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- Author or Editor: Anthony Arguez x
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Abstract
NOAA released the new 1981–2010 climate normals in July 2011. These included monthly and daily normals of minimum and maximum temperature. Monthly normals were computed from monthly temperature values that were corrected for biases (i.e., homogenized) due to changes in observing practices over the course of the normals period (station moves, changes in observation time, and changes in instrumentation). Daily temperature observations, however, are not homogenized, which could lead to inconsistencies between the daily and monthly normals. This study offers a constrained harmonic technique that forces the daily temperature normals to be consistent with the monthly temperature normals. This approach replaces the cubic spline interpolation of monthly temperature normals that was used to compute earlier versions of NOAA's daily temperature normals. It effectively passes the homogenization applied at the monthly scale down to the daily scale, resulting in a smooth annual cycle devoid of day-to-day sampling variability and intermonth discontinuities.
Abstract
NOAA released the new 1981–2010 climate normals in July 2011. These included monthly and daily normals of minimum and maximum temperature. Monthly normals were computed from monthly temperature values that were corrected for biases (i.e., homogenized) due to changes in observing practices over the course of the normals period (station moves, changes in observation time, and changes in instrumentation). Daily temperature observations, however, are not homogenized, which could lead to inconsistencies between the daily and monthly normals. This study offers a constrained harmonic technique that forces the daily temperature normals to be consistent with the monthly temperature normals. This approach replaces the cubic spline interpolation of monthly temperature normals that was used to compute earlier versions of NOAA's daily temperature normals. It effectively passes the homogenization applied at the monthly scale down to the daily scale, resulting in a smooth annual cycle devoid of day-to-day sampling variability and intermonth discontinuities.
No Abstract available.
No Abstract available.
On the Estimation of Daily Climatological Temperature VarianceAbstract
The climatological daily variance of temperature is sometimes estimated from observed temperatures within a centered window of dates. This method overestimates the true variance of daily temperature when the rate of seasonal temperature change is large, because the seasonal change within the date window introduces additional variance. The contribution of the seasonal change may be removed by performing the variance calculation using daily temperature anomalies, leading to a bias-free estimate of variance.
The difference between the variance estimation methods is illustrated using both idealized simulations of temperature variability and observed historical temperature data. The simulation results confirm that removing the climatological temperature cycle eliminates bias in the variance estimates. For several U.S. midlatitude locations, the difference in estimated standard deviation of daily mean temperature is on the order of a few percent near the seasonal peaks in climatological temperature change, but the maximum difference is larger in highly continental climates. These differences are shown to be significant when estimating the probability of temperature extremes under the assumption of a Gaussian distribution.
Abstract
The climatological daily variance of temperature is sometimes estimated from observed temperatures within a centered window of dates. This method overestimates the true variance of daily temperature when the rate of seasonal temperature change is large, because the seasonal change within the date window introduces additional variance. The contribution of the seasonal change may be removed by performing the variance calculation using daily temperature anomalies, leading to a bias-free estimate of variance.
The difference between the variance estimation methods is illustrated using both idealized simulations of temperature variability and observed historical temperature data. The simulation results confirm that removing the climatological temperature cycle eliminates bias in the variance estimates. For several U.S. midlatitude locations, the difference in estimated standard deviation of daily mean temperature is on the order of a few percent near the seasonal peaks in climatological temperature change, but the maximum difference is larger in highly continental climates. These differences are shown to be significant when estimating the probability of temperature extremes under the assumption of a Gaussian distribution.
