and their applications in Africa

Transcription

1 Satellite-based climate data records and their applications in Africa Steffen Kothe and CM SAF team

2 Climate Monitoring State of climate system Better understanding of climate change Basis are in situ measurements and more and more satellite-based data Global station network Global Radiosonde network 2

3 Requirements for Climate Data x x Value Time Long-term time series Calibration Homogeneity Point of measurement x Representativeness Quality control 3

4 The EUMETSAT SAF Network EUMETSAT Member States EUMETSAT Cooperating States 4

5 CM SAF Partners Deutscher Wetterdienst Swedish Meteorological and Hydrological Institute Royal Meteorological Institute of Belgium Finnish Meteorological Institute EUMETSAT Member States EUMETSAT Cooperating States CM SAF Member States Location of Partner NMHSs Met Office, United Kingdom (since 2017) 5

6 Global Energy and Water Cycle Source: Wild et al.,

7 Global Energy and Water Cycle Source: Wild et al.,

8 CM SAF Product Portfolio Long-term time series of Essential Climate Variables (ECVs) of parameters of the energy and water cycle: Global cloud parameters Global precipitation (planned) Global radiation at surface Global water vapor Regional (Europe & Africa) radiation at surface and TOA Regional land fluxes (planned) CM SAF provides TCDRs and ICDRs 8

9 CM SAF Users Others 0% State authorities 8% Research institutes 46% Pi Private companies 12% Meteorological Services 34% 9

10 CLAAS-2 Variables Cloud properties Liquid and ice water path Resolution Spatial: ( ) Temporal: hourly-, daily-, monthly means, mean monthly diurnal cycle Coverage Spatial: Meteosat disk Temporal: 2004 to 2015 Satellites Meteosat Second Generation (SEVIRI) DOI: /EUM_SAF_CM/CLAAS/V002 10

11 CLARA-A2 Variables Cloud properties Surface albedo Radiation Resolution Spatial: Temporal: daily-, pentad-, monthly mean Coverage Spatial: global Temporal: 1982 to 2015 Satellites NOAA, Metop (AVHRR) DOI: /EUM_SAF_CM/CLARA_AVHRR/V002 11

12 HOAPS Variables Wind, humidity (close to surface) Precipitation, evaporation Latent heat flux Fresh water flux Resolution Spatial: Temporal: 6-hourly-, monthly means Coverage Spatial: global ice free ocean Temporal: 1987 to 2015 Satellites DMSP (SSM/I) DOI: /EUM_SAF_CM/HOAPS/V002 12

13 FCDR SSM/I Variables Brightness temperature Resolution Spatial: native SSM/I Temporal: native SSM/I Coverage Spatial: global Temporal: 1987 to 2008 Satellites DMSP (SSM/I) DOI: /EUM_SAF_CM/FCDR_MWI/V002 13

14 SARAH-2 Variables Global radiation (SIS) Surface direct irradiance (SDI) Spectral a resolved ed irradiance a (SRI) Effective cloud albedo (CAL) Resolution Spatial: Temporal: 30 min instantaneous, daily-, monthly means Coverage Spatial: Meteosat disk Temporal: 1983 to 2015 Satellites Meteosat 2 to 10 (MVIRI/SEVIRI) DOI: /EUM_SAF_CM/SARAH/V002 14

15 Application Example Sunshine Duration Sunshine duration important parameter Tourism Health Sector Agriculture Vegetation modelling Solar energy DWD satellite-based sunshine duration 15

16 Application Example Sunshine Duration Sunshine duration important parameter Tourism Health Sector Agriculture Vegetation modelling Solar energy First satellite-based sunshine duration dataset for Europe and Africa DWD satellite-based sunshine duration 16

17 Application Example Sunshine Duration Sunshine duration important parameter Tourism Health Sector Agriculture Vegetation modelling Solar energy First satellite-based sunshine duration dataset for Europe and Africa Basis SARAH-2 radiation data Preliminary evaluation showed good agreement with station data (<10%) DWD satellite-based sunshine duration 17

18 Application Example Sunshine Duration Sunshine duration important parameter Tourism Health Sector Agriculture Vegetation modelling Solar energy First satellite-based sunshine duration dataset for Europe and Africa Basis SARAH-2 radiation data Preliminary evaluation showed good agreement with station data (<10%) Suitable for many applications DWD satellite-based sunshine duration 18

19 Application Example Sunshine Duration 19

20 Application Example Sunny Days Global Climatology of Sunny Days based on SARAH and CLARA Sunny day is defined by mean daily global radiation 80 % of maximum possible solar insolation Sunny period is characterized by five consecutive sunny days Sunny days interactive map on CM SAF webpage 20

