1. Introduction
The analysis of multidecadal climate trends and variability is commonly based on monthly and annual summaries of station-based weather data, and records of this time resolution have been widely available in digital form for decades (e.g., Jones et al. 1985, 1986; Vose et al. 1992). However, monthly means and averages are not sufficient for all climate applications. For example, the analysis of changes in the length of the growing season (Kunkel et al. 2004), changes in the frequency of heavy precipitation (Min et al. 2011), and changes in heat wave frequency and duration (Della Marta et al. 2007) all require data at least at the daily resolution. Unfortunately, daily data are comparatively less accessible than monthly values, in part because of impediments (e.g., mandates for cost recovery) in many countries for releasing daily climate data for widespread public use (Alexander et al. 2006). This relative paucity of daily data hampers climate change analysis and model comparison studies (Trenberth et al. 2007).
Here, a database is described whose aim is to address the need for daily climate data over global land areas. The database, known as the Global Historical Climatology Network (GHCN)-Daily dataset, contains daily data from over 80 000 surface stations worldwide, about two-thirds of which are for precipitation only. Like its counterpart for monthly data (Peterson and Vose 1997; Peterson et al. 1998; Lawrimore et al. 2011), GHCN-Daily is composed of daily weather reports from numerous sources that have been merged and subjected to a common suite of quality assurance (QA) reviews. Below, GHCN-Daily’s component data sources, methods for data integration and quality assurance, and the resulting spatial and temporal coverage of the dataset are described. The focus is on the core elements of temperature and precipitation, but the database also contains observations for snowfall, snow depth, and numerous other variables. Coverage of these elements is more limited in space and time.
2. Data sources
During the last several decades, the Global Telecommunication System (GTS), operated under the auspices of the World Meteorological Organization (WMO), has allowed National Meteorological and Hydrological Services (NMHSs) to share a wide variety of meteorological data regionally and worldwide. However, there has been no formal mechanism or requirement to share daily data via the GTS and no central repository for daily climate reports from national surface networks. In practice, the transmission of daily climate summaries has been treated as optional even for the network of stations that report temperature, precipitation, surface pressure, etc., at fixed synoptic hours [i.e., every 3 or 6 h for stations in the Regional Basic Synoptic Network (RBSN)]. Similarly, stations in the WMO’s Regional Basic Climate Network (largely a subset of the RBSN) are only required to provide a monthly climate summary known as CLIMAT and likewise do not consistently transmit daily summaries within their synoptic messages (WMO 2003).
Given this context, the goal in creating a global land surface daily dataset was to maximize the spatial coverage of daily weather data by acquiring historical data from as many stations in as many national observing networks as possible. Several complementary data acquisition tactics were used. The first was to exploit contacts with representatives from national meteorological and hydrological centers around the world to request contribution of their respective data collections. The earliest of these efforts lead to the development of the Global Daily Climatology Network (GDCN; Gleason et al. 2002) dataset. Not surprisingly, GDCN also contained a large collection of U.S. data. However, since GDCN’s release, a number of additional archives at National Oceanic and Atmospheric Administration/National Climatic Data Center (NOAA/NCDC) that contain daily data for the United States and its territories have been integrated comprehensively into GHCN-Daily. The second data collection tactic leveraged bilateral and international initiatives, such as the Global Climate Observing System (GCOS) program, which works to facilitate the free exchange of daily data from GCOS surface stations (Peterson et al. 1997). Bilateral agreements, in particular, have resulted in large contributions (hundreds to thousands of station records) from a number of countries. The last (and least preferred) tactic used the optional daily summaries that get transmitted as part of the GTS synoptic messages.
These varied attempts to acquire daily data can be loosely classified into four broad categories: 1) the international collection; 2) the U.S. collection; 3) government exchange data; and 4) the global summary of the day. A brief synopsis of each of these categories follows.
The international collection contains historical records for approximately 20 000 locations outside the United States (from over 100 different countries) and largely reflects the data collection efforts for GDCN. Well over 200 million values of maximum and minimum temperatures and total daily precipitation are included in this collection. As shown in Table 1, international collection records were generally obtained through personal contacts in various countries. As discussed in section 5, contributions to the international collection have resulted in particularly dense station networks with daily precipitation totals in Brazil, South Africa, and India, although the data from this collection are purely historical and are not updated. Precipitation records end generally in the late 1990s for Brazil and South Africa and in 1970 for India.
