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WG2 Trends in Agroclimatic Indices and Model Outputs. an attempt to summarize the WG2 questionnaires from 14 countries Vesselin Alexandrov. WG2 Trends in Agroclimatic Indices and Model Outputs. an attempt to summarize the WG2 questionnaires from 14 countries Vesselin Alexandrov.

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wg2 trends in agroclimatic indices and model outputs
WG2Trends in Agroclimatic Indices and Model Outputs

an attempt

to summarize the WG2 questionnaires

from 14 countries

Vesselin Alexandrov

wg2 trends in agroclimatic indices and model outputs2
WG2Trends in Agroclimatic Indices and Model Outputs

an attempt

to summarize the WG2 questionnaires

from 14 countries

Vesselin Alexandrov

wg2 trends in agroclimatic indices and model outputs3
WG2Trends in Agroclimatic Indices and Model Outputs
  • IMPORTANT NOTES:
  • Please consider ONLY data, models, methods, information, etc. which might be useful for implementation of the WG2 tasks and achievement of the respective WG2 deliverables (detailed within the COST 734 MoU)
  • Please skip a question/point if you are not able to provide information or try to obtain it from other colleagues in your country
  • The example attached is mainly to assist you – it should not be assumed as a mandatory one. Hence, fill free to use your own style
wg2 trends in agroclimatic indices and model outputs4
WG2Trends in Agroclimatic Indices and Model Outputs

Countries:

  • Bulgaria, Croatia
  • Czech Republic(2), France
  • Germany, Greece
  • Italy, Norway
  • Poland, Romania
  • Serbia, Slovakia,
  • Spain(3), Switzerland
wg2 trends in agroclimatic indices and model outputs5
WG2Trends in Agroclimatic Indices and Model Outputs

1. Please provide information on long-term (preferably at least 30 years) meteorological and agrometeorological data applied in your country:

information on long term meteorological and agrometeorological data
information on long-term meteorological and agrometeorological data

historical meteorological data: measured Météo-France datasets

- elements: maximum, minimum and mean air temperature, precipitation, global radiation, wind speed, PET…

- temporal resolution: daily (from 1880 to 2006)

- spatial resolution: may vary a lot, depending on parameters and period

- area/country/region: France (all major agricultural areas included)

- availability for the WG2 tasks implementation:secondary data (e.g. model outputs; maps) will be available for all COST 734 members.

slide7
number of data sets available for each parameter over the past 30 years (RR : precipitations, TT : temperature, FFM : wind speed, INST : solar duration, GLOT : global radiation).
information on long term meteorological and agrometeorological data8
information on long-term meteorological and agrometeorological data

historical meteorological data: measured data set provided by the Italian weather service

- elements: maximum, minimum and mean air temperature, precipitation

- temporal resolution: daily and monthly (1951-2005), climate 1961-1990 (CLINO)

- spatial resolution: near 40 weather stations across Italy

- area/country/region: Italy (all major agricultural areas included)

- availability for the WG2 tasks implementation:secondary data (e.g. indices; tables, maps)

- www.meteoam.it

information on long term meteorological and agrometeorological data9
information on long-term meteorological and agrometeorological data

historical meteorological data, Norway:

elements: Air temperature ( 2m, average, deviation from normal, average maximum , average minimum, absolute maximum, absolute minimum, number of days with minim temperature less than 0, degreedays base 5, degreedays base 17) , relative humidity of the air (2m, average), precipitation (2m, total precipitation, % of normal precipitation, , maximum daily precipitation, number of days with precipitation >=0.1 mm), cloud cover, number of days with sunshine.

- temporal resolution: monthly values

- spatial resolution: site specific measurements at 71’ meteorological stations in Norway mainland and islands in the Arctic

- area/country/region: site specific measurements at 71’ meteorological stations in Norway mainland and islands in the Arctic

- availability for the WG2 tasks implementation: ( most of the data since 1996 are available on the internet on the web-page of Norwegian Meteorological institute) Data on paper available since about 1960 .

