Verification and comparison of probabilistic precipitation forecasts using the TIGGE data in the upriver of Huaihe Basin

Abstract

Abstract. The precipitation forecasts of three ensemble prediction systems (EPS) and two multi-model ensemble prediction systems (MM EPS) were assessed by comparing with observations from 19 rain gauge stations located in the Dapoling-Wangjiaba sub-catchment of Huaihe Basin for the period from 1 July to 6 August 2008. The sample Probabilistic Distribution Functions (PDF) of gamma distribution, the Relative Operating Characteristic (ROC) diagrams, the percentile precipitation and a heavy rainfall event are analyzed to evaluate the performances of the single and multi-model ensemble prediction system (EPS).The three EPS were from the China Meteorological Administration (CMA); the United States National Centre for Environment Predictions (NCEP); and the European Centre for Medium-Range Weather Forecasts (ECMWF), all were obtained from the TIGGE-CMA archiving centre (THOR-PEX Interactive Grand Global Ensemble, TIGGE). The MM EPS were created using the equal weighting method for every ensemble member over the test area, the first ( MM-1) consisted of all three EPS, the second (MM-2) consisted of the ECMWF and NCEP EPS.The results demonstrate the level of correspondence between deterioration in predictive skill and extended lead time. Compared with observations and with a lead time of one day, ECMWF performs a little better than other centre's. With over five days in advance, all the three EPS and the two MM EPS don't give reliable probabilistic precipitation forecasts. Both MM EPS can outperform CMA and NCEP for most of the forecasted days, but still perform a little worse than ECMWF. Though variation of daily percentile precipitaCorrespondence to: F.-Y. Tian (tianfy@cma.gov.cn) tion and ROC areas show MM-2 outperforms MM-1, gamma distribution indicates much similar performances for all 10-day forecast, and neither is superior to ECMWF.

BMA Probabilistic Quantitative Precipitation Forecasting over the Huaihe Basin Using TIGGE Multimodel Ensemble Forecasts

“…As discussed above, the BMA PQPFs based on multicenter EGE EPSs with lead times of 1-7 days greatly improved the quality of PQPFs in most cases, demonstrating the potential of BMA methods and multicenter grand ensemble modeling. With the developing probabilistic forecasting methodology and the use of multimodel ensemble forecasting, extreme precipitation events forecasting and 3-10-day probability flood forecasting will become well developed, as pointed out by Thielen et al (2009) and Zhao et al (2011). Thus, an advanced statistical postprocessing approach and multicenter ensemble forecasting will greatly facilitate the development of the heavy-rainfall events forecasting and early probability flood forecasting.…”

“…A key component of The Observing System Research and Predictability Experiment (THORPEX), the THORPEX Interactive Grand Global Ensemble (TIGGE), provides a grand ensemble that incorporates the EPS forecasts from 10 forecast centers: the Australian Bureau of Meteorology (BoM), the China Meteorological Administration (CMA), the Meteorological Service of Canada (CMC), the Brazil Centro de Previsao Tempo e Estudos Climaticos (CPTEC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), M et eo-France, the National Centers for Environment Prediction (NCEP), and the Met Office (UKMO). This provides a very good basis for the production of PQPFs (Richardson 2005;Park et al 2008;Matsueda and Tanaka 2008;Zhao et al 2011). The primary aim of this work is to explore the advantage of the BMA strategy for PQPFs based on the TIGGE multicenter EPS.…”

Improvement of the SWAT model for event-based flood simulation on a sub-daily timescale

“…Since the catchment is dominated by a monsoon climate, approximately 60 % of the annual rainfall is received during the flood season ranging from mid-May to mid-October. Severe rainfall events within the study area typically transpire during the summer, frequently resulting in severe floods (Zhao et al, 2011).…”

Improvement of the SWAT model for event-based flood forecasting on a sub-daily time scale

“…Since the catchment is dominated by a monsoon climate, approximately 60 % of the annual rainfall is received during the flood season ranging from mid-May to mid-October. Severe rainfall events within the 25 study area typically transpire during the summer, frequently resulting in severe floods (Zhao et al, 2011). Apart from the WJB stream gauging station at the catchment outlet, other stations at Bantai (BT), Xixian (XX) and Huangchuan (HC) are located upstream of the study catchment, all of which are illustrated in Fig.…”