Williams, C. N., Jr., M. J. Menne, R. S. Vose, and D. R. Easterling, 2006. United States Historical Climatology Network Daily Temperature, Precipitation, and Snow Data. ORNL/CDIAC-118, NDP-070. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. doi: 10.3334/CDIAC/cli.ndp070
This document describes a database containing daily observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth from 1062 observing stations across the contiguous United States. These stations are a subset of the 1221-station U.S. Historical Climatology Network (HCN), a monthly database compiled by the National Climatic Data Center (Asheville, North Carolina) that has been widely used in analyzing U.S. climate. The earliest station record begins in 1871 (Charleston, South Carolina); records from 158 stations begin prior to 1900. Data from 1005 of these daily records extend through 2000, while 920 station records extend through 2005. Most station records are essentially complete for at least 50 years; the latest beginning year of record is 1948.
The daily resolution of these data makes them extremely valuable for studies attempting to detect and monitor long-term climatic changes on a regional scale. Studies using daily data may be able to detect changes in regional climate that would not be apparent from analysis of monthly temperature and precipitation data. Such studies may include analyses of trends in maximum and minimum temperatures, temperature extremes, daily temperature range, precipitation "event size" frequency, and the magnitude and duration of wet and dry periods. The data are also valuable in areas such as regional climate model validation and climate change impact assessment.
This database is available free of charge from CDIAC as a numeric data
package (NDP). This file describes the HCN/D station network and gives details of
the format and content of all files.
Keywords: United States; HCN; HCN/D; historical; climate; climatology; daily
data; temperature; maximum temperature; minimum temperature;
precipitation; snowfall; snow depth
1. Background Information
Over the past few decades, numerous global, hemispheric, and regional meteorological databases have been assembled for use in studying the nature and variability of the earth's climate. This work has been largely inspired by growing international concern over the potential climatic impacts of increasing atmospheric concentrations of greenhouse gases. While the parameters important in the study of climate change are myriad, those that seem to have received the most attention are near-surface air temperature (herein referred to as temperature) and precipitation. There are many reasons for this, including (1) the spatial and temporal variability of these parameters affects ecosystems, agriculture, water resources, human health, and energy needs and consumption; (2) instrumental records of these variables are relatively long, beginning in the 1800s in many regions of the northern hemisphere; and (3) analyses of temperature data from around the globe show an increase in global mean surface temperature of about and 0.6 deg C since the late 19th century (IPCC 2001).
The suitability of modern historical temperature and precipitation data for climate change studies depends on their reliability and accuracy. Most records of significant length, regardless of source, are likely to contain biases or inhomogeneities resulting from changes in the environment or operation of individual observing sites (e.g., urbanization, station moves, and instrument and time of observation changes). The process of identifying and removing these nonclimatic effects is complex and tedious, and has been undertaken on large scales in such studies as Jones (1994), Jones et al. (1986; 1997), Vinnikov et al. (1990), Peterson and Vose (1997), and Quinlan et al. (1987). The work of Quinlan et al. (1987) involved the compilation of a database containing monthly temperature and precipitation data from a network of 1219 U.S. stations known as the Historical Climatology Network (HCN). The compilation was performed at the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) in Asheville, North Carolina, and sponsored by the Carbon Dioxide Research Program of the U.S. Department of Energy. The project arose from the need for an accurate, unbiased, and modern historical climate record suitable for detecting and monitoring secular changes in regional climate in the contiguous United States. The quality of the HCN data was enhanced with the use of outlier and areal edits, and the data were corrected for time of observation differences, instrument changes, instrument moves, station relocations, and urbanization effects (Karl et al. 1986; Karl and Williams 1987). The HCN has been updated several times since its inception, most recently by Williams et al. (2004). Some of the stations in the HCN are first-order weather stations, but the majority were selected from approximately 5000 U.S. cooperative weather stations.
The first database released by NCDC to contain daily data from HCN
stations, the HCN/Daily (HCN/D; Hughes et al. 1992; hereafter H92)
contained daily maximum and minimum temperatures and precipitation
totals from 138 select U.S. stations. The temperature and precipitation
records from these stations were considered to be the most reliable,
internally consistent, and unbiased records from the HCN. These records
were compiled from digital and nondigital data sets archived at NCDC
that come from a variety of sources, including climatological
publications, universities, federal agencies, individuals, and data
archives. Records were subjected to extensive manual and automated
quality assurance (QA) checks. The selection of stations for inclusion
in H92 was performed with the following data quality issues in mind.