- additional information: yearly reports of measurements since about 1870 from this Norwegian meteorological institute.

information on long term meteorological and agrometeorological data10
information on long-term meteorological and agrometeorological data

historical meteorological data: measured data set are provided by 160 weather stations of the Romanian National Meteorological Administration (NMA):

elements: maximum, minimum and mean air temperature, rainfall

temporal resolution: daily, monthly, annually and multi-annual values covering the period from 1961 to 2006. Also, statistical data from extreme years (droughty, rainy..)

spatial resolution: 50 km

area/country/region: Romania (whole agricultural areas)

availability for the WG2 tasks implementation: for the Romanian COST 734 delegates only

additional information: secondary data (e.g. indices, multi-annual values and results from global or regional climatic model) could be provided for all COST 734 members

information on long term meteorological and agrometeorological data11
information on long-term meteorological and agrometeorological data

historical meteorological data: measured data of climatological stations of the German weather service (www.dwd.de)

- elements: maximum, minimum and mean air temperature, precipitation, vapour pressure, wind speed, cloudiness, sunshine duration

- temporal resolution: daily (minimum 30 years)

- spatial resolution: 524 weather stations across Germany

- area/country/region: Germany

- availability for the WG2 tasks implementation: data base of DWD, availability on request

- additional information:40 stations free available for download on the web:

http://www.dwd.de/en/FundE/Klima/KLIS/daten/online/nat/index_standardformat.htm

information on long term meteorological and agrometeorological data12
information on long-term meteorological and agrometeorological data

historical meteorological data: World Weather records

- elements: temperature (mean, mean maximum, mean minimum), pressure and precipitation

- temporal resolution: monthly, annual, 30 years (1951-1999)

- spatial resolution: 19 stations

- area/country/region: Germany

- availability for the WG2 tasks implementation: data base of DWD, free access http://www.dwd.de/de/FundE/Klima/KLIS/daten/online/wwr/formatinfo_wwr.htm

information on long term meteorological and agrometeorological data13
information on long-term meteorological and agrometeorological data

Historical meteorological data, Slovakia:

- elements: precipitation totals

- temporal resolution: monthly (1901-2005)

- spatial resolution: 25 km /cca 100 precipitation stations across Slovakia/

- area/country/region: Slovakia

- availability for the WG2 tasks implementation: primary data (no restrictions)

- additional information:

personal contact to P. Nejedlik /Pavol.Nejedlik@shmu.sk/

information on long term meteorological and agrometeorological data14
information on long-term meteorological and agrometeorological data

historical meteorological data: gridded CRU data set

- elements: maximum, minimum and mean air temperature, precipitation

- temporal resolution: monthly (1951-2005)

- spatial resolution: 50 km for CRU

- area/country/region: Europe

- availability for the WG2 tasks implementation: primary data from CRU

  • additional information: the CRU data set was provided by the IPCC DDC
  • references:

Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC DDC) 1999. Data/Information supplied by the IPCC Data Distribution Centre for climate change and related scenarios for impact assessments, Norwich, UK

information on long term meteorological and agrometeorological data15
information on long-term meteorological and agrometeorological data

historical meteorological data: gridded CRU data set CRU TS 1.2

- elements: pre, tmp, dtr, vap, cld

- temporal resolution: monthly (1901-2000)

- spatial resolution: 10’

- area/country/region: Europe

- availability for the WG2 tasks implementation: primary data (ftp upon e-mail)

- references:

Mitchell et al, 2003, http://www.cru.uea.ac.uk/~timm/data/index-table.html

information on long term meteorological and agrometeorological data16
information on long-term meteorological and agrometeorological data

historical meteorological data: reanalysis from NCEP-NCAR

- elements: maximum, minimum and mean air temperature, precipitation

- temporal resolution: daily and monthly (1951-2005)

