Mesoclimatic analysis of severe weather and ENSO interactions in North Carolina

Rhome, Jamie; Niyogi, D.; Raman, Sethu

doi:10.1029/1999gl011327

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…From Figure 3b, we see clearly that SST girds over ENSO region (170E–90W and 5S–5N), the North Atlantic region (80W–40W and 10N–20N), and the NC Coast region (75W–65W and 22.5N–32.5N) influence the summer flows into Falls Lake. This is in line with earlier findings [ Roswintiarti et al , 1998; Rhome et al , 2000] suggesting that warm conditions in the tropical Pacific and tropical North Atlantic result in above-normal inflow conditions in Falls Lake. It is important to note that we consider SST regions whose correlations are significant and greater than the threshold value of

\pm 1.96 ∕ \sqrt{n - 3}

where n is the total number of years ( n = 78 years for Falls Lake) of observed records used for computing the correlation.…”

Section: Seasonal Streamflow Forecasts Development For the Neuse Bsupporting

confidence: 93%

“…Most of the studies focusing on climatic variability over the South Eastern US have shown that warm tropical Pacific conditions during October–December lead to above-normal precipitation during winter and below-normal precipitation during summer if warm pool conditions prevail in the topical Pacific during the spring [ Schmidt et al , 2001; Lecce , 2000; Hansen et al , 1998; Zorn and Waylen , 1997]. Studies have also reported ENSO related teleconnection between precipitation and temperature over NC during both winter and summer seasons [ Roswintiarti et al , 1998; Rhome et al , 2000]. We basically develop a low-dimensional model by identifying SST conditions that influence the seasonal streamflow forecasts into Falls Lake during July–September (JAS).…”

Section: Seasonal Streamflow Forecasts Development For the Neuse Bmentioning

confidence: 99%

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Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

Devineni

Sankarasubramanian

Ghosh

2008

Water Resources Research

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[1] A new approach for developing multimodel streamflow forecasts is presented. The methodology combines streamflow forecasts from individual models by evaluating their skill, represented by rank probability score (RPS), contingent on the predictor state. Using average RPS estimated over the chosen neighbors in the predictor state space, the methodology assigns higher weights for a model that has better predictability under similar predictor conditions. We assess the performance of the proposed algorithm by developing multimodel streamflow forecasts for Falls Lake Reservoir in the Neuse River Basin, North Carolina (NC), by combining streamflow forecasts developed from two lowdimensional statistical models that use sea-surface temperature conditions as underlying predictors. To evaluate the proposed scheme thoroughly, we consider a total of seven multimodels that include existing multimodel combination techniques such as combining based on long-term predictability of individual models and by simple pooling of ensembles. Detailed nonparametric hypothesis tests comparing the performance of seven multimodels with two individual models show that the reduced RPS from multimodel forecasts developed using the proposed algorithm is statistically significant from the RPSs of individual models and from the RPSs of existing multimodel techniques. The study also shows that adding climatological ensembles improves the multimodel performance resulting in reduced average RPS. Contingency analyses on categorical (tercile) forecasts show that the proposed multimodel combination technique reduces average Brier score and total number of false alarms, resulting in improved reliability of forecasts. However, adding multiple models with climatology also increases the number of missed targets (in comparison to individual models' forecasts) which primarily results from the reduction of increased resolution that is exhibited in the individual models' forecasts under various forecast probabilities.Citation: Devineni, N., A. Sankarasubramanian, and S. Ghosh (2008), Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations, Water Resour. Res., 44, W09404,

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\pm 1.96 ∕ \sqrt{n - 3}

where n is the total number of years ( n = 78 years for Falls Lake) of observed records used for computing the correlation.…”

