Homogenization of the Hellenic cloud amount time series

Transcription

1 Homogenization of the Hellenic cloud amount time series A Argiriou 1, A Mamara 2, E Dimadis 1 1 Laboratory of Atmospheric Physics, 2 Hellenic Meteorological Service October 19, 2017 A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

2 Clouds A significant meteorological parameter, affecting: the radiative balance of Earth (albedo - long wave absorptivity / emmissivity), precipitation and snowfall, the energy sector (consumption of buildings, yield of solar power plants) High quality and homogenized cloud amount datasets are therefore important A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

3 Cloud amount measurement Result of an observation; reported in octas or eighths with the additional convention that: 0 oktas represents the complete absence of cloud, 1 okta represents a cloud amount of 1 eighth or less, but not zero, 7 oktas represents a cloud amount of 7 eighths or more, but not full cloud cover, 8 oktas represents full cloud cover with no breaks, 9 oktas represents full cloud cover with no breaks sky and also obscured by fog or other meteorological phenomena A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

4 Data Synoptic daily cloud amount observations for thirty years ( ), from 36 WMO stations of the Hellenic National Meteorological Service A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

5 Monthly averages Calculated as the arithmetic mean of the daily synoptic observations of the month, provided that there are no more than: three consecutive missing daily averages in the month and five in total missing daily averages in the month A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

6 Daily averages Calculated as the arithmetic mean of the synoptic observations of the day, provided that : there are at least 3 synoptic observations within the day and the time difference between the first and last synoptic observation equals 12 hours A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

7 Quality Control Initial QC on raw synoptic data performed by the HNMS Criteria on daily and monthly averages calculation led to the exclusion of 7 time series (stations) 25 outliers detected and excluded from further processing A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

8 Homogenization method HOMER (deliverable of the ESSEM COST Action ES0601 Advances in homogenisation methods of climate series: an integrated approach (HOME) ) A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

9 Selection of stochastic model If the variable of interest represents an: Additive model intensity (eg temperature, pressure, etc) and the absolute difference of the values is of importance, then additive model and the normal distribution are used Multiplicative model accumulation and has a natural zero (eg precipitation or snowfall) then percentage changes, rather than differences between values, are more important Therefore the multiplicative model and the quasi-log normal, or the inverse Gaussian distribution are used Selection is neither straightforward and not always obvious A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

10 Additive model O t TC t S t I t original data series, trend cycle, seasonal effect, the irregular effects component O t = TC t + S t + I t The seasonality adjusted data SA t are: SA t = O t S t Applied when homogenizing temperature time series and other weather variables following approximately a normal distribution A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

11 Multiplicative model O t = TC t S t I t, with SA t = O t S t Suggested when homogenizing biased variables with a natural zero, such as precipitation, that follows an L-shaped distribution or wind speed, that follows the Weibull distribution A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

12 Testing models Suitability tested on the cloud amount time series of WMO Station (Serres, Northern Greece) Complete 30-year data set; no outliers or gaps (a) Time series and trend (b) Histogram A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

13 Holt-Winters exponential method Model parameters Table: Estimated α, β and γ parameters Method α β γ Additive Multiplicative Performance statistics Table: Performance statistics of the additive and multiplicative Holt-Winters model on cloudiness Method ME RMSE MAE MAPE SSE Additive Multiplicative A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

14 Holt-Winters models - Histograms of residuals (a) Additive model (b) Multiplicative model A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

15 Holt-Winters models - QQ plots vs normal distribution (a) Additive model (b) Multiplicative model A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

16 Shapiro-Wilk test Table: Shapiro-Wilk test Method W p-value Additive < 005 Multiplicative < 005 The null hypothesis that the residuals are normally distributed is rejected at the 5% level A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

17 Final model selection Results of all tests do not allow for a clear choice of either the additive or the multipilicative model, but several indicators are slightly better when using the additive model Cloud amount has a natural zero (clear sky conditions) but physically it is closer to an intensity rather than to an accumulation (in the sense that it affects solar irradiance and sky long wave emissivity) Due to the above we decided to perform homogenization using the additive model A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

18 Homogenization Pairwise detection feature of HOMER, provided by PRODIGE Additive model Stations that passed the quality control tests were used as a single network, ie as a single climatic zone Geographic inter-comparison neighborhood applied, (maximum distance of 200 km and a minimum of 5 neighbors) This takes into account the orography of the mainland (Pindos mt), that changes the precipitation regime in the mainland A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

19 Results example 1 Figure: Pairwise comparisons between the Ierapetra station with its neighboring stations; bold vertical lines denote the year of probable break point, σ is the standard deviation of the noise A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

20 Results example cc Ierapetra cc DIFF (okta) Figure: Average magnitude and sign (+ or -) of the probable break-points detected by pairwise comparison of the Ierapetra station with its neighboring stations A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

21 Conclusions Hellenic cloud amount time series were found to be rather inhomogeneous; out of the 25 time series from the corresponding weather stations: 19 with no possible breakpoints, 9 with one possible breakpoint, 1 with two possible breakpoints Limited metadata (available for only six stations) Only one potential breakpoint confirmed by metatdata A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

22 Future work Cross-validate current findings by applying more homogenization methods Correction of the available time series Cloud amount trend analysis Development of a cloud amount climatology A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23

23 Thank you for your attention! A Argiriou 1, A Mamara 2, E Dimadis 1 (LAPUP-HNMS) Gr-Cloudiness Homogenization October 19, / 23