1. Process of User-oriented interactive flooding-leading rain forecast system Chen Jing 1 Zhongwei Yan 2 Jiarui Han 3 Jiao Meiyan 4 1. Numerical Weather Prediction Center, CMA 2. 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. User’s needs & decision-making information Conceptual User-oriented Interactive Forecast System Meteorological forecast system What to be user-oriented? Key variable Initial condition with sensitive perturbations Target/local observations Key decisions Downscaling Experience-calibration User-based assessment Climate background

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 threshold in Wangjiaba sub-basin

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

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

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. 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: Pa n = (Pa n-1 +γPa n-2 ) ×γ, where Pa n is the effective preceding rainfall for day n counting from the first day of the flood season, Pa n- 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 Pa n = (Pa n-1 + Pa n-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. Analyzing user-end flood risk, to figure out dynamic forecasting target for forecasting system

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

11. risk Flood Discharge (m 3 /s) Water Level (m) Preceding Rain (mm) Average rainfall (mm) 75 percentile708.7524.30541.6165.302925 85 percentile1094.525.984556.4337511.58525 90 percentile152026.90967.67617.0295 92 percentile1674.427.443273.45921.7 95 percentile208027.987585.3467528.7455 97 percentile2392.928.341694.0347538.5765 99 percentile275028.8643124.297558.14125 Water level, preceding rain, flood discharge and average rainfall, under a certain risk condition (a certain percentile in statistical sense) Risk Identification The statistic relationship of Flood risk and 3 influence factors Risk Target is also the Flood Limiting Water Level for Wangjiaba sluice

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

13. 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% 。 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% 。 Preceding rain has a significant import impact on flood risk in Wangjiaba sub-basin All cases that water level exceeded Flood Limiting water level in 1 Jun.-31 Sep. 2003-2010 49.1mm Water Level Preceding Rain

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

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+αFLRT n+1 +βQ n +γPR n In which ， 27.5 is the supposed water level on the n+1th day, W n is the known water level on the nth day; δ is a constant, equal to -0.01; FLRT n+1 is the FLRT on the n+1th day, its coefficient α=-0.387; Q n is flood discharge on the nth day, its coefficient β=1.486 ； PR n is the preceding rain on the nth day, PR n = （ PR n-1 +PR n-2 ×γ ） ×γ ， γ=0.85, its coefficient γ=0.713;

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

18. What TIGGE could provide?

19. TIGGE Center (model name) Forecast length membersUTC Period of forecasts used in the case CMA (babj) 10 days15121Jun.-31 Sep. 2007-2010 ECMWF (ecmf) 15 days51121Jun.-31 Sep. 2007-2010 JMA (rjtd) 9 days50121Jun.-31 Sep. 2007-2010 NCEP (kwbc) 16 days21121Jun.-31 Sep.2007-2010 UKMO (egrr) 15 days23121Jun.-31 Sep.2007-2010 Grand Ensemble -160121Jun.-31 Sep.2007-2010 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 O t,,correspondingly precipitation forecasts could also form a forecasts distribution F t.. Because of systematic forecast bias, on the same x percentile, forecast P f was different from observation P ob, that is F t (x)≠ O t (x).  If x P ob, if x>δ% ， P f < P ob ； and if x=δ% ， P f = P ob  theoretically, precipitation observations (O t ) and precipitation forecasts (F t ) were identically distributed, F t = O t （ Gneiting et al., 2007 ）. That is, in the same x percentile, forecast P f and observation P ob should be the same.  Therefore, supposing (O t ) and precipitation forecasts (F t ) were identically distributed, let F t (x)= O t (x) in the same x percentile to calibrate the forecast on user-end.

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

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 System Preliminary results

26. Dynamic Forecast Target——FLRT FLRT in Regression method The gap of water level FLRT in Hydrological model method FLRT in Regression method FLRT in Hydrological model method 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). The gap of water level to 27.5m FLRT in Regression method FLRT in Hydrological model method 1Jun.-31 Sep. 2008

27. FLRT v.s. TIGGE grand ensemble mean FLRT in Regression method FLRT in Hydrological model method TIGGE 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