Sources and contacts for the international collection.
The U.S. collection contains daily data from a dozen separate datasets archived at NOAA/NCDC. As shown in Table 2, these archives include some of the earliest observations available for the United States (from the U.S. Forts and Voluntary Observer Program covering much of the nineteenth century; Dupigny-Giroux et al. 2007) as well as the latest measurements from the state-of-the-art climate monitoring stations that make up the U.S. Climate Reference Network (deployed early in the twenty-first century). GHCN-Daily thus contains the most complete collection of U.S. daily data available. Data for the United States are comprehensively updated in GHCN-Daily from a number of real-time and time-delayed data feeds. In addition, beginning with the 2011 data year, dataset index (DSI) 3200 (U.S. cooperative summary of the day) is no longer updated, having been superseded by GHCN-Daily (DSI 9101), which now serves as the official archive for U.S. daily Cooperative Observer Network data.
Data sources composing the U.S. collection.
Government exchange data (Table 3) refer to data collected through official GCOS or bilateral agreements. Under such agreements, Environment Canada and the Australian Bureau of Meteorology, for example, have provided their complete digital, daily database for inclusion in GHCN-Daily (with more than 7500 and 17 000 station records, respectively). Other NMHSs, such as the All-Russian Research Institute of Hydrometeorological Information, have provided large subsets (hundreds of station records) of their digital archives. The European Climate Assessment and Dataset (ECA&D; Klein Tank et al. 2002) project also provides a large collection of government exchange data and currently contains daily data from over 1500 stations in more than 50 countries. Early versions (before 2004) of the ECA&D data were used to form part of the international collection; however, more recently (beginning in 2011), the latest version of the ECA&D is operationally ingested into GHCN-Daily to incorporate monthly updates to the European data. Finally, under the auspices of GCOS, 76 different NMHSs have officially provided daily data for just over half of the 1000+ GCOS Surface Network (GSN) stations that make up the GSN network implementation. Although mechanisms have been set up to regularly update (at least monthly or annually) much of the government exchange data (e.g., Canada, Australia, ECA&D, Uzbekistan, Cypress, Iran, Latvia), such mechanisms have yet to be implemented for most GCOS stations, although new sets of historical data are periodically added.
Sources for government exchange data.
The global summary of the day contains 24-h summaries encoded in the special “climatological code” group transmitted with SYNOP reports on the GTS. These reports are archived in NCDC’s Integrated Surface Dataset (DSI-3505) and the 24-h summary period purportedly ends at midnight (i.e, 2400) UTC. Daily maximum and minimum temperatures from this source are included in GHCN-Daily only when provided as a nominal 24-h climatological summary as indicated in the SYNOP messages, whereas daily precipitation totals are used if they can be summed from two 12-h or four 6-h subtotals (as provided in standard SYNOP code groups). Subdaily summations are identified by associated “measurement” flag codes in the GHCN-Daily data format. Daily summaries from the global summary of the day may differ significantly from climate summaries with 24-h periods ending at local midnight (or at other hours used by convention at a particular NMHS), particularly in the case of precipitation. Nevertheless, data from this GTS source are available for a number of locations that are not contained in any other data archive available to NCDC, and they provide the only source of updates for many stations.
3. Data integration
As shown in Table 4, the process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: 1) eliminating source data for stations whose location is unknown or questionable; 2) classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and 3) combining the data from the different source datasets to form comprehensive station records. The first two of these steps are performed whenever a new source dataset or additional stations become available. The combining of data is part of an automated process that fully regenerates GHCN-Daily on a regular basis (usually once per week) using the latest versions of all sources. These three steps are explained further below.
Basic procedure for adding new source data to GHCN-Daily.
In the initial step, a station’s record from a particular source dataset is considered for inclusion in GHCN-Daily provided it meets the following conditions: First, it must be identified with a location name, latitude, and longitude using the metadata associated with the source dataset or from other standard station history information. Second, its period of record must contain 100 or more daily values for at least one of five core GHCN-Daily elements (maximum temperature, minimum temperature, precipitation, snowfall, or snow depth). Third, the record must not fail the “intra-source” duplicate check, which compares records from all stations within a source dataset. If more than 50% of a station’s record is identical to the data from another station in the same source, the longer of the two records is retained for inclusion in GHCN-Daily, provided that the metadata indicate that the two sites are in close proximity (i.e., within 40 km). However, if two stations with matching records are more than 40 km apart, neither is incorporated into the dataset.
