Updating statewide monthly temperature extremes lynxstyle dating
Since the dataset was at the divisional spatial scale, it naturally lent itself to agricultural and hydrological applications. For each climate division, monthly station temperature and precipitation values are computed from the daily observations.
The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. In March 2015, historical data for thirteen Alaskan climate divisions were added to the n Clim Div database and will be updated each month with the CONUS n Clim Div data.
The outcome of these improvements is a new divisional dataset that maintains the strengths of its predecessor while providing more robust estimates of areal averages and long-term trends.
The NCEI's Monitoring Branch transitioned from the Drd964x dataset to the more modern the n Clim Div dataset in early 2014.
The n Clim Div dataset is designed to address the following general issues inherent in the Drd964x dataset: The first (and most straightforward) improvement to the n Clim Div dataset involves updating the underlying network of stations, which now includes additional station records and contemporary bias adjustments (i.e., those used in the U. Historical Climatology Network version 2; Menne et al., 2009).
City Information - Detailed information on select cities across Pennsylvania.
In the same way, annual temperature anomalies feature a few outstanding (warm or cold) years, and a large "pack".
Slight changes to any one year can result in a "bump" in rank in the "middle of the pack".
More information on this new dataset can be accessed in Alaska FAQs.
Traditionally, climate division values have been computed using the monthly values for all of the Cooperative Observer Network (COOP) stations in each division, which are then averaged to compute divisional monthly temperature and precipitation averages/totals. For the 1895-1930 period, statewide values were computed directly from stations within each state.