Comparisons between Testbed and Finnish national observation network Jani Poutiainen Finnish Meteorological Institute (FMI) Observation Services

2 Observations - Background -Information production of 1D, 2D and 3D state of the atmosphere. -Users and applications include: meteorologists, citizens, government organizations, researchers, companies, collaborative organizations, automated processes (like weather and air quality forecasting models). fsl/lg/bams_cover_lt.gif fsl/lg/bams_cover_lt.gif fsl/lg/bams_cover_lt.gif Snow depth

3 Finnish national operational observation networks Helsinki Testbed Consortium, project phase 1,

4 National network - FMI weather stations, Automatic weather station (AWS) 169Data are collection: stations via public switched modem line and 1-3h interval, - 14 stations have frequent modem collection, 30 minute interval, - 53 stations leased/packet switched line (GSM/GPRS, Ethernet- LAN, VTS-network) typically 10 minute interval. - Of the total number, 20 stations are Milos 200 and the rest Milos 500 stations. - Of the AWS stations, 39 are marine weather stations. - At 43 AWS stations are also done manual observations 1). Mast station10Leased line, real-time data display. Radar station8Leased line, 5 minute interval. Country wide coverage. Lightning locating network receiver 5+3Leased lines, real-time data transmission. Country wide coverage. Satellite reception for operational weather services 2NOAA, Meteosat (& readyness to Metop) Air quality measurement station 18Leased line or GSM/GPRS, once an hour. Radioactivity measurement station 8Public switched modem line, once a day. Solar radiation/sun shine duration measurement station 31TCP/IP-connection (leased line or GSM/GPRS), sun shine data at 1 minute interval, solar radiation one hour interval. Part of observations at AWS stations, collection interval varies. Transmitting precipitation station 119Public switched telephone network via operator’s commercial service, once a day. Mailing precipitation station156Via mail, once a month. 2) Manual weather station25Via data collection terminal, 3h interval. 1) Sounding station3Twice a day. 1) Manual observation work will end ) Manual observation work will end almost entirely. About 100 precipitation stations will be replaced with automatic gages.

5 Surface observations / Estimated changes Base AWS network (WMO requirements, safety weather services, climatological time series, geoclimatological coverage) Complementing AWS network (special service needs: nowcasting, agricultural observations, commercial special needs, other reasons) Automated precipitation measurements (included in base and complementing AWS networks) AWS stations total Precipitation stations; manual Weather stations; manual (FMI)20-2 Climate stations; manual All stations total Sounding stations; manual/automated 2/10/2 Estimated changes in the national FMI weather observation network and new classification - Definitions and grounds for definitions - Numbers of stations

6 National network - various observations Automatic surface station Marine station Radio sonde auto launcher Satellite dishes Lightning detection Radio mast

7 8 radar sites C-Band Doppler radars Built in Gematronik HW & Sigmet SW Fully automated Using 128 kB fixed lines Design, planning, purchase, installation and maintenance by FMI Data availability 99% Weather Radars

8 Special measurement sites: Jokioinen observatory, since 1957 (FMI information database) Sodankylä Arctic Research Centre, since 1858 (Kivi et al. 1999), and 1949 (FMI information database). Nearby special sites: Luosto weather radar and measurement platform, Pallas Global Atmospheric Watch station. Versatile infrastructure Long-term high quality routine operations (incl. human observations) Special instrumentation, observations and research SODANKYLÄ JOKIOINEN Jokioinen: 60  49'N, 23  30'E, 104 m asl Sodankylä: 67  22'N, 26  39'E, 178 m asl

9 Testbed observation stations status

10 Red: Functioning WXT510 mast stations Blue: Traditional network Road weather stations Masts with 3 heights, middle height doubled