The second step is to determine whether, thanks to a different source, data for the same location are already contained in GHCN-Daily or whether the location of the station record is new to GHCN-Daily. Whenever possible, station records from a new source are matched to records already in GHCN-Daily via the station identification numbers (IDs). However, it is common for a single meteorological station to have multiple network affiliations, which means that different source datasets may index the same station data to different IDs. Station lists (e.g., as supplied by an NMHS) are sometimes available that cross-reference IDs used by different organizations. To illustrate, data for Alabaster Shelby County Airport, Alabama, are indexed by Cooperative Observer Network ID 010116 in NCDC’s 3200 and 3206 datasets (among others). Given the common ID, data from these two sources should likely be combined into one GHCN-Daily record. In NCDC DSI-3210 (Table 2), however, and in the various other sources for airport observations, data for this location are stored under WBAN ID 53864, which must be matched with the corresponding cooperative station ID using NCDC’s Multinetwork Metadata System.
If cross-reference lists are not available, a new source of data for a particular station may be compared to station records already contained in GHCN-Daily. If data from the new source match the data for a station already added to GHCN-Daily at a rate of at least 50% for all elements during their common overlap period and the new station and the preexisting GHCN-Daily station are identified to be within 40 km of one another (based on their respective coordinates), then the new station data are added as an additional data source to the relevant GHCN-Daily station record already present in the dataset.
Finally, stations may be matched on the basis of their names and location alone. This strategy is more difficult to automate than the other two approaches because multiple stations within the same city or town may be identified with the same name and small differences in coordinates can be the result of either differences in accuracy or the existence of multiple stations in close proximity to each other. This type of matching was conducted for stations outside the United States whose data from the global summary of the day needed to be matched with data from the international collection.
The implementation of the above classification strategies yields a list of GHCN-Daily stations and an inventory of the source datasets to be integrated for each station. These lists form the basis for step 3, the integrating (or combining) of the data from the various sources to create GHCN-Daily. Combining takes place according to a hierarchy of data source precedence and in a manner that attempts to maximize the amount of data included while also minimizing the degree to which data from sources with different characteristics such as times of observation are mixed. Although precipitation, snowfall, and snow depth are allowed to come from separate sources during a particular month, maximum and minimum temperatures are considered together in order to ensure that the temperatures for a particular station and day always originate from the same source. This is important, for example, in the case of the real-time data feeds for the United States and the global summary of the day data, which tend to have observations that apply to 24-h summary periods that differ from those reported by other sources. For this reason, these sources are used only if no observations are available from any other source for that station, month, and element. Among the other sources, each day is considered individually; if an observation for a particular station and day is available from more than one source, the observation from the most preferred source available is used in GHCN-Daily. The hierarchy of data sources used in cases of overlap is based on several criteria. In general, data that have received the greatest amount of scrutiny are chosen over fully automated, real-time data streams. At stations operated by the United States, sources providing a cooperative summary of the day are given preference over other data streams because they contribute the largest amount of data. For stations outside the United States, the official governmental exchange data are preferred over the international collection when summaries from these two sources are available for the same station, element, and day.
4. Quality assurance
The QA approach to GHCN-Daily is based on several basic design considerations. First, given the large number of station records, a growing number of meteorological elements, and frequent additions of both historical and real-time data, it is impractical to rely on network-wide manual verification of the outcome of quality assurance algorithms as is commonly done in many existing QA systems (e.g., Guttman and Quayle 1990; Hubbard et al. 2005; Kunkel et al. 2005). Rather, a fully automated QA system is necessary for GHCN-Daily that is reliable enough to run “unsupervised.” Automated systems also have the advantage of providing traceable and reproducible results, which is a necessary component to tracking the provenance of climate data. At the same time, integration of new station records can introduce data problems that may go undetected by routine, automated QA checks. Such problems include undocumented changes to units of measure and the assignment of data records to incorrect station identifiers (Peterson et al. 1998). Consequently, the occasional application of additional automatic and semiautomatic fundamental data integrity checks is also necessary. Because of these design considerations, a multi-tiered QA approach was used. This approach consists primarily of routine, fully automated procedures with some additional overall data record integrity checks that are implemented occasionally (e.g., when a significant amount of historical data are added to the dataset) and that require some manual evaluation. Each of these procedures is described briefly below.