21 Application Example Sunny Days 21

22 Application Example Sunny Days 22

23 Application Example Sunny Days 23

24 Application Example Sunny Days 24

25 Application Example Sunny Days 25

26 Application Example Sunny Days 26

27 Application Example Microphysical Cloud Properties Analysis of cloud optical thickness and effective particle radius offshore Namibia Area known for large stratocumulus fields Data basing on MSG/SEVIRI Investigation area 27

28 Application Example Microphysical Cloud Properties Analysis of cloud optical thickness and effective particle radius offshore Namibia Area known for large stratocumulus fields Data basing on MSG/SEVIRI Typical diurnal cycle was found for cloud optical thickness 17 October 2006 Investigation area and cloud optical thickness 28

29 Application Example Microphysical Cloud Properties Analysis of cloud optical thickness and effective particle radius offshore Namibia Area known for large stratocumulus fields Data basing on MSG/SEVIRI Typical diurnal cycle was found for cloud optical thickness and effective particle radius CM SAF CLAAS data for long-term climatological analysis 17 October 2006 Investigation area and cloud optical thickness 29

30 Application Example Solar Atlas South Africa Colleagues from SAWS and JRC produced a solar Atlas for South Africa using CM SAF radiation data Long-term Annual Mean for Global Horizontal Irradiance, period Photovoltaic (PV) energy yield of Global Irradiation on optimally inclined plane for the year (kwh/m2) Kindly provided by Lucky Ntsangwane (SAWS) 30

31 Application Example Solar Energy Production forecast, planning, monitoring of solar energy needs radiation data for at least 10 years PVGIS tool of Joint Research Centre Free PV online tool Uses CM SAF solar radiation data Production forecast and potential estimation Easy to apply For all African countries Solar energy potential ti from PVGIS for Rwanda 31

32 Application Example Solar Energy Solar energy potential from PVGIS for Rwanda 32

33 Application Example Solar Energy 33

34 Application Example Solar Energy 34

35 Application Example Agriculture Ethiopia Reconstructing gpre-agricultural expansion vegetation cover of Ethiopia (a) Reconstructed natural vegetation cover of agricultural land. (b) Reconstructed Land cover map of Ethiopia (agricultural areas replaced by the simulated natural vegetation cover). Hailu et al., 2015, Applied Geography 35

36 Data Access Web User Interface Easy selection and online ordering Possibility of regular data delivery Postprocessing: Spatial, temporal Data format (netcdf, hdf) Selection of region Ftp or All data free of charge 36

37 Data Access EUMETCast The following CM SAF products are disseminated via EUMETCast: Monthly mean fractional cloud cover Monthly mean surface incoming shortwave radiation Product format is netcdf. EUMETCast Africa: Channel: SAF-Africa PID: 301 Multicast Address:

38 Toolbox CM SAF provides CM SAF R Toolbox for free cmsaf R-package collection of functions for simply analysis and manipulation of CM SAF netcdf data (see cran.r-project.org) R-scripts for analysis and visualization of CM SAF data R-scripts, which help unexperienced R-users to apply functions of cmsaf R-package 38

39 Application Example Toolbox Examples by User Forum Participants Unexplained seasonal patterns over Mauritius Large annual variability in Senegal 39

40 Application Example Toolbox Kigali 40

41 Training Training workshops in cooperation with EUMETSAT Practical exercises with CM SAF data For content of previous courses see the community site 41

42 Satellite based assessment of extreme weather e events e and agricultural a production potential in Uganda Barasa Bernard barasagis@cedat.mak.ac.ug g

43 Results Annual mean rainfall Annual mean SIS

44 Results Floods events in September, 2007, April, 2008, September, 2012 and May, 2013

45 Results Drought (February, 1998, 2005, 2010, 2012)

46 By: Tiro Nkemelang

47 - Rainfall in Botswana mainly convective. y - Good correlation between rainfall distribution and cloud top height

48 CM SAF Training Workshop Pretoria (South Africa) 8 12 June 2015 Training Project COULIBALY Kolotioloma Alama from National Meteorological Service of Cote d Ivoire kcoulibaly2@yahoo.fr

49 Climatology pattern 10 9 Odienné Korhogo 8 Man Bouaké Bondoukou 7 Daloa Yamoussoukro Dimbokro 6 Gagnoa 5 Abidjan Adiaké Sassandra San-Pédro Tabou

50 CM SAF Data Analysis: Burkina Faso Case Study Guillaume NAKOULMA Burkina Faso Meteorological Service

51 Spatial Distribution Climatology SIS CFC PRECIP

52 Summary Products and services in connection with global energy and water cycle Especially geostationary products excellent suitable for Africa Thoroughly quality insurance and control mechanisms Extensive exchange and support with / of users Free and uncomplicated data access So far more about 140 peer-reviewed e e ed publications using CM SAF data Contact data: Steffen.Kothe@dwd.de 52