Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data

2004 ◽  
Vol 21 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Qingxiang Li ◽  
Xiaoning Liu ◽  
Hongzheng Zhang ◽  
Peterson Thomas C. ◽  
Easterling David R.
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Temperature Data ◽  
Temporal Inhomogeneity

scholarly journals <i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

2010 ◽  
Vol 17 (3) ◽  
pp. 269-272 ◽  
Author(s):  
S. Nicolay ◽  
G. Mabille ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Air Temperature ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Weather Forecast ◽  
Temperature Data ◽  
Temperature Time Series ◽  
Climatic Data ◽  
Statistical Validation ◽  
Confidence Levels ◽  
Temperature Time

Abstract. Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

Calibrating and Evaluating Reanalysis Surface Temperature Error by Topographic Correction

2008 ◽  
Vol 21 (6) ◽  
pp. 1440-1446 ◽  
Author(s):  
Tianbao Zhao ◽  
Weidong Guo ◽  
Congbin Fu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Original Data ◽  
Reanalysis Data ◽  
Temperature Data ◽  
Temperature Error ◽  
Topographic Correction ◽  
Temperature Errors

Abstract Based on the observed daily surface air temperature data from 597 stations over continental China and two sets of reanalysis data [NCEP–NCAR and 40-yr ECMWF Re-Analysis (ERA-40)] during 1979–2001, the altitude effects in calibrating and evaluating reanalyzed surface temperature errors are studied. The results indicate that the accuracy of interpolated surface temperature from the reanalyzed gridpoint value or the station observations depends much on the altitudes of original data. Bias of interpolated temperature is usually in proportion to the increase of local elevation and topographical complexity. Notable improvements of interpolated surface temperature have been achieved through “topographic correction,” especially for ERA-40, which highlights the necessity of removal of “elevation-induced bias” when using and evaluating reanalyzed surface temperature.

scholarly journals The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

2014 ◽  
Vol 6 (1) ◽  
pp. 61-68 ◽  
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Temperature Data ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Data Set ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

Spatial and temporal variation of surface air temperature at different altitude zone in recent 30 years over Nepal

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 417-428
Author(s):  
JANAK LAL NAYAVA ◽  
SUNIL ADHIKARY ◽  
OM RATNA BAJRACHARYA
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Temperature Data ◽  
Lapse Rate ◽  
Spatial And Temporal Variation ◽  
Mountain Regions ◽  
Temperature Lapse Rate ◽  
Air Temperatures ◽  
Long Term Trend

This paper investigates long term (30 yrs) altitudinal variations of surface air temperatures based on air temperature data of countrywide scattered 22 stations (15 synoptic and 7 climate stations) in Nepal. Several researchers have reported that rate of air temperature rise (long term trend of atmospheric warming) in Nepal is highest in the Himalayan region (~ 3500 m asl or higher) compared to the Hills and Terai regions. Contrary to the results of previous researchers, however this study found that the increment of annual mean temperature is much higher in the Hills (1000 to 2000 m asl) than in the Terai and Mountain Regions. The temperature lapse rate in a wide altitudinal range of Nepal (70 to 5050 m asl) is -5.65 °C km-1. Warming rates in Terai and Trans-Himalayas (Jomsom) are 0.024 and 0.029 °C/year respectively.  

scholarly journals A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data

2013 ◽  
Vol 14 (3) ◽  
pp. 929-945 ◽  
Author(s):  
Brian Henn ◽  
Mark S. Raleigh ◽  
Alex Fisher ◽  
Jessica D. Lundquist
Keyword(s):  
Missing Data ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Accurate Method ◽  
Temperature Data ◽  
Lapse Rate ◽  
Horizontal Distance ◽  
Method Performance ◽  
Near Surface ◽  
Temperature Observations

Abstract Near-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate–based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available or for 1-h gaps. Lapse rate–based filling was most accurate for intermediate numbers of stations. The accuracy of the lapse rate and EOF methods was found to be sensitive to the vertical separation of stations and the degree of correlation between them, which also explained some of the regional differences in performance. Horizontal distance was less significantly correlated with method performance. From these findings, guidelines are presented for choosing a filling method based on the duration of the missing data and the number of stations.

An Estimate of the Sampling Error Variance of the Gridded GHCN Monthly Surface Air Temperature Data

2007 ◽  
Vol 20 (10) ◽  
pp. 2321-2331 ◽  
Author(s):  
S. S. P. Shen ◽  
H. Yin ◽  
T. M. Smith
Keyword(s):  
Error Estimation ◽  
Air Temperature ◽  
Sampling Error ◽  
Surface Air Temperature ◽  
Error Variance ◽  
Correlation Factor ◽  
Temperature Data ◽  
Climatic Research Unit ◽  
Spatial Variance ◽  
Two Parameters

Abstract The sampling error variances of the 5° × 5° Global Historical Climatological Network (GHCN) monthly surface air temperature data are estimated from January 1851 to December 2001. For each GHCN grid box and for each month in the above time interval, an error variance is computed. The authors’ error estimation is determined by two parameters: the spatial variance and a correlation factor determined by using a regression. The error estimation procedures have the following steps. First, for a given month for each grid box with at least four station anomalies, the spatial variance of the grid box’s temperature anomaly, σ̂2s, is calculated by using a 5-yr moving time window (MTW). Second, for each grid box with at least four stations, a regression is applied to find a correlation factor, αs, in the same 5-yr MTW. Third, spatial interpolation is used to fill the spatial variance and the correlation factor in grid boxes with less than four stations. Fourth, the sampling error variance is calculated by using the formula E2 = αsσ̂2s/N, where N is the total number of observations for the grid box in the given month. The two parameters of the authors’ error estimation are compared with those of the University of East Anglia’s Climatic Research Unit for the decadal data. The comparison shows a close agreement of the parameters’ values for decadal data. An advantage of this new method is the generation of monthly error estimates. The authors’ error product will be available at the U.S. National Climatic Data Center.

scholarly journals CHANGE IN SURFACE AIR TEMPERATURE REGİME AND İTS POTENTIAL İMPACT ON THE CONDITIONS OF TAKE-OFF AND LANDING OF AIRCRAFT İN GANJA-GAZAKH REGİON

2021 ◽  
Vol 103 (4) ◽  
pp. 48-52
Author(s):  
M.F. Suleymanov ◽  
Keyword(s):  
Air Temperature ◽  
Temperature Regime ◽  
Surface Air Temperature ◽  
Temperature Data ◽  
Potential Impact

The article analyzes changes in surface air temperature regime in the Ganja-Gazakh region using the relevant temperature data for 1961...2014 on the example of Agstafa, Dashkasan, Shamkir, Gadabay, Ganja, and Goygol-resort meteorological stations. Assumptions are made about the influence of the found changes in the temperature regime on the conditions of take-off and landing of aircraft in the Ganja-Gazakh region.

Sign in / Sign up

Export Citation Format

Share Document