- spatial resolution: 2.5deg

- area/country/region: enlarged Euro-Mediterranean region

- availability for the WG2 tasks: primary data from NCEP-NCAR, (no restrictions)

- additional information: NCEP-NCAR from CDC

- references: http://www.nomad2.ncep.noaa.gov/ncep_data/

Kalnay, E., et al., The NCEP/NCAR 40-year Reanalysis Project, Bull. Am. Meteorol. Soc., 7, 437–471, 1996.

information on long term meteorological and agrometeorological data17
information on long-term meteorological and agrometeorological data

historical agrometeorological data, Bulgaria: from near 50 meteo and agrometeo NIMH stations

- elements: soil moisture, soil temperature, phеnological stages, agrotechnological data (required for crop model input)

- temporal resolution: soil moisture (each 10-days during the agricultural year, 1961-2000); soil temperature (monthly, 1961-2000), phеnological stages (1961-1990); agrotechnological data (1981-1993)

- area/country/region: Bulgaria

- availability for the WG2 tasks implementation:secondary data (indices, model outputs)

information on long term meteorological and agrometeorological data18
information on long-term meteorological and agrometeorological data

historical agrometeorological data, Romania – measured data set are provided by 55 weather stations of the National Meteorological Administration:

- elements: temperature at soil surface (maximum, minimum, mean), soil temperature at different depth (5, 10 , 20, 50, 100 cm), number of days with extreme events (frost, heat, etc), heat and cold units, degree days, ETP (2001-2006), soil moisture (1971-2006), phenological data (1981-2006)

- temporal resolution: monthly, seasonally, annually, multi-annually values covering the period from 1961 to 2006.

Phenological data base: agricultural crops: winter wheat, maize, sunflower, potato, sugar-beet, grapevine, fruits: apple, plum, peach

paper and electronic format, and starting with 2005, BBCH-codes are used.

- availability for the WG2 tasks implementation: the results (thematic maps covering whole agricultural territory from Romania, indices and trends) can be provided

information on long term meteorological and agrometeorological data19
information on long-term meteorological and agrometeorological data

historical agrometeorological data, Norway:

- elements: air temperature, diurnal mean, maximum, and minimum temperatures, solar radiation, wind speed and direction, relative humidity, precipitation, evapotranspiration, soil temperature

- temporal resolution: diurnal and partly hourly recordings during the years 1958, 1959, and 1960.

- spatial resolution: All over the community, 42 stations, 130 to 423 m a.s.l., of two types

- area/country/region: Nes community (95.42 km2) in South East Norway

- availability for the WG2 tasks implementation:printed

- additional information: The tables, analyses of the data, the derived respiration equivalent index and a scientific discussion are all published in a PhD thesis

information on long term meteorological and agrometeorological data20
information on long-term meteorological and agrometeorological data

historical agrometeorological data, Germany:phenological seasons

- elements: average beginning, end and duration of 10 phenological seasons

- temporal resolution: average (1961-1990)

- spatial resolution: 40 stations

- area/country/region: Germany

- availability for the WG2 tasks implementation:available from web (see ref.)

- references (incl. web pages):

http://www.dwd.de/de/FundE/Klima/KLIS/daten/online/nat/index_phaeno.htm

wg2 trends in agroclimatic indices and model outputs21
WG2Trends in Agroclimatic Indices and Model Outputs

2. Please indicate any models (e.g., numerical weather models, regional climate models, weather generators) and/or their related outputs used in your country:

models and or their related outputs
models and/or their related outputs

Numerical weather models (Serbia):

  • Nonhydrostatic Mesoscale Model (NMM) (National Centers for Environmental Prediction (NCEP), USA)

- inputs/outputs: standard outputs from mesoscale numerical weather prediction model

- temporal resolution: 2-5 days

- spatial resolution: 10 km

- area/country/region: globe

- availability for the WG2 tasks implementation:for the Serbian COST 734 delegates

models and or their related outputs23
models and/or their related outputs

Numerical weather models (Croatia):