Since the release of H92, much more work has been conducted at NCDC
involving compilation and digitizing of daily data. However, to enable
the compilation of a database providing better spatial coverage of the
contiguous United States, the four station selection criteria listed
above were not strictly adhered to in later versions of the HCN/D.
The data presented in this package are daily observations of maximum and minimum temperature, precipitation amount (liquid equivalent), snowfall amount, and snow depth from 1062 of the 1221 stations comprising the HCN. Data from 1005 of these daily records extend through 2000, while 920 of these extend through 2005. Most station records are essentially complete for at least 50 years; the latest beginning year of record is 1948. Records from 158 stations begin prior to 1900, with that of Charleston, South Carolina beginning the earliest (1871).
While the stations selected in H92 were determined to be superior
with regard to the above station selection criteria, the resulting
network's spatial coverage of the United States was lacking in several
regions. By including many more stations (mainly from the U.S.
cooperative station network), and performing the needed QA checks,
coverage has now been vastly improved. This figure
shows the distribution of the 1062 HCN/D stations. All of the contiguous
48 states are represented by stations in the database.
For users to correctly interpret records from the HCN/D in their analyses, it is important to describe a few caveats inherent in the recording of daily meteorological data in the United States. These relate primarily to observations of maximum and minimum temperature at U.S. cooperative network stations.
As pointed out in Sect. 2, the criterion deemed most important in the H92 station selection process was the degree to which a station maintained a constant observing time, i.e., a fixed observing "day," for maximum and minimum temperatures. The importance of maintaining a consistent schedule for observing daily maximum and minimum temperature has been illustrated by several studies, such as Mitchell (1958), Baker (1975), and Schaal and Dale (1977). These studies examined the effects of changing observation time on the daily mean temperature, customarily determined for U.S. stations by adding the maximum and minimum temperature observed over a prescribed 24-hour observing day and dividing by 2. At first-order National Weather Service (NWS) stations (some of which are included in the HCN/D), the 24-hour observing day ends at or near local midnight. Monthly and annual mean temperatures derived using the mean of the daily maximum and minimum temperatures from such stations have been shown by Baker (1975) and Mitchell (1958) to correspond closely with those computed using the stations' hourly observations. While this evidence lends clear support to the practice of ending the observing day at midnight, cooperative observers (comprising most of the HCN/D stations) generally do not take readings at this hour. Most end their observing day in the late afternoon or early evening, with a smaller number choosing a time between 0700 and 0800 local standard time (LST). The systematic biases introduced to the daily means by varying observing times can have far-reaching effects, as the daily mean temperatures form the basis of monthly and annual mean temperatures, and also monthly, seasonal, and annual heating degree days, cooling degree days, and growing degree days. Information on the LST of maximum/minimum temperature observations at each station is contained in the station history file for the HCN/D which is described in Sect. 5. Users are strongly urged to make use of this time of observation information in analyses where homogeneity of observing practices across a network of selected stations would be considered important.
While combining daily temperature (or precipitation) data from stations which use different observing days complicates data compilation and quality control and also distorts areal patterns, of perhaps more fundamental importance is the degree of homogeneity over time of observing practices at individual stations and the associated implications for studies of climatic trends. Many stations in the HCN/D depart to varying degrees from a single, fixed observation time for maximum and minimum temperatures over their period of record (see the station history file). This often results from observing responsibilities being transferred between individuals and may even result in a station moving to a new location in the area (relocation information is also contained in the station history file). The user is referred to the work of Mitchell (1958), Baker (1975), and Schaal and Dale (1977) for detailed illustrations of how such changes in observing time are likely to bias calculations involving maximum and minimum temperature data. Two main conclusions common to all three studies are (1) mean temperature calculations using 24-hour maximum/minimum temperatures from PM observations are biased high with respect to midnight observations, while those from AM observations are biased low, and (2) the magnitude of these biases is dependent upon time of year and a station's climatic regime.
