Search Results

You are looking at 11 - 20 of 22 items for

  • Author or Editor: Imke Durre x
  • Refine by Access: All Content x
Clear All Modify Search
Matthew J. Menne, Imke Durre, Russell S. Vose, Byron E. Gleason, and Tamara G. Houston

Abstract

A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias).

Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.

Full access
Scott Applequist, Anthony Arguez, Imke Durre, Michael F. Squires, Russell S. Vose, and Xungang Yin

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.

Full access
Imke Durre, Matthew J. Menne, Byron E. Gleason, Tamara G. Houston, and Russell S. Vose

Abstract

This paper describes a comprehensive set of fully automated quality assurance (QA) procedures for observations of daily surface temperature, precipitation, snowfall, and snow depth. The QA procedures are being applied operationally to the Global Historical Climatology Network (GHCN)-Daily dataset. Since these data are used for analyzing and monitoring variations in extremes, the QA system is designed to detect as many errors as possible while maintaining a low probability of falsely identifying true meteorological events as erroneous. The system consists of 19 carefully evaluated tests that detect duplicate data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of the values flagged as errors is used to set the threshold for each procedure such that its false-positive rate, or fraction of valid values identified as errors, is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is enhanced by the error detection capabilities of the earlier tests. Based on an assessment of each individual check and a final evaluation for each element, the system identifies 3.6 million (0.24%) of the more than 1.5 billion maximum/minimum temperature, precipitation, snowfall, and snow depth values in GHCN-Daily as errors, has a false-positive rate of 1%−2%, and is effective at detecting both the grossest errors as well as more subtle inconsistencies among elements.

Full access
Melissa Free, James K. Angell, Imke Durre, John Lanzante, Thomas C. Peterson, and Dian J. Seidel

Abstract

The utility of a “first difference” method for producing temporally homogeneous large-scale mean time series is assessed. Starting with monthly averages, the method involves dropping data around the time of suspected discontinuities and then calculating differences in temperature from one year to the next, resulting in a time series of year-to-year differences for each month at each station. These first difference time series are then combined to form large-scale means, and mean temperature time series are constructed from the first difference series. When applied to radiosonde temperature data, the method introduces random errors that decrease with the number of station time series used to create the large-scale time series and increase with the number of temporal gaps in the station time series. Root-mean-square errors for annual means of datasets produced with this method using over 500 stations are estimated at no more than 0.03 K, with errors in trends less than 0.02 K decade−1 for 1960–97 at 500 mb. For a 50-station dataset, errors in trends in annual global means introduced by the first differencing procedure may be as large as 0.06 K decade−1 (for six breaks per series), which is greater than the standard error of the trend. Although the first difference method offers significant resource and labor advantages over methods that attempt to adjust the data, it introduces an error in large-scale mean time series that may be unacceptable in some cases.

Full access
Imke Durre, Xungang Yin, Russell S. Vose, Scott Applequist, and Jeff Arnfield

Abstract

The Integrated Global Radiosonde Archive (IGRA) is a collection of historical and near-real-time radiosonde and pilot balloon observations from around the globe. Consisting of a foundational dataset of individual soundings, a set of sounding-derived parameters, and monthly means, the collection is maintained and distributed by the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI). It has been used in a variety of applications, including reanalysis projects, assessments of tropospheric and stratospheric temperature and moisture trends, a wide range of studies of atmospheric processes and structures, and as validation of observations from other observing platforms. In 2016, NCEI released version 2 of the dataset, IGRA 2, which incorporates data from a considerably greater number of data sources, thus increasing the data volume by 30%, extending the data back in time to as early as 1905, and improving the spatial coverage. To create IGRA 2, 40 data sources were converted into a common data format and merged into one coherent dataset using a newly designed suite of algorithms. Then, an overhauled version of the IGRA 1 quality-assurance system was applied to the integrated data. Last, monthly means and sounding-by-sounding moisture and stability parameters were derived from the new dataset. All of these components are updated on a regular basis and made available for download free of charge on the NCEI website.

