THORPEX Asia, Kunming, 1 Nov 2012

1. Process of User-oriented interactive flooding-leading rain forecast systemChen Jing1 Zhongwei Yan2 Jiarui Han3 Jiao Meiyan41. Numerical Weather Prediction Center, CMA2. RCE-TEA, Institute of Atmospheric Physics, Beijing 3. Research Center for Strategic Development, CMA THORPEX Asia, Kunming, 1 Nov 2012

2. Why user-oriented?The meteorological model, as a chaotic system, is of limited predictability. General improvement of large-scale forecast has, asymptotically, been limited. However, for a given user, at a specified scale, there is still great potential of improvement, especially in the context of ensemble forecast.

3. What to be user-oriented? Key variable Initial condition with sensitive perturbations Target/local observations Key decisions User’s needs & decision-making information Meteorological forecast system Downscaling Experience-calibration User-based assessment Climate background Conceptual User-oriented Interactive Forecast System

4. Components in User-oriented Interactive Forecast System

5. How user-end information could provide a dynamic forecast target for forecast system? Focus on dynamic flood-leading rainfall thresholdin Wangjiaba sub-basin

6. Target region：Wangjiaba sub-basin Wangjiaba Sluice Precipitation station Huaihe river basin Observation:1 Jun.-31 Sep. 2003-2010

7. Target region：Wangjiaba sub-basin Huaihe river basin 3×3 grid boxes

8. Hydrological user’s experience:Heavy rainfall over 50mm/day usually causes floods in the next days;However, less heavy rainfall may lead to floods if there has been rainfall in preceding days

9. Analyzing user-end flood risk, to figure out dynamic forecasting target for forecasting system Hydrological user’s need: flood-leading rainfall forecast (considering 3 factors)preceding rainfall : determines to some extent the current local soil water content, among other hydrological conditions The effective preceding rainfall is defined as: Pan = (Pan-1 +γPan-2) ×γ, where Pan is the effective preceding rainfall for day n counting from the first day of the flood season, Pan-1 is the same quantity for a day before, and γ= 0.85 is an empirical coefficient based on users' experience in Linyi. The effective preceding rainfall is then iteratively estimated as Pan = (Pan-1 + Pan-2×0.85)×0.85. water levels: In general, the flood-leading risk increases as the water level rises. stream flow: In general, the flood-leading risk increases as the water flow increases.

10. Main research flow Reliable suggest feedback to end-user Identify risk water level Risk assessment Exclusion of low-risk cases Precipitation Probabilistic forecast TIGGE build a regression model Using 3 factors Dynamic flood-leading rainfall threshold

11. Risk Identification The statistic relationship of Flood risk and 3 influence factors is also the Flood Limiting Water Level for Wangjiaba sluice Risk Target Water level, preceding rain, flood discharge and average rainfall, under a certain risk condition (a certain percentile in statistical sense)

12. Flood Limiting water level As the possibility of flood risk increasing, all three influence factors increase correspondingly.

13. Preceding rain has a significant import impact on flood risk in Wangjiaba sub-basin In 76 cases (water level exceeded Flood Limiting water level in 1 Jun.-31 Sep. 2003-2010), minimum of preceding rain is 49.1mm. )水位超过汛限共76个案例，其中区域的前期影响雨量超过60mm的72个案例，占94%。 Water Level Preceding Rain 49.1mm All cases that water level exceeded Flood Limiting water level in 1 Jun.-31 Sep. 2003-2010

14. D1 Preceding rain<49.1mm D3 Preceding rain≥49.1mm Water level>=24.5 FLRT=50mm/d FLRT (Flood leading rain threshold) Identify Dynamic Forecasting Target（Flood Leading Rain Threshold） Water level gap in 24hrs hadn’t exceeded 3m, so 24.5m had no chance to increase to 27.5m (FLW) in target region Excluding low-risk cases for users, according to historical statistic results D2 Water level<24.5m ??? we need to solve