To begin, during routine processing, the data are first passed through a “format checking program” that looks for problems such as nonexistent months or days, invalid characters in data fields, and so forth. This routine sets offending records to missing. The primary purpose of this program is to ensure that our integration methods do not either introduce or retain records that violate the intended and documented GHCN-Daily data format. Next, a comprehensive sequence of fully automated QA procedures identifies daily values that violate 1 of 19 quality tests. Described in greater detail in Durre et al. (2010), these tests identify a variety of data problems, including the duplication of data records; exceedance of physical, absolute, and climatological limits; excessive temporal persistence; excessively large gaps in the distributions of values; internal inconsistencies among elements; and inconsistencies with observations at neighboring stations. This system flags approximately 0.3% of over 2 billion data values, and it has been estimated that 98%–99% of the values flagged are true data errors and only 1%–2% are false positives (i.e., valid observations erroneously flagged as bad; Durre et al. 2010). This level of performance was achieved through careful selection and evaluation of procedures and test thresholds using the techniques described by Durre et al. (2008).
Manual review of random samples of flagged values was used to set the test threshold of each procedure such that its false-positive rate is minimized. In addition, the tests are arranged in a deliberate sequence in which the performance of the later checks is thought to be enhanced by the error detection capabilities of the earlier ones. As a result of this comprehensive manual assessment during the QA development phase, the algorithms are effective at detecting the grossest errors as well as more subtle inconsistencies among elements without the typically higher rate of false positives of automated QA procedures (Schmidlin et al. 1995; Kunkel et al. 2005; You and Hubbard 2006).
The second tier of quality assurance includes record integrity checks, which are implemented only occasionally. These consist of checks for
climatological means that are inconsistent with a station’s location;
large, systematic jumps in the annual mean of a record (such as might be caused by a shift in reporting units); and
concentrations of values that fail automated QA procedures.
A semiautomatic method for identifying large jumps and other erratic behavior in time series of annual totals was also applied. Gross shifts in precipitation time series were identified by means of the standard normal homogeneity test (Alexandersson 1986) applied to station time series of annual precipitation totals computed from the daily data. The two major problems revealed by this test included a two- to threefold increase in precipitation (likely caused by a transition in reporting units) and completely dry multiyear periods at locations that normally report abundant precipitation. The affected stations with large jumps were eliminated from the integrated dataset.
A manual examination of station records where maximum and minimum temperatures failed the outlier and/or inconsistency checks at least 300 times revealed two problems. In one case, five stations reported only maximum temperatures before 1981 and these temperatures were around 10°C lower than the maximum temperatures reported during the latter part of the record. Second, a set of about 100 stations was removed from the dataset because their time series exhibited shifts on the order of 5°–10°C during some portion of their records or failed to follow an annual cycle where one would be expected.
In the last record integrity check, U.S. temperature records for which the time of observation has been documented are tested for inconsistencies between the reported observation time and the reported temperatures. Such inconsistencies are known to be present in the data as a result of various observing and digitization practices and errors (e.g., Reek et al. 1992; Kunkel et al. 2005). Such errors are best identified by means of comparison with hourly temperature observations at neighboring synoptic stations (Janis 2002). Whenever the daily maximum temperatures within a month are judged to be inconsistent with corresponding maximum temperatures derived from the hourly data (see the appendix), all temperatures in the month are flagged accordingly.
5. Description of the dataset and processing
Figures 1 and 2 depict the locations of stations that have at least 10 years of records during successive 30-yr intervals starting in 1861. Like its monthly counterpart, the concentration of stations with observations of temperature or precipitation in GHCN-Daily is denser over North America and Eurasia than over Africa, Antarctica, and South America. In the case of GHCN-Daily, however, the densest historical station networks come from the United States, Canada, and Australia, a reflection of the comprehensive contributions from these countries. Nevertheless, Brazil, India, and South Africa have also contributed records from very dense national precipitation networks. The maps for the year 2010 provide an indication of the density of stations that can be updated in GHCN-Daily.