Abstract
Time series filtering (e.g., smoothing) can be done in the spectral domain without loss of endpoints. However, filtering is commonly performed in the time domain using convolutions, resulting in lost points near the series endpoints. Multiple incarnations of a least squares minimization approach are developed that retain the endpoint intervals that are normally discarded due to filtering with convolutions in the time domain. The techniques minimize the errors between the predetermined frequency response function (FRF)—a fundamental property of all filters—of interior points with FRFs that are to be determined for each position in the endpoint zone. The least squares techniques are differentiated by their constraints: 1) unconstrained, 2) equal-mean constraint, and 3) an equal-variance constraint. The equal-mean constraint forces the new weights to sum up to the same value as the predetermined weights. The equal-variance constraint forces the new weights to be such that, after convolved with the input values, the expected time series variance is preserved. The three least squares methods are each tested under three separate filtering scenarios [involving Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO) time series] and compared to each other as well as to the spectral filtering method—the standard of comparison. The results indicate that all four methods (including the spectral method) possess skill at determining suitable endpoints estimates. However, both the unconstrained and equal-mean schemes exhibit bias toward zero near the terminal ends due to problems with appropriating variance. The equal-variance method does not show evidence of this attribute and was never the worst performer. The equal-variance method showed great promise in the ENSO project involving a 5-month running mean filter, and performed at least on par with the other realistic methods for almost all time series positions in all three filtering scenarios.
Abstract
Time series filtering (e.g., smoothing) can be done in the spectral domain without loss of endpoints. However, filtering is commonly performed in the time domain using convolutions, resulting in lost points near the series endpoints. Multiple incarnations of a least squares minimization approach are developed that retain the endpoint intervals that are normally discarded due to filtering with convolutions in the time domain. The techniques minimize the errors between the predetermined frequency response function (FRF)—a fundamental property of all filters—of interior points with FRFs that are to be determined for each position in the endpoint zone. The least squares techniques are differentiated by their constraints: 1) unconstrained, 2) equal-mean constraint, and 3) an equal-variance constraint. The equal-mean constraint forces the new weights to sum up to the same value as the predetermined weights. The equal-variance constraint forces the new weights to be such that, after convolved with the input values, the expected time series variance is preserved. The three least squares methods are each tested under three separate filtering scenarios [involving Arctic Oscillation (AO), Madden–Julian oscillation (MJO), and El Niño–Southern Oscillation (ENSO) time series] and compared to each other as well as to the spectral filtering method—the standard of comparison. The results indicate that all four methods (including the spectral method) possess skill at determining suitable endpoints estimates. However, both the unconstrained and equal-mean schemes exhibit bias toward zero near the terminal ends due to problems with appropriating variance. The equal-variance method does not show evidence of this attribute and was never the worst performer. The equal-variance method showed great promise in the ENSO project involving a 5-month running mean filter, and performed at least on par with the other realistic methods for almost all time series positions in all three filtering scenarios.
Abstract
The air-freezing index (AFI) is a common metric for determining the freezing severity of the winter season and estimating frost depth for midlatitude regions, which is useful for determining the depth of shallow foundation construction. AFI values represent the seasonal magnitude and duration of below-freezing air temperature. Departures of the daily mean temperature above or below 0°C (32°F) are accumulated over each August–July cold season; the seasonal AFI value is defined as the difference between the highest and lowest extrema points. Return periods are computed using generalized extreme value distribution analysis. This research replaces the methodology used by the National Oceanic and Atmospheric Administration to calculate AFI return periods for the 1951–80 time period, applying the new methodology to the 1981–2010 climate normals period. Seasonal AFI values and return period values were calculated for 5600 stations across the coterminous United States (CONUS), and the results were validated using U.S. Climate Reference Network temperature data. Return period values are typically 14%–18% lower across CONUS during 1981–2010 versus a recomputation of 1951–80 return periods with the new methodology. For the 100-yr (2 yr) return periods, about 59% (83%) of stations show a decrease of more than 10% in the more recent period, whereas 21% (2%) show an increase of more than 10%, indicating a net reduction in winter severity that is consistent with observed climate change.