15. FLRT dynamic forecasting target —— based on regression model • Goal：to quantify rainfall, which lead to water level increase to Flood Limiting Water level (27.5m) from the nth day to the n+1th day under a certain risk condition ( in a certain preceding rain, discharge and rainfall), and to build a regression model based on the historical cases (958 cases). And this rainfall is the dynamic flood-leading forecast target. Flood Limiting Water level (27.5m) FLRT——Dynamic forecasting target How much rainfall will lead the water level up to Flood limiting water level on the n+1th day under current risk conditions (preceding rain, discharge and rainfall)? Water Level on the nth day

16. FLRT dynamic forecasting target —— based on regression model • Hence, the formula is 27.5—Wn=δ+αFLRTn+1+βQn+γPRn • In which，27.5 is the supposed water level on the n+1th day, Wn is the known water level on the nth day; • δ is a constant, equal to -0.01; • FLRTn+1 is the FLRT on the n+1th day, its coefficient α=-0.387; • Qn is flood discharge on the nth day, its coefficient β=1.486； • PRn is the preceding rain on the nth day, PRn =（PRn-1+PRn-2×γ）×γ， γ=0.85, its coefficient γ=0.713;

17. FLRT Regression result Regression result Water level gap Water level gap to 27.5m FLRT 1 Jun.-31 Sep. 2003-2010

18. What TIGGE could provide?

19. TIGGE 114~121°E，32~37°N, a 3°×3° grid-box Resolution 0.5°×0.5°

20. TIGGE Bias ---percentile distribution of the all TIGGE forecasts and observations If TIGGE forecast are accurate, the distribution of TIGGE forecasts and OB are almost the same. But there exists systematic forecast bias in all ensemble system, especially for more than 14.6mm. For this systematic bias, How to calibrate the bias ? 14.6mm

21. Distribution Calibration Method • When samples size t was sufficiently large, precipitation observations on user-end could form a distribution Ot, ,correspondingly precipitation forecasts could also form a forecasts distribution Ft. . Because of systematic forecast bias, on the same x percentile, forecast Pf was different from observation Pob, that is Ft(x)≠ Ot(x). • If x<δ%， Pf > Pob , if x>δ%， Pf < Pob；and if x=δ%， Pf = Pob • theoretically, precipitation observations (Ot) and precipitation forecasts (Ft ) were identically distributed, Ft = Ot（Gneiting et al., 2007）. That is, in the same x percentile, forecast Pf and observation Pob should be the same. • Therefore, supposing (Ot) and precipitation forecasts (Ft ) were identically distributed, let Ft(x)= Ot(x) in the same x percentile to calibrate the forecast on user-end.

22. ETS verification results After calibration, forecasts improved. Perfect score is 1; and 0 means no skill.

23. BIAS Score After calibration, all ensemble forecasts improved. Perfect score is 1

24. Brier Score 0 is perfect score, and all ensemble forecasts improved after calibration

25. User-oriented Interactive Forecasting SystemPreliminary results

26. Dynamic Forecast Target——FLRT FLRT in Regression method FLRT in Hydrological model method The gap of water level to 27.5m 1Jun.-31 Sep. 2008 FLRT in Regression method FLRT in Regression method FLRT in Hydrological model method FLRT in Hydrological model method The gap of water level the dynamic FLRT reflect a change of flood-risk on user-end, but it ignored the low-risk cases, which is the different from the hydrological model. And it not only shows users to prevent high-flood-risk cases, but provides a forecast target for forecast system (TIGGE).

27. FLRT in Regression method FLRT in Hydrological model method TIGGE ensemble mean FLRT v.s. TIGGE grand ensemble mean Although, there are several heavy rainfall events in 1Jun.-31 Sep. 2008, not every heavy rain could lead to a flood-risk. TIGGE ensemble mean could catch some heavy rainfall events but not flood-leading events.

28. Flood Leading rain risk probabilistic forecast TIGGE Grand Ensemble（162 members） the predicted probability of occurrence of the FLR events in Wangjiaba sub-basin, based on TIGGE grand ensemble forecast and the dynamic FLRT with the user-end information.

29. Conclusion Forecasting flood-leading rainfall at a specific user-scale is feasible with TIGGE data, as long as the ensemble products are well analyzed according to user-end information.

30. Thank you