Section: Seasonal Streamflow Forecasts Development For the Neuse Bsupporting

confidence: 93%

Section: Seasonal Streamflow Forecasts Development For the Neuse Bmentioning

confidence: 99%

Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

Devineni

Sankarasubramanian

Ghosh

2008

Water Resources Research

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“…The soil moisture availability and weather patterns in the semiarid Tropics are dominated by mesoscale convection and monsoonal flow (cf. Bollé et al 1993;Dolman et al 1997), whereas the midlatitudinal weather patterns and corresponding soil moisture availability are modulated through synoptic weather and mesoscale frontal activity (Rhome et al 2000). This difference has far-reaching implications on the diverse hydrometeorological feedbacks in the two domains.…”

Section: Assessing Midlatitudinal and Semiarid Tropical Surface Effectsmentioning

confidence: 99%

Hydrological Land Surface Response in a Tropical Regime and a Midlatitudinal Regime

Niyogi¹,

Xue²,

Raman³

2002

J. Hydrometeor

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“…Surface emissions such as nitrogenous gases Vol. 160, 2003 Assessing Seasonal Transport marked by intermittent precipitation, the surface resistance for dry deposition is often the lowest (cf., PLEIM et al, 1999) and can lead to significantly high dry deposition values (cf., FINKELSTEIN et al, 2000;NIYOGI et al, 2000). Such a winter case thus represents possible optimal conditions for long-range transport and deposition of chemical emitted in eastern North Carolina, to eastern South Carolina and Georgia as shown in Figure 8.…”

Section: A) December 12-19 1998mentioning

confidence: 98%

“…Recent research has also shown that a significant teleconnection exists between climate in North Carolina and global patterns such as El Nin˜o-Southern Oscillation (ROSWINTIARTI et al, 1998;RHOME et al, 2000). Recent research has also shown that a significant teleconnection exists between climate in North Carolina and global patterns such as El Nin˜o-Southern Oscillation (ROSWINTIARTI et al, 1998;RHOME et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Assessing Seasonal Transport and Deposition of Agricultural Emissions in Eastern North Carolina, U.S.A.

Rhome¹,

Niyogi²,

Raman³

2003

Pure and Applied Geophysics

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There is an increasing interest regarding the fate of nitrogenous compounds emitted from agricultural activities in the southeastern United States. Varying climate, topography and proximity to the Atlantic Ocean particularly complicates the problem. An increased understanding of the interaction of synoptic scale flow with mesoscale circulations would constitute a significant improvement in the assessment of regional scale transport and deposition potential. This knowledge is necessary to facilitate current and future modeling attempts in the region as well as for planning future monitoring sites to develop a cohesive regional policy for the abatement strategies. The eastern portion of North Carolina is used as a case example due to its high, localized emission of nitrogen compounds from agricultural waste. to three different seasons were studied. Surface wind and thermodynamic patterns were analyzed using surface observing stations and archived-model analysis results centered over eastern North Carolina. Diurnal and seasonal patterns were identified for dispersion and concentration values obtained using an air pollution transport and dispersion model. This mesoscale information was used to draw qualitative conclusions regarding the possible trends and deviations in the dynamic trajectories as well as the resulting near-surface concentrations and deposition potential in eastern North Carolina. Results show that highly variable seasonal and diurnal atmospheric circulations characterize the study domain. These variations can significantly impact the transport and fate of pollutants released in this region. Generally, summer provides the highest potential for localized deposition, while fall can provide opportunity for long-range transport. The results also suggest that mean climatological or seasonally averaged flow patterns may not be sufficient for analyzing the fate of the agricultural releases in this region. At the very least, mean and variance based analysis is required to capture the climatology of the dispersion and deposition patterns. These patterns in eastern North Carolina appear to be sensitive to the strength and location of air mass boundaries along the coastal plain, indicating diverse scale interactions affecting the variability and uncertainty in the regional pollutant transport.

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Mesoclimatic analysis of severe weather and ENSO interactions in North Carolina

Cited by 11 publications

References 9 publications

Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations

Hydrological Land Surface Response in a Tropical Regime and a Midlatitudinal Regime

Assessing Seasonal Transport and Deposition of Agricultural Emissions in Eastern North Carolina, U.S.A.

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