Reprint 879
Application of Satellite Rain Rate Estimates to the
Prediction of Tropical Cyclone Rainfall
S.T. Chan & M.Y. Chan
The 42th Session of ESCAP/WMO Typhoon Committee,
Singapore, 25-29 January 2010
Application of Satellite Rain Rate Estimates to the
Prediction of Tropical Cyclone Rainfall
Chan Sai-tick and Chan Man-yee
Hong Kong Observatory
Abstract
A major calamity brought by typhoon is the flooding due to heavy
rain. In the lecture, a new forecasting tool which combines subjective
tropical cyclone forecast tracks with satellite rain rate estimates to
generate point and areal rainfall predictions associated with tropical
cyclones will be introduced. Here, the rain rate estimates are extracted
from the QMORPH precipitation analyses supplied by the Climatic
Prediction Center of NOAA in near real time.
Performance of the technique based on selected cases of tropical
cyclones which affected Hong Kong in 2008 and 2009 and Hong Kong
Observatory’s subjective forecast tracks will be shown. The potential
application of EPS TC track information in the technique to generate both
deterministic and probabilistic predictions will also be discussed.
1
1.
Introduction
Apart from bringing high winds, tropical cyclones (TCs) also cause
torrential rain, leading to calamities like floods and landslides. An
accurate analysis and prediction of precipitation association with TC is
essential to the timely issuance of warnings. Due to the scarcity of direct
observations of precipitation over the ocean, the remote sensing
equipment is indispensable to forecasters in estimating the amount of
rainfall accompanying a TC. Among the remote sensing observations,
radar reflectivity demonstrates good correlation with the actual rain rates
and its spatial resolution is also high. Yet the radars are only useful when
the TCs are close enough to the radar sites. For satellites, the passive
microwave channel signals detected by the polar-orbiting satellites could
generate high quality and high resolution rain rate estimates, though the
update frequency of once to twice a day is still inadequate. The infra-red
(IR) observations from the geostationary satellites are updated much more
frequently (e.g., twice an hour for MTSAT-1R), but the cloud top
temperatures deduced from IR channels are less correlated with the rain
rates. Combining the advantages of both types of satellite observations,
high quality, high spatiotemporal resolution rainfall analysis products can
be made. Notable examples include the CMORPH/QMORPH products
from the Climate Prediction Centre (CPC) of NOAA (Joyce et al. 2004),
the TRMM Multi-satellite Precipitation Analysis (TMPA) by NASA (Huffman
et al. 2009), the blended satellite technique by the Naval Research
Laboratory (NRL), Monterey of the Naval Postgraduate School (Turk and
Hawkins 2004), the PERSIANN (Precipitation Estimation from Remotely
Sensed Information using Artificial Neural Networks) analyses by the
University of California, Irvine (Sorooshian et al. 2000) and the GSMaP
(Global Satellite Mapping of Precipitation) products developed by the
Japan Aerospace Exploration Agency (JAXA) (Aonashi et al. 2009).
The Hong Kong Observatory (HKO) has developed an operational
point and areal TC rainfall forecasting tool by combining the subjective
TC forecast track and the satellite microwave rain rate analysis. Attempts
have also been made to utilize the ensemble prediction system (EPS) TC
tracks to generate deterministic and probabilistic predictions of TC
rainfall.
2.
TC rainfall prediction based on satellite rain rate estimates
2.1 Data and methodology
Sapiano and Arkin (2009) evaluated a number of rainfall analysis
products by using the rain gauge data collected over the US continent and
2
the Pacific. They found that all of the products examined were able to
resolve the diurnal variation in the regional rainfall totals. Among the
others, the CMORPH products by NOAA CPC yielded the highest
correlation with rain gauge observations. The CMORPH technique uses
precipitation estimates derived from low orbiter satellite microwave
observations and the motion vectors derived from consecutive IR
observations from geostationary satellites. At a given location, the shape
and intensity of the precipitation in the intervening time periods between
microwave scans are determined by performing a time-weighted
interpolation between the precipitation features propagated forward in
time from the previous microwave scan and those propagated backward in
time from the following microwave scan. Based on the above method,
NOAA CPC produces global precipitation analyses at a spatial resolution
of 8 km every half-hourly. CMORPH estimates are available about 18
hours past real time, but NOAA CPC also produces the QMORPH
estimates, which are similar to CMORPH, except that the microwave
precipitation features are propagated via IR data forward in time only.
