Daily Temperature Time Series Research Articles

Spatiotemporal distributions of thermal peaks in summer and winter over South Korea

This study investigated the spatial distributions and long-term trends of the annual highest and lowest temperatures (summer peak temperature, SPT; winter peak temperature, WPT) and their timings (summer peak day, SPD; winter peak day, WPD) in South Korea and analyzed their relationship with the general circulation patterns. The two peaks were determined by selecting the highest and the lowest points after extracting temperature variations longer than the seasonal scale (91 days) in the time series of daily mean temperatures. For the long-term trend, we examined data for 100 years (1909–2008) for five stations and data for 35 years (1974–2008) for 61 stations. The SPD in South Korea is August 4 on average. It is earliest (July 31) in the central inland region, the central hilly sections, and the southern inland region and latest (August 10) in the southern coastal region and on Jeju Island (Seogwipo). The WPD in South Korea is January 16 on average. It is earliest (January 13) in the central inland region and southern inland region and latest (January 24) on Jeju Island (Jeju) and in Ulleungdo. The SPT and WPT are highest on Jeju Island (Seogwipo; 27.3 and 6.4 °C, respectively) and lowest in the central hilly sections (Daegwallyeong; 20.2 and −7.9 °C, respectively). The interannual variations in the WPD and WPT are greater than those in SPD and SPT. A significant increasing trend in the WPT was observed for all of the analyzed stations only for the second half of the 100-year period (1959–2008). In the case of the 35-year period, the SPD did not show any clear changes at all stations, but the WPD tended to occur earlier at three stations in the east coastal area. The WPT showed an increasing trend at 55 stations for 35 years, but the SPT showed an increasing trend only in Seogwipo (0.041 °C/year) and even showed a decreasing trend in Mungyeong (−0.049 °C/year). General circulation patterns were indexed and their correlations with the seasonal peaks were investigated. No correlations were found with the SPD. However, the WPD showed a significant positive correlation with the day of the highest Siberian High Intensity (SHI) and the day of the lowest Arctic Oscillation Index (AOI). Furthermore, the SPT showed negative correlations with the intensities of the Okhotsk High and North Pacific High, whereas the WPT showed a negative correlation with SHI and a positive correlation with AOI and with the intensity of the northerly wind that flows into the Korean Peninsula.

Trends in extreme temperature indices in Austria based on a new homogenised dataset

AbstractInstrumental time series are often affected by inhomogeneities which can mask or amplify climate change signals. Various procedures for the detection and adjustment of breaks exist for monthly and annual time series. Homogenization methods on a daily basis are scarce and often disregard uncertainties accompanying the break adjustment. We present a complete homogenization procedure for daily extreme temperature series based on the break detection method PRODIGE and SPLIDHOM for break correction. Both parts of the homogenization rely on the existence of highly correlated reference stations. After the statistical comparison with neighbouring stations, detected breaks are verified and further localized by metadata. Uncertainties of the break adjustments are estimated by altering reference stations and by applying a bootstrapping technique providing an objective indication about the reliability of the homogenization.The method was tested and applied to 71 time series of daily minimum (TN) and maximum temperatures (TX) in Austria covering the period 1948–2009. For some series homogenization was not possible due to large uncertainties in the adjustments or a lack of suitable reference series. In the remaining 57 TN and 54 TX series a total number of 139 breaks were detected. Seventy‐five percent of those breaks are documented in the metadata archive, with most of them being caused by station relocations and instrumentation changes. In general, the mean over the temperature dependent adjustments of all stations show a temperature reduction. However, the majority of breaks have mean amplitudes of less than 0.5 °C. A comprehensive analysis was performed on the new homogenized daily dataset, showing a widespread warming trend in both TN and TX series. The warming trend is in general amplified due to the homogenization. However, significant changes in the trend are only observed at very few stations. In autumn, however, the trend is reversed in many temperature based ‘climate change detection indices’. Copyright © 2012 Royal Meteorological Society

A Stochastic Conceptual Modeling Approach for Examining the Effects of Climate Change on Streamflows in Mountain Basins