  • ECMWF (UK), ALADIN (France)

- inputs/outputs: average air temperature, precipitation, sea level pressure

- temporal resolution: 6-hourly, down to a month ago (ECMWF); 3-hourly, since 2004 (ALADIN)

- spatial resolution: 25 km for ECMWF and 8 km for ALADIN

- area/country/region: Central Europe and Adriatic sea

- availability for the WG2 tasks implementation:for the Croatian COST 734 delegates only

Poland, Romania, Bulgaria, Slovakia – ALADIN;

Greece, Spain, etc. - ECMWF

models and or their related outputs24
models and/or their related outputs

Numerical weather models (Italy):

  • RAMS (originally from CSU-USA, now distributed by ASTER)

- temporal resolution: outputs at daily and hourly values

- spatial resolution: varies depending on the application – possible nested domains

- area/country/region: enlarged Euro-Mediterranean

- availability for the WG2 tasks implementation: belongs to ASTER and a license is needed

http://www.aster.com/index.shtml

http://www.lamma.rete.toscana.it

models and or their related outputs25
models and/or their related outputs

Numerical weather models (Germany):

  • Local model (LM) of the German Weather Service and the “Consortium for Small-Scale Modelling“ (COSMO).

- inputs/outputs: hourly boundary conditions from GME (Global model), 48 hours forecast

- temporal resolution: hourly forecast

- spatial resolution: 7 km, 35 layers

- area/country/region: Germany, Europe

http://www.dwd.de/de/FundE/Veroeffentlichung/Dokumentation/promet27%203_4.pdf

Germany, Italy, Greece, Romania, Poland

models and or their related outputs26
models and/or their related outputs

Numerical weather models (Romania):

The forecasters from NMA use also numerical models such as:

  • HRM, integrated twice a day (00 and 12 TU) for an anticipation of 72 hours (horizontal spatial resolution of 20 km);
  • MM5, integrated 4 times a day (00, 06, 12, and 18 TU) for an anticipation of 24 hours (horizontal resolution of 15 km);
models and or their related outputs27
models and/or their related outputs

Numerical local area models (Italy):

  • LAMI (by Servizio Meteorologico dell'Aeronautica)
  • LAMBO (by ARPA Emilia Romagna – SIM - http://www.arpa.emr.it/sim/)
  • BOLAM by CNR Isac, managed by Servizio Agrometeorologico Regionale della Sardegna (http://www.sar.sardegna.it/) and Università di Genova (http://www.fisica.unige.it/atmosfera/bolam_avn.htm)

applied also in Greece

  • DALAM (by Ucea – www.ucea.it)
  • LILAM (by Meteoliguria – http://www.meteoliguria.it)
models and or their related outputs28
models and/or their related outputs

Global climate models (Italy, Czech Republic):

  • General Circulation Model PUMA (Portable University Model of Atmosphere) and Planet Simulator. Germany

- temporal resolution: monthly values

- spatial resolution: 3.5 deg

- area/country/region: globe

- availability for the WG2 tasks: free from owner

- additional information: other free weather generators - upon request

www.mi.uni-hamburg.de/plasim

  • GCMs: IPCC DDC data, PRUDENCE outputs

- availability for the WG2 tasks: IPCC DDC, PRUDENCE server

  • HadCM3 - Greece
models and or their related outputs29
models and/or their related outputs

Regional climate models (Croatia):

  • RegCM3 (Italy)

- inputs/outputs: air temperature, precipitation,

- temporal resolution: 6-hourly for winter and summer (1961-1990)

- spatial resolution: 50 km RegCM3

- area/country/region:Central and southern Europe and the northern Mediterranean sea

- availability for the WG2 tasks implementation:for the Croatian COST 734 delegates only

In Spain the following models are used: HIRLAM/INM,MESO-NH; MM5 mesoscale model (PSU/NCAR).