QMORPH estimates are available within 3 hours of real time and are
therefore more suitable for use in operation. The new forecasting tool
takes the hourly QMORPH estimates at 0.25-degree resolution as one of
the key input data.
For the TC tracks, the HKO’s subjective forecast tracks are used,
which include the hourly forecast positions in the coming 72 hours. The
tracks are available within 2 hours past real time and are updated every 3
hourly whenever a TC enters the HKO warning area, viz. within 10N-30N
and 105E-125E.
To obtain a TC rainfall prediction, the QMORPH rain rates are
advected with the forecast track to obtain the hourly forecast positions of
the rain areas, from which the forecast hourly rainfall at HKO as well as
the daily rainfall totals over the coast of Guangdong and the northern part
of the South China Sea in the next 3 days are computed (Fig. 1). The new
product is updated every hour based on the following assumptions:
(1)
The rain rate analysis from QMORPH is accurate.
(2)
The forecast TC track is accurate.
(3)
The rain areas associated with the TC move in the same
direction and speed as the storm centre during the whole
forecast range (72 hours).
(4)
The shape and intensity of the rain areas remain unchanged
during the whole forecast range.
2.2 Validation based on 2008 dataset
Validation of the new tool was made with the 6 TCs which affected
3
Hong Kong in 2008 by using the 3-hourly CMORPH estimates at 0.25degree resolution. The predictions from the tool were verified against
the observed rainfall at HKO, and compared with the corresponding
predictions from the deterministic system of the European Centre for
Medium-Range Weather Forecasts (ECMWF) and the Global Spectral
Model (GSM) of the Japan Meteorological Agency (JMA).
Verification results showed that for the cases of Typhoon Neoguri
and Typhoon Fengshen (Table 1), the errors in the day-1 prediction of the
tool were the smallest among the three forecast guidance. Besides, the
new tool captured well the arrival time of the rainbands associated with
Fengshen and Nuri (Figure 2).
Although the microwave-based TC rainfall prediction for 19 April
2008 was closer to actual than NWP predictions, the error was indeed
very high (176.7 mm). The exceptional heavy rain was believed to be due
to the interaction of Neoguri with the pre-existing northeast monsoon
(Fig. 3). In general, the root mean squared error (RMSE) of the rainfall
predictions increased from day 1 to day 3 (Fig. 4), the heavy rain during
Neoguri was one of those cases with significant error recorded
(highlighted with blue circles in Fig. 4). Negative biases were noted in
the verification, in particular in day 1. This could be related to the
enhancement of TC rainbands upon interaction with the terrain during the
landfall phase of the TCs. The biases improved in day 2 and day 3,
possibly due to the counterbalancing effect of the diminishing supply of
moisture when the TCs were approaching land.
Based on the validation results obtained above, the new tool was put
into operation at HKO in the 2009 TC season.
2.3 Case studies using 2009 data
Verification of point forecasts
Verification of the new product was made against the observed
rainfall at HKO for all 8 TC cases which affected Hong Kong in 2009.
Same as before, comparison was made with the corresponding predictions
from the global models of ECMWF and JMA. The results given in Table
2 show that out of the 8 cases, the errors of the new product for day 1
during Severe Tropical Storm Linfa, Tropical Storm Soudelor and
Typhoon Molave were the lowest among all three forecast guidance.
Besides, the new product successfully predicted the arrival time of
Typhoon Molave’s rainbands in Hong Kong (Fig. 5).