Another factor users should be aware of pertains to thermometers used at the HCN/D stations. In 1984, the NWS introduced a new Maximum/Minimum Temperature System (MMTS) at cooperative network observing stations. Through 1994, 645 out of 1062 (about 60%) of the HCN/D stations had installed an MMTS (the station history file identifies these stations). Concerns have arisen about the calibration of this system as compared to that of the earlier thermometric system. The new system is thermistor-based with a "beehive like" instrument shelter, whereas the older systems consisted of liquid-in-glass thermometers, mounted inside a Cotton Region Shelter (Stevenson Screen). Quayle et al. (1991) looked into performance differences of the two systems and found that the new system produces maximum temperatures about 0.3 deg C lower and minimum temperatures about 0.4 deg C higher than the old system. Unfortunately, because large samples of side-by-side overlapping measurements are not available, site-specific corrections cannot yet be derived and only large-scale temperature changes can be corrected. Furthermore, daily biases, which are likely to be dependent on synoptic conditions, are unlikely to be the same from day to day. Thus, to date there has been no attempt to adjust the daily temperature data from the HCN/D for these instrument-induced biases.
In summary, while the HCN/D stations represent the best long-term
climate records available for the contiguous U.S., no station is
completely free of changes that could possibly affect its instrumental
record; therefore, it is recommended that users make full use of the
information contained in the station histories when performing analyses
with these data. The data have not been adjusted for station
relocations, heat island effects, instrument changes, or time of
observation biases. The nature of inhomogeneities arising from such
factors depends on a station's climatic regime.
An important part of the numeric data packaging process at CDIAC is the quality assurance (QA) of data before distribution. Data received at CDIAC are rarely in perfect condition for immediate distribution, regardless of their source. To guarantee data of the highest possible quality, CDIAC conducts extensive QA reviews. Reviews involve examining the data for completeness, reasonableness, and accuracy. Although they have common objectives, these reviews are tailored to each data set, often requiring extensive programming efforts. Although time-consuming, the QA process is an important component in the value-added concept of ensuring accurate, usable data for researchers.
Through the years, NCDC has conducted extensive manual and
automated QA assessments of the HCN/D data. Although the data sent by
NCDC were in excellent condition, CDIAC still conducted QA checks on the
data and found some minor discrepancies. The following summarizes the
major aspects of QA work performed by NCDC and CDIAC, respectively.
Users may also find additional details of QA work performed at NCDC in
NCDC's Summary of the Day (SOD, TD-3200) documentation, available over
the internet via NCDC's web site (http://www.ncdc.noaa.gov). (From the
NCDC homepage, use the search feature to search for "Summary of the Day"
or "TD-3200".)
NCDC QA Checks and Adjustments
A general overview of the history of HCN/D QA efforts conducted at NCDC, paraphrased from NCDC's SOD documentation, is as follows. In 1982, historical data were converted from various digital files to an "element" (observation type; e.g., maximum temperature, precipitation amount, etc.) type of file structure. At the time, data were only processed through a gross value check. Shortly thereafter, NCDC instituted a greatly enhanced computer algorithm for operational, automated validation of digital archives. The revised edit system performed internal consistency checks and evaluated against surrounding stations in addition to climatological limits and serial checks. Quality control flags were appended to each element to show how they fared during the edit procedures and to indicate what, if any, action was taken. Prior to 1982, the files consisted only of raw, observed data values; both observed and edited values (as necessary) have been supplied from 1982 onward.
Since 1982, the operational edit system at NCDC has evolved into a Geographical Edit and Analysis (GEA) expert system which affords interactive graphics presentations for the human editors. As of 1991, additional capabilities to detect systematic errors in the daily data have been incorporated using the Validation of Historical Daily Data (ValHiDD) system. Furthermore, in November 1993, the entire historical period of record was independently processed (no human editing) through the ValHIDD system for five data elements (the five variables included in NDP-070). Hence, the entire period of record for these elements now comprises observed (raw) and edited values.
The following is a list of items from H92 that constitute some of
the main human and automated QA checks performed on the data by NCDC.