AbstractThis study presents a modeling approach for examining how changes in climate affect streamflow in mesoscale mountain basins dominated by snowmelt runoff. A conceptual snowmelt-runoff model was developed that is forced by daily time series of temperature and precipitation. The model can be run using either observed climate data or artificial climate data generated from a GCM or a stochastic model. The model was applied to a case-study basin, the north fork of the Clearwater River in Idaho, using stochastically generated climate scenarios. Climate scenarios were generated using a contemporaneous auto-regressive integrated moving average (CARIMA) model for temperature and a precipitation model based on a two-state first-order Markov process. A baseline climate scenario was developed that represents recently observed temperature and precipitation conditions and then 15 additional climate scenarios that represent shifts in recent conditions. For each scenario, model application produced an ensemble of 50 streamflow traces each spanning 30 yr. Results show that an increase in temperature among scenarios leads to a decrease in streamflow and vice versa. Decreases in temperature shift the basin runoff to fully snowmelt dominated, whereas increases in temperature increase the frequency of midwinter runoff events. Increasing precipitation leads to increased runoff in cases where the temperature remains the same as the observed record, but not in cases where the temperature increases. The modeling approach presented here can be used by water managers to examine which types of climate change could require modifications in water planning and operations.

시공간 상관성을 고려한 일기산출기 모형을 이용한 4대강 유역별 미래 일기 변수 산출

일기 산출기 모형은 가상의 일기 자료를 생성하는 통계 모형이다. 본 연구는 시공간 상관성이 고려된 다중지점에서의 일기산출 모형을 제안하고, 온실가스 배출 미래 시나리오에 따라 강수량과 평균 기온 일기산출이 가능한 알고리즘을 개발하였다. 제안된 알고리즘은 다단계 일반화 선형모형 하에서 필요한 모수들을 추정하고, 적합된 모형 하에서 일기변수들을 랜덤하게 산출하는 절차이다. 과거 30년간 관측된 우리나라 4대강 유역의 일 강수량 자료와 평균 기온 자료를 가지고 모형을 적합하고, 미래 일별 일기자료 산출에 적용하였다. Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.

A procedure for the detection of undocumented multiple abrupt changes in the mean value of daily temperature time series of a regional network

AbstractThis paper presents the new procedure MAC‐D for the automated detection of undocumented Multiple Abrupt Changes in the mean value of Daily temperature series, recorded in a network of meteorological stations. MAC‐D can be applied to series containing seasonality, multiple change points, outliers, and with a noise component that can be autocorrelated and non‐normally distributed. The main novelties of the procedure are (1) the pretreatment of the observed series, to derive a series of daily values that complies with the theoretical requirements of the change point detection tests and in (2) the combined use of the reference series and pairwise comparison approaches. MAC‐D consists of three phases in sequence. In phase 1, the seasonal and climatic fluctuations are estimated and removed, using the reference series approach. Phase 2 combines a linear filtering with a change point detection test in an iterative algorithm, which runs until full compliance between the characteristics of the filtered series and the test requirements is achieved. Phase 3 is aimed at removing the false change points, due to error propagation in the reference series analysis, by double checking the detected change points with the pairwise comparison approach. Monte Carlo estimations of the actual significance and overall performance of the procedure for different series features and test resolutions are provided. Results demonstrate that MAC‐D performs very well with daily series having a wide range of different characteristics. Copyright © 2012 Royal Meteorological Society

Altitudinal temperature lapse rates in an Alpine valley: trends and the influence of season and weather patterns

AbstractThe vertical temperature structure in the area of Garmisch‐Partenkirchen between the Loisach river valley and the highest mountain in Germany (Zugspitze, 2962 m a.s.l.) is analysed. Using long time series (1936–2008) of daily mean, minimum and maximum temperatures from two meteorological stations, in the valley floor and on the mountain top, we studied seasonal variations in temperature lapse rates. By using daily data on weather types, significantly different altitudinal temperature lapse rates and daily temperature ranges for low and high pressure weather situations were found. There was no significant influence of snow cover in the valley on the lapse rate. At both stations, there has been an increase in temperature during recent decades. As there was a slight difference between these trends, the lapse rate has tended to become more negative, i.e. the difference between the mountain top and the valley has become more pronounced. In order to investigate the lapse rates in more detail, unpublished temperature data from 1989 to 1990 on slope transects near Garmisch‐Partenkirchen were evaluated. Despite the fact that an exact description of the vertical structure of the atmospheric temperature is possible only from free air measurements, there are some indications that major inversions occur in the first 500 m above the valley ground, depending on season, weather situation and time of day. Copyright © 2012 Royal Meteorological Society