The new product failed to predict the heavy rain brought by Typhoon
4
Koppu on 15 September 2009. An analysis of the synoptic weather
pattern on that day suggested that an easterly airstream had converged
with the southerly flow associated with Koppu near Hong Kong and
caused the heavy rain (Fig. 6). The forecast rainfall map actually showed
that the rain areas would be getting close to Hong Kong (Fig. 7), should
there exist a slight error in the forecast track or the rain rate analysis,
heavy rain could have affected Hong Kong.
The RMSE of the new product increased with the forecast range (Fig.
8) and the rate of increase was even speedier than the validation dataset in
2008. This is not surprising as the TC forecast tracks used in the
derivation of the rainfall forecasts would increasingly deviate from actual
as the forecast hour progresses. Tropical Storm Nangka and Severe
Tropical Storm Goni are examples in which large errors in the forecast
track have led to significant errors (red circles in Fig. 8 and Fig. 9). In
general, the 2009 verification showed that the rainfall amounts for the
first two days were under-estimated as in 2008, but the negative biases
have been much improved for day 2. The predictions for day 3 even gave
more rain than actual, due to the significant over-predictions in the cases
of Tropical Storm Nangka and Severe Tropical Storm Goni.
Verification of areal forecasts
The capability of the new tool in forecasting areal rainfall was
examined with the case of Tropical Storm Soudelor. On 10 July 2009, the
rainbands associated with Soudelor were affecting the northern part of the
South China Sea, at a distance away from the south China coastal region.
The coverage of the rain areas associated with Soudelor was well
captured by the forecast rainfall map with base time at 12 UTC, 9 July
2009 (Fig. 10a). Soudelor’s rainbands arrived at Hong Kong and the
south China coastal region the next day, and that was also well predicted
by the new tool (Fig. 10b). On the third day, the tool successfully
predicted that the major rainbands of Soudelor would move away from
Hong Kong and the eastern part of Guangdong and bring torrential rain in
excess of 100 mm to Hainan Island (Fig. 10c).
On the other hand, ECMWF and JMA predicted a slower than actual
movement for Soudelor, resulting in the failure of the models in correctly
predicting the rain areas on day 1 and day 2, as well as the torrential rain
that followed on day 3 on Hainan Island (Fig. 11).
3.
Ensemble rainfall prediction incorporating EPS information
3.1 Data and methodology
5
Attempts have been made to combine the microwave rain rates with
the forecast TC tracks output from an EPS. The 0.25-degree resolution
CMOPRH estimates and the 51 forecast TC tracks from the ECMWF
EPS were used in the experiments. The forecast tracks include 12hourly forecast positions up to T+120 hours. The following two
approaches were tested in the derivation of rainfall predictions:
（1）Ensemble rainfall approach
51 rainfall maps are produced using each of the 51 forecast tracks
from the ECMWF EPS, and then a final forecast map is produced by
averaging the 51 maps. In addition, the probability of rainfall totals
exceeding various thresholds can also be obtained based on the 51
forecast maps.
（2）Ensemble track approach
An ensemble TC track is first computed by taking the geographical
average of all 51 forecast tracks and then a forecast map is obtained by
advecting the microwave rain rates along the ensemble track.
3.2 Evaluation
The performance of the ensemble predictions was evaluated using
the same 6 cases which affected Hong Kong in 2008.
Point deterministic forecast
Predictions from the ensemble rainfall approach and ensemble track
approach were verified against the observed rainfall at HKO (Table 3).
The results showed that the performance of both approaches were
comparable for day 1 and day 2, but the errors for day 3 were smaller and
correlation with actual higher for the ensemble rainfall approach. When
compared with the point forecasts derived using the best track dataset in
Section 2.3 above (Fig. 4), the day-3 errors of the ensemble rainfall
approach were also smaller, which demonstrated the benefits of
introducing perturbations in the forecast track to the tool. However, due
to the smoothing resulted from taking average of the 51 forecast maps in
the ensemble rainfall approach, a lot more light rain areas were generated
when compared with the ensemble track approach (Fig. 12). Due to the
same reason, the peak rainfall rates were also smaller in the ensemble
rainfall approach.
