1. Performance of Nowcast System BJANC during Beijing 2008 Olympics
Ming-Xuan Chen
Ladies and gentlemen, my name is Chen Ming-Xuan. I come from Institute of Urban Meteorology of CMA. My talk is about performance of nowcast system named BJANC during the Beijing 2008 Olympics.
1 July 2018

2. Outline Brief introduction of BJANC
Performance and application of BJANC during the 2008 Olympics
First, I will give a brief introduction of the system BJANC.

3. BJANC is a fruit of an international joint project between BMB/IUM and NCAR/RAL from 2004 to 2008
BJANC is formed by cooperative research and further development of NCAR’s nowcasting technique at BMB/IUM
The skill of BJANC is focused on convective storm analysis and nowcasting in support of Beijing 2008 Olympics (B08)
BJANC is one of nowcasting systems of WMO’s forecast demonstration project during 2008 Olympics (B08FDP )
BJANC is a fruit of an international joint project between Beijing Meteorological Bureau (BMB), IUM and Research and Application Laboratory of National Center of Atmospheric Research from 2004 to 2008;
BJANC is formed by cooperative research and further development of NCAR’s nowcasting technique at BMB/IUM;
The skill of BJANC is focused on convective storm analysis and nowcasting in support of Beijing 2008 Olympics (B08);
BJANC is one of nowcasting systems of WMO’s forecast demonstration project during 2008 Olympics (B08FDP ). About B08FDP, another my colleague will give a detailed presentation tomorrow.
This is a work flow chart of the system research, testing and application from 2004 till In 2004, we introduce NCAR’s nowcasting technique; From 2004 till 2006, further development and improvement of key algorithms and modules were performed; From 2006 to 2007, integration and setup of BJANC were finished, and trial during flood season and application training to forecasters were performed; In 2008, supported the Olympics and took part in the formal B08FDP demonstration.
Trial during flood season
Performance and service for B08
Further development and improvement of key algorithms and modules
Drilling for B08
Introduction of NCAR’s ANC into BMB
Integration and setup of BJANC
Application training
B08FDP trial
Formal B08FDP demonstration
2004
2004~2006
2006~2007
2008

4. Operational flow chart of BJANC
CINRAD(4), Satellite(2), AWS(106), Radiosonde(5), Meso-NWP(BJ-RUC)
Data ingesting
Removal of radar clutter echo (AP, NP and brightband)
Data QC
QPE
3-D radar mosaic +
Local Z-R
VIL
Algorithms
Probability of hail (POH)
Low-level thermodynamical fields
Storm tracking and fx
Human entry
Integration
Forecasts of storm evolution
This is operational flow chart of the system. The first is data ingesting from radar, satellite, AWS, radiosonde and meso-NWP. The second is radar data quality control including removal of AP, NP and brightband echo. Then, radar reflectivity mosaic is produced. And with local Z-R relationship, quantitative precipitation estimates (QPE) can be produced. Several algorithms of data analysis, storm analysis and tracking, echo extrapolation can form many forecast factors for storm nowcast and produce some products, for example, probability of hail, storm tracking and forecast, low-level thermodynamical fields. Then, all forecast factors are integrated by fuzzy logic and form final forecast products, including forecasts of storm evolution and reflectivity, and with local Z-R relationship, quantitative precipitation forecast (QPF) can be formed. Main products are put into a interactive storm nowcast platform named VIPS for forecaster use. We can also perform human entry to modify the system auto nowcast.
Products +
Reflectivity fx
Local Z-R
QPF
VIPS
Verification and validation

5. Outline Brief introduction of BJANC
Performance and application of BJANC during the 2008 Olympics
The second is performance and application of the system during the Games.

6. List of Ingested Real-time Data during 2008 Summer
Data type
Data source
Data fields
CINRAD radar data (synchronized)
BJRS, TJRS, SJZRS, ZBRC, 6-min interval
Reflectivity, Radial velocity
AWS data
106 stations from Beijing, Tianjin and Hebei areas, 5-min interval
Wind, Temp, Dewpoint, Pressure
Satellite data
FY2C & FY2D, 15-min interval
Visible, IR, Cloud type
Radiosonde data
5 stations (Chifeng, Zhangjiakou, Taiyuan, Xingtai and Beijing), 6-h interval
U, V, W, Pressure, Temp, RH, Height
Meso-NWP outputs
BMB’s operational BJ-RUC model, 3-h updating and 1-h interval
Forecast fields, model soundings
This is the list of ingested realtime data during 2008 summer-time, including synchronized data from 4 radars with 6-min interval, surface observations from 106 AWS with 5-min interval, cloud data from two geostationary meteorological satellites with 15-min interval, high-level data from 5 rawinsondes with 6-h interval, and meso-NWP with 3-h update cycling and 1-h interval.