Prediction of harvest start date in highbush blueberry using time series regression models with correlated errors

This work proposes a model for predicting the harvest start date sufficiently far ahead to enable the farmer to make a well-informed plan. The heat unit method is widely used in agriculture as the phenological unit of time, which offers the least variation in date predictions, and heat units have been used to estimate the start of harvesting in various crops. The problem is that the farmer needs to know the number of days and not the number of heat units that are needed until the harvest can begin. It is proposed that the daily maximum and minimum temperature time series be modelled through regression models with errors correlated using a sine curve. Using the requirements reported by Carlson and Hancock (1991) for the start of harvest of 13 varieties of blueberry over 15 years, a model has been developed that allows the requirements of heat units to be translated into days remaining until harvest. The models are estimated at intervals of 3 months, 2 months, 1 month, 14 days and 7 days before the date at which the heat unit requirements are reached. Three months ahead, the error was less than 10 days late, and 7 days ahead, it was 2 days late. A blueberry orchard in Temuco, Chile, was used as a case study and had similar results. All the errors are within the variability of the heat unit models. The models can be used by farmers to predict and plan the blueberry harvest with adjustments for location and variety.

Mortality caused by influenza and respiratory syncytial virus by age group in England and Wales 1999–2010

Please cite this paper as: Hardelid et al. (2012) Mortality caused by influenza and respiratory syncytial virus by age group in England and Wales 1999–2010. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750‐2659.2012.00345.x. Background: The mortality burden caused by influenza cannot be quantified directly from death certificates because of under‐recording; therefore, the estimated number of influenza deaths has to be obtained through statistical modelling. Objective: To estimate the number of deaths caused by influenza and respiratory syncytial virus (RSV) in England and Wales between 1999 and 2010 using a multivariable regression model. Methods: Generalised linear models were used to estimate weekly deaths by age group (<15, 15–44, 45–74 and 75+ years) as a function of positive influenza and RSV isolates. Adjustment was made for temperature variation (using weekly means of daily Central England temperature time series), underlying seasonal variation and temporal trends. The parameters from the model were used to predict the number of deaths caused by influenza and RSV across winter seasons. Results: Between 7000 and 25 000 deaths across all ages were associated with influenza in the winter periods 1999–2009. The mortality burden was the highest among the over 75 age group, among whom 2·5–8·1% of deaths were caused by influenza. The lowest number of influenza deaths was estimated for the winter 2009/2010 when pandemic influenza A/H1N1 (2009) was the predominant circulating strain. RSV accounted for 5000–7500 deaths each winter season. Conclusions: The model presented provides a robust and reasonable approach to estimating the number of deaths caused by influenza and RSV by age group at the end of each winter.

Climate change: a new metric to measure changes in the frequency of extreme temperatures using record data

Consensus on global warming is the result of multiple and varying lines of evidence, and one key ramification is the increase in frequency of extreme climate events including record high temperatures. Here we develop a metric—called “record equivalent draws” (RED)—based on record high (low) temperature observations, and show that changes in RED approximate changes in the likelihood of extreme high (low) temperatures. Since we also show that this metric is independent of the specifics of the underlying temperature distributions, RED estimates can be aggregated across different climates to provide a genuinely global assessment of climate change. Using data on monthly average temperatures across the global landmass we find that the frequency of extreme high temperatures increased 10-fold between the first three decades of the last century (1900–1929) and the most recent decade (1999–2008). A more disaggregated analysis shows that the increase in frequency of extreme high temperatures is greater in the tropics than in higher latitudes, a pattern that is not indicated by changes in mean temperature. Our RED estimates also suggest concurrent increases in the frequency of both extreme high and extreme low temperatures during 2002–2008, a period when we observe a plateauing of global mean temperature. Using daily extreme temperature observations, we find that the frequency of extreme high temperatures is greater in the daily minimum temperature time-series compared to the daily maximum temperature time-series. There is no such observable difference in the frequency of extreme low temperatures between the daily minimum and daily maximum.