Areal probability forecast
Verification was made of the probability forecasts derived from the
6
ensemble rainfall approach with the verification domain defined to be
within 12.8N-30.2N and 102.8E-127.7E. CMORPH estimates were taken
as the ground truth for the purpose of verification. The results (Fig. 13)
revealed that the ensemble method generally over-estimated the
probability of rainfall reaching various rainfall thresholds in day 1.
Besides, the forecast skill dropped rapidly as the rainfall threshold
increased from 50 mm to 200 mm.
4.
Discussion and conclusions
The verification results showed that, given the assumptions made in
Section 2.1, point and areal rainfall predictions based on microwave rain
rates deliver satisfactory performance, even beating the NWP models in
individual cases. As hourly rainfall amounts can be generated, the new
product could more effectively assist the forecasters in assessing when the
rain associated with TC would start or cease. The new product will
particularly be useful when the TC motion predicted by NWP models
deviates from the official forecast. With a forecast update available every
hour at about 3 hours after observation time, the new tool will be able to
capture the latest developments more rapidly than NWP models.
Nevertheless, the following limitations of the new product should be
noted:
（1）it has been assumed that the rain areas associated with the TC
will move in tandem with the storm. Rotation of rainbands around the TC
centre have not been considered;
（2）the shape and intensity of the rainbands may actually change
with the intensity of the TC within the forecast horizon;
（3）the interaction between the TC and other weather systems has
not been taken into account, in particular the interaction of TC with
frontal systems will enhance the rainfall intensity whereas cold air
intrusion into the TC will weaken it;
（4）the tool has not considered the impact on rainfall intensity due
to the interaction of TC with the land mass.
As the forecast hour increases, the assumptions made in the tool may
become less valid. The forecast position error of the TC will also
increase. As such, the skill of the tool may drop significantly in day 2
and day 3.
In the attempt to extend the tool to incorporate EPS information, we
have studied two different approaches. The ensemble rainfall approach
7
made less significant errors in general but the forecast peak rainfall rate
was usually dampened due to the smoothing applied. Since the peak
rainfall rate is one of the most critical information in TC warnings, the
above limitation should be duly taken into account in operation. The
verification results revealed a rapid decrease in the reliability of the
probability forecasts with an increase in the rainfall threshold. This could
be related to the uncertainty in the microwave-based rain rates and the
rain areas associated with TC.
The method that we have tested
incorporates only perturbations in the motion of TCs. The introduction of
perturbations in the rain rates as well as rain areas may further improve
the forecast accuracy.
In summary, the new TC rainfall forecasting tool based on microwave
observations provides high spatial and temporal resolution prediction. It offers a
useful guidance other than NWP predictions for forecasters’ reference when TCs
approach the northern part of the South China Sea and Hong Kong.
8
5.
References
[1] Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A
method that produces global precipitation estimates from passive microwave
and infrared data at high spatial and temporal resolution. J. Hydromet., 5, 487503.
[2] Huffman, G.J., R.F. Adler, D.T. Bolvin, E.J. Nelkin, 2009: The TRMM
Multi-satellite Precipitation Analysis (TMPA). Chapter in Satellite Applications
for Surface Hydrology, F. Hossain and M. Gebremichael, Eds. Springer Verlag,
accepted. PDF available at
ftp://meso.gsfc.nasa.gov/agnes/huffman/papers/TMPA_hydro_rev.pdf.
[3] Turk, F.J., J.D. Hawkins, 2004: Gauging Tropical Rainfall Potential with a
Blended Satellite Technique. 26th Conference on Hurricanes and Tropical
Meteorology, Miami, FL, 2-7 May, paper 5A.7. PDF available at
http://ams.confex.com/ams/pdfpapers/76007.pdf.
[4] Sorooshian, S., K-L Hsu, X. Gao, H.V. Gupta, B. Imam, and D.
Braithwaite, 2000: Evaluation of PERSIANN System Satellite–Based Estimates
of Tropical Rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
[5] Aonashi K., J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. Liu, S. Shige, S.