Regional climate model data:PRUDENCE server !!!!

models and or their related outputs30
models and/or their related outputs

Regional climate models (Greece):

  • PRECIS

Kotroni et al. Climatic projections in the eastern Mediterranean using the regional climatic model PRECIS. 8th Conference on Meteorology - Climatology – Atmospheric Physics, Athens, May 24-26, 2006.

In order to investigate climate change and impacts in Greece as well as in the Eastern Mediterranean area, the regional climate model PRECIS, has been implemented in the National Observatory of Athens (NOA). For the application of the PRECIS model at NOA a horizontal analysis of 25 km was selected, which is the finest resolution used so far in the area as well as the complex land-sea distribution.

models and or their related outputs31
models and/or their related outputs

Weather generators (Czech Republic, Croatia, Serbia)

  • Met&Roll and M&Rfi, Czech Republic

- inputs/outputs:in a standard use daily “learning” series of max and min air temperature, solar radiation or sunshine duration, precipitation; wind speed and humidity + …; daily weather values as outputs

- temporal resolution: unlimited series of daily values)

- spatial resolution: for given locations where inputs are available

- area/country/region: any site within the Czech Republic

- availability for the WG2 tasks implementation:free to use for research (particularly for COST 734 participants) details at: http://www.ufa.cas.cz/dub/dub.htm or contact mirek_trnka@yahoo.com

models and or their related outputs32
models and/or their related outputs

Weather generators (Germany):

  • CLIMGEN (USA)

- inputs/outputs:daily or monthly max and min air temperature, solar radiation or sunshine duration, precipitation / daily weather values as outputs: Precipitation, Maximum and Minimum temperature, Solar radiation, Maximum and Minimum relative humidity, Maximum and Minimum dew point temperature, Windspeed, Vapor pressure deficit, reference evapotranspiration (Penman-Monteith, Priestley-Taylor, Hargreaves).

- temporal resolution: daily / 1 to 1440 minutes (storm events)

- spatial resolution: depending on data availability

  • availability for the WG2 tasks implementation:registering and free download

http://www.bsyse.wsu.edu/climgen/

models and or their related outputs33
models and/or their related outputs

Weather generators (Spain):

  • WGEN (USA)

- inputs/outputs:daily or monthly norms of max and min air temperature, solar radiation or sunshine duration, precipitation + statistical parameters (in case of monthly norms)/ daily weather values as outputs (ITACYL)

- temporal resolution: unlimited series of daily values

- spatial resolution: for given locations where inputs are available

- area/country/region: every weather station in Castilla y León

- availability for the WG2 tasks implementation:belongs to DSSAT and a license is needed

- additional information: other free weather generators could be provided upon request

http://www.itacyl.es/opencms/opencms/proyectos/investigacion/proyectos/proyecto_0026.html

models and or their related outputs34
models and/or their related outputs

Weather generators (Switzeland):

  • LARS-WG

Racsko P., Szeidl L. & Semenov M. (1991): A serial approach to local stochastic weather models. Ecological Modelling 57, 27-41.

Semenov M.A., Brooks R.J., Barrow E.M. & Richardson C.W (1998): Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Climate Research 10, 95-107.

Semenov M.A. & Brooks R.J. (1999): Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11, 137-148.

wg2 trends in agroclimatic indices and model outputs35
WG2Trends in Agroclimatic Indices and Model Outputs

3. Please name and shortly describe any homogenization tests/procedures applied to meteorological and agricultural related time series in your country:

homogenization tests procedures
homogenization tests/procedures

Croatia, etc.:

  • Standard Normal Homogeneity Test

- short description: Homogeneity testing of the temperature time series was performed by Alexandersson´s SNHT test. The test requires a time series of monthly values from the test station and one or more reference series. The reference series are compared with the test series to estimate the relative homogeneity of the test series. The test series and reference series are obtained from monthly data on a seasonal and annual basis.

