IMPROVEMENT OF QUANTITATIVEPRECIPITATION FORECAST IN HUAIHE BASIN
BASED ON DOWNSCALING BY WRF MODEL
Hao Ying*(MEE 13625)

Supervisors: Dr. Tomoki Ushiyama**
Dr. Takahiro Sayama,
Prof. Shinji Egashira
ABSTRACTIn this study, an attempt was made to improve QPF (Quantitative Precipitation Forecast) skill in
Huaihe Basin based on Numerical Weather Predictionto increase the accuracy and resolution of flood
forecasting as well as prolong the lead time for flood fighting preparedness. In order to achieve thetarget, JMA (Japan Meteorological Agency) and ECMWF (European Centre for Medium-Range
Weather Forecast)data were utilized.The Weather Research and Forecasting (WRF) model wasemployed for dynamical downscaling. Downscaling corrected the locations of the misplaced ECMWF
forecasts and the underestimated rainfall amount derived from JMA. Downscaled forecasts predicted
more accurate amount of rainfall in the right location 3 days ahead of the rainfall occurrence.
Meanwhile, the resolution of precipitation forecast became 5 times and 19 times higher in the outerand inner frame respectively. Finally, flood peak can be predicted 5 days in advance.
Keywords: Quantitative, Precipitation, Forecast, downscaling, parameterization
INTRODUCTION
HuaiheRiver which is located in the eastern part of China (112°E -121°E, 31°N -36°N) is considered
as one of the seven largest rivers in China with a catchment area of approximately 270,000 km2(Figure 1).This basin suffers from flood frequently. As a result,the improvement of flood forecast skill
is urgently demanded. Numerical Weather Prediction (NWP) as the most essential method has been astrong support for the predicted precipitation forecast period of hydrological model. However, theNWP has low resolution and doesn’t focus on the meso-scale weather system. As a result,downscaling is carried out with the help of WRF model to produce more accurate QPF with longer
lead time and higher resolution. This research focus on a heavy rainfall case which occurred on July
8th during flood 2007. The heavy rainstorm (daily precipitation ≥100 mm) appeared in the 26 stations
along Huaihe River, among them Anhui Yingshang County and Fengtai County reach 221.5 mm and219.4 mm respectively. Due to this rainfall process, the discharge of Wangjiaba climbed to the peakvalue (4600 m3/s) and water level reached 29.59 m, exceeding highest safety stage on July 11th.
METHODOLOGYFirst of all, NWP data (JMA, ECMWF) were used as input to WRF model for downscaling. TheWeather Research and Forecasting (WRF) Model is a next-generation meso-scale numerical weather
prediction system designed to serve both atmospheric research and operational forecasting needs.
*Senior Engineer, Anhui Meteorological Observatory**Dr., Researcher, International Centre for Water Hazard and Risk Management1

Figure 1 Geographic location of HRB

Figure 2 Accumulated precipitation
distributionon July 8th

The design of WRF model about parameters and domain are shown in Table 1. During downscaling,
11 combinations of physical parameterization were selected for inter-comparison to customize WRF
model.The 11 sets include 2 land-surface schemes(thermal diffusion scheme, unified Noah scheme), 4
cumulus convection parameterization schemes(Kain-Fritsch scheme,Betts-Miller-Janjic scheme, GrellDevenyi ensemble scheme, New Grell 3D scheme) and 7 microphysical schemes(Kessler scheme, Linet al., scheme,WSM 3-class simple ice scheme, WSM 5-class scheme, Ferrier microphysics, WSM 6class graupel scheme, Thompson graupel scheme). After forecast skill of each set of physicalparameterization schemes were evaluated by objective and quantified metrics, such as threat score,regional average precipitation, and absolute error, the optimal scheme was selected. Next, it wasapplied to ECMWF and JMA forecast with different initial time and compared with the originalversion to check the improvement of predictability. Lastly, the application of downscaling based on
the parameterization were evaluated and the importance of application for flood mitigation wasanalyzed.
Table 1The overview of the WRF model configuration used in the present study.
Model configuration
Horizontal grid spacing
Number of horizontal grid points
Centre of domain
Horizontal grid system
Integration time step
Number of vertical levels
Top of model
Initial conditions
Lateral boundary conditions
Shortwave radiation
Longwave radiation
Surface layer
Planetary boundary layer

Model specifications
Outer frame
Inner frame
20Km
5Km
80*70
157*11733.206°N, 116.104°E
Arakawa C-grid
60s
2850hPa
ECMWF
ECMWF
Dudhia (1989)
RRTM
Monin-Obukhov scheme
Mellor-Yamada-Janjic (Eta) TKE scheme