INTEGER STCODE,CNI,ELEV,MOTMAX,YRTMAX,MOTMIN,YRTMIN,
+MOPRCP,YRPRCP,MOSNOW,YRSNOW,MOSNWD,YRSNWD
REAL LAT,LON
CHARACTER*2 STATE
CHARACTER*30 STNAME
READ(5,100,END=99)STATE,STCODE,CNI,STNAME,LAT,LON,
+ELEV,MOTMAX,YRTMAX,MOTMIN,YRTMIN,MOPRCP,YRPRCP,
+MOSNOW,YRSNOW,MOSNWD,YRSNWD
100 FORMAT(A2,1X,I2,I4,1X,A30,1X,F5.2,1X,F7.2,2X,
+ I4,5(1X,I2,1X,I4)
DATA INVENT;
LENGTH STNAME $ 30;
INFILE 'INVENT.TXT';
INPUT STATE $ 1-2 STCODE 4-5 CNI 6-9 STNAME $ 11-40
LAT 42-46 LON 48-54 ELEV 57-60 MOTMAX 62-63
YRTMAX 65-68 MOTMIN 70-71 YRTMIN 73-76
MOPRCP 78-79 YRPRCP 81-84 MOSNOW 86-87
YRSNOW 89-92 MOSNWD 94-95 YRSNWD 97-100;
Variable Variable Starting Ending
Variable type width column column
STATE Character 2 1 2
STCODE Numeric 2 4 5
CNI Numeric 4 6 9
STNAME Character 30 11 40
LAT Numeric 5 42 46
LON Numeric 7 48 54
ELEV Numeric 4 57 60
MOTMAX Numeric 2 62 63
YRTMAX Numeric 4 65 68
MOTMIN Numeric 2 70 71
YRTMIN Numeric 4 73 76
MOPRCP Numeric 2 78 79
YRPRCP Numeric 4 81 84
MOSNOW Numeric 2 86 87
YRSNOW Numeric 4 89 92
MOSNWD Numeric 2 94 95
YRSNWD Numeric 4 97 100
The station history file provides valuable information concerning each station in the
HCN/D. This file documents station moves and instrument changes, lists station observers and
observation times, and identifies suspect fields. For each station in the file there is an
identification record followed by multiple data records describing station observing details
over its period of record.
The file may be read using the following FORTRAN code:
CHARACTER*2 STATE
CHARACTER*30 CURRNAME
CHARACTER*16 COUNTY
CHARACTER*25 XREF
CHARACTER*150 BLANK
CHARACTER*1 STATUS, DISTUNIT, POUNIT
CHARACTER*6 LATNORTH
CHARACTER*7 LONGWEST
CHARACTER*3 DIRECT, DIRECTPO
CHARACTER*28 NAME
CHARACTER*10 QUALIF
CHARACTER*4 TIMEOBS
CHARACTER*2 PCPHT, PCTHT
CHARACTER*46 OBSNAME
INTEGER STANUM, STANUM2, DIVISION, MOBEG, DAYBEG, YRBEG
INTEGER MOEND, DAYEND, YREND, SUSP(15), DISTANCE, ELEV
INTEGER DISTPO, INSTRU(36), PUB(16), NUMOBS
OPEN(UNIT=5,FILE='history.txt')
10 READ (5,100) STANUM, STATE, STATUS, DIVISION, CURRNAME,
1 COUNTY, XREF, BLANK
20 READ (5,110,END=999) STANUM2
BACKSPACE 5
IF (STANUM .NE. STANUM2) GOTO 10
READ (5,115) STANUM2, MOBEG, DAYBEG, YRBEG,
1 MOEND, DAYEND, YREND, (SUSP(I),I=1,15), LATNORTH,
1 LONGWEST, DISTANCE, DISTUNIT, DIRECT,
1 ELEV, DISTPO, POUNIT, DIRECTPO, NAME, QUALIF,
1 (INSTRU(I),I=1,36), TIMEOBS, PCPHT, PCTHT,
1 (PUB(I),I=1,16), OBSNAME, NUMOBS
GOTO 20
100 FORMAT(I6,1X,A2,A1,I2,1X,A30,1X,A16,1X,A25,A150)
110 FORMAT(I6)
115 FORMAT(I6,2(2(1X,I2),1X,I4),1X,15A1,1X,A6,1X,A7,1X,
1 I3,A1,A3,1X,I5,1X,I4,A1,A3,1X,A28,1X,A10,1X,36A1,
1 1X,A4,1X,2A2,1X,16A1,1X,A46,1X,I2)
999 CLOSE(UNIT=5)
STOP
END
or using the SAS code:
DATA HISTORY (DROP=X);