- temporal resolution: monthly, the period depends on the input time series (e.g. 1951-2005)

- spatial resolution: at least 10-20 weather stations are necessary

- availability for the WG2 tasks implementation:a license is needed

homogenization tests procedures37
homogenization tests/procedures

Czech Republic, Slovakia, Italy, Bulgaria,etc.

  • AnClim – software for statistical analysis and homogenization

- short description: TXT files, working with one station at a time. Menu is ordered in a sequence (steps) to be taken during data processing: viewing data, adjusting (transformation), testing distribution, finding outliers, homogeneity testing (both absolute and relative homogeneity tests), analysis, filtering.

http://www.climahom.eu/AnClim.html

http://www.klimahom.com/software/AnClim.html

- availability for the WG2 tasks implementation:freeware, fully functional version with support upon contact and agreement with the author (Dr.Stepanek)

homogenization tests procedures38
homogenization tests/procedures

Italy:

  • Software ClimDex

- short description:a Microsoft Excel program designed to assist researchers in the analysis of climate change and detection. More specifically, ClimDex guides a user through a four-step analysis process, using a graphical user interface. This process consists of the following steps:1. Quality Control; 2. Homogeneity Testing; 3. Calculate Indices; 4. Region Analysis

- temporal resolution: monthly, or daily: the time resolution depends on the input time series

- availability for the WG2 tasks implementation:No restrictions

- additional information: fully available from the web site

homogenization tests procedures39
homogenization tests/procedures

France, Bulgaria:

  • PRODIGE (Meteo France)

- short description: The currently used in Météo-France homogenization procedure, does not require computation of regional reference series. The methodology of homogenization is valuable for practical use such as on climate data, even with poor or missing metadata, and allows the detection of multiple breaks.

- temporal resolution: monthly, the period depends on the input time series (e.g. 1951-2005)

- spatial resolution: at least 10-20 weather stations are necessary

- availability for the WG2 tasks implementation: Meteo France applies restrictions

- additional information: a new COST ES0601 action just started

homogenization tests procedures40
homogenization tests/procedures

Spain:

  • Von Neumann ratio test

Von Neumann, I. 1941. ‘Distribution of the ratio of the mean square successive difference to the variance’. Annals of Mathematical Statistics 13:367-395.

  • Precipitation: SNHT and Wald-Wolfowitz tests. Applied to the monthly precipitation values in stations with more than 20 years of data from 1960 (approximately 1200 series).
  • Temperature: Mann test (applied to the monthly temperature values in stations with more than 20 years of data from 1960). Petit test (applied to the monthly temperature values in stations with long series).
homogenization tests procedures41
homogenization tests/procedures

Greece:

  • N. Dalezios et al. Testing homogeneity of temperature and precipitation series in Greece. International symposium / workshop on climatic variability and impact to agriculture, Volos, April 21, 1994.

This paper deals with tests on homogeneity of temperature and time series in Greece. The emphasis is placed on the identification of inhomogeneities in temperature and precipitation time series as well as on the specification of certain years, in which inhomogeneities occur. Moreover in this study correction factors are identified to artificially homogenize the time series. This accomplished by employing various homogeneity tests to monthly data over 37 years (1951-1987) at 31 stations over Greece which has been classified in 5 regions using Factor analysis.

homogenization tests procedures42
homogenization tests/procedures

Switzerland:

  • Auer I, et al., 2006: HISTALP-historical instrumental climatological surface time series of the Greater Alpine Region. Int. J. Climatology,27,17-46.
  • Begert M, Schlegel T, Kirchhofer W:, 2005: Homogeneous temperature and precipitation series of Switzerland from 1864 to 2000. Int. J. Climatol.,25,65-80
  • Della-Marta PM, 2006: A method of homogenizing the extremes and mean of daily temperature measurements. J. of Climate,19/17,4179-4197
wg2 trends in agroclimatic indices and model outputs43
WG2Trends in Agroclimatic Indices and Model Outputs

4. Please provide any information on the statistical methods for analyses of meteorological and simulation model output related time series

statistical methods for analyses of time series
statistical methods for analyses of time series

Italy:

Trend Calculation:Least squares; Minimum Absolute Deviation; Significance Testing: Confidence intervals for least squares, the Mann-Kendall and Spearman rank statistics; Indices for Extremes as in ECA&D

- software: mainly in MATLAB. Some specific software for extremes available from ECA&D (ClimDex).

