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- Author or Editor: Michael J. Janis x
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Abstract
Non-calendar-day observations of 24-h minimum and maximum air temperatures can be considerably different from calendar-day or midnight observations. This paper examines the influence of time-of-observation on 24-h temperature observations. Diurnal minimum and maximum temperatures measured at common observation times (0700 and 1700 LST) are compared with minimum and maximum temperatures measured at midnight. The principal methods make use of hourly temperature observations, sampled over 24-h moving windows, to approximate once-daily observations. Surprisingly, non-calendar-day observations are similar to calendar-day observations on a majority of days. When differences do occur, however, they can be large and of either sign. Differences between 1700 LST observations and midnight observations are typically smaller than those arising from 0700 LST observations. Daily differences can be grouped by temperature extrema recorded on the incorrect day (a bookkeeping problem) or temperature extrema recorded on two successive days (bias). Bias scenarios arise when very cold mornings or very warm afternoons influence the temperature measured on successive days. Locations or seasons with the least day-to-day temperature variability often display the least time-of-daily-observation influence on temperature. Determining those days on which large departures and biases are likely to occur is possible by measuring day-to-day temperature persistence. First-order differences of daily time series may be used explicitly in adjustment procedures for morning observations of maximum temperature. Otherwise, first-order differences may be used to determine those days on which large observation-time differences are likely or those days on which observation-time dependencies are trivial.
Abstract
Non-calendar-day observations of 24-h minimum and maximum air temperatures can be considerably different from calendar-day or midnight observations. This paper examines the influence of time-of-observation on 24-h temperature observations. Diurnal minimum and maximum temperatures measured at common observation times (0700 and 1700 LST) are compared with minimum and maximum temperatures measured at midnight. The principal methods make use of hourly temperature observations, sampled over 24-h moving windows, to approximate once-daily observations. Surprisingly, non-calendar-day observations are similar to calendar-day observations on a majority of days. When differences do occur, however, they can be large and of either sign. Differences between 1700 LST observations and midnight observations are typically smaller than those arising from 0700 LST observations. Daily differences can be grouped by temperature extrema recorded on the incorrect day (a bookkeeping problem) or temperature extrema recorded on two successive days (bias). Bias scenarios arise when very cold mornings or very warm afternoons influence the temperature measured on successive days. Locations or seasons with the least day-to-day temperature variability often display the least time-of-daily-observation influence on temperature. Determining those days on which large departures and biases are likely to occur is possible by measuring day-to-day temperature persistence. First-order differences of daily time series may be used explicitly in adjustment procedures for morning observations of maximum temperature. Otherwise, first-order differences may be used to determine those days on which large observation-time differences are likely or those days on which observation-time dependencies are trivial.
Abstract
The National Oceanic and Atmospheric Administration is establishing the U.S. Climate Reference Network (CRN) to improve the capacity for observing climatic change and variability. A goal of this network is to provide homogeneous observations of temperature and precipitation from benchmark stations that can be coupled with historical observations for detection and attribution of climatic change. The purpose of this study was to estimate the number and distribution of U.S. CRN observing sites. The analysis was conducted by forming hypothetical networks from representative subsamples of stations in an existing higher-density baseline network. The objective was to have the differences between the annual temperature and precipitation trends computed from reduced-size networks and the full-size networks not greater than predetermined error limits. This analysis was performed on a grid cell basis to incorporate the expectation that a greater station density would be required to achieve the monitoring goals in areas with greater spatial gradients in trends. Monte Carlo resampling techniques were applied to stations within 2.5° latitude × 3.5° longitude grid cells to successively lower the resolution compared to that in the reference or baseline network. Differences between 30-yr trends from lower-resolution networks and full-resolution networks were generated for each grid cell. Grid cell densities were determined separately for temperature and precipitation trends. In practice densities can be derived for any parameter and monitoring goal. A network of 327 stations for the contiguous United States satisfied a combined temperature-trend goal of 0.10°C decade−1 and a precipitation-trend goal of 2.0% of median precipitation per decade.
Abstract
The National Oceanic and Atmospheric Administration is establishing the U.S. Climate Reference Network (CRN) to improve the capacity for observing climatic change and variability. A goal of this network is to provide homogeneous observations of temperature and precipitation from benchmark stations that can be coupled with historical observations for detection and attribution of climatic change. The purpose of this study was to estimate the number and distribution of U.S. CRN observing sites. The analysis was conducted by forming hypothetical networks from representative subsamples of stations in an existing higher-density baseline network. The objective was to have the differences between the annual temperature and precipitation trends computed from reduced-size networks and the full-size networks not greater than predetermined error limits. This analysis was performed on a grid cell basis to incorporate the expectation that a greater station density would be required to achieve the monitoring goals in areas with greater spatial gradients in trends. Monte Carlo resampling techniques were applied to stations within 2.5° latitude × 3.5° longitude grid cells to successively lower the resolution compared to that in the reference or baseline network. Differences between 30-yr trends from lower-resolution networks and full-resolution networks were generated for each grid cell. Grid cell densities were determined separately for temperature and precipitation trends. In practice densities can be derived for any parameter and monitoring goal. A network of 327 stations for the contiguous United States satisfied a combined temperature-trend goal of 0.10°C decade−1 and a precipitation-trend goal of 2.0% of median precipitation per decade.