RETAIN STANUM STATE STATUS DIVISION CURRNAME COUNTY XREF BLANK;
INFILE 'history.txt' MISSOVER LS=236;
INPUT @45 x $1. @;
IF X NE ' ' THEN DO;
INPUT STANUM 1-6 STATE $ 8-9 STATUS $ 10 DIVISION 11-12 CURRNAME $ 14-43
COUNTY $ 45-60 XREF $ 62-86 BLANK $ 150;
END;
ELSE
INPUT STANUM2 1-6 MOBEG 8-9 DAYBEG 11-12 YRBEG 14-17 MOEND 19-20
DAYEND 22-23 YREND 25-28 SUSPLAT 30 SUSPLONG 31 SUSPLOC 32
SUSPELEV 33 SUSPPO 34 SUSPNAME 35 SUSPQUAL 36 SUSPINST 37
SUSPTIME 38 SUSPHTS 39 SUSPPUBS 40 SUSPBEG 41 SUSPEND 42 SUSPOBS 43
SUSPOTHR 44 LATNORTH $ 46-51 LONGWEST $ 53-59 DISTANCE 61-63 DISTUNIT $ 64
DIRECT $ 65-67 ELEV 69-73 DISTPO 75-78 POUNIT $ 79 DIRECTPO $ 80-82
NAME $ 84-111 QUALIF $ 113-122 ADDINST 124
COTTON 125 DBULB 126 EVAPSTA 127 FISHPORT 128 HYGRO 129
MINTHERM 130 MAXTHERM 131 NORIVGAG 132 RAINGAGE 133
SHELTER 134 RECRIVER 135 RECRAIN 136 SNOW 137 STORAGE 138
STDRAIN 139 STDSHELT 140 THERMOGR 141 DIGTHERM 142
TIPBUCK 143 OTHEVAP 144 MAXMIN 145 TELSYS 146 HYGRO 147 HY6 148
HY8 149 SFP 150 SRRNG 151 SSG 152 SSRG 153 STB 154 AMOS 155 AUTOB 156
PSY 157 TIMEOBS $ 161-164 PCPHT $ 166-167
PCTHT $ 168-169 BULLETW 171 COMBBUL 172 CLIMDATA 173
RIVSTAGE 174 HYDROBUL 175 PRECDATA 176 SNOWBULL 177
NOTPUB 178 CWB 179 MONTHREV 180 STATEPUB 181 LCD 182 BQ 183
SGPD 184 WWR 185 MYB 186 OBSNAME $ 188-233 NUMOBS 235-236;
RUN;
Stated in tabular form, the contents of the station history file include the following:
Variable Variable Starting Ending
Variable type width column column
Identification record:
X Alphanumeric 1 45 45
STANUM Numeric 6 1 6
STATE Character 2 8 9
STATUS Character 1 10 10
DIVISION Numeric 2 11 12
CURRNAME Alphanumeric 30 14 43
COUNTY Alphanumeric 16 45 60
XREF Alphanumeric 25 62 86
BLANK Character 150 87 236
Data record:
STANUM2 Numeric 6 1 6
MOBEG Numeric 2 8 9
DAYBEG Numeric 2 11 12
YRBEG Numeric 4 14 17
MOEND Numeric 2 19 20
DAYEND Numeric 2 22 23
YREND Numeric 4 25 28
SUSPLAT Numeric 1 30 30
SUSPLONG Numeric 1 31 31
SUSPLOC Numeric 1 32 32
SUSPELEV Numeric 1 33 33
SUSPPO Numeric 1 34 34
SUSPNAME Numeric 1 35 35
SUSPQUAL Numeric 1 36 36
SUSPINST Numeric 1 37 37
SUSPTIME Numeric 1 38 38
SUSPHTS Numeric 1 39 39
SUSPPUBS Numeric 1 40 40
SUSPBEG Numeric 1 41 41
SUSPEND Numeric 1 42 42
SUSPOBS Numeric 1 43 43
SUSPOTHR Numeric 1 44 44
LATNORTH Alphanumeric 6 46 51
LONGWEST Alphanumeric 7 53 59
DISTANCE Numeric 3 61 63
DISTUNIT Character 1 64 64
DIRECT Alphanumeric 3 65 67
ELEV Numeric 5 69 73
DISTPO Numeric 4 75 78
POUNIT Character 1 79 79
DIRECTPO Alphanumeric 3 80 82
NAME Character 28 84 111
QUALIF Alphanumeric 10 113 122
ADDINST Numeric 1 124 124
COTTON Numeric 1 125 125
DBULB Numeric 1 126 126
EVAPSTA Numeric 1 127 127
FISHPORT Numeric 1 128 128
HYGRO Numeric 1 129 129
MINTHERM Numeric 1 130 130
MAXTHERM Numeric 1 131 131