- availability for the WG2 tasks implementation: MATLAB is proprietary software.

Software to calculate indices

eca.knmi.nl

www.knmi.nl/samenw/eca/index.html

www.ncdc.noaa.gov/oa/wmo/ccl

www.cru.uea.ac.uk/projects/stardex

statistical methods for analyses of time series45
statistical methods for analyses of time series

Germany:

Trend Calculation through non-linear approximation of stochastic processes.

- short description: The method for nonlinear approximation of stochastic processes is derived for calculations of climatic trends of long-term meteorological data sets. The method uses among others spline approximation, Green´s function and spectral transfer function of the Chauchy problem.

- software: in PASCAL

- availability for the WG2 tasks implementation:EXE file would be available for the WG2 members

statistical methods for analyses of time series46
statistical methods for analyses of time series

Serbia:

  • Time series analysis using quantitative parameters of chaos

- short description: This method includes deriving low attractors in atmospheric data time series and calculations corresponding quantities as the Lyapunov exponent, Kolmogorov entropy and Kaplan-Yorke dimension. It is also combining with filtering techniques for time series, particularly with the 4253H filter.

- input/output: time series/quantitative parameters for detection of the weak chaos

- software: in FORTRAN and C language

- availability for the WG2 tasks implementation:yes

statistical methods for analyses of time series47
statistical methods for analyses of time series

Spain:

  • Trend Calculation: Least squares (used in diagnostic tool); Minimum Absolute Deviation; Significance Testing: Confidence intervals for least squares, the Mann-Kendall and Spearman rank statistics

- software:free

Libiseller C. and Grimvall A., 2002 .Performance of Partial Mann Kendall Tests for Trend Detection in the Presence of Covariates, Environmetrics 13,71-84

statistical methods for analyses of time series48
statistical methods for analyses of time series
  • Czech Republic
  • Cluster Analysis; Various techniques for assessment links with the agrometeorologicaly relevant events and e.g. regional circulation patterns (e.g. GWL)

- software:

standard statistical packages SPSS, Statistica or Unistat: licensed – use restricted

AnClim, neural networks, wavelet analysis packages

statistical methods for analyses of time series49
statistical methods for analyses of time series

Croatia, Poland, etc.

  • software:STATISTICA

- availability for the WG2 tasks implementation: a license is needed

statistical methods for analyses of time series50
statistical methods for analyses of time series

Switzerland:

  • Trend analysis, Fourier and spectral analysis, and others.

Mostly using available FORTRAN routines

  • Recently, an increasing number of investigations have been carried out using the R language:

http://www.r-project.org

, which is becoming a standard.

statistical methods for analyses of time series51
statistical methods for analyses of time series

Greece:

  • 8th Conference on Meteorology - Climatology – Atmospheric Physics, Athens, May 24-26, 2006.

Anagnostopoulou et al. Extreme rainfall events over the Greek area: theory and application of the generalized extreme value (GEV) distribution and pareto distribution.

Skourkeas et al.Model estimation of mean maximum and minimum temperatures in Greece.

  • 6th Hellenic conference in meteorology climatology and atmospheric physics, 25-28 September, 2002.

Metaxas et al.,Contribution to the seasonal air temperature forecast in the northern hemisphere with the use of statistical methods(Principal Components Analysis, Canonical Correlation Analysis)

wg2 trends in agroclimatic indices and model outputs52
WG2Trends in Agroclimatic Indices and Model Outputs

5. Please specify any additional data/information/problems/questions related to the implementation of the WG2 tasks and the achievement of the respective WG2 deliverables

additional data info problems questions
additional data/info/problems/questions

Norway:

- short description:By means of GIS weather and soil data are combined for application of a plant development model for barley to make estimates of crop security in a marginal area for cereal production. Daily weather data were interpolated to soil type units.