Full access
Peter E. Goble, Nolan J. Doesken, Imke Durre, Russ S. Schumacher, Abigail Stewart, and Julian Turner

Abstract

Every day, thousands of volunteers across the United States report the amount of precipitation they have received in the past 24 hours. This study focuses on the largest of these volunteer-submitted reports for each day, using precipitation measurements from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) from January 2010 to December 2017 as well as observations from the U.S. Cooperative Observer Program (COOP) network from January 1981 through December 2017. Results provide clarity on spatial variability, temporal variability, and seasonal cycle of contiguous U.S daily precipitation extremes (DPEs). During 2010–17, the DPEs ranged from 11 mm on 28 March 2013 in Oregon to 635 mm on 27 August 2017 in Texas during Hurricane Harvey. Coastal states are most prone to high daily precipitation totals, especially those bordering the Gulf of Mexico or Atlantic Gulf Stream. The average DPE value varies with season; it is greater than 175 mm in late August and less than 100 mm through meteorological winter. These observations also show that location of the DPE varies with season as well. For example, 28.5% of February extremes fall in Pacific states, whereas all August extremes occur east of that region. Perhaps most importantly, these findings demonstrate strength in numbers. The large daily sample size of CoCoRaHS and COOP networks forms a basis for monitoring, mapping, and categorizing DPEs, and other aspects of extreme precipitation, with considerable spatial detail.

Free access
Imke Durre, Michael F. Squires, Russell S. Vose, Xungang Yin, Anthony Arguez, and Scott Applequist

Abstract

The 1981–2010 “U.S. Climate Normals” released by the National Oceanic and Atmospheric Administration’s (NOAA) National Climatic Data Center include a suite of monthly, seasonal, and annual statistics that are based on precipitation, snowfall, and snow-depth measurements. This paper describes the procedures used to calculate the average totals, frequencies of occurrence, and percentiles that constitute these normals. All parameters were calculated from a single, state-of-the-art dataset of daily observations, taking care to produce normals that were as representative as possible of the full 1981–2010 period, even when the underlying data records were incomplete. In the resulting product, average precipitation totals are available at approximately 9300 stations across the United States and parts of the Caribbean Sea and Pacific Ocean islands. Snowfall and snow-depth statistics are provided for approximately 5300 of those stations, as compared with several hundred stations in the 1971–2000 normals. The 1981–2010 statistics exhibit the familiar climatological patterns across the contiguous United States. When compared with the same calculations for 1971–2000, the later period is characterized by a smaller number of days with snow on the ground and less total annual snowfall across much of the contiguous United States; wetter conditions over much of the Great Plains, Midwest, and northern California; and drier conditions over much of the Southeast and Pacific Northwest. These differences are a reflection of the removal of the 1970s and the addition of the 2000s to the 30-yr-normals period as part of this latest revision of the normals.

Full access
Anthony Arguez, Imke Durre, Scott Applequist, Russell S. Vose, Michael F. Squires, Xungang Yin, Richard R. Heim Jr., and Timothy W. Owen

The National Oceanic and Atmospheric Administration (NOAA) released the 1981–2010 U.S. Climate Normals in July 2011, representing the latest decadal installment of this long-standing product line. Climatic averages (and other statistics) of temperature, precipitation, snowfall, and numerous derived quantities were calculated for ~9,800 stations operated by the U.S. National Weather Service (NWS). They include estimated normals, or “quasi normals,” for approximately 2,000 active short-record stations such as those in the U.S. Climate Reference Network. The 1981–2010 installment features several new products and methodological enhancements: 1) state-of-the-art temperature homogenization at the monthly scale, 2) extensive utilization of quality-controlled daily climate data, 3) new statistical approaches for calculating daily temperature normals and heating and cooling degree days, and 4) a comprehensive suite of precipitation, snowfall, and snow depth statistics. This paper provides a general overview of this new suite of climate normals products.

Full access
Russell S. Vose, Scott Applequist, Mike Squires, Imke Durre, Matthew J. Menne, Claude N. Williams Jr., Chris Fenimore, Karin Gleason, and Derek Arndt

Abstract

This paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895–2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research.

Full access

CREATING CLIMATE REFERENCE DATASETS

CARDS Workshop on Adjusting Radiosonde Temperature Data for Climate Monitoring

Melissa Free, Imke Durre, Enric Aguilar, Dian Seidel, Thomas C. Peterson, Robert E. Eskridge, James K. Luers, David Parker, Margaret Gordon, John Lanzante, Stephen Klein, John Christy, Steven Schroeder, Brian Soden, Larry M. McMillin, and Elizabeth Weatherhead

Homogeneous upper-air temperature time series are necessary for climate change detection and attribution. About 20 participants met at the National Climatic Data Center in Asheville, North Carolina on 11–12 October 2000 to discuss methods of adjusting radiosonde data for inhomogeneities arising from instrument and other changes. Representatives of several research groups described their methods for identifying change points and adjusting temperature time series and compared the results of applying these methods to data from 12 radiosonde stations. The limited agreement among these results and the potential impact of these adjustments on upper-air trends estimates indicate a need for further work in this area and for greater attention to homogeneity issues in planning future changes in radiosonde observations.

Full access