Quantifying uncertainty in monthly and seasonal forecasts of indices
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1 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Quantifying uncertainty in monthly and seasonal forecasts of indices Christoph Spirig, Irina Mahlstein, Jonas Bhend, and Mark Liniger
2 Current monthly and seasonal forecasts tercile probabilities 3-monthly (weekly) averages of temperature for seasonal (monthly) forecasts bar plots, maps, climagrams 2
3 Customers of ER and SFC commercial customers (re)insurance energy providers global perspective general public 3
4 Improve usability of seasonal forecasts forecasts of indices in addition to basic met. variables indices: (non-linear) aggregation of meteorological parameter over given period indices often include thresholds direct relevance for users forecasts with a user perspective while avoiding complex impact models 4
5 Vision Soil moisture index combining monitoring and forecasts (IFS-ENS, IFS-ENS- EXT,, seamless) input: long-term resolution, calibrated 5
6 Vision Soil moisture index appropriate representation of uncertainties skill of index forecasts? 6
7 Analysis scheme Daily time series from seasonal forecasts + hindcasts Calibration Calibrated daily series Observations/ ERA-Interim Indicators from observations Forecasts of indicators Verification 7
8 Challenge: bias correction of daily data Problem: 30 years of observations not enough to calculate daily climatology approach: apply low-pass filter evaluated using perfect model approach (Mahlstein et al., JGR 2015) 30 yrs average hindcast mean fit to 30yrs mean 8
9 Bias correction of daily data CRPS of average temperature forecast in JJA (May init) raw corrected 9
10 Skill of CDD forecast CRPSS of average temperature forecast in JJA (May init) raw calibrated worse better than climatology worse better than climatology 10
11 Example of index forecasts temperature based indices Heating Degree Days (HDD) and Cooling Degree Days (CDD) sums of daily temperatures T i below / above given threshold (TH) proxies for heating/cooling energy demand 11
12 Skill in forecasts of indicators vs skill of the underlying variable Summer Winter HDD/CDD temperature 12
13 Skill in forecasts of indicators vs skill of the underlying variable Summer Winter HDD/CDD temperature 13
14 Skill of absolute vs tercile forecasts CDD forecast JJA, May initialization CRPSS (absolute) RPSS (terciles) worse better than climatology 14
15 Verification against observation data sets HDD/CDD forecasts for months 2-4, Nov/May init Summer Winter E-OBS ERA-Interim E-OBS data set: 15
16 Lessons learnt Daily calibration improves skill in forecasts of climate indices Skill largely insensitive to choice of calibration method Skill in index at most as large as skill in underlying variable Skill in seasonal index predictions is limited 16
17 Implications for use of forecasts 17
18 Forecast presentation Basis: HDD forecast as tercile summary lower tercile prob. middle tercile prob. upper tercile prob. 18
19 Forecast presentation Basis: HDD forecast as tercile summary combine with skill information! lower tercile prob. middle tercile prob. upper tercile prob. 19
20 User perspective for aggregation of forecast information example of CDD forecast energy perspective: cooling energy demand health perspective how many people are affected? use population density for weighting of CDD forecast/obs 20
21 Skill of CDD forecast (RPSS) CDD prediction for August (May initialization) same analysis for CDD weighted by population density, aggregated to country level 21
22 Conclusions potential to provide added value in certain areas be careful in not provoking overinterpretation combine skill and forecast information promote the use of «climatological forecast» added value by considering natural variability 22
23 Thank you! 23
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