Extreme value and cluster analysis of European daily temperature series

Time series of daily mean temperature obtained from the European Climate Assessment data set is analyzed with respect to their extremal properties. A time-series clustering approach which combines Bayesian methodology, extreme value theory and classification techniques is adopted for the analysis of the regional variability of temperature extremes. The daily mean temperature records are clustered on the basis of their corresponding predictive distributions for 25-, 50- and 100-year return values. The results of the cluster analysis show a clear distinction between the highest altitude stations, for which the return values are lowest, and the remaining stations. Furthermore, a clear distinction is also found between the northernmost stations in Scandinavia and the stations in central and southern Europe. This spatial structure of the return period distributions for 25-, 50- and 100-years seems to be consistent with projected changes in the variability of temperature extremes over Europe pointing to a different behavior in central Europe than in northern Europe and the Mediterranean area, possibly related to the effect of soil moisture and land-atmosphere coupling.

Estimation of forest harvesting-induced stream temperature changes and bioenergetic consequences for cutthroat trout in a coastal stream in British Columbia, Canada

Data from a paired-catchment study in south coastal British Columbia, Canada, were analyzed to assess the thermal effects of clearcut harvesting with no riparian buffer on a fish-bearing headwater stream. The approach used time series of daily mean water temperatures for East Creek (control) and A Creek (treatment), both before and after harvest. Statistical models were developed to predict (a) what the temperatures would have been in the post-harvest period had harvesting not occurred, and (b) what temperatures would have been in the pre-harvest period had harvesting already occurred. The Wisconsin Bioenergetics Model was used to simulate growth of coastal cutthroat trout (Oncorhynchus clarki clarki) for the first year following fry emergence using the predicted and observed stream temperatures to generate scenarios representing with-harvest and no-harvest thermal regimes. A Monte Carlo approach was used to quantify the effects of uncertainty associated with the regression models on predicted stream temperature and trout growth. Summer daily mean temperatures in the with-harvest scenario were up to $$5^{\circ}\hbox{C}$$ higher than those for the no-harvest scenario. Harvesting-induced warming reduced growth rates during summer, but increased growth rates during autumn and spring. In the with-harvest scenario, trout were 0.2–2.0 g (absolute weight) smaller throughout the winter period than in the no-harvest scenario. However, the bioenergetic simulations suggest that trout growth may be more sensitive to potential changes in food supply following harvesting than to direct impacts of stream temperature changes.

Criteria for heat and cold wave duration indexes

Many areas of society are susceptible to the effects of extreme temperatures. Without an adequate definition of what constitutes heat and cold waves, it is impossible to assess either their changes in the past or their possible consequences for the future. The Intergovernmental Panel on Climate Change recommended criteria for heat wave duration indexes based on two arbitrarily defined constants. The principal weakness of this approach is that it does not yield comparable results for different geographical locations. This paper remedies the current lack of a meteorologically based definition of heat and cold waves and offers a preliminary test of its performance. Having previously shown that maximum daily temperature values follow normal frequency distribution, we derive statistical thresholds (e.g., below and above normal) from that distribution. These thresholds are thus climate specific and their change can be compared across geographical locations. These criteria are then tested on the homogeneous time series of maximum daily temperature observed for the period 1961–2008 with respect to three different geographical locations. The results obtained show an increase in the frequency of heat waves for the period 1991–2008 in comparison with the normal climatological period 1961–1990.

Modelling Long-Term Variability in Daily Air Temperature Time Series for Southern Hemisphere Stations

Customarily, climate studies of long-range temperature variability have been carried out using annual or monthly averages. The approach mixes the details of short- and long-range variability that are different for air temperature series. This work shows that a useful method for eliminating short-range variability on long-range variability is to apply a sufficiently long (about 2 months) time step to the daily series. An autoregressive integrated moving average model is fitted to daily maximum and minimum temperature anomalies from the mean seasonal cycle, using data from a number of Australian and New Zealand weather stations. The fitted model can be considered as a sum of random walk plus white noise. This enables us to obtain a quantitative long-term description of the temperature variability.

