scholarly journals Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

2015 ◽  
Vol 28 (3) ◽  
pp. 1166-1183 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman
Keyword(s):  
United States ◽  
Spatial Variability ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Total Variance ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

scholarly journals Representation of MJO Variability in the NCEP Climate Forecast System

2011 ◽  
Vol 24 (17) ◽  
pp. 4676-4694 ◽  
Author(s):  
Scott J. Weaver ◽  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
El Nino ◽  
La Niña ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
La Nina ◽  
The Indian Ocean ◽  
Ncep Climate Forecast System

The Madden–Julian oscillation (MJO) is arguably the most important intraseasonal mode of climate variability, given its significant modulation of global climate variations and attendant societal impacts. Advancing the current understanding and simulation of the MJO using state-of-the-art climate data and modeling systems is thus a necessary goal for improving MJO prediction capability. MJO variability is assessed in NOAA/NCEP reanalyses and two versions of the Climate Forecast System (CFS), CFS version 1 (CFSv1) and its update version 2 (CFSv2). The analysis leans on a variety of diagnostic procedures and includes MJO sensitivity to varying El Niño–Southern Oscillation (ENSO) phases. It is found that significant improvements have been realized in the representation of MJO variations in the new NCEP Climate Forecast System reanalysis (CFSR) as evidenced by outgoing longwave radiation (OLR) power spectral analysis and more coherent propagation characteristics of precipitation and 850-hPa zonal winds over the Eastern Hemisphere in CFSR-only depictions. Conversely, while modest improvements are realized in the CFSv2 as compared to CFSv1, in general the simulation of the MJO continues to be a challenge. Both versions produce strong eastward propagating variance of convection and wind fields in the intraseasonal frequency band. However, the simulated MJO propagates slower than the observed with difficulties traversing the Maritime Continent into the western Pacific, as noted in many previous modeling studies. The CFS shows robust intraseasonal simulations over the west Pacific during El Niño years with diminished simulation capability over the Indian Ocean during La Niña years. This is likely a manifestation of the preference for La Niña MJO activity to occur over the Indian Ocean and the simulation challenges over that domain.

Mixed layer heat budget of the El Niño in NCEP climate forecast system

2011 ◽  
Vol 39 (1-2) ◽  
pp. 365-381 ◽  
Author(s):  
Boyin Huang ◽  
Yan Xue ◽  
Hui Wang ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Mixed Layer ◽  
El Niño ◽  
El Nino ◽  
Heat Budget ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Mixed Layer Heat Budget ◽  
Ncep Climate Forecast System

How distinct are the two flavors of El Niño in retrospective forecasts of Climate Forecast System version 2 (CFSv2)?

2016 ◽  
Vol 48 (11-12) ◽  
pp. 3829-3854 ◽  
Author(s):  
Prasanth A. Pillai ◽  
Suryachandra A. Rao ◽  
Gibies George ◽  
D. Nagarjuna Rao ◽  
S. Mahapatra ◽  
...  
Keyword(s):  
El Niño ◽  
El Nino ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System

scholarly journals Drought Indices Based on the Climate Forecast System Reanalysis and Ensemble NLDAS

2011 ◽  
Vol 12 (2) ◽  
pp. 181-205 ◽  
Author(s):  
Kingtse C. Mo ◽  
Lindsey N. Long ◽  
Youlong Xia ◽  
S. K. Yang ◽  
Jae E. Schemm ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
North American ◽  
Root Zone ◽  
The United States ◽  
Drought Indices ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.

scholarly journals Comparison of Daily Precipitation Statistics for the United States in Observations and in the NCEP Climate Forecast System

2008 ◽  
Vol 21 (22) ◽  
pp. 5993-6014 ◽  
Author(s):  
R. W. Higgins ◽  
V. B. S. Silva ◽  
V. E. Kousky ◽  
W. Shi
Keyword(s):  
United States ◽  
Lead Time ◽  
Daily Precipitation ◽  
The United States ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Station Data ◽  
Forecast Lead Time ◽  
Ncep Climate Forecast System

Abstract An intercomparison of the statistics of daily precipitation within seasonal climate over the conterminous United States is carried out using gridded station data and output from the NCEP Climate Forecast System (CFS). Differences in the occurrence of daily precipitation between the observations and a set of CFS reforecasts are examined as a function of forecast lead time for 1982–2005. Difference patterns show considerable evolution depending on season and lead time, with positive biases in CFS at most locations and leads except along the southern tier of states during the spring and summer months. An examination of differences in daily precipitation statistics by ENSO phase and in the frequencies of wet and dry spells is also conducted using a longer period of gridded daily station data (1948–2006) and a pair of 100-yr CFS coupled simulations. These comparisons expose additional details of the regional and seasonal dependence of the bias in the CFS simulations and reforecasts over the conterminous United States. The analysis motivates additional synoptic studies aimed at improving the linkage between daily precipitation and related circulation features in CFS. Prospects for using this information to develop more reliable ensemble-based probabilistic forecasts in real time at leads of 2–4 weeks (e.g., risks of heavy rain events) are also considered.

scholarly journals Seasonal Prediction of Regional Reference Evapotranspiration Based on Climate Forecast System Version 2

2014 ◽  
Vol 15 (3) ◽  
pp. 1166-1188 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez ◽  
Wendy D. Graham
Keyword(s):  
El Niño ◽  
Reference Evapotranspiration ◽  
El Nino ◽  
Maximum Temperature ◽  
Climate Forecast ◽  
Coarse Scale ◽  
Forecast System ◽  
Climate Forecast System ◽  
Sensitivity Coefficients ◽  
Spatial Disaggregation