Abstract
The air-freezing index (AFI) is a common metric for determining the freezing severity of the winter season and estimating frost depth for midlatitude regions, which is useful for determining the depth of shallow foundation construction. AFI values represent the seasonal magnitude and duration of below-freezing air temperature. Departures of the daily mean temperature above or below 0°C (32°F) are accumulated over each August–July cold season; the seasonal AFI value is defined as the difference between the highest and lowest extrema points. Return periods are computed using generalized extreme value distribution analysis. This research replaces the methodology used by the National Oceanic and Atmospheric Administration to calculate AFI return periods for the 1951–80 time period, applying the new methodology to the 1981–2010 climate normals period. Seasonal AFI values and return period values were calculated for 5600 stations across the coterminous United States (CONUS), and the results were validated using U.S. Climate Reference Network temperature data. Return period values are typically 14%–18% lower across CONUS during 1981–2010 versus a recomputation of 1951–80 return periods with the new methodology. For the 100-yr (2 yr) return periods, about 59% (83%) of stations show a decrease of more than 10% in the more recent period, whereas 21% (2%) show an increase of more than 10%, indicating a net reduction in winter severity that is consistent with observed climate change.
Abstract
Wind data from the SeaWinds instrument on NASA’s Quick Scatterometer (QuikSCAT) satellite are investigated to ascertain how well the surface manifestation of the Madden–Julian oscillation (MJO) can be resolved. The MJO signal is detected in nonfiltered gridded data using extended EOF analysis of the zonal wind field, overshadowed by annual, semiannual, and monsoon-related modes. After bandpass filtering with Lanczos weights, MJO signals are clearly detected in several kinematic quantities, including the zonal wind speed, the zonal pseudostress, and the velocity potential. Extraction of the MJO using QuikSCAT winds compares favorably with extraction using NCEP Reanalysis 2, except that the QuikSCAT signal appears to be more robust.
In addition, an alternative bandpass-filtering technique using variable filter weights near time series endpoints is presented. The method uses least squares minimization to match newly created frequency response functions in edge zones as closely as possible to the predetermined frequency response function of interior points. This method stands in contrast to the common practice of simply discarding those endpoints where a convolution cannot be computed.
Abstract
Wind data from the SeaWinds instrument on NASA’s Quick Scatterometer (QuikSCAT) satellite are investigated to ascertain how well the surface manifestation of the Madden–Julian oscillation (MJO) can be resolved. The MJO signal is detected in nonfiltered gridded data using extended EOF analysis of the zonal wind field, overshadowed by annual, semiannual, and monsoon-related modes. After bandpass filtering with Lanczos weights, MJO signals are clearly detected in several kinematic quantities, including the zonal wind speed, the zonal pseudostress, and the velocity potential. Extraction of the MJO using QuikSCAT winds compares favorably with extraction using NCEP Reanalysis 2, except that the QuikSCAT signal appears to be more robust.
In addition, an alternative bandpass-filtering technique using variable filter weights near time series endpoints is presented. The method uses least squares minimization to match newly created frequency response functions in edge zones as closely as possible to the predetermined frequency response function of interior points. This method stands in contrast to the common practice of simply discarding those endpoints where a convolution cannot be computed.
The 1981–2010 U.S. Climate Normals released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) include a suite of descriptive statistics based on hourly observations. For each hour and day of the year, statistics of temperature, dew point, mean sea level pressure, wind, clouds, heat index, wind chill, and heating and cooling degree hours are provided as 30-year averages, frequencies of occurrence, and percentiles. These hourly normals are available for 262 locations, primarily major airports, from across the United States and its Pacific territories. We encourage use of these products specifically for examination of the diurnal cycle of a particular variable, and how that change may shift over the annual cycle.
The 1981–2010 U.S. Climate Normals released by the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) include a suite of descriptive statistics based on hourly observations. For each hour and day of the year, statistics of temperature, dew point, mean sea level pressure, wind, clouds, heat index, wind chill, and heating and cooling degree hours are provided as 30-year averages, frequencies of occurrence, and percentiles. These hourly normals are available for 262 locations, primarily major airports, from across the United States and its Pacific territories. We encourage use of these products specifically for examination of the diurnal cycle of a particular variable, and how that change may shift over the annual cycle.
Daily High-Resolution Temperature and Precipitation Fields for the Contiguous United States from 1951 to PresentAbstract
In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.
Significance Statement
Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.
Abstract
In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales.
Significance Statement
Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.