7. Four CINRAD radars for B08
This is reflectivity image from the four radars.
Four CINRAD radars for B08

8. List of BJANC Real-time Products during 2008 Summer
All results are REALTIME products from BJANC during this summer 
List of BJANC Real-time Products during 2008 Summer
Name
Description
Available for B08FDP
Available for Operation
Reflectivity mosaic
3-D and composite, 6-min interval, from 4 radars, 1-km resolution
Yes
Storm tracking and extrapolation
Detection, tracking and 30-, 60- and 120-min extrapolation of 35dBZ reflectivity from TITAN, 6-min interval
TREC wind
TREC wind vectors from radar and satellite data, 1-km resolution No
Thermodynamical fields at lower layer
Retrieved horizontal wind, vertical velocity, convergence/divergence, perturbation temperature and its gradient, relative humidity, QR at lower layer from VDRAS, 12-min interval, 3-km resolution and 375-m vertical interval
Forecasts of storm evolution trend
30- and 60-min forecasts of storm evolution trend (growth, steady, decay, initiation), 6-min interval, 1-km resolution
Forecasts of storm reflectivity
30- and 60-min forecasts of storm reflectivity, 6-min interval, 1-km resolution
QPE
6-min, 30-min, 60-min, 3-h and 6-h quantitative precipitation estimates (QPE) based on merged reflectivity and a local Z-R relation, 6-min interval, 1-km resolution
QPF
30- and 60-min quantitative precipitation forecast (QPF) based on storm reflectivity forecasts and a local Z-R relation, 6-min interval, 1-km resolution
Boundary location and its extrapolation
location and its extrapolation of entered boundary layer convergence line
Probability of hail
Probability of hail based on storm analysis, 6-min interval
This is list of the system realtime products during 2008 summer, including reflectivity mosaic, storm tracking and extrapolation, TREC wind, thermodynamical fields at lower layer, forecast of storm evolution trend and reflectivity, QPE and QPF, boundary location and its extrapolation, and probability of hail. Most of these products have 1-km resolution and 6-min update interval. All of products are available for operation and most for B08FDP.

9. Real-time products are displayed on BMB’s operational website
Operational nowcasting platform at BMB’s WFO
This is operational nowcasting platform at BMB’s weather forecast office, and BJANC display is in both Chinese and English.
Also real-time products is displayed on BMB’s operational website for much more forecasters and BMB’s other operational staff.
This is BJANC forecast domain 500km by 500km. Beijing, Tianjin and most of Hebei province are covered.
BJANC display in both Chinese and English
BJANC forecast domain (500km by 500km)

10. Products and verification results were displayed on B08FDP website in real-time during the Olympics
Also products and verification results from BJANC were displayed on B08FDP website in real-time during the Olympics with from other B08FDP systems. This is a demo for 1-h QPF display.

11. Cross-section along arbitrary direction
Reflectivity mosaic from the four radars (10 August 2008) 4 3
Mosaic of composite reflectivity 2 1
Mosaic of 4.5 km reflectivity
This is an example of reflectivity mosaic and cross section along arbitrary direction from the system. The mosaic include composite reflectivity mosaic and 3 dimensional mosaic. Forecasters can use the products to estimate storm structure and watch storm evolution in much bigger domain than the only one radar range.
Cross-section along arbitrary direction

12. Storm tracking and extrapolation
(1159 UTC 8 August 2008) – Opening Day of the Games
120-min extrapolation
30-min extrapolation
Storm detection
60-min extrapolation
Storm characteristics:
Speed (km/hr)
Evolution (+/-)
Top (km)
This is an example of storm tracking and extrapolation for opening day of the Olympics at just start time of the opening ceremony, including storm detection, identification of storm characteristics of speed, evolution and echo top, and 30-min, 60-min and 2-h extrapolation. We can see the storm to south-west of Beijing area has threat probability to National Stadium. Based on the storm tracking product, weather modification were performed to the storm cell for accelerate its decay.
National Stadium

13. Echo tracking based on TREC
(1059UTC 24 August 2008) – Closing day of the Games
This is an example echo tracking based on TREC technique for closing day of the Olympics at just ahead of 1 hour of the closing ceremony. According to this product, forecaster only need to pay attention to the storm in the north-east of Beijing area. Other storms will not affect National Stadium.
National Stadium

14. 1-h forecasts from BJ-ANC (14 August 2008)
growth
steady
decay
initiation
Radar mosaic at 0423 UTC
1-h forecast of storm evolution at 0423 UTC
This is an example of 1-h forecast of storm evolution and reflectivity from the system for 14 August storm event. This is radar mosaic at 0423 UTC time and this is 1-h forecast of storm evolution, and this is reflectivity forecast at the same time. Thin line is boundary layer convergence line location entered by human and its extrapolation to indicate instability of low layer and enhance forecasts of storm evolution and intensity. A successful 1-h forecast of storm initiation is shown and validated by radar observations 1-h later.
Radar mosaic at 0523 UTC
1-h forecast of storm reflectivity at 0423 UTC
Thin line is boundary location and its extrapolation

