“High-quality” climate data ... ... “accurate and representative” measurements ... “efficient and reliable” data quality control ... “standardized and relevant” metadata ... “ rational and efficient” homogenization procedures ... „???“ grid datasets 1
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss What does „high quality“ mean with regard to spatial climate datasets? Practice and experience at MeteoSwiss Christoph Frei and Francesco Isotta Rebekka Erdin, Denise Keller, David Masson, Reinhard Schiemann, Raphaela Vogel, Bettina Weibel, Marco Willi (former collaborators) Data Management Workshop 28.-30.10.2015, St. Gallen, Switzerland
Application and Users Hydrology Research Runoff forecasting, ETH, Univ., FHS Flood protection, Univ. outside CH Land slide risks, … Agriculture Agencies Crop suitablity maps, Federal, Crop desease and pests, Regional Subsidies, … Private Sector Insurance Energy & Construction Engineering Renewable energy, Heating/cooling design, … Internal Climate monitoring Snow & ice Model verification Local forecasts Avalange risk, CC-Scenarios, … Slope stability, 3 Glacier monitoring, …
User Requirements high accuracy (small random errors) fine spatial resolution (km) high temporal resolution (1 day, 1 hour) multi-parameter – physically consistent multi-decadal – climate consistent timely – possibly real-time 4
Trends in Viticulture 1981-2010 Trend in Huglin Index change in station network based on high-res daily grid data HI=sum((T m +T x )/2 – 10degC) April – September 5
Effects from network changes Interpolation Bias 6 Frei 2014
Purpose-Design Philosophy Data products targeted to application groups Methods depend on intended application Individual balance between method and data Limitations / uncertainties are openly cummunicated 7
The MCH Grid-Data Suite Precipitation (mm) Relative Sunshine Duration (%) Temperature (degC) ~ 40 products, Temp (m, n, x), Precip, Sun, Radiation territory of Switzerland, 2 km norm, monthly, daily, (hourly), anomaly 1961-actual, 2004-actual (radar-gauge) automatic production and delivery 8 web, reports
Precipitation – Products 23.07.2009 22.08.2005 Real-time (hourly) Real-time (daily) Radar-Gauge combination, t-KED Statistical reconstruction (RSOI) Erdin et al. 2012, Sideris et al. 2014 Schiemann et al. 2012 Aug. 2005 22.08.2005 -> talk by Francesco on Friday Anomaly wrt Norm High-resolution (daily, monthly, ...) 9 >1960, SYMAP PRISM & SYMAP Shepard 1984, Frei et al. 1998 Schwarb et al. 2001, Frei et al. 1998, 2004
Relative Sunshine Duration High-resolution SSD Merging satellite (MSG Clear-sky index) and in-situ data (Heliometer, ~75 stations) Non-contemporaneous, PCA & KED Frei et al. 2015, Stöckli 2013 10 Frei et al. 2015
Relative Sunshine Duration SS : Fraction of explained spatial variance (spatial Nash-Sutcliffe efficiency) better Leave-one-out crossvalidation all days 1998-2001 11 Frei et al. 2015
Documentation for Users www.meteoswiss.ch Search for „Gitterdaten“ Underlying data Analysis method Applications and target users Overview sheet Accuracy and limitations with list of products Grid structures Update cycle 12 Versions
User Wishes (2013) Your applications could benefit from new products/developments with priority on ... ... higher spatial resolution 250 m ... finer time resolution (hourly) wind, humidity, snowfall, ... more parameters soil moisture, PET, soil moisture, ... ... more real-time products ... uncertainty information (quantitative) ... longer time coverage (<1961) ... better long-term consistency „Hydrological“ Switzerland ... areal extent beyond Swiss border 0 25% 50% 75% Number of responses: 26 13
Our Experience • “High quality” = USEFUL. Meeting the requirements of applications. • Requirements are diverse. Products tailored for applications. • There is not “a best method”. • Users need to grapple with requirements and specifications. User-friendly product information. • Be honest about limitations. • Improving through collaboration. Bridging producer – user gap. • “High-quality climate services” is about sharing thought, not just data. 14
Publications Erdin, R., C. Frei, and H. R. Künsch, 2012: Data transformation and uncertainty in geostatistical combination of radar and rain gauges. J. Hydrometeorol., 13, 1332–1346, doi:10.1175/JHM-D-11-096.1. Frei, C., 2014: Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances. Int. J. Climatol., 34, 1585–1605, doi:10.1002/joc.3786. Frei, C., M. Willi, R. Stöckli, and B. Dürr, 2015: Spatial analysis of sunshine duration in complex terrain by non- contemporaneous combination of station and satellite data. Int. J. Clim., doi:10.1002/joc.4322. Hiebl, J., and C. Frei, 2015: Daily temperature grids for Austria since 1961 - concept, creation and applicability. Theor. Appl. Clim., doi:10.1007/s00704-015-1411-4. Isotta, F. A. and Coauthors, 2014: The climate of daily precipitation in the Alps: Development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Clim., 34, 1657–1675, doi:10.1002/joc.3794. Isotta, F. A., R. Vogel, and C. Frei, 2015: Evaluation of European regional reanalyses and downscalings for precipitation in the Alpine region. Meteorol. Z., 24, 15-37. Masson, D., and C. Frei, 2014: Spatial analysis of precipitation in a high-mountain region: Exploring methods with multi-scale topographic predictors and circulation types. Hydrol. Earth Syst. Sci., 18, 4543–4563, doi:10.5194/hess-18-4543-2014. Masson, D., and C. Frei, 2015: Long-term variations and trends of mesoscale precipitation in the Alps: Recalculation and update for 1901-2008. Int. J. Clim., (in press). Vogel, R., 2013: Quantifying the uncertainty of spatial precipitation analyses with radar-gauge observation ensembles. Scientific Report MeteoSwiss, 95, 80 pp. Willi, M., 2010: Gridding of daily sunshine duration by combination of station and satellite data. Technical Report MeteoSwiss, 232, 89 pp. 15
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