### Extended Ensemble Nowcasting Technique Using Recognition on

```Toward the Extended Ensemble
Nowcasting Technique Using Pattern
Trengshi Huang PhD.
Weather Forecast Center, Central Weather Bureau
Multi-stage Quantify Precipitation Forecast
Weekly (Qualitative)
GFS, Synaptic Analysis, statistics, analogy,
conceptual
data assimulatoin
Storm scale data
assimulation
Extrapolation
1-3 daily QPF (Quantitative)
Regional (Ensemble) Forecast System,Statistics,
anvanced Ensemble Forecast
0-6 hr (or 0-12 hr) QPF
LAPS/STMAS, ARPS, VDRAS, Cloud model
0-1 hr QPF
Pattern
Recognition ?
0 hr Nowcasting
lighting
2
Very Short Term Forecast/Nowcast
Monitor and Nowcasting Systems by Weather Satellite Center

(W.P. Huang, WSC)
Big data and Ensemble Forecast
 Big data is an all-encompassing term for any
collection of data sets so large and complex
that it becomes difficult to process using
 The challenges include analysis, capture,
curation, search, sharing, storage, transfer,
visualization, and privacy violations.
 如何在大量的系集預報資料中取得有用的

http://en.wikipedia.org/wiki/Big_data
Basic Ensemble QPF Products
 Most probable single solution (deterministic forecast ):
Ensemble mean, median, etc…
QPESUMS Analysis
 CWB WRF EPS (Ensemble Prediction System): 20 members,
5km (李志昕&洪景山，2011)
 Take the ensemble mean among the ensemble dimension
standard deviation, max., min., max. 10% mean, …
 Evaluation from mean and standard deviation
(丘台光、陳嘉榮、張保亮、林品芳，2007)
Mean
MAX.
STD
MIN
 The ensemble average "smears" the rain rates so that the
maximum rainfall is reduced and area of light rain is enhanced
 Probability Density Function (PDF) approach on QPF
 The same spatial shape as the ensemble mean
 The same PDF of the entire ensemble system (PM, Elbert 2001).
 Or Averaging the PDFs among the ensemble dimension (newPM, 葉等2014)
 Deal not on spatial distribution.
Mean
PM
newPM
QPESUMS Analysis
From PQPFx to QPFP-y%
Select a threshold, say 50% chance, in probability space to make QPF
exceeding 50% chance
POP=PQPF0.1
Assign hatched
area to 0.1 mm
PQPF130
Assign hatched
area to 130 mm
PQPF10
Assign hatched
area to 10 mm
PQPF200
Assign hatched
area to 200 mm
PQPF25
Assign hatched
area to 25 mm
PQPF350
Assign hatched
area to 350 mm
PQPF50
Assign hatched
area to 50 mm
PQPF500
Assign hatched
area to 500 mm
PQPF100
QPFP-50%
Assign hatched
area to 100 mm
QPF exceeding y% probability threshold: QPFP-y%
Deterministic but probabilistic inside
 Easy for Forecasters to make decision
 Easy to use since it is QPF

QPFP-5%
MAX
QPFP-10%
QPFP-20%
QPFP-30%
QPFP-40%
QPFP-50%
MEAN
QPFP-60%
QPFP-70%
QPFP-90%
QPFP-100%
MIN
Predictability Evolution with Forecast Hours
1.0
Skill Score
 High resolution NWPs &
Ensemble Prediction System
assimulation and forecast
NWPs
0 1
3
6
Fct. hours
Integration on the Extended Nowcasting Precipitation
 Combining the CWB WRF-EPS (Li et al, 2013) and QPESUMS observation to
develop the extended nowcasting on QPF
 Objective: to improve the 0-6 h
QPF
 Key factors：
 Pattern recognigition
skill
(Moment invarant, Chen et al.
2014)
et al, 2014; Yeh et al., 2014)
Integration on the Extended Nowcasting Precipitation

Originial
(extrapolation)

(ARMOR)
Combing the
observation.
WEPS
and
(NWP model)
Assimulation
…
1. Develop a pattern recognition to rank
the WEPS forecast during a time window.
