned by forecast probability obtained across a model ensemble ( Fig. 1 , left), which then enables the observed fraction to be obtained over the whole climatology. However, using area-pooling techniques considering individual grid points in a homogeneous region ( Lott et al. 2014 ), an unbinned diagram may also be produced ( Fig. 1 , right), with each point

Full access
Xinrong Wu

– 406 , doi: 10.1038/nature03301 . Toth , Z. , and E. Kalnay , 1993 : Ensemble forecasting at NMC: The generation of perturbations . Bull. Amer. Meteor. Soc. , 74 , 2317 – 2330 , doi: 10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2 . Wei , M. , Z. Toth , R. Wobus , and Y. Zhu , 2008 : Initial perturbations based on the ensemble transform (ET) technique in the NCEP global ensemble forecast systems . Tellus , 60A , 62 – 79 , doi: 10.1111/j.1600-0870.2007.00273.x . Whitaker , J

Full access
Renguang Wu, Ben P. Kirtman, and Huug van den Dool

1. Introduction El Niño–Southern Oscillation (ENSO) is a coupled ocean–atmosphere phenomenon in the tropical Pacific. It involves interactions among sea surface temperature (SST), thermocline depth, atmospheric convection, and surface winds ( Zebiak and Cane 1987 ; Philander 1990 ). ENSO events exert substantial impacts on short-term climate variability on the globe. Considerable efforts have been made to improve ENSO forecasts and to understand the physical processes that limit its

Full access
Gerald A. Meehl, Julie M. Arblaster, and Grant Branstator

Abstract

A linear trend calculated for observed annual mean surface air temperatures over the United States for the second-half of the twentieth century shows a slight cooling over the southeastern part of the country, the so-called warming hole, while temperatures over the rest of the country rose significantly. This east–west gradient of average temperature change has contributed to the observed pattern of changes of record temperatures as given by the ratio of daily record high temperatures to record low temperatures with a comparable east–west gradient. Ensemble averages of twentieth-century climate simulations in the Community Climate System Model, version 3 (CCSM3), show a slight west–east warming gradient but no warming hole. A warming hole appears in only several ensemble members in the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset and in one ensemble member of simulated twentieth-century climate in CCSM3. In this model the warming hole is produced mostly from internal decadal time-scale variability originating mainly from the equatorial central Pacific associated with the Interdecadal Pacific Oscillation (IPO). Analyses of a long control run of the coupled model, and specified convective heating anomaly experiments in the atmosphere-only version of the model, trace the forcing of the warming hole to positive convective heating anomalies in the central equatorial Pacific Ocean near the date line. Cold-air advection into the southeastern United States in winter, and low-level moisture convergence in that region in summer, contribute most to the warming hole in those seasons. Projections show a disappearance of the warming hole, but ongoing greater surface temperature increases in the western United States compared to the eastern United States.

Full access