scholarly journals An Improved QC Process for Temperature in the Daily Cooperative Weather Observations

2007 ◽  
Vol 24 (2) ◽  
pp. 206-213 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Nathaniel B. Guttman ◽  
Jinsheng You ◽  
Zhirong Chen
Keyword(s):  
Quality Assurance ◽  
Data Quality ◽  
Quality Assessment ◽  
Air Temperature ◽  
Spatial Regression ◽  
Daily Maximum ◽  
Correct Identification ◽  
Climatic Data ◽  
Regression Test ◽  
Minimum Air Temperature

Abstract TempVal is a spatial component of data quality assurance algorithms applied by the National Climatic Data Center (NCDC), and it has been used operationally for about 4 yr. A spatial regression test (SRT) approach was developed at the regional climate centers for climate data quality assurance and was found to be superior to currently used quality control (QC) procedures for the daily maximum and minimum air temperature. The performance of the spatial quality assessment procedures has been evaluated by assessing the rate with which seeded errors are identified. A complete dataset with seeded errors for the year 2003 for the contiguous United States was examined for both the maximum and minimum air temperature. The spatial regression quality assessment component (SRT), originating in the Automated Climate Information System (ACIS), and TempVal, originating in the NCDC database, were applied separately and evaluated through the ratio of identified seeded errors to the total number of seeds. The spatial regression test applied in the ACIS system was found to perform better in identifying the seeded errors. For all months, the relative frequency of correct identification of wrong data is 0.72 and 0.83 for TempVal and SRT, respectively. The goal of the comparison was to evaluate quality assurance techniques that could improve data quality assessment at the NCDC, and the results of the comparison led to the recommendation that the SRT be included in the NCDC quality assessment methodology.

Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

Evaluation of estimating daily maximum and minimum air temperature with MODIS data in east Africa

2012 ◽  
Vol 18 ◽  
pp. 128-140 ◽  
Author(s):  
Shengpan Lin ◽  
Nathan J. Moore ◽  
Joseph P. Messina ◽  
Mark H. DeVisser ◽  
Jiaping Wu
Keyword(s):  
East Africa ◽  
Air Temperature ◽  
Daily Maximum ◽  
Modis Data ◽  
Minimum Air Temperature

scholarly journals Performance of Quality Assurance Procedures for an Applied Climate Information System

2005 ◽  
Vol 22 (1) ◽  
pp. 105-112 ◽  
Author(s):  
K. G. Hubbard ◽  
S. Goddard ◽  
W. D. Sorensen ◽  
N. Wells ◽  
T. T. Osugi
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Climatic Variability ◽  
Spatial Regression ◽  
Type I ◽  
Type Ii ◽  
Random Errors ◽  
Multiple Thresholds ◽  
Regression Test ◽  
Type Ii Errors

Abstract Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of ±1°C for temperature, not too different from the sensor field accuracy. The study underscores the fact that precipitation is more difficult to quality control than temperature. The new spatial regression test presented in this document outperformed all the other tests, which together identified only a few errors beyond those identified by the spatial regression test.

Stomatal conductance in three conifer species at different elevations during summer in Wyoming

1988 ◽  
Vol 18 (2) ◽  
pp. 242-246 ◽  
Author(s):  
Gregory A. Carter ◽  
William K. Smith ◽  
Julian L. Hadley
Keyword(s):  
Air Temperature ◽  
Early Morning ◽  
Early Summer ◽  
Subalpine Forest ◽  
Daily Maximum ◽  
Interspecific Differences ◽  
Lower Elevation ◽  
Central Rocky Mountains ◽  
Maximum Conductance ◽  
Minimum Air Temperature

Stomatal conductances to water vapor diffusion in Engelmann spruce (Piceaengelmannii Parry ex Engelm.), subalpine fir (Abieslasiocarpa (Hook.) Nutt.), and lodgepole pine (Pinuscontorta Engelm.) were compared to determine environmental influences on conductance at higher (3220 m) and lower (2860 m) elevations in the central Rocky Mountains. Measurements were taken on clear days, and soil water potentials remained at or greater than −0.1 MPa. Interspecific differences were small between spruce and fir at either site, but pine conductance was generally higher than spruce or fir at 2860 m. Daily maximum conductance in spruce and fir at 3220 m did not increase above 1.0 mm s−1 until daily minimum air temperature (early morning) increased to near 1 °C in early summer. Increases in maximum conductance above 2.0 mm s−1 occurred at both elevations when minimum air temperature rose above approximately 5 °C. At the lower elevation site, increases in maximum conductance during late July and mid-August appeared to depend strongly on soil temperature increasing above 7–8 °C. The persistence of cold soil temperatures in the highest elevations of the subalpine forest may serve to inhibit stomatal opening in spruce and fir in comparison to spruce, fir, and pine in lower elevation forests.