Kida, S. Seto, N. Takahashi, and Y.N. Takayabu, 2009: GSMaP passive,
microwave precipitation retrieval algorithm: Algorithm description and
validation. J. Meteor. Soc. Japan, 87A, 119-136.
[6] Sapiano, M.R.P., and P.A. Arkin, 2009: An Intercomparison and
Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly
Gauge Data. J. Hydromet., 10, 149-166.
9
Name of
tropical
cyclone
Date
Daily
Rainfall
recorded
Prediction
from the
new tool
Prediction
from
ECMWF
Prediction
from JMA
Neoguri
17/4/2008
Trace
Trace
Trace
Trace
18/4/2008
Trace
2.3
1.3
0.8
19/4/2008
237.4
60.7
19.9
4.0
23/6/2008
0
0
0
0
24/6/2008
0.6
6.9
10.9
5.3
25/6/2008
146.1
88.8
323.8
266.6
5/8/2008
6.1
55.4
38.2
64.7
6/8/2008
74.1
29.5
22.5
40.0
7/8/2008
72.3
0.2
26.5
35.0
20/8/2008
0
0
0
0.1
21/8/2008
Trace
1.7
4.7
3.0
22/8/2008
61.6
35.4
47.4
26.5
22/9/2008
0
0
1.0
2.2
23/9/2008
34.1
23.7
26.5
30.7
24/9/2008
43.7
9.6
37.9
20.7
2/10/2008
3
0
0.9
0.1
3/10/2008
2.4
17.5
9.6
9.2
4/10/2008
14.0
8.9
8.2
23.0
Fengshen
Kammuri
Nuri
Hagupit
Higos
Unit：mm
Table 1
The predicted accumulated rainfall for day 1 from the new tool and
ECMWF and JMA global models and the observed amount for TCs
which affected Hong Kong in 2008
10
Name of
tropical
cyclone
Date
Daily
Rainfall
recorded
Prediction
from the
new tool
Prediction
from
ECMWF
Prediction
from JMA
Linfa
20/06/2009
0
0
Trace
43
21/06/2009
0
0.6
5
3
26/06/2009
17.7
0
10
17
27/06/2009
46.9
35.5
28
55
10/07/2009
Trace
0
1
1
11/07/2009
8.1
6.1
12
18
12/07/2009
Trace
0
5
20
17/07/2009
0.4
0
Trace
2
18/07/2009
11.7
0.2
51
34
19/07/2009
124.6
83.6
32
20
03/08/2009
21.4
0
4
4
04/08/2009
21.3
0
62
29
05/08/2009
92.5
0
61
34
10/09/2009
0.9
0
8
9
11/09/2009
11.8
0
16
29
13/09/2009
23.4
0
3
10
14/09/2009
38.8
56.3
52
13
15/09/2009
190.3
0.2
25
19
27/09/2009
0
0.2
2
1
28/09/2009
52.7
0.2
62
68
Nangka
Soudelor
Molave
Goni
Mujigae
Koppu
Ketsana
Unit：mm
Table 2
The predicted accumulated rainfall for day 1 from the tool and
ECMWF and JMA global models and the observed amount for TCs
which affected Hong Kong in 2009
11
Approach
RMSE
(mm)
Correlation
coefficient
Table 3
Day 1
Day 2
Day 3
Ensemble Ensemble Ensemble Ensemble Ensemble Ensemble
rainfall
track
rainfall
track
rainfall
track
49
49
60
61
65
75
0.64
0.64
0.30
0.29
0.14
0.05
Verification statistics of microwave-based TC rainfall predictions for
TCs which affected Hong Kong in 2008
12
Figure 1 Microwave-based TC rainfall prediction during the passage of
Typhoon Molave (base time 23 UTC, 17 July 2009)
13
Figure 2a Observed rainfall at HKO (red line) and corresponding hourly
predictions by microwave-based TC rainfall during the passage
of Typhoon Fengshen (base time 03 UTC, 22 June 2008)
Figure 2b Observed rainfall at HKO (red line) and corresponding hourly
predictions by microwave-based TC rainfall during the passage
of Typhoon Nuri (base time 18 UTC, 20 August 2008)
Figure 3
Synoptic pattern on 19 April 2008
14