AbstractReference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman–Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño–Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.

scholarly journals Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range

2018 ◽  
Vol 32 (1) ◽  
pp. 161-182 ◽  
Author(s):  
Baoxiang Pan ◽  
Kuolin Hsu ◽  
Amir AghaKouchak ◽  
Soroosh Sorooshian ◽  
Wayne Higgins
Keyword(s):  
United States ◽  
West Coast ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Prediction Skill ◽  
Precipitation Prediction ◽  
Skill Scores ◽  
Extended Range ◽  
To Receive

Abstract Precipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region’s short- to extended-range precipitation prediction skill using the hindcast database of the Subseasonal-to-Seasonal Prediction Project (S2S). The prediction skill–lead time relationship is evaluated, using both deterministic and probabilistic skill scores. Results show that the S2S models display advantageous deterministic skill at week 1. For week 2, prediction is useful for the best-performing model, with a Pearson correlation coefficient larger than 0.6. Beyond week 2, predictions generally provide little useful deterministic skill. Sources of extended-range predictability are investigated, focusing on El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO). We found that periods of heavy precipitation associated with ENSO are more predictable at the extended range period. During El Niño years, Southern California tends to receive more precipitation in late winter, and most models show better extended-range prediction skill. On the contrary, during La Niña years Oregon tends to receive more precipitation in winter, with most models showing better extended-range skill. We believe the excessive precipitation and improved extended-range prediction skill are caused by the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, we verified that active MJO events systematically modulate the area’s precipitation distribution. Our results show that most models do not represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models exhibit enhanced extended-range prediction skills under active MJO conditions.

Changes in Observed Daily Precipitation over the United States between 1950–79 and 1980–2009

2013 ◽  
Vol 14 (1) ◽  
pp. 105-121 ◽  
Author(s):  
R. W. Higgins ◽  
V. E. Kousky
Keyword(s):  
United States ◽  
Great Plains ◽  
El Niño ◽  
Daily Precipitation ◽  
El Nino ◽  
Southern Oscillation ◽  
Heavy Precipitation ◽  
The United States ◽  
Enso Cycle ◽  
Precipitation Events

Abstract Changes in observed daily precipitation over the conterminous United States between two 30-yr periods (1950–79 and 1980–2009) are examined using a 60-yr daily precipitation analysis obtained from the Climate Prediction Center (CPC) Unified Raingauge Database. Several simple measures are used to characterize the changes, including mean, frequency, intensity, and return period. Seasonality is accounted for by examining each measure for four nonoverlapping seasons. The possible role of the El Niño–Southern Oscillation (ENSO) cycle as an explanation for differences between the two periods is also examined. There have been more light (1 mm ≤ P < 10 mm), moderate (10 mm ≤ P < 25 mm), and heavy (P ≥ 25 mm) daily precipitation events (P) in many regions of the country during the more recent 30-yr period with some of the largest and most spatially coherent increases over the Great Plains and lower Mississippi Valley during autumn and winter. Some regions, such as portions of the Southeast and the Pacific Northwest, have seen decreases, especially during the winter. Increases in multiday heavy precipitation events have been observed in the more recent period, especially over portions of the Great Plains, Great Lakes, and Northeast. These changes are associated with changes in the mean and frequency of daily precipitation during the more recent 30-yr period. Difference patterns are strongly related to the ENSO cycle and are consistent with the stronger El Niño events during the more recent 30-yr period. Return periods for both heavy and light daily precipitation events during 1950–79 are shorter during 1980–2009 at most locations, with some notable regional exceptions.

What Made El Niño 1997-1998 Famous?: The Key Events Associated with a Unique Climatic Event

2000 ◽  
Author(s):  
Stanley A. Changnon
Keyword(s):  
United States ◽  
News Media ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Climate Conditions ◽  
The Pacific ◽  
Climate Event ◽  
The Media ◽  
Key Events

El Niño 97-98 provided one of the most interesting and widely known climatic events of this century. It garnered enormous attention not only in the scientific community but also in the media and from the American public. El Niño developed rapidly in the tropical Pacific during May 1997, and by October “El Niño “had become a household phrase across America. Television and radio, newspapers and magazines pummeled America with the dire tales of El Niño during the fall of 1997 as the climate disruption battered the West Coast and the southern United States with storm after storm. Worried families changed vacation plans, and insurance executives pondered losses and raised rates. Victims of every type of severe weather blamed El Niño . After a winter filled with unusual weather, the head of the National Oceanic and Atmospheric Administration (NOAA) declared, “This winter’s El Niño ranks as one of the major climatic events of this century.” It was the first El Niño observed and forecast from start to finish. The event was noteworthy from several perspectives. • First, it became the largest and warmest El Niño to develop in the Pacific Ocean during the past 100 years. • Second, the news media gave great attention to the event, and El Niño received more attention at all levels than had any previous climate event. • Third, scientists were able to use El Niño conditions to successfully predict the climate conditions of the winter six months in advance. • Fourth, the predictive successes brought new credibility to the science of long-range prediction and, in general, acted to increase the public’s understanding of the climate and oceanic sciences. • Fifth, there were notable differences in how weather-sensitive decision makers reacted to the predictions, some used them for great gain, while others, fearing failure, did not. • Sixth, the great strength of El Niño brought forth claims that the phenomenon was the result of anthropogenic-induced global warming. This possibility was debated and added to the scientific-policy debates surrounding climate change. • Seventh, the net effect of the El Niño -influenced weather on the United States was an economic benefit, after early fears and predictions of great damages.

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