Time Series Of Meteorological Variables Research Articles

Estimating the potential predictability of Western Australian surface temperature using monthly data

The seasonal mean of a climate variable is considered to be a statistical random variable with two components: a slow component related to slowly varying (time scales of a season or more) forcings from external and internal atmospheric sources, and an intraseasonal component related to forcings from weather variability with time scales less than a season. Here, an extension of a previous Analysis of Variance method is proposed which deals with climate data in all seasons when estimating the intraseasonal variability. By removing this from the total variability, an estimate for the slow component, and hence the long range predictability, of the seasonal mean is made. The method is applied to monthly surface temperature data for Western Australia from 1951--2000. References R. J. B. Fawcett, D. A. Jones and G. S. Beard. A verification of publicly issued seasonal forecasts issued by the Australian Bureau of Meteorology: 1998--2003. Aust. Meteor. Mag., 54:1--13, 2005. C. S. Frederiksen and X. Zheng. Coherent Structures of Interannual Variability of the Atmospheric Circulation: The Role of Intraseasonal Variability. Frontiers in Turbulence and Coherent Structures, World Scientific Lecture Notes in Complex Systems, Vol. 6, Eds Jim Denier and Jorgen Frederiksen, World Scientific Publications, 87--120, 2007. S. Grainger, C. S. Frederiksen and X. Zheng. Estimating the potential predictability of Australian surface maximum and minimum temperature. Climate Dynamics, Accepted, 2008. D. A. Jones and B. C. Trewin. The spatial structure of monthly temperature anomalies over Australia. Aust. Meteor. Mag., 49:261--276, 2000. E. N. Lorenz. On the existence of extended range predictability. J. Appl. Meteor., 12:543--546, doi:10.1175/1520-0450(1973)012<0543:OTEOER>2.0.CO;2, 1973. I. G. Watterson. The diurnal cycle of surface air temperature in simulated present and doubled CO2 climates. Climate Dynamics, 13:533--545, doi:10.1007/s003820050181, 1997. X. Zheng and C. S. Frederiksen. Variability of seasonal-mean fields arising from intraseasonal variability. Part 1, methodology. Climate Dynamics, 23:177--191, doi::{10.1007/s00382-004-0428-7}, 2004. X. Zheng, H. Nakamura and J. A. Renwick. Potential predictability of seasonal means based on monthly time series of meteorological variables. J. Climate, 13:2591--2604, doi:10.1175/1520-0442(2000)013<2591:PPOSMB>2.0.CO;2, 2000. X. Zheng, M. Sugi and C. S. Frederiksen. Interannual variability and predictability in an ensemble of climate simulations with the MRI-JMA AGCM. J. Meteor. Soc. Jap., 82:1--18, doi:10.2151/jmsj.82.1, 2004.

A method for estimating the potential long-range predictability of precipitation over Western Australia

A methodology for estimating the potential predictability of seasonal mean climate variables where the daily data consist of dichotomous (on/off) events, such as precipitation, is described and applied to Western Australian rainfall for the period 1951--2000. The method relies on determining an estimate for the intermonth correlations for precipitation and utilizes a stochastic, two state, first order, Markov chain model fitted to the daily data. References W. Feller. An Introduction to Probability Theory and Its Applications . Vol. 2. John Wiley and Sons, 626pp, 1966. C. S. Frederiksen and X. Zheng. Coherent Structures of Interannual Variability of the Atmospheric Circulation: The Role of Intraseasonal Variability. Frontiers in Turbulence and Coherent Structures, World Scientific Lecture Notes in Complex Systems , Vol. 6, Eds Jim Denier and Jorgen Frederiksen, World Scientific Publications, 87--120, 2007. D. A. Jones and G. Weymouth. An Australian monthly rainfall data set. Technical Report No. 70 , Bur. Met. Australia, 1997. R. W. Katz and X. Zheng. Mixture Model For Overdispersion of Precipitation. J. Climate , 12:2528--2537, 2.0.CO;2>doi:10.1175/1520-0442(1999)012 2.0.CO;2 , 1999. E. N. Lorenz. On the existence of extended range predictability. J. Appl. Meteor. , 12:543--546, 2.0.CO;2>doi:10.1175/1520-0450(1973)012 2.0.CO;2 , 1973. R. A. Madden, D. J. Shea, R. W. Katz and J. W. Kidson. The potential long-range predictability of precipitation over New Zealand. Int. J. Clim. , 19:405--421, http://ejournals.ebsco.com/direct.asp?ArticleID=UWLQUYU54E2X7BYBW3DT , 1999. D. S. Wilks. Statistical Methods in the Atmospheric Sciences. (second edition). Academic Press, 627pp, 2006. X. Zheng and C. S. Frederiksen. A study of predictable patterns for seasonal forecasting of New Zealand rainfall. J. Climate , 19:3320--3333, doi:10.1175/JCLI3798.1 , 2006. X. Zheng, H. Nakamura and J. A. Renwick. Potential predictability of seasonal means based on monthly time series of meteorological variables. J. Climate , 13:2591--2604, 2.0.CO;2>doi:10.1175/1520-0442(2000)013 2.0.CO;2 , 2000.