DATAThe THORPEX Interactive Grand Global Ensemble (TIGGE) provides operational global
ensembleforecast data quasi-operationally (2 days delay). ECMWF and JMA precipitation forecast
data which have the horizontal resolution of 0.5*0.5 spacing grid were downloaded from TIGGE
2portal. The longest forecast time of ECMWF is 360 hours (15 days) with 6 hours interval, and JMA is
264 hours (9 days) with the same time interval. 9 parameters were selected for the downscaling in thesurface layer. In the pressure level, 5 meteorological elements (geopotential height, specific humidity,temperature, U velocity, V velocity) in 9 levels (1000 hPa, 925 hPa, 850 hPa, 700 hPa, 500 hPa, 300hPa, 250 hPa, 200 hPa, 50 hPa) were collected. All of the data are written in Grib2 format.The Grid
Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access,manipulation, and visualization of earth science data (from GrADS homepage)
RESULTS AND DISCUSSION
1. Customization Result by Inter-comparison of Physical Parameterization Schemes
Customization of WRF model was done by following the procedures: First of all, I used “Kain-Fritschscheme” as cumulus convection scheme and “WSM 5-class” as microphysical scheme. Next,
comparison was made between "thermal diffusion scheme" and "Noah schemes" to select the optimalland-surface scheme. After two runs, I used the selected land-surface scheme and fixed microphysicalscheme (WSM 5-class) to search the suitable cumulus convection scheme among "Kain-Fritschscheme" "Betts-Miller-Janjic scheme", "Grell-Devenyi ensemble scheme" and "New Grell 3Dscheme". Afterward, land-surface scheme and cumulus convection scheme have been selected,comparisons among"Kessler", "Lin", "WSM 3-class", "WSM 5-class", "Ferrier", "WSM 6-class" and"Thompson" was conducted to fulfill purpose of selection. At last, an optimal combination ofparameterization schemesthat is the configuration of Noah scheme, WSM 6-class scheme and BettsMiller-Janjic scheme (set 10)was selected,.
Quantification was conducted with the help of different statistical metrics including threat score(Figure 3) and regional average rainfall (Figure 4).In outer frame, comparing the best scheme with theWRF default schemes (set 1), the threat score of light rain increased from 0.23 to 0.55 while the TS ofrainstorm increased from 0.04 to 0.13. On the other hand, the best scheme decreased the absolute biasof regional average rainfallfrom -21 mm to -3 mm. Thus, parameterization played an important role on
the improvement of precipitation forecast skill.

Figure 3The threat scores of different
parameterization schemes in outer frame

Figure 4 The regional average rainfall
generated from different parameterization
scheme and original JMA forecast32. Improvement of QPF by Downscaling ECMWF Forecasts
Downscaling was performed using the optimal parameterization scheme with different lead time. The
simulated precipitation predictions was initialized from July 1 to July 7, 2007. Through the analysis, it
was found out that if the downscaling wasn’t carried out, no heavy rainfall could have been predictedin Huaihe Basin. Thus, the precipitation forecast in ECMWF original forecasts can’t be helpful forflood control. After the downscaling, a heavy rainfall belt is predicted 6 days earlier and the maximum
precipitation is over 200 mm which matched the actual condition. Even the location of rain band isnorth, when the lead time becomes shorter, the predicted precipitation area become closer to observed
precipitation. After downscaling, the QPF captured the horizontal scale and intensity of weather
system to some extent which can give a good clue to the forecasters.
3. Improvement of QPF by Downscaling JMA Forecasts
A subjective comparison was done between the QPF from original JMA forecasts and its
downscaling.Before downscaling, a rain belt was predicted in the correct location by JMA. However,the intensity was greatly underestimated. The maximum rainfall was predicted to be only 80 mm in theforecast by JMA initialized on July 5th(Figure 5). It is difficult to be taken note by flood fighters anddecision makers. According to this QPF, the early warning alarm may not have been issued.
However,the downscaling forecast with the initial time on July 5th(Figure 6) predicted the location ofheavy rain band and rainfall center as well as rainfall intensity much better than the previous forecast.
More details about heavy rainfall centers are shown in Figure 6. As we knew, there are five storm
centers in observed precipitation distribution. Four of them which lie in (112.5°E,31.2°N),(114.6°E,32.3°N),(116.5°E,32.5°N),(119°E,32.5°N) respectively are predicted in a proper location
close to observed rainfall centers. In addition, the mainstream of Huaihe River is predicted to receive
more than 60 mm precipitation which is similar to the rain gauge data. In the aspect of maximum
rainfall, it is predicted to be 160 mm with 20 km resolution and 200 mm with 5 km resolution. The
observation shows that 12 stations have accumulated precipitation of over 150 mm. The daily
precipitation in Fengtai county and Yingshang county in Anhui Province reach 219.4 mm and 221.5mm respectively. So the intensity also agreed with the observed rainfall very well.

