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SPDSS_Task53-2_Collect and Fill Missing Monthly Climate Data
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SPDSS_Task53-2_Collect and Fill Missing Monthly Climate Data
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Last modified
4/17/2013 9:29:55 AM
Creation date
5/27/2008 10:35:00 AM
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Decision Support Systems
Title
SPDSS Task 53.2 - Collect and Fill Missing Monthly Climate Data
Description
SPDSS Task 53.2 –Collect and Fill Missing Monthly Climate Data memo discusses the identification of and filling techniques for the key climate stations selected to represent climatic conditions throughout the South Platte, North Platte and Laramie River basins (SPDSS study area).
Decision Support - Doc Type
Task Memorandum
Date
2/7/2005
DSS Category
Consumptive Use
DSS
South Platte
Basin
South Platte
Contract/PO #
C153954
Grant Type
Non-Reimbursable
Bill Number
SB01-157, HB02-1152, SB03-110, HB04-1221, SB05-084, HB06-1313, SB07-122
Prepared By
Leonard Rice Engineering
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156 <br />152 <br />148 <br />144 <br />140 <br />+' 136 <br />R <br />D <br />~ 132 <br />'i 128 <br />a <br />`a~ 124 <br />0 <br />M 120 <br />,__ 116 <br />c <br />H 112 <br />`w <br />108 <br />00 104 <br />100 <br />96 <br />92 <br />88 <br />100 110 120 130 140 150 160 <br />Longmont 2 ESE Spring 32 Degree Frost Date <br />Figure 5 -Boulder vs. Longmont 2 ESE Scatterplot -Frost Dates <br />Based on this assessment, an alternate approach was developed using monthly linear regression <br />with nearby climate stations to fill daily minimum temperature data. Annual frost dates were <br />then calculated from the filled minimum daily temperature data using June 30 as the last spring <br />frost date and July 1 as the first fall frost date. As described further in the Recommendations <br />section, we recommend that an algorithm be added to TSTooI to allow calculation of frost dates <br />from daily data. <br />3. Fill Missing Monthly Climate Data <br />3.1 Temperature and Precipitation <br />The first step in filling missing monthly temperature and precipitation data was to select an <br />independent station to fill the missing data. Close proximity and the highest correlation <br />coefficients (R) using monthly linear regression were the main criteria for selecting an <br />independent station. Preference was also given to an independent station with historical data that <br />provided a complete record for the dependent station (as opposed to first filling the independent <br />station and then using it to fill the dependent station). Table 3 and Table 4 provide the <br />temperature and precipitation regression analysis results, respectively. If the dependent station <br />was filled with an independent station that had already been filled, then the independent station <br />name is followed by the word "filled". <br />Monthly linear regression generally produced high correlations for most of the dependent <br />stations being filled with temperature data. Correlations were not as high for precipitation data <br />filling. It is important to note that if a dependent station had only a few months to fill, the <br />independent station was selected based on the R-value for only the months being filled. This <br />r' <br /> <br />i <br />i <br />'. <br />~. <br />' <br />• ~i <br />i ~~ <br />. ~~' . <br />~` <br />i~ <br />less <br /> <br />. . <br />. <br />i <br />,s <br />•~ <br />•~ . <br />i <br />i <br />i . <br />~~ . <br /> - - Estimated trendline excluding outlying data <br />® Trendline including all data <br />Task 53_2.doc 10 of 17 <br />
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