NORIVGAG Numeric 1 132 132
RAINGAGE Numeric 1 133 133
SHELTER Numeric 1 134 134
RECRIVER Numeric 1 135 135
RECRAIN Numeric 1 136 136
SNOW Numeric 1 137 137
STORAGE Numeric 1 138 138
STDRAIN Numeric 1 139 139
STDSHELT Numeric 1 140 140
THERMOGR Numeric 1 141 141
DIGTHERM Numeric 1 142 142
TIPBUCK Numeric 1 143 143
OTHEVAP Numeric 1 144 144
MAXMIN Numeric 1 145 145
TELSY Numeric 1 146 146
HYGRO Numeric 1 147 147
HY6 Numeric 1 148 148
HY8 Numeric 1 149 149
SFP Numeric 1 150 150
SRRNG Numeric 1 151 151
SSG Numeric 1 152 152
SSRG Numeric 1 153 153
STB Numeric 1 154 154
AMOS Numeric 1 155 155
AUTOB Numeric 1 156 156
PSY Numeric 1 157 157
TIMEOBS Alphanumeric 4 161 164
PCPHT Alphanumeric 2 166 167
PCTHT Alphanumeric 2 168 169
BULLETW Numeric 1 171 171
COMBBUL Numeric 1 172 172
CLIMDATA Numeric 1 173 173
RIVSTAGE Numeric 1 174 174
HYDROBUL Numeric 1 175 175
PRECDATA Numeric 1 176 176
SNOWBULL Numeric 1 177 177
NOTPUB Numeric 1 178 178
CWB Numeric 1 179 179
MONTHREV Numeric 1 180 180
STATEPUB Numeric 1 181 181
LCD Numeric 1 182 182
BQ Numeric 1 183 183
SGPD Numeric 1 184 184
WWR Numeric 1 185 185
MYB Numeric 1 186 186
OBSNAME Alphanumeric 46 188 233
NUMOBS Numeric 2 235 236
Where:
The next 34 variables represent the following instruments and classifications. If an
instrument was used at a particular station or if a particular classification is appropriate
for that station, the variable will have a value of 1; if it was not used, the variable will
have a value of 0.
The 48 HCN/D data files (one for each state of the contiguous United States) contain
daily maximum and minimum temperatures (°F), precipitation amounts (hundredths of inches),
snowfall amounts (tenths of inches), snow depths (whole inches), and data flags from the 1062
HCN/D stations. The files are sorted by six-digit station number (the two-digit state code
followed by the four-digit Cooperative Network Index), year, and month, with one record per
month containing station number, data type, data units, year, month, number of days in the
month, and 31 daily data values with their respective flags.
The files may be read using the following FORTRAN format:
INTEGER YEAR,MON,DAYS,VALUE(31)
CHARACTER*1 SF(31),DMF(31),DQF(31)
CHARACTER*4 DATTYP
CHARACTER*6 STAID
CHARACTER*2 UNITS
1 CONTINUE
READ(5,100,END=99) STAID,DATTYP,UNITS,YEAR,MON,
+ DAYS,(SF(I),VALUE(I),DMF(I),DQF(I),I=1,31)
100 FORMAT(A6,1X,A4,A2,I4,I2,1X,I2,31(1X,A1,I4,2A1))
or by using the SAS format:
DATA HCND;
ARRAY DAY {31} $ DAY1-DAY31;
INFILE IN LRECL=270;
INPUT STAID $ 1-6 DATTYP $ 8-11 UNITS $ 12-13 YEAR 14-17
MON 18-19 DAYS 21-22 @23 (DAY1-DAY31) ($CHAR8.);
Stated in tabular form (using variable names from the FORTRAN format), the contents of a
record in an HCN/D data file include the following.
