1 / 21

VI Seminar Homogenization, Budapest 2008

VI Seminar Homogenization, Budapest 2008. “Characterization of data sets for the assessment of inhomogeneities of climate data series, resulting from the automation of the observing network in Mainland Portugal. M.Mendes, J.Neto, A.Silva, L.Nunes , P.Viterbo Instituto de Meteorologia, Portugal.

aadi
Télécharger la présentation

VI Seminar Homogenization, Budapest 2008

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. VI Seminar Homogenization, Budapest 2008 VI Seminar Homogenization, Budapest 2008 “Characterization of data sets for the assessment of inhomogeneities of climate data series, resulting from the automation of the observing network in Mainland Portugal M.Mendes, J.Neto, A.Silva, L.Nunes, P.Viterbo Instituto de Meteorologia, Portugal

  2. VI Seminar Homogenization, Budapest 2008 Current IM network withoverlapping observations • 30 sites with Automatic Weather Stations (AWS) and Conventional Stations (CS) • 2 sites also with Present Weather Sensors (WW)

  3. VI Seminar Homogenization, Budapest 2008 The problem: continuation of conventional dataseries with data from Automatic Weather Station?

  4. VI Seminar Homogenization, Budapest 2008 Overlapping periods of AWS and CS data

  5. VI Seminar Homogenization, Budapest 2008 Station Features

  6. VI Seminar Homogenization, Budapest 2008 Conventional Observationsand Instruments Thermo-hygrograph Sunshine-recorder Mercury Barometer Visual observations Piranómetro Radiation screen Evaporation pan Thermometers Rain gauge

  7. VI Seminar Homogenization, Budapest 2008 Pyranometer Automatic weather station sensors and equipments Wind vane and anemometer GSM Antena Radiation shield with temp. & hum. sensors Data acquisition system Rain gauge Rain detector AWS with solar panel

  8. VI Seminar Homogenization, Budapest 2008 Data records/failures AWS vs CS(10 years data)

  9. <-0.25 -0.25 to +0.25 >+0.25 Bias results for air temperature (Differences between observations AWS-CS) -> 11 cases for T09, 21 cases for Tmin, 19 cases for Tmax

  10. Spatial distribution of Bias results for air temperature (AWS-CS)

  11. Bias monthly results for air temperature

  12. Example of statistic analysis for individual series (1/4) Tmin.: Cabril

  13. Example of statistic analysis for individual series (2/4) Tmax.: Cabril

  14. Example of statistic analysis for individual series (3/4) Tmin: Lisboa

  15. Example of statistic analysis for individual series (4/4) Tmax: Lisboa

  16. Statistical Testing: Total data mean values (AWS-CS) Z- values: two tailed test (significance levels: 10% , 5% and 1%) For each month, results are significant (90%) for most of the stations; For each station results may change between Tmax and Tmin

  17. Statistical testing of monthly data differences to normal values 1961-90 Z- values: two tailed test (significance levels: 10% , 5% and 1%) Tmax Tmin

  18. Climatological analysis of extreme values At Lisboa AWS detects more tropical nights than the CS, the opposite at Cabril

  19. Climatological analysis of extreme values At Lisboa AWS detects less warm, summer and tropical days than CS, at Cabril there is seasonal dependancy

  20. Connection with the Project “SIGN”:Signatures of environmental change in the observationsof the Geophysical Institutes Recovery of 19th and early 20th century Portuguese historical meteorological data M.Valente,M.Barros,L.Nunes,E.Alves,R.Trigo,E.Pinhal,F.Coelho,M.Mendes,J.Miranda • This work presents the joint efforts of the 3 Portuguese Geophysical Institutes (of Lisbon, Oporto and Coimbra) and the Portuguese Meteorology Institute to convert to a digital database the historical meteorology data, recorded since 1856 until 1940 in several publications by the institutes. The different sets of historical data contain monthly, daily and sometimes hourly records of pressure, temperature, precipitation, humidity, wind speed and direction, cloud cover, evaporation & ozone. • The published data cover several stations in mainland Portugal, the Azores and Madeira islands and in former Portuguese African and Asian colonies. One of the aims is to use the data to study the changes that have taken place in the historical records during the last 150 years, when the recovered data are joined with the post-1941 data stored in the Meteorology Institute digital database. • The other aim is to make the data available to the meteorology community at large. Direct observations of pressure data for Lisbon and for the 1856-1940 period were prioritized and have been manually digitized, being later subjected to quality control tests. Digital historical records of Lisbon temperature, relative humidity and precipitation data have been obtained through corrected OCR techniques applied to published hourly or bi-hourly tables. • Preliminary digital results are also available for several stations in mainland Portugal, Azores and Madeira. All datasets are subjected to an initial quality control test, to detect wrong values, with more comprehensive tests to be applied at later stages. At the same time, detailed metadata files are being compiled for each station. First analysis results for the digital historical database are available.

  21. Final remarks/questions • Availability of 10 years of daily data x 30 stations • Overlapping data series have been characterized and compared, • Some results regarding air temperature have been shown, but many other variables (humidity, pressure, …) have also been analyzed, • For Tmax & Tmin 2/3 of stations have bias +/-0.25ºC, for T09 only 1/3 of stations • There is a problem with missing data from AWS, which lowers the confidence, • Climatological extremes are different if calculated with AWS or CS! • For air temperature (well behaved variable, 2 types of inhomogenities were shown: • seasonal dependence and offset • For most of the stations, conventional observations will stop in a couple of years (only few sites will remain for more years), so, we’ll have to rely on AWS data, • Then, most recent “break-point” of the series will be known (CS=>AWS), • An homogenization plan is required! First for monthly data and then daily data... • Continuation of the SIGN project is desirable • IM-Portugal welcomes cooperation in this filed (in relation with COST HOME?)

More Related