11 Helsinki Kaivopuisto testbed Helsinki Bulevardi testbed Helsinki Roihupeltotestbed Helsinki city center (YTV) testbed Helsinki Vallila (YTV) testbed Vantaa Tikkurila (YTV) testbed Espoo Leppävaara (YTV) testbed Helsinki Olympic stadium (x3)testbed/WCA2005 Espoo Otaniemi testbed/WCA2005 Helsinki Hietalahtitestbed/WCA2005 Helsinki Hietaniemitestbed/WCA2005 Helsinki Finlandia housetestbed/WCA2005 Helsinki Market squaretestbed/WCA2005 Helsinki KaisaniemiFMI AWS Helsinki Itä-Pakilaheat island Vantaa Pakkalaheat island Vantaa Asolaheat island Helsinki Tapulikaupunkiheat island Sipoo, Östersundomheat island Kirkkonummi Sundsbergheat island Vantaa Riipilä hillheat island Vantaa Riipilä valleyheat island Helsinki Harju heat island Helsinki Pitäjänmäkiheat island Helsinki Itä-Pakilaheat island Total27 pcs. © Kaupunkimittaus Helsinki 2005 Central city real-time stations/WCA2005 Urban surface stations in August 2005

12 Testbed mast station photos Liljendahl, Mickelspiltom Hyvinkää, Kaukas Ground level 81m

13 Helsinki Roihupelto 100m N

14 Testbed function vs. Operational network (Dabberdt et al 2004)

15 Observation network characteristics Traditional networkHelsinki Testbed Designed, operated and maintained FMITestbed project (incl. FMI,Vaisala, Unibase) Statusoperationalquasi-operational Characteristicshigh accuracy, continuity, climatological time series, synoptic scale representativity, nation wide geospatial coverage, data submission to international data banks and GTS multilateral: operations (incl. models), service and research orientation high temporal resolution, high spatial resolution, real-time data, bulk surface layer profiles, lower-cost technology, R&D environment for atmospheric study and modeling, observations, service and business models, and ICT-development AWS reporting interval10 min-3h (selected data 1 min)5 min (selected data 1 min) Testbed estimate (2005) for 90x90 km intense area: 16 (FMI), Vaisala, Kumpula, 30 link masts, 20 other WXTs and standalone loggers, 9 precip stations, and 23 road stations.  100 pcs.  Avg station distance of 9 km Compare to 169 automated FMI stations in the whole country  Avg station distance of 45 km

16 Future vision on mesoscale networks (Dabberdt et al. 2004) Surface network requirements: Station distance: flat country 25 km, coast 10 km, mountainous land <25 km, urban area <10 km Reporting interval: 5 min for basic variables; wind, temperature, humidity, pressure, rain amount. Additional variables: precipitation type, ground temperature, ground moisture, radiation, cloud height, and visibility. Buoy distances: 100 km for basic variables; wave height and direction, salinity and temperature profiles. Surface observation network supporting other measurements: wind profiler network (100km distances), radar and satellite observations, and lightning locating system. Boundary layer observations are essential for forecasting of mesoscale phenomena. Single methods produce usable data only for some parts, variables or weather conditions (e.g. cloudy vs. clear) in the boundary layer.  Combination and integration of data is necessary as well as network design as a whole having observations complementing each other. Dabberdt, W. F., T. W. Schlatter and F. H. Carr with E. W. J. Friday, D. Jorgensen, S. Koch, M. Pirone, M. Ralph, J. Sun, P. Welsh, J. Wilson and X. Zou (2004): Design and Development of Multifunctional Mesoscale Observing Networks in Support of Integrated Forecasting Systems, Submitted October 1, 2004 to Bulletin of the American Meteorological Society.

17 Future visions cont’d / challenges and steering ideas for network evolution: Mesoscale data-assimilation and modeling requirements: In temporal and space scale it is needed at least 6-8 measurements with respect to the size of the phenomenon 3D distribution (mass, wind, humidity) 10 km (200 m) in horizontal (in vertical) resolution in the lower troposphere, and km (500 m) upper troposphere; temporal resolution 1-3 hours. More precise rain intensity measurements including quality control 3D distribution of hydrometeors Diabatic heating profile in cloud Cloud and other microphysical measurements (e.g. mixing rate, drop size distribution) Ground temperature and moisture profiles at daily level, canopy type and condition, snow cover observation and snow depth, sea surface temperature Turbulent flow, fluxes, and stability at 0-2 km level; temporal resolution 15 minutes Boundary layer height and structure More detailed observations in coastal and mountainous areas Tropopause topology with 10 km horizontal resolution O 3, CO 2, H 2 O and cloudiness for radiative transfer models E.g. aerosols, chemical compounds, and surface temperature Preceding list excludes the types of observations considered to be detailed enough (in US), i.e. radio soundings, weather radars, wind profilers.