(a)–(f) Density of GHCN-Daily stations with daily maximum and minimum temperature.
Citation: Journal of Atmospheric and Oceanic Technology 29, 7; 10.1175/JTECH-D-11-00103.1
(a)–(f) Density of GHCN-Daily stations with daily precipitation.
Citation: Journal of Atmospheric and Oceanic Technology 29, 7; 10.1175/JTECH-D-11-00103.1
With over 80 000 station records from 180 countries and territories (Table 5), GHCN-Daily is likely the most comprehensive global collection of in situ land surface daily climate summaries available. The total number of values for all elements in the dataset is well over 2 billion, including nearly 300 million maximum and minimum temperatures and more than 800 million daily precipitation totals (as well as 240 million observations of daily snowfall and about 220 million daily snow depths). Additional elements are available at select U.S. stations, most notably temperature at observation time, snow water equivalent, pan evaporation, and the occurrence of various weather phenomena. About 70% of all values come from North American stations.
List of countries and territories with data in GHCN-Daily and their corresponding Federal Information Processing Standard (FIPS) codes. (Source:
Figure 3 depicts the temporal evolution of the station network. Daily summaries are available from a relatively small number of stations before 1890 when the number of stations reporting maximum and minimum temperature (precipitation) is about 2.5% (8.9%) of the peak number. The total number, spatial distribution, and temporal completeness generally increase through time for all variables, although both the temperature and precipitation networks attain their maximum density in the 1960s. The interval covered by GHCN-Daily station reports varies from less than 1 year up to 245 years, with the average temperature record spanning 36.7 years and the average precipitation record lasting 33.1 years.
Time series of the number of stations in GHCN-Daily with (a) maximum and minimum temperature, (b) precipitation, (c) snowfall, and (d) snow depth.
Citation: Journal of Atmospheric and Oceanic Technology 29, 7; 10.1175/JTECH-D-11-00103.1
The number of temperature stations as well as the total number of snowfall and snow depth stations remains roughly the same at near-peak levels through the present. The precipitation network, in contrast, declines in size abruptly in the late 1960s, largely because a source for thousands of Indian precipitation records ends around 1970. The decline in the number of available precipitation reports continues until the mid-2000s, when the rapid development of the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS; http://www.cocorahs.org) in the United States contributes to a rebound in precipitation station numbers. Throughout the record, the vast majority of temperature stations is in North America because of the comprehensiveness of the U.S. and Canadian contributions, whereas the number of precipitation stations is more evenly split between North America and the rest of the world for most of the twentieth century. Nearly all snowfall and snow depth stations are from the Northern Hemisphere, and snowfall is commonly measured only in North America.
GHCN-Daily is updated each day using a number of near-real-time data streams such that recent observations are added within 1 or 2 days of their availability at many thousands of stations. In these cases, the latest daily climate summaries should be only 1 or 2 days behind the calendar date. In the case of time-delayed updates, values are generally delayed by 1 or 2 months. However, it should be noted that, although more than 20 000 stations in GHCN-Daily can be regularly updated (and more than 30 000 stations contain values within the past year), most participating countries have provided historical daily station records only once. Some of these stations are not necessarily currently active but, in the absence of ongoing formal exchange mechanisms, the sole potential for updates for many GHCN-Daily stations is through the daily synoptic summaries archived in NCDC’s global summary of the day. Potential updates from the global summary of the day have yet to be fully exploited in GHCN-Daily; however, values from this source tend to be incomplete and have the timing issues mentioned in section 2. For this reason, other mechanisms for data sharing are encouraged, as outlined in the summary and conclusions. Nevertheless, new and existing bilateral agreements for routine data sharing will continue to enhance the database.
In addition to the near-real-time and time-delayed updates, GHCN-Daily is fully reprocessed on a regular basis (usually once per week), which entails reconstructing the dataset from its component sources from start to finish. During the reprocess, the most recent version of each source dataset is reintegrated to form the comprehensive (combined) GHCN-Daily station records, and all period of record values are subjected to the latest suite of QA checks. This type of reprocessing helps to ensure that GHCN-Daily is synchronized with its source archives and that all daily climate records are uniformly subjected to the latest set of QA tests. This approach to dataset construction and maintenance honors the intent of a key research need required to ensure the climate record for climate studies, which was highlighted as a “lesson learned” from the Intergovernmental Panel on Climate Change Fourth Assessment Report (Doherty et al. 2009).