Bias = -18.6 mm
RMSE = 46.5 mm
Bias = -15.6 mm
Bias = -11.5 mm
RMSE = 54.3 mm
Figure 4
RMSE = 67.0 mm
Verification of the microwave-based TC rainfall predictions in
2008
15
35
Actual Rainfall
Rainfall (mm)
30
Forecast Rainfall
25
20
15
10
2009071916
2009071914
2009071912
2009071910
2009071908
2009071906
2009071904
2009071902
2009071824
2009071822
2009071820
2009071818
2009071816
2009071814
2009071812
2009071810
2009071808
2009071806
2009071802
0
2009071804
5
Time
Figure 5 Observed rainfall at HKO and the corresponding hourly
predictions by microwave-based TC rainfall during the passage
of Typhoon Molave (base time 01 UTC, 18 July 2009)
Figure 6
Synoptic pattern on 15 September 2009
16
Figure 7 Day-1 prediction from the microwave-based TC rainfall during
the passage of Typhoon Koppu (base time 16 UTC, 14
September 2009)
17
Rainfall for Day 1 (T+0 to T+24)
250
LINFA
Actual Rainfall (mm)
200
NANGKA
SOUDELOR
150
MOLAVE
GONI
100
MUJIGAE
Bias = -25.3 mm
RMSE = 49.5 mm
50
KOPPU
KETSANA
0
0
50
100
150
200
250
Forecast Rainfall (mm)
Rainfall for Day 3 (T+48 to T+72)
Rainfall for Day 2 (T+24 to T+48)
250
Bias = -21.0 mm
RMSE = 63.0 mm
200
Actual Rainfall (mm)
Actual Rainfall (mm)
250
150
100
50
0
Bias = 12.5 mm
RMSE = 78.2 mm
200
150
100
50
0
0
50
100
150
200
250
0
Forecast Rainfall (mm)
Figure 8
50
100
150
200
250
Forecast Rainfall (mm)
Verification of the microwave-based TC rainfall predictions in
2009
18
28
27
28
27
27
27
26
26
26
25
HKO best track
2009062500H
2009062600H
2009062400H
06
07
06
05
05
04
08
03
04
03
09
02
HKO best track
02
2009080300H
2009080200H
Figure 9 The HKO best track and the forecast tracks of Tropical Storm
Nangka (top) and Severe Tropical Storm Goni (down)
19
(a)
(b)
(c)
Figure 10 Microwave-based rainfall predictions (base time 12 UTC, 9
July 2009, left panel) and corresponding daily rainfall analyses
(right panel) for (a) day-1; (b) day-2 and (c) day-3 during the
passage of Tropical Storm Soudelor
20
Figure 11 The synoptic pattern and 12-hour accumulated rainfall
predicted by ECMWF and JMA valid at (i) 00 UTC, 12 July
2009 (top) and (ii) 12 UTC, 12 July 2009 (down)
21
Figure 12 Predictions by the ensemble rainfall approach (left) and the
ensemble track approach (right) during the passage of Typhoon
Nuri (base time 18 UTC, 19 August 2008)
1.0
1.0
0.8
Observed Frequency
Observed Frequency
Threshold = 50 mm
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
0.8
0.8
0.6
0.4
0.2
0.0
1.0
0.0
Probability forecast
1.0
0.2
0.4
0.6
Probability forecast
0.8
1.0
0.8
1.0
1.0
Threshold = 150 mm
Threshold = 200 mm
0.8
Observed Frequency
Observed Frequency
Threshold = 100 mm
0.6
0.4
0.2
0.0
0.8
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
Probability forecast
Figure 13
0.8
1.0
0.0
0.2
0.4
0.6
Probability forecast
Reliability diagrams for day-1 prediction by microwave-based
TC rainfall for rainfall thresholds of 50 mm, 100 mm, 150 mm
and 200 mm
22