scholarly journals Sensitivity Analysis of Quality Assurance Using the Spatial Regression Approach—A Case Study of the Maximum/Minimum Air Temperature

2005 ◽  
Vol 22 (10) ◽  
pp. 1520-1530 ◽  
Author(s):  
Kenneth G. Hubbard ◽  
Jinsheng You
Keyword(s):  
Air Temperature ◽  
Time Window ◽  
Spatial Regression ◽  
Optimal Number ◽  
Quality Data ◽  
Distance Weighting ◽  
Time Offset ◽  
Inverse Distance ◽  
Minimum Air Temperature

Abstract Both the spatial regression test (SRT) and inverse distance weighting (IDW) methods have been applied to provide estimates for the maximum air temperature (Tmax) and the minimum air temperature (Tmin) in the Applied Climate Information System (ACIS). This is critical to the processes of estimating missing data and identifying suspect data and is undertaken here to ensure quality data in ACIS. The SRT method was previously found to be superior to the IDW method; however, the sensitivity of the performance of both methods to input parameters has not been evaluated. A set of analyses is presented for both methods whereby the sensitivity to the radius of inclusion, the regression time window, the regression time offset, and the number of stations used to make the estimates are examined. Comparisons were also conducted between the SRT and the IDW methods. The performance of the SRT method stabilized when 10 or more stations were applied in the estimates. The optimal number of stations for the IDW method varies from only a few to 30. The results indicate that the best estimates obtained using the IDW method are still inferior to the worst estimates obtained using the SRT method.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

scholarly journals Data quality assessment in the routine health information system: an application of the Lot Quality Assurance Sampling in Benin

2014 ◽  
Vol 30 (7) ◽  
pp. 837-843 ◽  
Author(s):  
Yolaine Glèlè Ahanhanzo ◽  
Edgard-Marius Ouendo ◽  
Alphonse Kpozèhouen ◽  
Alain Levêque ◽  
Michel Makoutodé ◽  
...  
Keyword(s):  
Information System ◽  
Quality Assurance ◽  
Data Quality ◽  
Quality Assessment ◽  
Health Information ◽  
Health Information System ◽  
Data Quality Assessment ◽  
Lot Quality Assurance Sampling

scholarly journals Spatial Accuracy of Climate Networks: A Case Study in Nebraska

2014 ◽  
Vol 53 (8) ◽  
pp. 1932-1942 ◽  
Author(s):  
Andrea J. Coop ◽  
Kenneth G. Hubbard ◽  
Martha D. Shulski ◽  
Jinsheng You ◽  
David B. Marx
Keyword(s):  
Air Temperature ◽  
Weather Data ◽  
Reference Network ◽  
Daily Maximum ◽  
Spatial Accuracy ◽  
Climate Data ◽  
Historical Climatology ◽  
Mean Square Errors ◽  
Minimum Air Temperature

AbstractClimate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.

scholarly journals Modeling hourly air temperature based on internationally agreed times and the daily minimum temperature

2019 ◽  
Vol 23 (11) ◽  
pp. 807-811
Author(s):  
Sidinei Z. Radons ◽  
Arno B. Heldwein ◽  
Luís H. Loose ◽  
Mateus P. Bortoluzzi ◽  
Silvane I. Brand ◽  
...  
Keyword(s):  
Temperature Variation ◽  
Air Temperature ◽  
Linear Models ◽  
Daily Minimum Temperature ◽  
Mean Deviation ◽  
Data Series ◽  
Climatic Data ◽  
Daily Minimum ◽  
Model Adjustment ◽  
Minimum Air Temperature

ABSTRACT There are several fields that require knowledge of air temperature variation throughout the day, such as disease prediction or calculation of chill-hours. However, automatic meteorological stations are not always located in the vicinity to accurately monitor this variable. In this sense, models that describe the daily temporal variation of air temperature can be used to meet this demand, and transform the climatic data series of conventional meteorological stations into an estimated hourly series. The aim of this study was to adjust and validate models for the hourly air temperature variation through data obtained at internationally agreed times (0, 12 and 18 h Universal Time Coordinated: UTC) and the daily minimum air temperature. The hourly database of the automatic station was used for model adjustment and validation. Functions were adjusted based on values measured at internationally agreed times and the daily minimum air temperature for certain daily variation patterns. The air temperature estimation was performed on an hourly basis using sinusoidal and linear models. The model that presented the lowest root mean square error (RMSE) was used for the estimation. The accuracy of the air temperature estimates varied according to the time, presenting RMSE from 0.7 to 1.6 °C, with maximum mean deviation of 0.4 °C. The results of this study showcase the necessity of knowledge of the daily air temperature variation, as well as a series of data from conventional meteorological stations, which can be estimated using hourly models.

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