Variable Variable Starting Ending
Variable type width column column
STAID Character 6 1 6
DATTYP Character 4 8 11
UNITS Character 2 12 13
YEAR Numeric 4 14 17
MON Numeric 2 18 19
DAYS Numeric 2 21 22
SF(1) Alphanumeric 1 24 24
VALUE(1) Numeric 4 25 28
DMF(1) Alphanumeric 1 29 29
DQF(1) Alphanumeric 1 30 30
SF(2-31) Alphanumeric 1 * *
VALUE(2-31) Numeric 4 * *
DMF(2-31) Alphanumeric 1 * *
DQF(2-31) Alphanumeric 1 * *
*May be obtained using: COL(N) = COL(1) + (N * 8) - 8, where COL(N) is the starting/ending
column for SF(N), VALUE(N), DMF(N), or DQF(N); COL(1) is the starting/ending column for SF(1),
VALUE(1), DMF(1), or DQF(1); and N is the day of the month (2-31).
The HCN/D database is available free of charge from CDIAC. The data and a
plain text version of the documentation are available from CDIAC's anonymous FTP (file
transfer protocol) area via the Internet. Please note: your computer needs to have FTP software
loaded on it (this is built in to most modern day operating systems). Commands used to obtain
the database are shown below. For additional information, contact CDIAC.
ftp cdiac.esd.ornl.gov or ftp 128.219.24.36
(When the system asks you to login, enter "anonymous")
(When the system asks for your password, enter your e-mail address.)
Change the directory to pub/ndp070 (i.e., "cd pub/ndp070")
Retrieve the file you want (e.g., "get invent.txt")
The data and an HTML version of the documentation may also be obtained from
CDIAC's web site at http://cdiac.esd.ornl.gov/.
For non-internet data acquisitions (e.g., 8mm tape, CD-ROM, etc.), users should contact
CDIAC directly.
Address:
Carbon Dioxide Information Analysis Center
Oak Ridge National Laboratory
P.O. Box 2008
Oak Ridge, Tennessee 37831-6335, U.S.A.
Telephone:
(865) 574-3645 (Voice)
(865) 574-2232 (Fax)
Email: cdiac@ornl.gov
Baker, D. G. 1975. Effect of observation time on mean temperature estimation. J. Appl. Meteor. 14:471-76.
Easterling, D. R., T. R. Karl, E. H. Mason, P. Y. Hughes, and D. P. Bowman. 1996. United States Historical Climatology Network (U.S. HCN) Monthly Temperature and Precipitation Data. ORNL/CDIAC-87, NDP-019/R3. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 280 pp.
Hughes, P. Y., E. H. Mason, T. R. Karl, and W. A. Brower. 1992. United States Historical Climatology Network Daily Temperature and Precipitation Data. ORNL/CDIAC-50, NDP-042. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 140 pp.
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Jones, P. D. 1994. Northern Hemisphere surface air temperature variations : a reanalysis and an update to 1993. J. Climate 7:2548-2568.
Jones, P. D., S. C. B. Raper, R. S. Bradley, H. F. Diaz, P. M. Kelly, and T. M. L. Wigley. 1986. Northern Hemisphere surface air temperature variations 1851 1984. J. Clim. Appl. Meteor. 25:161-79.
Jones, P. D., T. J. Osborn, and K. R. Briffa. 1997. Estimating sampling errors in large-scale temperature averages. J. Climate 10:1794-1802.
Karl, T. R., G. Kukla, and J. Gavin. 1986. Relationship between decreased temperature range and precipitation trends in the United States and Canada, 1941 80. J. Clim. Appl. Meteor. 25:1878-86.
Karl, T. R., and C. N. Williams, Jr. 1987. An approach to adjusting climatological time series for discontinuous inhomogeneities. J. Clim. Appl. Meteor. 26:1744-63.
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