A spectral analysis of biosphere–atmosphere trace gas flux densities and meteorological variables across hour to multi-year time scales

The advent of long-term studies on CO 2 and water vapor exchange provides us with new information on how the atmosphere and biosphere interact. Conventional time series analysis suggests that temporal fluctuations of weather variables and mass and energy flux densities occur on numerous time scales. The time scales of variance that are associated with annual time series of meteorological variables, scalar flux densities and their covariance with one another, however, remain unquantified. We applied Fourier analysis to time series (4 years in duration) of photon flux density, air temperature, wind speed, pressure and the flux densities of CO 2 and water vapor. At the daily time scale, strong spectral peaks occurred in the meteorological and flux density records at periods of 12 and 24 h, due to the daily rising and setting of the sun. At the synoptic time scale (3–7 days) the periodic passage of weather fronts alter available sunlight and temperature. In turn, variations in these state variables affect carbon assimilation, respiration and transpiration. At the seasonal and semi-annual time scales, a broad spectral peak occurs due to seasonal changes in weather and plant functionality and phenology. In general, 21% of the variance of CO 2 exchange is associated with the annual cycle, 43% of the variance is associated with the diurnal cycle and 9% is associated with the semi-annual time scale. A pronounced spectral gap was associated with periods 3–4 weeks long. Interactions between CO 2 flux density ( F c) and sunlight, air temperature and latent heat flux density were quantified using co-spectral, coherence and phase angle analyzes. Coherent and in-phase spectral peaks occur between CO 2 exchange rates and water vapor exchange on annual, seasonal and daily time scales. A 180° phase shift occurs between F c and photon flux density ( Q p ) on seasonal and daily time scales because the temporal course of sunlight corresponds with the withdrawal of CO 2 from the atmosphere, a flux that possesses a negative sign. Covariations between F c and T air experience a 180° phase shift with one another at the seasonal time scale because rising temperatures are associated with more carbon uptake. At daily time scales the phase angle between F c and T air is on the order of 130°. This phase lag can be explained by the strong dependence of canopy photosynthesis on available light and the 2–3 h lags, which occur between the daily course of sunlight and air temperature. Spectral analysis was used to investigate the performance of a biophysical model (CANOAK) across a spectrum of time scales. By varying meteorology, leaf area index and photosynthetic capacity, the model was able to replicate most of the spectral gaps and peaks that were associated with CO 2 exchange, when soil moisture was ample.

Potential Predictability of Seasonal Means Based on Monthly Time Series of Meteorological Variables

Abstract Based only on monthly mean data, an analysis of variance method is proposed for decomposing the interannual atmospheric variability in seasonal-mean time series into components related to “weather noise” and to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter two components. A study using synthetic data showed that the method proposed here is comparable in performance to conventional methods requiring daily data. The technique was applied to gridded global data of monthly surface temperature, 500-hPa height, and 300-hPa wind in order to examine the geographical and seasonal dependencies of their potential predictability. For all the variables, the highest potential predictability tends to be found in the Tropics, where seasonal anomalies in the atmosphere are strongly coupled with the underlying sea surface temperature anomalies and the weather noise com...

Separating Different Scales of Motion in Time Series of Meteorological Variables

The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.

Some Effects of Finite Sample Size and Persistence on Meteorological Statistics. Part I: Autocorrelations

Abstract Time series of meteorological variables typically exhibit a pronounced annual cycle and persistence and samples are of finite size. This paper analyses the impact of these complicating features on certain statistics computed from the time series. The presence of an annual cycle means that statistics are nonstationary unless computed from multiyear samples of limited duration. Persistence leads to lack of independence of observations. Large amplitude weather (high frequency) events induce natural variability at low frequencies, known as climatic noise, that is enhanced by the presence of persistence. This natural variability should be, taken into account when estimating population statistics from a finite sample, but generally this has not been done in meteorology. A number of studies in meteorology have computed statistics from daily data by 1) removing the annual cycle; 2) computing second moment statistics over each individual season; and 3) averaging the second moment statistics over all years...

Fluctuations in Humidity, Temperature, and Horizontal Wind as Measured by a Subcloud Tethered-Balloon System

A system is described for measuring atmospheric temperature, humidity, and wind speed continuously and simultaneously at more than one level between 50 and 600 m above the ocean or land surface. The design of the system is presented. This consists of a modulator-transmitter unit which is suspended from a tether line supported by a balloon and controlled by a winch. Data are transmitted by radio link to a receiver-demodulator and recorder at the surface. Results are given which show that the system is capable of acquiring simultaneous time series of meteorological variables at two levels. This presents a viable, though not complete, method for acquiring time series data in a region of the atmosphere previously poorly sampled. The data presented suggest that turbulent mixing in this layer is governed by moist plumes on a scale of 100 m or more. Spectra of humidity are in good agreement with the Kolmogoroff minus-five-thirds law in the inertial subrange between 0.1 and 0.5 Hz. These spectra seem consistent with previous findings and support the suggested scale and role of the moist plumes in the turbulent transfer processes in the subcloud layer.