Verification of precipitation and drought indices forecasts at subseasonal to seasonal time scales
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1 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Verification of precipitation and drought indices forecasts at subseasonal to seasonal time scales Christoph Spirig, Jonas Bhend, Samuel Monhart, and Mark Liniger
2 Overview User tailoring of climate forecasts: prediction of indices Verification of monthly forecasts surface temperature and precipitation against ECA&D drought index SPEI in Switzerland Verification of seasonal forecasts precipitation and water balance in E. Africa
3 Prediction of indices indices: (non-linear) aggregation of meteorological parameter(s) over given period direct relevance for users forecasts with a user perspective while avoiding complex impact models
4 Prediction of drought indices Interest from various sectors eg. agriculture, energy, public health Ongoing projects: hydrological ensemble predictions for hydropower operations Improve usability of seasonal forecasts Nant de Drance
5 Analysis scheme Daily time series from monthly + seasonal hindcasts Calibration Calibrated daily series Observations/ Re-analyses packages: SpecsVerification easyverification Verification
6 Analysis scheme Daily time series from monthly + seasonal hindcasts Calibration Calibrated daily series Observations/ Re-analyses Indicators from observations Forecasts of indicators Verification
7 Verification monthly forecasts ECA&D data set ( ~ 1000 observation sites Hindcasts of cycle 40r1, complete yearly cycle
8 Skill of raw and bias-corrected hindcasts raw bc add. bc qmap week 1 (days 5-11) week 2 (days 12-18) week 3 (days 19-25) week 4 (days 26-32)
9 CRPSS temperature precipitation
10 ROCSS temperature precipitation
11 D5-11 D12-18 CRPSS Temp D19-25 D26-32 CRPSS Temp
12 D5-11 D12-18 CRPSS Prec. D19-25 D26-32 CRPSS Prec.
13 Skill temperature and precipitation Skillful forecasts (T) for up to 4 weeks lead time (discrimination) and up to two weeks (CRPSS) Quantile mapping outperforms mean debiasing technique Spatial skill patterns quite homogenous Winter and autumn with higher skill and with more pronounced regional differences
14 SPEI Index cumulative water balance (WB) different time periods: 1 24 months index = value of standardized WB, negative values = dryer than norm positve values = wetter than norm model case for long term forecasts: system with different degrees of memory Vicente-Serrano et al., 2010, Buegería et al., 2014
15 Climate observation network
16 SPEI verification 27 observation sites hindcasts of current operational IFS cycle i.e. March April initial dates of
17 SPEI verification --- climatological T/prec forecasts
18 SPEI verification
19 Skill for different drought classes
20 SPEI predictions
21 Seasonal forecasts in East Africa Ethiopia s food security early warning system Main cropping season: June-September Skill of seasonal forecasts (ECMWF System 4) for predicting precipitation and cumulative water balance?
22 May forecasts for JJAS RPSS of cum. water balance from raw model output RPSS of cum. water balance from bias-corrected model output bias-corrrection of T min, T max, and precipitation
23 Resolution ROCSS (lower tercile) precip sum ROCSS (lower tercile) water balance
24 Precipitation vs water balance ROCSS (lower tercile) precip sum ROCSS (lower tercile) water balance Similar skill for water balance (= f (T min, T max, prec) ) and precip Signifcant regional and temporal (skill of ind. months) differences
25 Conclusions Skill of SPEI monthly forecasts Better skill for dry anomalies? Water balance seasonal forecast for E Africa with similar or better skill than precipitation Indicators with inherent memory call for seamless approaches
26 Thank you
27 Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss MeteoSwiss Operation Center 1 CH-8058 Zurich-Airport T MeteoSvizzera Via ai Monti 146 CH-6605 Locarno-Monti T MétéoSuisse 7bis, av. de la Paix CH-1211 Genève 2 T MétéoSuisse Chemin de l Aérologie CH-1530 Payerne T
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