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Prediction Center (prediction + center)

Distribution by Scientific Domains
Earth and Environmental Science83%

Kinds of Prediction Center

  • climate prediction center
    		•

    Selected Abstracts

    Global summer monsoon rainy seasons

    INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2008
    Suping Zhang
    Abstract A concise and objective definition of monsoon rainy season characteristics is proposed for worldwide monsoon regions. The result highlights six major summer monsoon rainy season domains and the mean dates of the local onset, peak and withdrawal phases of the summer monsoon rainy season. The onset phases occur progressively later poleward in the continental domains but primarily eastward in the oceanic monsoon regions. The rainy season retreats equatorward over the continental and oceanic monsoon regions. The length of the rainy season decreases poleward and shorter rainy season can also be found over the outskirts of warm water. Some exceptions exist in terms of the characteristics of rainy season, e.g. the westward advance of rainy season over North Africa and an apparently prolonged rainy season in the Korean peninsula. The results here are basically compatible with those obtained in previous studies on regional monsoons. A definition of the seasonal wind overturning is proposed. Combining rainfall and winds, we stratify the global monsoon into strong and weak categories. The strong monsoons are typically in the regions with both concentration of summer rainfall and annual reversal of low-level winds, while the weak monsoon features only a contrasting wet,dry season. Seemingly, some mid-latitude regions with wind reversals are not monsoonal because of the reversals being opposite to the monsoon overturning and the rainfall patterns being more or less Mediterranean. The comparison between the monsoon domains derived from Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the 40-year European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40), the Japanese 25-year Reanalysis (JRA-25) and National Centers for Environmental Prediction (NCEP) datasets show good capabilities of the reanalyses in demarcation of the major monsoon rainy season domains in the tropics and subtropics. But the reanalyses are less realistic in the mid-latitudes of Eurasia and North America. The result here provides the simple yet objective definitions of monsoon domain, onset, peak and withdrawal which are useful for validation of GCMs. Copyright © 2008 Royal Meteorological Society [source]

    Detecting trends in tropical rainfall characteristics, 1979,2003

    INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 8 2007
    K.-M. Lau
    Abstract Analyses of two state-of-the-art, blended space-based and ground-based global rainfall data sets from the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center Merged Analysis Product (CMAP) reveal that there was a significant shift in the probability distribution functions of tropical rainfall during the period 1979,2003. This shift features a positive trend in the occurrence of heavy (top 10% by rain amount) and light (bottom 5%) rain events in the tropics during 1979,2003 and a negative trend in moderate (25,75%) rain events. These trends are consistent in both data sets and are in overall agreement with the Climate Research Unit's (CRU) gauge-only rainfall data over land. The relationships among the trends and the possible long-term changes in rainfall characteristics are discussed. Copyright © 2006 Royal Meteorological Society [source]

    Spatial and temporal variabilities of rainfall in tropical South America as derived from Climate Prediction Center merged analysis of precipitation

    INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 2 2002
    H. Matsuyama
    Abstract We investigated the spatial and temporal variabilities of Climate Prediction Center merged analysis of precipitation (CMAP) in tropical South America from 1979 to 1998. First, we validated CMAP using other hydrometeorological data. In comparison with the high-density precipitation data of the Global Historical Climatology Network (GHCN) Ver. 2, CMAP reproduces the spatial pattern well, although it underestimates (overestimates) heavy (light) precipitation. CMAP also reproduces the interannual variability well, compared with the discharge data of the River Amazon. Next, we applied the rotated empirical orthogonal function (REOF) to CMAP after subtracting the annual cycle. Simultaneous and lag correlations were calculated among the scores of REOFs 1 to 4, the southern oscillation index, and the dipole index of the Atlantic. REOF 1 (15%) represents the north,south pattern that exhibits the maximum precipitation in the summer hemisphere. REOF 2 (12%) indicates the gradual decrease of precipitation in the northern part of tropical South America, reflecting the effect of the Atlantic. REOF 3 (11%) exhibits an east,west pattern related to El Niño. In REOF 4 (7%), the centre of the factor loading is located in Colombia, and the score jumps abruptly around 1985,86. The Lepage test detected the abrupt increase of CMAP in 1985,86 around Colombia. Since such a jump is not found in GHCN Ver. 2, the discontinuous changes of CMAP and REOF 4 around 1985,86 are artificial and peculiar to CMAP. In this region, CMAP should be applied with caution when evaluating recent trends and the interannual variability. The importance of the abrupt increase of precipitation around Colombia is also addressed. Copyright © 2002 Royal Meteorological Society. [source]

    Prediction of sea surface temperature from the global historical climatology network data

    ENVIRONMETRICS, Issue 3 2004
    Samuel S. P. Shen
    Abstract This article describes a spatial prediction method that predicts the monthly sea surface temperature (SST) anomaly field from the land only data. The land data are from the Global Historical Climatology Network (GHCN). The prediction period is 1880,1999 and the prediction ocean domain extends from 60°S to 60°N with a spatial resolution 5°×5°. The prediction method is a regression over the basis of empirical orthogonal functions (EOFs). The EOFs are computed from the following data sets: (a) the Climate Prediction Center's optimally interpolated sea surface temperature (OI/SST) data (1982,1999); (b) the National Climatic Data Center's blended product of land-surface air temperature (1992,1999) produced from combining the Special Satellite Microwave Imager and GHCN; and (c) the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis data (1982,1999). The optimal prediction method minimizes the first- M -mode mean square error between the true and predicted anomalies over both land and ocean. In the optimization process, the data errors of the GHCN boxes are used, and their contribution to the prediction error is taken into account. The area-averaged root mean square error of prediction is calculated. Numerical experiments demonstrate that this EOF prediction method can accurately recover the global SST anomalies during some circulation patterns and add value to the SST bias correction in the early history of SST observations and the validation of general circulation models. Our results show that (i) the land only data can accurately predict the SST anomaly in the El Nino months when the temperature anomaly structure has very large correlation scales, and (ii) the predictions for La Nina, neutral, or transient months require more EOF modes because of the presence of the small scale structures in the anomaly field. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Integrating climate forecasts and natural gas supply information into a natural gas purchasing decision

    METEOROLOGICAL APPLICATIONS, Issue 3 2000
    David Changnon
    This paper illustrates a key lesson related to most uses of long-range climate forecast information, namely that effective weather-related decision-making requires understanding and integration of weather information with other, often complex factors. Northern Illinois University's heating plant manager and staff meteorologist, along with a group of meteorology students, worked together to assess different types of available information that could be used in an autumn natural gas purchasing decision. Weather information assessed included the impact of ENSO events on winters in northern Illinois and the Climate Prediction Center's (CPC) long-range climate outlooks. Non-weather factors, such as the cost and available supplies of natural gas prior to the heating season, contribute to the complexity of the natural gas purchase decision. A decision tree was developed and it incorporated three parts: (a) natural gas supply levels, (b) the CPC long-lead climate outlooks for the region, and (c) an ENSO model developed for DeKalb. The results were used to decide in autumn whether to lock in a price or ride the market each winter. The decision tree was tested for the period 1995,99, and returned a cost-effective decision in three of the four winters. Copyright © 2000 Royal Meteorological Society [source]