Reprint 879

Application of Satellite Rain Rate Estimates to the Prediction of Tropical Rainfall

S.T. Chan & M.Y. Chan

The 42 th Session of ESCAP/WMO Committee,

Singapore, 25-29 January 2010

Application of Satellite Rain Rate Estimates to the Prediction of Rainfall

Chan Sai-tick and Chan Man-yee 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 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 ’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 and the northern part of the South 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 0.25- degree 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 (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 Soudelor and 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 , 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 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 12- hourly 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, 487- 503. [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 Date Daily Prediction Prediction Prediction tropical Rainfall from the from from JMA cyclone recorded new tool ECMWF 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 Fengshen 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 Kammuri 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 Nuri 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 Hagupit 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 Higos 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

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 Date Daily Prediction Prediction Prediction tropical Rainfall from the from from JMA cyclone recorded new tool ECMWF

Linfa 20/06/2009 0 0 Trace 43

21/06/2009 0 0.6 5 3

Nangka 26/06/2009 17.7 0 10 17

27/06/2009 46.9 35.5 28 55

Soudelor 10/07/2009 Trace 0 1 1

11/07/2009 8.1 6.1 12 18

12/07/2009 Trace 0 5 20

Molave 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

Goni 03/08/2009 21.4 0 4 4

04/08/2009 21.3 0 62 29

05/08/2009 92.5 0 61 34

Mujigae 10/09/2009 0.9 0 8 9

11/09/2009 11.8 0 16 29

Koppu 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

Ketsana 27/09/2009 0 0.2 2 1

28/09/2009 52.7 0.2 62 68

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 Day 1 Day 2 Day 3 Approach Ensemble Ensemble Ensemble Ensemble Ensemble Ensemble rainfall track rainfall track rainfall track RMSE 49 49 60 61 65 75 (mm) Correlation 0.64 0.64 0.30 0.29 0.14 0.05 coefficient Table 3 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 (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 RMSE = 67.0 mm

Figure 4 Verification of the microwave-based TC rainfall predictions in 2008

15

35 Actual Rainfall Forecast Rainfall 30

) 25 20 15

Rainfall (mm 10 5 0 2009071802 2009071804 2009071806 2009071808 2009071810 2009071812 2009071814 2009071816 2009071818 2009071820 2009071822 2009071824 2009071902 2009071904 2009071906 2009071908 2009071910 2009071912 2009071914 2009071916 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 (base time 16 UTC, 14 September 2009)

17

Rainfall for Day 1 (T+0 to T+24) 250

LINFA

) 200 NANGKA SOUDELOR 150 MOLAVE

100 GONI MUJIGAE Bias = -25.3 mm Actual Rainfall (mm 50 KOPPU RMSE = 49.5 mm KETSANA 0 0 50 100 150 200 250

Forecast Rainfall (mm)

Rainfall for Day 2 (T+24 to T+48) Rainfall for Day 3 (T+48 to T+72) 250 250 Bias = -21.0 mm Bias = 12.5 mm ) 200 ) 200 RMSE = 63.0 mm RMSE = 78.2 mm 150 150

100 100 Actual Rainfall (mm 50 Rainfall (mmActual 50

0 0 0 50 100 150 200 250 0 50 100 150 200 250

Forecast Rainfall (mm) Forecast Rainfall (mm)

Figure 8 Verification of the microwave-based TC rainfall predictions in 2009

18

28 28

27 27 27 27

26 26 26

25

HKO best track 2009062500H 2009062600H 2009062400H

06 06 07 05 04 05 03 08 04

09 03 02 02

HKO best track 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 Threshold = 50 mm Threshold = 100 mm 0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2 Observed Frequency Observed Frequency 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Probability forecast Probability forecast 1.0 1.0 Threshold = 150 mm Threshold = 200 mm 0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2 Observed Frequency Observed Frequency 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Probability forecast Probability forecast Figure 13 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