Long-term trend and multi-annual variability of water temperature in the pristine Bela River basin (Slovakia)

► We identify changes in water temperature in mountainous stream during 1959–2008. ► The water temperature within 50-years increased not significantly by 0.12 °C. ► Multi-annual water temperature variability includes significant cycle of 11 years. ► The impact of the riparian vegetation was evaluated prior and after the wind-throw. Biological processes in surface waters appreciably depend on temperature of water. This paper summarizes our investigations of water temperature in the Bela River. The Bela River is a mountainous stream not influenced by direct human activities, draining the headwaters of the Vah River basin in the Tatra National Park (TANAP), Slovakia. Our primary aim was to identify the long-term trends and multi-annual variability of the annual water temperature at the Podbanske gauging station, using temperature readings taken at 7.00 am for the period of 50 years (1959–2008). Long-term mean of the annual water temperature of the Bela River at the Podbanske gauging station (922 m a.s.l.) was 4.2 °C, the air temperature at Podbanske meteorological station (972 m a.s.l.) was 5.0 °C. Both, air and water temperature, show an increasing trend. While the air temperature within 50-years increased significantly by 1.5 °C, in the case of water temperature this increase was merely by 0.12 °C. On November 19, 2004, a wind-throw brushed the investigated area with an aftermath of 15.4% destroyed forest in the Bela basin, mainly along the area adjacent to the river. Therefore, in the second part of the study, the impact of the riparian vegetation growing along the river banks was evaluated for two distinctive periods, i.e. the period prior and after the wind-throw. We statistically analysed the changes in water temperature on 6-year time series of daily water temperature (November 2001 through November 2007). The results presented herein may be useful for defining boundary values for surface water temperature, as required by the EC Water Framework Directive.

Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model

In this study, impacts of climate change on streamflow in the Yellow River Basin are investigated. A semi-distributed hydrological model (SWAT) is calibrated and validated with records at Huayuankou, Lanzhou and Huaxian hydrological stations. Using outputs from a global circulation model (HadCM3), a statistical downscaling model (SDSM) and a combination of ‘bilinear-interpolation and delta’ are applied to generate daily time-series of temperature and precipitation (1961–2099). The generated data is integrated into SWAT to simulate streamflow under current and future climate conditions. The results show that the results modeled responding to SDSM fit natural or measured records better than responding to the combination method. Responses of runoff are assessed based on climate change scenarios constructed with SDSM using HadCM3 outputs. The projections show that the Yellow River Basin tends to become warmer. Annual average maximum and minimum temperature would rise by 5.0 °C in the 2080s. Annual precipitation would also increase by 54 mm–150 mm. The change in summer precipitation is most significant with an increase by 75 mm–115 mm. Annual average streamflow tends to increase gradually 17.9%–42.9% with time under scenario A2 and 11.5%–34.5% under scenario B2 relative to the current runoff (1960.5 m 3/s). Seasonal average streamflow would increase in spring, summer and winter in most time horizons under both scenarios, but decrease in autumn under scenario A2. Increasing runoff in spring and summer can favor crop growth, and increasing annual precipitation and runoff can alleviate water demand stress to some degree in the Yellow River Basin.

Mid-21st century climate and weather extremes in Cyprus as projected by six regional climate models

We present climate change projections and apply indices of weather extremes for the Mediterranean island Cyprus using data from regional climate model (RCM) simulations driven by the IPCC A1B scenario within the ENSEMBLES project. Daily time-series of temperature and precipitation were used from six RCMs for a reference period 1976–2000 and for 2026–2050 (‘future‘) for representative locations, applying a performance selection among neighboring model grid-boxes. The annual average temperatures of the model ensemble have a ±1.5°C bias from the observations (negative for maximum and positive for minimum temperature), and the models underestimate annual precipitation totals by 4–17%. The climatological annual cycles for the observations fall within the 1σ range of the 6-model average, highlighting the strength of using multi-model output. We obtain reasonable agreement between models and observations for the temperature-related indices of extremes for the recent past, while the comparison is less good for the precipitation-related extremes. For the future, the RCM ensemble shows significant warming of 1°C in winter to 2°C in the summer for both maximum and minimum temperatures. Rainfall is projected to decrease by 2–8%, although this is not statistically significant. Our results indicate the shift of the mean climate to a warmer state, with a relatively strong increase in the warm extremes. The precipitation frequency is projected to decrease at the inland Nicosia and at the coastal Limassol, while the mountainous Saittas could experience more frequent 5–15 mm/day rainfall. In future, very hot days are expected to increase by more than 2 weeks/year and tropical nights by 1 month/year. The annual number of consecutive dry days shows a statistically significant increase (of 9 days) in Limassol. These projected changes of the Cyprus climate may adversely affect ecosystems and the economy of the island and emphasize the need for adaptation strategies.