- input/output: daily weather records and digital soil type maps

- software: FORTRAN

- elements: air temperature, cloud cover, relative humidity, wind speed, precipitation

- temporal resolution: diurnal recordings during the years 1991 through 2000.

- spatial resolution: On about 31,000 soil type units of about 21,000 ha arable land

- area/country/region: On the arable land of the communities Steinkjer and Inderøy (approx. 64oN) in Central Norway

- availability for the WG2 tasks implementation:printed article

additional data info problems questions54
additional data/info/problems/questions
  • Lack of sufficient dataor poor quality data

Spain: “Lack of data or poor quality data when the need is for data of high temporal and spatial resolution. In many cases it could be difficult to provide good quality gridded data of daily meteorological values for grids smaller than 40 km2”

Slovakia: “pure qality of some agrometeorological data /soil moisture and agrotechnical data/; some data are still stored on the paper sheets /part of soil moisture and phenological data/

additional data info problems questions55
additional data/info/problems/questions
  • Time demanding tasks
  • Some short visits between COST 734 members would be of help (Bulgaria, Romania, Serbia)
additional data info problems questions56
additional data/info/problems/questions

Bibliography (Italy)

  • Mariani L., 2002. Reti di osservazione e banche di dati meteorologici in Italia, Workshop Climagri del 7 marzo 2002, http://www.climagri.it/prog7_8.htm
  • Maugeri M., 2002. Disponibilità, affidabilità e omogeneità di serie storiche di dati meteorologici in Italia, http://www.climagri.it/prog7_8.htm
  • Nanni M.T., 2002. Metodi per la valutazione dell’omogeneità e per l’estrazione di segnali da serie storiche di dati meteorologici,http://www.climagri.it/prog7_8.htm
  • Buzzi A., 2002. L'evoluzione dei modelli previsionali a breve scadenza, http://www.climagri.it/prog7_8.htm.
  • Brunetti, M., Buffoni, L., Maugeri, M., Nanni, T., 2000. Trends of minimum and maximum daily temperatures in Italy from 1865 to 1996. Theor. Appl. Climatol., 66, 49-60.
  • Brunetti, M., Mangianti, Maugeri, M., Nanni, T., 2000. Climate variation in Italy from 1866 to 1995. World Resources Review, 12, 31-43.   Brunetti, M., Maugeri, M., Nanni, T., 2000. Variations of temperature and precipitation in Italy from 1866 to 1995. Theor. Appl. Climatol., 65, 165-174.
summarizing trends in agroclimatic indices and crop model outputs in europe
Summarizing Trends in Agroclimatic Indices and Crop Model Outputs in Europe
  • Author(s)
  • Abstract
  • Introduction
  • Goals, task and material
  • Short overview of previous results – WMO (S), IPCC (RO,BG), national studies, etc.
summarizing trends in agroclimatic indices and crop model outputs in europe59
Summarizing Trends in Agroclimatic Indices and Crop Model Outputs in Europe
  • Summarizing WG2 questionnaires:

Question by question – 4 parts

  • Concluding remarks
  • Acknowledgements
  • References
  • ANNEX : all questionnaires (redo if necessary)?

Map of the countries submitted questionnaires

summarizing trends in agroclimatic indices and crop model outputs in europe60
Summarizing Trends in Agroclimatic Indices and Crop Model Outputs in Europe
  • Tables related to the questions: data, models, methods, contact persons
  • Figures, etc.
working plan next year
Working plan – next year
  • Determination of case studies – S, RO, BG, PL, !!!
  • Collecting meteo and agrometeo data for the selected regions (WG1, WG4)
  • Data quality control, homogenization data