Moreover, to ensure version control and traceability each updated and reprocessed version of GHCN-Daily is assigned a unique three-part version code, and every version of GHCN-Daily is archived in its entirety as a separate dataset (along with the latest processing source code). The first component of the version code is incremented only when there are changes to the processing algorithms and/or major additions to the database itself. The second part indicates whether it is an update or a newly reprocessed version. The third part is a timestamp that indicates when the update or reprocessing was done. To illustrate, the descriptive statistics in this section were generated from GHCN-Daily version 2.90-upd-2011112910, an update from 29 November 2011 that initiated at 1000 UTC (and which was produced by appending recently available data updates to the last fully reprocessed version 2.90-por-2011112514). This version can be retrieved from NOAA/NCDC by requesting DSI 9101 version 2.90-upd-2011112910. Authors are requested to cite the relevant version number and timestamp when GHCN-Daily is used for analysis.
6. Summary and conclusions
GHCN-Daily supersedes the Global Daily Climate Network dataset, which was released in 2002. Compared to the GDCN, GHCN-Daily includes a more expansive set of historical data sources, numerous data streams that improve the latency of the dataset through frequent updates, and a much more comprehensive set of QA checks. GHCN-Daily also serves as the official archive for daily records from the GSN. The merged GSN station records from all available data sources are provided as a distinct subset of stations for ease of access to the GSN archive. Daily records from stations in the widely used U.S. Historical Climatology Network (USHCN) are also provided as a separate subset of GHCN-Daily and have been used for updating the USHCN version 2 monthly temperatures (Menne et al. 2009) since 2006. In 2011, GHCN-Daily also became the official database for all U.S. daily data.
In spite of the label Global Historical Climatology Network, it is important not to interpret this name to mean that the dataset can be used to quantify all aspects of climate variability and change without any additional processing. Historically (and in general), the stations providing daily data were not managed to meet the desired standards for climate monitoring (e.g., Karl et al. 1995). Rather, the stations were deployed to meet the demands of agriculture, hydrology, weather forecasting, aviation, etc. Notably, GHCN-Daily has not been homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias). Users, therefore, must consider whether the potential for changes in systematic bias might be important to their application. In addition, GHCN-Daily and GHCN-Monthly are not currently internally consistent (i.e., GHCN-Monthly is not necessarily derived from the data in GHCN-Daily); however, GHCN-Daily is anticipated to be a major source of future updates and enhancements to GHCN-Monthly.
Finally, although GHCN-Daily has already found applications in climate monitoring and assessments (e.g., Alexander et al. 2006; Caesar et al. 2006), its utility could always be enhanced with additional data for regions outside of North America. For this reason, we encourage new data contributions and particularly welcome the addition of complete national daily climate archives. These contributions can be made as part a new initiative to create a more comprehensive global surface temperature databank (Thorne et al. 2011). In cases where routine updates of such national data contributions are not possible via web services or other routine and preferably automated means, the development and exchange of official “climate quality” daily messages over the GTS analogous to the monthly CLIMAT messages should be encouraged. In summary, GHCN-Daily is best viewed as a dynamic, integrated daily dataset to which new data sources and variables will continue to be added. Enhancements to the methods for quality assurance are also likely to be developed over time, with routine homogeneity assessments a likely future addition.
Acknowledgments
The authors wish to thank the many National Meteorological and Hydrological Services and their representatives for contributions to GHCN-Daily. We would also like to acknowledge the many people at NCDC who worked to collect daily data from around the world, especially Tom Peterson and Pasha Groisman. We thank Peter Thorne, Anthony Arguez, Tom Peterson, and two anonymous reviewers for helpful review comments. Partial support for this work was provided by the U.S. Department of Energy Office of Biological and Environmental Research (Grant DE-AI02-96ER62276) and the NOAA/Office of Global Programs, Climate Change Data and Detection Element.