15. Thermodynamical retrieval fields at lower layer (0429UTC 14 August 2008)
Vertical velocity and wind vector at m
Perturbation temp and wind vector at m level
1-h later
Updraft
Cool pool
Gust front
This is an example of thermodynamical retrieval fields at lower layer for the same 14 August event based on radar data assimilation and analysis, including vertical velocity, wind vector, perturbation temperature, gradient of perturbation temperature, convergence and divergence. And black thick curves indicate storm echo more than 35 dBZ. We can see there are strong updraft, cool pool and convergence at lower layer along with storm development. And gradient of perturbation temperature indicates strong gust front. All of these thermodynamical fields indicate the new storm will initiate. 1-h later, radar observations make sure this prediction.
Gradient of perturbation temp and wind vector at m
Convergence/divergence and wind vector at m level
Convergence
Black thick curves indicate storm echo more than 35 dBZ

16. QPE and QPF (10 August 2008) 30-min QPE: 0930-1000 UTC
30-min QPF: UTC
This is a comparison of QPE and QPF to the same 30-min and 60-min periods.
1-h QPE: UTC
1-h QPF: UTC

17. Forecasts from BJANC for 10 August 2008
(Strongest convective rainfall day during 2008 Olympics period)
Reflectivity mosaic (more than 35 dBZ) from 4 radar observations
1-h forecasts of thunderstorm reflectivity more than 35 dBZ
1-h quantitative precipitation forecasts (QPF)
This is an example of forecasts from the system for 10 August event. 10 August is the strongest convective rainfall day during 2008 Olympics period. And find the system has a good nowcasting performance to the event.
animation period：0200UTC~1130UTC
animation interval：30-min

18. Verification to 1-h forecast of storm reflectivity more than 35 dBZ with radar reflectivity mosaic
This is verification to 1-h forecast of storm reflectivity more than 35 dBZ with radar reflectivity mosaic from BJANC and persistence method. The verification domain is the above 500km by 500km with resolution 1km by 1km. The verified events include 42 convection cases during 2008 summer time. BJANC has much better skill than persistence to storm reflectivity nowcasting.
Verification domain: 500km by 500km
Verification resolution: 1km by 1km
Verification events: 42 convective events during 2008 summer time

19. 106 AWS stations for verification
Verification to 1-h QPF from BJANC with 106 AWS observations using B08FDP’s real-time forecast verification system
Threshold (mm)
CSI (TS)
Bias
0.2
0.4
0.6
1.0
0.3
0.5
2.0
5.0
0.1
106 AWS stations for verification
Verification domain: 500km by 500km
Observation: Precipitation from 106 AWS
Forecast: 1-h QPF from BJ-ANC
Verification period: 0000UTC 1 August 2008 ~ 0000 UTC 21 September 2008
This is verification results to 1-h QPF from BJANC with 106 AWS observations using B08FDP’s real-time forecast verification system. The verification domain is 500km by 500km and verification periodis from 0000UTC 1 August 2008 till 0000 UTC 21 September 2008.
The verification results include Quantile-Quantile (Q-Q) plot and CSI.
Q-Q plot to compare the distribution of forecast values against the distribution of observed values to indicate conditional bias in the precipitation, and CSI (TS) and Bias is how well the occurrence and frequency of “point-point” rain forecasts and observations exceeding a given threshold.
We can see BJANC has robust skill to precipitation from Q-Q plot and CSI. But there is a smaller precipitation forecast area than observations from Bias results.
（1）某个时次的QQ图：将某个时次所有站点上的数据按照从小到大的顺序排序；将该时次所有预报格点插值到站点上；把所有插值到站点上的预报按照从小到大排序；把排序号相等的预报和观测作为一个坐标点点在图上。对应于某个时次有多少个有效的观测站点（缺测的视为无效），则在QQ图上就该有多少个点。（2）某段时间的QQ图：将时段内所有时次、所有站点的观测从小到大排序；预报插值排序方法同上。
Quantile-Quantile (Q-Q) plot: to compare the distribution of forecast values against the distribution of observed values to indicate conditional bias in the precipitation
CSI (TS) and Bias: how well the occurrence and frequency of “point-point” rain forecasts and observations exceeding a given threshold
Q-Q Plot

20. Forecasted precipitation (mm/h) Observed precipitation (mm/h)
Q-Q plot verification to 1-h QPF from B08FDP systems with 106 AWS observations during 0000UTC 1 August 2008 ~ 0000 UTC 21 September 2008
Forecasted precipitation (mm/h)
This is the same Q-Q plot verification to 1-h QPF from not only BJANC but also other B08FDP systems with 106 AWS observations during the same period as the above. We can see BJANC has much better skill to convective precipitation than some other systems from other organizations in the world.
Observed precipitation (mm/h)

21. That's All
That’s all.
Thank you for your attention!
谢谢！