1.Pattern Recognition of Observed CV and WEPS-Based
QPF (PROCWB-QPF)
Extended Nowcasting on QPF or QPN
(Chen et al., 2014)
Pattern Recognition (I)
 Moment invariants (Hu 1962)
OBS
WEPS

 1O 
 
  
 O 

 7 
 1i 
 
 
 i 

 7

DS i  AS i
S 
2
i
 介於0到1之間，愈接

(Chen et al., 2014)
Pattern Recognition (II)
 Piecewise recognition
S ni
S ni 1
S1i
S 2i
N
S
i
ave


S ni / N
n 1
(Chen et al., 2014)
Sampling, Recognizing, and Shifting
 Targeted time window (±6hr, 3hourly) based on
observation.
 22 members：WRFD, TWRF, WRF-EPS (20 members)
 4 Lag Runs
 5x22x4=440 samples
Fro ensemble member N (N=1, 2, …, 22)
forecast at target time (say 0 hr)
05/15 00Z
05/14 18Z

lag run
05/14 12Z
05/14 06Z
05/15 1200Z
-6
-3
6
9
12
15
18
21
24
18
21
24
27
30
24
27
30
33
36
‒6 h
0
3
6
1212h 15
18
+6 h
Shifting Forecast: Ranking 1 to 10 in 440 samples
Hindcast
05/15 1200Z
-3
0
3
6
9
12
Forecast
Rank
1-10
Hindcast
 Ensemble
Mean
among Ranks 1 to 10
 Ensemble
mean
smears
members’
extreme QPF values
 Ensemble QPFP-20%
 Why 20%?
 At least 2 of 10
members
 Overestimate
the
overall
QPF
sometimes
 PM, newPM?
To be continue…
-3
0
3
6
9
12
Forecast
2014 May & Jun Verification on 0-3h QPF
 May and June verification
on 0-3h QPF.
larger then 10% for
recognition or not.
TS
POD
ETS
FAR
134/488 Cases, 37000(1902)/370000 km2
Predictability Evolution with Forecast Hours
1.0
Skill Score
 High resolution NWPs &
Ensemble Prediction System
assimulation and forecast
 Pattern Recognition/ Single
Model Forecast/ Ensemble
Forecast
Ensemble Forecasting
 Fitting and Calibration??
Fitting?
NWPs
0 1
3
6
Ptn Rcg./EPS
Ptn Rcg./Single
Fct. hours
Fung-Wang (2014) at 09/21 0000 UTC
CWB Operational Typhoon QPF
 Analog Approach
 Historical Typhoon tracks and precipitation data base
 Climatology Approach
 Climatological Typhoon positions and precipitation rate
 Numerical Weather Prediction
 Global Forecast System: GFS, ECMWF, NCEP-GFS, JMA, UK
 Regional Forecast Model: WRF-D, TWRF, NFS …
 Storm Scale NWP: LAPES-WRF, STAMAS-WRF …
 Ensemble Based NWP Forecast
 CWB WRF Ensemble Forecast System: 20 members
 TTFRI Ensemble Forecast System: 16-20 members
 ETQPF (Ensemble Typhoon QPF): QPF from a selected track
 And more…
 Subjective Adjustment by Senior Forecasters
 Recent Error Checking and Verification
 Forecaster’s Experiment
Concluding Remark
 Ensemble QPF Approach:
 Deterministic Ensemble QPF:
 Basic: Mean, median, variance, …
 Advanced: PM, newPM, ETQPF, …
 Probability of QPF (PQPF)
 Probability
 Most models need not probability but QPF
 Probabilistic but deterministic QPF (QPFP)
 Easy to make decision for Forecasters
 Probability inside
 Pattern-Recognition/Analog QPF on Observed Radar CV & WEPS
for (Extending) Nowcasting
 Better Pattern-Recognition method or strategy
 Fitting forecast among Radar QPF and Nowcasting
 Calibration or modification technology
 Storm Scale/ Radar/ Cloud resolved data assimulation and
prediction
```