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72534.016200
684706.335400
28986.938300
495049.291800
1
002
Projected
GCS_NAD_1983_2011
Linear Unit: Meter (1.000000)
NAD_1983_2011_Oregon_Statewide_Lambert
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20191106
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20191106
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dataset
20191106
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Historical U.S. Monthly Average Temperature and Precipitation from Global Historical Climate Network - Daily (GHCN-D) from 1981 through 2010.
<div style='text-align:Left;'><p><span>This point layer contains monthly summaries of daily temperatures (means, minimums, and maximums) and precipitation levels (sum, lowest, and highest) for the period January 1981 through December 2010 for weather stations in the Global Historical Climate Network Daily (GHCND). Data in this service were obtained from web services hosted by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc.</span></p><p><span></span></p><p><span>The data were compiled at Esri from publicly available sources hosted and administered by NOAA. Because the ACIS data is updated and corrected on an ongoing basis, the date of collection for this layer was Jan 23, 2019. The following process was used to produce this dataset: </span></p><p><span>Download the most current list of stations from ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt. Import this into Microsoft Excel and save as CSV. In ArcGIS, import the CSV as a geodatabase table and use the XY Event layer tool to locate each point. Using a detailed U.S. boundary extract the points that fall within the 50 U.S. States, the District of Columbia, and Puerto Rico. </span></p><p><span>Using Python with DA.UpdateCursor and urllib2 access the ACIS Web Services API to determine whether each station had at least 50 monthly values of temperature data for each station. Delete the other stations. Using Python add the necessary field names and acquire all monthly values for the remaining stations. Thus, there are stations that have some missing data. </span></p><p><span>Using Python Add fields and convert the standard values to metric values so both would be present. Thus, there are four sets of monthly data in this dataset: </span></p><p><span>a. Monthly means, mins, and maxes of daily temperatures - degrees Fahrenheit.</span></p><p><span>b. Monthly mean of monthly sums of precipitation and the level of precipitation that was the minimum and maximum during the period 1981 to 2010 - mm. </span></p><p><span>c. Temperatures in 3a. in degrees Celsius. </span></p><p><span>d. Precipitation levels in 3b in Inches.</span></p><div style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'><b>Sources:</b> </div><div style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'><ul><li>Sources data for station locations: </li><ul><li>Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Accessed April 10, 2019. doi:10.7289/V5NV9G8D. </li><li>Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3. [indicate subset used following decimal, e.g. Version 3.24]. Accessed online on 1/18/2019 at ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt </li></ul><li>Source for monthly averages for precipitation and temperature: Applied Climate Information System (ACIS). ACIS Web Services API: Accessed 1/18/2019 at https://www.rcc-acis.org/docs_webservices.html. The Applied Climate Information System (ACIS) was developed and is maintained by the NOAA Regional Climate Centers (RCCs). It was designed to manage the complex flow of information from climate data collectors to the end users of climate data information. The main purpose of ACIS is to alleviate the burden of climate information management for people who use climate information to make management decisions.</li></ul><div>Subset of data originally retrieved from: <a href='https://www.arcgis.com/home/item.html?id=b8df6517ceac42af9ab483089296ed04' style='color:rgb(0, 97, 155); text-decoration-line:none;' target='_blank' rel='nofollow ugc noopener noreferrer'>https://www.arcgis.com/home/item.html?id=b8df6517ceac42af9ab483089296ed04</a>. </div></div></div>
Esri, GHCND, and ACIS
PrecipitationTemperatureClimateMonthlyearth observationslandscapeglobalhistoricalGHCNDaverage
<div style='text-align:Left;'><p><span>None, the original NOAA data is licensed as public domain, the compiled Esri dataset is covered by the Esri Master License.</span></p><p><span>THE DATA AND RELATED MATERIALS MAY CONTAIN SOME NONCONFORMITIES, DEFECTS, OR ERRORS. ESRI DOES NOT WARRANT THAT THE DATA WILL MEET USER'S NEEDS OR EXPECTATIONS; THAT THE USE OF THE DATA WILL BE UNINTERRUPTED; OR THAT ALL NONCONFORMITIES, DEFECTS, OR ERRORS CAN OR WILL BE CORRECTED. ESRI IS NOT INVITING RELIANCE ON THIS DATA, AND THE USER SHOULD ALWAYS VERIFY ACTUAL DATA.THE DATA AND RELATED MATERIALS ARE PROVIDED "AS-IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.IN NO EVENT SHALL ESRI AND/OR ITS LICENSOR(S) BE LIABLE FOR COSTS OF PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOST PROFITS, LOST SALES, OR BUSINESS EXPENDITURES, INVESTMENTS, OR COMMITMENTS IN CONNECTION WITH ANY BUSINESS; LOSS OF ANY GOODWILL; OR FOR ANY INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THIS AGREEMENT OR USE OF THE DATA AND RELATED MATERIALS, HOWEVER CAUSED, ON ANY THEORY OF LIABILITY, AND WHETHER OR NOT ESRI OR ITS LICENSOR(S) HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THESE LIMITATIONS SHALL APPLY NOTWITHSTANDING ANY FAILURE OF ESSENTIAL PURPOSE OF ANY EXCLUSIVE REMEDY. In the event that the data vendor(s) has (have) granted the end user permission to redistribute the Geodata, please use proper proprietary or copyright attribution for the various data vendor(s), and provide the associated metadata file(s) with the Geodata.</span></p><p>Data Contact:</p><p>Training Services Librarian </p><p>Esri | 380 New York Street | Redlands, CA 92373 | USA </p><p>DataLibrary@esri.com</p><p><span></span></p></div>
434
EPSG
8.5(10.3.1)
Climate
Feature Class
434
OBJECTID_1
OBJECTID_1
OID
4
0
0
Internal feature number.
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Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
OBJECTID
OBJECTID
Integer
4
0
0
NCDC_ID
NCDC_ID
Integer
4
0
0
GHCND_ID
GHCND_ID
String
32
0
0
StnName
StnName
String
64
0
0
Lat_DD
Lat_DD
Double
8
0
0
Lon_DD
Lon_DD
Double
8
0
0
Elev_Gnd_F
Elev_Gnd_F
Integer
4
0
0
Elev_Gnd_m
Elev_Gnd_m
Integer
4
0
0
Mean_T_Jan
Mean Jan Temp
Single
4
0
0
Mean_T_Feb
Mean Feb Temp
Single
4
0
0
Mean_T_Mar
Mean Mar Temp
Single
4
0
0
Mean_T_Apr
Mean Apr Temp
Single
4
0
0
Mean_T_May
Mean May Temp
Single
4
0
0
Mean_T_June
Mean June Temp
Single
4
0
0
Mean_T_July
Mean July Temp
Single
4
0
0
Mean_T_Aug
Mean Aug Temp
Single
4
0
0
Mean_T_Sept
Mean Sept Temp
Single
4
0
0
Mean_T_Oct
Mean Oct Temp
Single
4
0
0
Mean_T_Nov
Mean Nov Temp
Single
4
0
0
Mean_T_Dec
Mean Dec Temp
Single
4
0
0
GlobalID
GlobalID
String
38
0
0
DistanceFromCoast
DistanceFromCoast
Single
4
0
0
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FALSE
434
TRUE
FALSE
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