Historical time series of climate observations from in-situ observing stations are essential for assessing climate variability and trend. However, changes in instrument, in observing practices/procedures, and in the location and exposure of station, etc. cause discontinuities in the time series of climate data. Such discontinuities must be eliminated to make the time series of climate data suitable for accurate assessment of climate trends and variability.
The effort to detect and eliminate artificial changes from data records is called data homogenization. This is a complex task involving two steps. The first step is to detect changepoint (time of change), which involves the use of station history information (metadata) and/or statistical tests. The second step is to adjust the data to eliminate the detected artificial changes, to produce homogeneous climate data records. This is most commonly attained using statistical methods to estimate the adjustments needed to homogenize the data record; but physically based adjustments can be obtained occasionally.
During the past decade, Dr. Xiaolan Wang has developed two major algorithms, the PMTred and PMFred algorithms, for climate data homogenization. One is for use in cases where a reference series is available; another is for use in cases where a reference series is not available. She wrote two large sets of FORTRAN codes to implement the algorithms, which work well for annual and monthly data series but its computing speed needs to be improved for homogenization of daily data records – crucial data for studying weather/climate extremes. She has developed the new computing algorithm to improve the computing speed and needs a contractor to incorporate it into the two sets of her FORTRAN codes.
PERIOD OF CONTRACT:
The contract will commence on the “Date of Award” to September 9, 2016.
The Crown retains the right to negotiate with suppliers on any procurement.
Documents may be submitted in either official language of Canada.