Figure 5 Prediction of accumulated rainfall
by JMA original dataset initialized onJuly 5th

Figure 6 Downscaled precipitation
forecast of JMA initialized on July 5th4

4. Evaluation of Downscaled Forecasts
When we evaluate the simulation result of WRF model with the initial time on July 5th, several
statistical metrics are commonly used [e.g., Yu et al., 2006; Han et al., 2008], such as correlation
coefficient (corr) and root mean-square error (RMSE). All the formulas of these metrics can be foundin Seigneur et al. [2000].
① Correlation Coefficient
There is a higher correlation between forecasting and observation with the correlation coefficient of0.664. According to correlation coefficient significant checking table, under α=0.01 significance level,
the correlation coefficientpassed the significance test. It is concluded that there is a significant
correlation between downscaled forecast and observed rainfall.In addition, we selected the correlation
coefficient of precipitation forecast whose lead time is 24h from JMA and CMA for comparison. The
correlation coefficients are 0.4-0.6 in JMA and 0.2-0.5 in CMA. So the correlation coefficient (0.664)
of QPF downscaled by JMA is higher than both JMA and CMA. The improvement was made bydownscaling.
②Threat Score
On July 5th, very high scores of light rain (0.84) and rainstorm (0.67) are displayed. Comparisons weredone between the best annual threat scores of precipitation forecasting in Anhui MeteorologicalObservatory and downscaled JMA forecasts with similar lead time. In 2011, the threat score of lightrain (0-9.9 mm), moderate rain (10.0-24.9 mm), heavy rain (25-49.9 mm) and rainstorm (>50 mm) inAMO with the lead time of 3 days are 0.39, 0.17, 0.10 and 0.09 respectively. On the other hand,downscaled forecasts with the same lead time which have the TS of 0.84, 0.17, 0.20 and 0.67
sequentially show significant improvement of predictability.
5. The Application of improved QPF in Flood Disaster Mitigation
Hydrograph of Huaibin hydrologic station (Figure 7) illustrates that about 2 days after the heavy
rainfall event on July 8thwhich
was predicted 3 days before,
the flood peak appeared.Thus,

2d

the first floodgate can bepredicted 5 days in advance.
On the other hand, another3d

flood peak occurred 3 days

5xc

11daysafter the third rainfall process.
The third rainfall event waspredicted 8 days before.

Forecast time

1stFloodpeak

2ndFlood
peak

Figure 7 The observed discharge and predicted rainfall initialized on July 5th5

Therefore, the second flood peak can be forecasted 11 days in advance.That means, after the decision
makers receive the forecast information produced by downscaled JMA forecasts on July 5th, they will
have more than 5 days to take action in advance to minimize the losses caused by the coming flood. Sothe QPF products which obtained from our study are quite useful for flood disaster mitigation.
CONCLUSIONS(1)Before downscaling, inter-comparison of physical parameterization schemes is proved to benecessary to customize WRF model. The suitable cumulus convection scheme, land-surface scheme
and microphysical scheme can simulate precipitation system better. It was proven that the best scheme
has more predictability than the default scheme.
(2).Resolution is increased by downscaling. After downscaling, the resolution of QPF become 5 times
higher in the outer frame and 19 times higher in the inner frame.
(3)Through subjective evaluation, improvements are made by downscaling both in the aspect ofintensity and vastness. Both downscaled JMA outputs and ECMWF outputs indicate a large scale
heavy rainfall event during July 8thand July 9th.
(4)Through objective evaluation, it was proven that downscaling can obtain QPF with more accuracy.
The performances of downscaled JMA forecast were evaluated by threat score and correlation
coefficient. The results show that after downscaling, the threat scores of heavy rainfall is 58% higher
than the prediction level of AMO. The correlation coefficient of downscaled version is higher than thatof JMA and CMA and has significant relation with the observed rainfall.
(5)The improved QPF with longer lead time creates more time for flood fighting preparation. Decision
makers have more than 5 days in advance to take action before flood peak occur if they refer to theQPF products obtained from our study.As a result it plays an important role on flood mitigation.
ACKNOWLEDGEMENT

I would like to thank JICA,ICHARM and GRIPS in giving me the wonderful opportunity to study
the advanced technology of flood disaster mitigation in Japan.I would also like to take thisopportunity to express my deepest gratitude and special thanks to Dr. Tomoki Ushiyama,Dr.Takahiro Sayama and Professor Shinji Egashira for supervising me and giving me very usefuladvices and suggestions during my individual study.
REFERENCES
Han, Z. W., H. Ueda, and J. L. An (2008), Evaluation and intercomparison of meteorologicalpredictions by five MM5-PBL parameterizations in combination with three land-surface models,Atmos. Environ., 42(2), 233–249,doi:10.1016/j.atmosenv.2007.09.053.
Seigneur, C., et al. (2000), Guidance for the performance evaluation of threedimensional air quality
modeling systems for particulate matter and visibility, J. Air Waste Manage., 50(4), 588–599.
Yu, S. C., B. Eder, R. Dennis, S. H. Chu, and S. E. Schwartz (2006), New unbiased symmetric metrics
for evaluation of air quality models, Atmos Sci Lett, 7(1), 26–34, doi:10.1002/Asl.125.
6