A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

Abstract Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Trends of maximum temperature, minimum temperature and diurnal temperature range and their correlations with urbanisation in Xiamen, China

This study examines variations of maximum temperature, minimum temperature and diurnal temperature range (DTR) and their possible correlations to urbanisation in Xiamen, a coastal city in SE China. The underlying meteorological data used are the daily temperature time series from Xiamen Meteorological Station from 1954 to 2007. We selected four urbanisation indices, urban population, gross domestic product, built-up area and gross electricity consumption, to investigate correlations with temperature variables over the period 1981–2007. The results show that annual maximum temperature, minimum temperature and DTR increased from 1954 to 2007 at a rate of 0.33°C/10 years, 0.18°C/10 years and 0.15°C/10 years, respectively. Similarly, the seasonal maximum temperature, minimum temperature and DTR rose over the same period, with stronger increasing trends in winter and summer than in spring and autumn. The four selected urbanisation indices were all positively correlated with the three temperature variables, although more significantly with maximum and minimum temperatures than with DTR. The influence of urbanisation appears to be slightly stronger on maximum temperature than on minimum temperature in this area.

Nonlinear dynamics of mean daily temperature and dewpoint time series at Babolsar, Iran, 1961–2005

Abstract Chaos theory and its applications are at the cutting edge of investigations in environmental sciences (e.g. Environmental Physics, Biometeorology and Atmospheric Sciences among many others). However, we still need to take advantage of some recent computational efforts for nonlinear investigations of empirical meteorological variables. The objectives of this work were (i) to quantify the nonlinear variability of observed time series of mean daily temperature and dewpoint records collected at Babolsar, Iran and (ii) to stimulate the use of recent computational tools for investigating environmental data. Positive Lyapunov exponents were found for both time series ( λ max = 0.0174 and λ max = 0.0169 for mean daily air temperature and dewpoint temperature, respectively). In addition, determinism tests showed that both time series masked strong determinist components ( | κ → | = 0.721 and | κ → | = 0.693 for mean daily temperature and dewpoint, respectively). That nonlinear test could be of use in future research for making decisions in applying deterministic, stochastic or combinations of both models with climate data. There were found interesting evidences on the existence of empirical attractors in univariate climate time series as expressed by reconstructing both phase spaces.

A Study of the NOAA Near-Nadir AMSU-A Brightness Temperatures over Antarctica

Abstract Daily mean brightness temperatures over Antarctica derived from measurements by three Advanced Microwave Sounding Unit-A (AMSU-A) radiometers on board NOAA-18, NOAA-19, and MetOp-A satellites are studied. To demonstrate the characteristics of the data over this test site, time series of daily averages of the brightness temperatures are constructed. These time series provide a useful pattern of annual variation of the AMSU-A measurements for intercalibration of microwave radiometers on board multiple satellites. To investigate the diurnal effect on the measurements, the time series of daily averaged brightness temperatures are constructed separately for the ascending and descending passes. Results show that there are little diurnal differences in measurements during the Antarctic winter months from each satellite. Therefore these measurements provide a practical approach to obtain relative channel biases of intersatellite data. The monthly averages of the measurements over July 2009 are employed to obtain the relative channel biases because it is the coldest month in Antarctica. The resultant channel biases for the three satellites are within the range of ±0.1 K for channels 1–5 and ±0.3 K for channels 6–15. This is strong evidence that Antarctica is a potentially good test site for intercalibration of microwave radiometers on board multiple satellites. The small relative biases at channels 1–5 indicate that Antarctica is a very stable test site that is particularly useful for intercalibration of measurements from the window channels. The establishment of a natural test site for calibration references is important for calibration and validation of spaceborne microwave instruments.