APPENDIX
Testing for Discrepancies in the Timing of Daily Maximum Temperature
Although there are numerous potential causes of discrepancies in the timing of daily maxima and minima, a common discrepancy arises in the United States with observations from Cooperative Observers whose 24-h daily summary period ends in the local morning hours. Because the maximum temperature attained during the 24 h that precede a morning observation time is usually reached sometime during the previous afternoon, a number of volunteer observers who observe in the morning attribute the daily maximum to the previous calendar day when recording the value (Reek et al. 1992). In such cases, the observer usually records the 24-h minimum on the current calendar day (i.e., the day on which the summary period actually ended, which is the desired practice for recording all daily variables, including daily maximum temperature). Moreover, historically, Cooperative Observer paper forms were commonly keyed in a similar way: that is, whereby daily maximum temperatures were systematically assigned to the previous day for morning observers.
A target series is identified as having an apparent systematic issue with the timing of daily temperatures when (i) there is at least one surrogate series available for comparison and (ii) the index of agreement between the target series and all available surrogate series is higher when the surrogate maximum temperature series are systematically shifted forward or backward by one day. More specifically, the d values between the target and all shifted surrogate series must improve by more than 0.2 relative to the value calculated between the target and unlagged surrogate series. The use of a minimum improvement in d as well as the requirement for d to be at least 0.7 for an unlagged comparison comes from a systematic manual evaluation of potential thresholds as described in Durre et al. (2008).
REFERENCES
Alexander, L. V., and Coauthors, 2006: Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res., 111, D05109, doi:10.1029/2005JD006290.
Alexandersson, H., 1986: A homogeneity test applied to precipitation data. Int. J. Climatol., 6, 661–675.
Caesar, J., Alexander L. , and Vose R. S. , 2006: Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set. J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280.
Della-Marta, P. M., Haylock M. R. , Luterbacher J. , and Wanner H. , 2007: Doubled length of western European summer heat waves since 1880. J. Geophys. Res., 112, D15103, doi:10.1029/2007JD008510.
Doherty, S. J., and Coauthors, 2009: Lessons learned from IPCC AR4: Scientific developments needed to understand, predict, and respond to climate change. Bull. Amer. Meteor. Soc., 90, 497–513.
Dupigny-Giroux, L.-A., Ross T. F. , Elms J. D. , Truesdell R. , and Doty S. R. , 2007: NOAA’s Climate Database Modernization Program: Rescuing, archiving, and digitizing history. Bull. Amer. Meteor. Soc., 88, 1015–1017.
Durre, I., Menne M. J. , and Vose R. S. , 2008: Strategies for evaluating quality assurance procedures. J. Appl. Meteor. Climatol., 47, 1785–1791.
Durre, I., Menne M. J. , Gleason B. E. , Houston T. G. , and Vose R. S. , 2010: Robust automated quality control of daily surface observations. J. Appl. Meteor. Climatol., 49, 1615–1633.
Gleason, B. E., Peterson T. C. , Groisman P. Ya. , Easterling D. R. , Vose R. S. , and Ezell D. S. , 2002: A new global daily temperature and precipitation data set. Preprints, 13th Symp. on Global Change and Climate Variations, Orlando, FL, Amer. Meteor. Soc., P1.16. [Available online at https://ams.confex.com/ams/annual2002/webprogram/Paper27803.html.]
GLOBE Task Team, and Coauthors, 1999: The Global Land One-Kilometer Base Elevation (GLOBE) Digital Elevation Model, version 1.0. National Oceanic and Atmospheric Administration National Geophysical Data Centerdigital database. [Available online at http://www.ngdc.noaa.gov/mgg/topo/globe.html.]
Guttman, N. B., and Quayle R. G. , 1990: A review of cooperative temperature data validation. J. Atmos. Oceanic Technol., 7, 334–339.
Hubbard, K. G., Goddard S. , Sorensen W. D. , Wells N. , and Osugi T. T. , 2005: Performance of quality assurance procedures for an applied climate information system. J. Atmos. Oceanic Technol., 22, 105–112.
Janis, M. J., 2002: Observation-time-dependent biases and departures for daily minimum and maximum air temperatures. J. Appl. Meteor., 41, 588–603.
