Reprint 879 Application of Satellite Rain Rate Estimates to the Prediction of Tropical Cyclone Rainfall S.T. Chan & M.Y. Chan The 42 th 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 0.25- 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 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.