Jones, P. D., and Coauthors, 1985: A grid point surface air temperature data set for the Northern Hemisphere. U.S. Department of Energy Carbon Dioxide Research Division Tech. Rep. TRO22, 251 pp.
Jones, P. D., Raper S. C. B. , Cherry B. S. G. , Goodess C. M. , and Wigley T. M. L. , 1986: A grid point surface air temperature data set for the Southern Hemisphere 1851-1984. U.S. Department of Energy Carbon Dioxide Research Division Tech. Rep. TR027, 73 pp.
Karl, T. R., and Coauthors, 1995: Critical issues for long-term climate monitoring. Climatic Change, 31, 185–221, doi:10.1007/BF01095146.
Klein Tank, A. M. G., and Coauthors, 2002: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol., 22, 1441–1453.
Kunkel, K. E., Easterling D. R. , Hubbard K. , and Redmond K. , 2004: Temporal variations in frost-free season in the United States: 1895–2000. Geophys. Res. Lett., 31, L03201, doi:10.1029/2003GL018624.
Kunkel, K. E., Easterling D. R. , Redmond K. , Hubbard K. , Andsager K. , Kruk M. , and Spinar M. , 2005: Quality control of pre-1948 cooperative observer network data. J. Atmos. Oceanic Technol., 22, 1691–1705.
Lawrimore, J. H., Menne M. J. , Gleason B. E. , Williams C. N. , Wuertz D. B. , Vose R. S. , and Rennie J. , 2011: An overview of the Global Historical Climatology Network monthly mean temperature dataset, version 3. J. Geophys. Res., 116, D19121, doi:10.1029/2011JD016187.
Legates, D. R., and Willmott C. J. , 1990a: Mean seasonal and spatial variability in gauge corrected global precipitation. Int. J. Climatol., 10, 111–127.
Legates, D. R., and Willmott C. J. , 1990b: Mean seasonal and spatial variability in global surface air temperature. Theor. Appl. Climatol., 41, 11–21.
Legates, D. R., and McCabe G. J. Jr., 1999: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model evaluation. Water Resour. Res., 35, 233–241.
Menne, M. J., Williams C. N. Jr., and Vose R. S. , 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 993–1007.
Min, S.-K., Zhang X. , Zwiers F. W. , and Hegerl G. C. , 2011: Human contribution to more-intense precipitation extremes. Nature, 470, 378–381, doi:10.1038/nature09763.
Peterson, T. C., and Vose R. S. , 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc., 78, 2837–2849.
Peterson, T. C., Daan H. , and Jones P. , 1997: Initial selection of a GCOS surface network. Bull. Amer. Meteor. Soc., 78, 2145–2152.
Peterson, T. C., Vose R. S. , Razuvaev V. N. , and Schmoyer R. L. , 1998: Global Historical Climatology Network (GHCN) quality control of monthly temperature data. Int. J. Climatol., 18, 1169–1179.
Reek, T., Doty S. R. , and Owen T. W. , 1992: A deterministic approach to the validation of historical daily temperature and precipitation data from the Cooperative Network. Bull. Amer. Meteor. Soc., 73, 753–765.
Schmidlin, T. W., Wilks D. S. , McKay M. , and Cember R. P. , 1995: Automated quality control procedure for the “water equivalent of snow on the ground” measurement. J. Appl. Meteor., 34, 143–151.
Thorne, P. W., and Coauthors, 2011: Guiding the creation of a comprehensive surface temperature resource for twenty-first-century climate science. Bull. Amer. Meteor. Soc., 92, ES40–ES47.
Trenberth, K. E., and Coauthors, 2007: Observations: Surface and atmospheric climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 235–336.
Vose, R. S., Schmoyer R. L. , Steurer P. M. , Peterson T. C. , Heim R. , Karl T. R. , and Eischeid J. K. , 1992: The Global Historical Climatology Network: Long-term monthly temperature, precipitation, sea level pressure, and station pressure data. Oak Ridge National Laboratory Environmental Sciences Division Publ. 3912, 324 pp.
WMO, 2003: Manual on the Global Observing System: Volume 1—Global aspects. World Meteorological Organization Document WMO 544, 58 pp.
You, J., and Hubbard K. G. , 2006: Quality control of weather data during extreme events. J. Atmos. Oceanic Technol., 23, 184–197.
