Reprint 1355 Adjustment of Automatic Gridded

Reprint 1355 Adjustment of Automatic Gridded Wind Field Forecasts in the Presence of Tropical Cyclones WU Man-tsun* and HO Chun-kit The 32nd Guangdong - Hong Kong - Macao Seminar on Meteorological Science and Technology and The 23rd Guangdong - Hong Kong - Macao Meeting on Cooperation in Meteorological Operations (Macau 8-10 January 2018) 修订热带气旋出现时的自动网格风场预报胡文津 1 何俊杰 2 1 香港中文大学 2 香港天文台摘要现时香港天文台应用多个数值天气预报模式的预测，透过加权的客观共识方法制作出未来九天的数码天气预报。但当有热带气旋出现在西北太平洋或南海时，各预报模式对热带气旋的预报位置和强度时有差异，往往导致风场预报和天文台的主观预报出现不一致的情况。本文探讨利用两种方法来修订自动网格风场预报，以尽量减少模式风场预报和主观预报的差异。这两种方法分别为「制造气旋风场」和「移动模式风场」。前者利用天文台对热带气旋强度和风力分布的预测构建一个新的风场，后者则在模式的集成预报群组中选取一个预报位置及强度与主观预报路径最接近的成员并采用其风场。本文讨论上述两种方法的利弊，并以在 2016 年至 2018 年影响香港的热带气旋作为验证对象。结果显示，若以香港离岸站点的风速作为指标，两种修订方法的预测风速对比实测风速的误差，均较使用多模式客观共识方法为低。 - 1/35 - Adjustment of Automatic Gridded Wind Field Forecasts in the Presence of Tropical Cyclones WU Man-tsun1 HO Chun-kit2 1 2 The Chinese University of Hong Kong Hong Kong Observatory Abstract The Hong Kong Observatory currently utilizes weather forecasts from various numerical weather prediction models and applied a weighted objective consensus approach to produce digital weather forecasts for the next nine days. However, when there are tropical cyclones over the western North Pacific or the South China Sea, different models often give different predictions of positions and intensities of the tropical cyclones, resulting in possible inconsistencies between the wind field given by models and the Observatory's subjective forecast. This paper explores two methods to adjust automatic gridded wind field forecasts in order to minimize the discrepancies between the wind field forecasts and subjective forecasts. The two methods are "Generate Cyclone" and "Model Wind Field Relocation". The former uses parameters of the - 2/35 - Observatory's forecast intensity and wind distribution to generate a new wind field, while the latter selects a member from the model ensemble for which the forecast positions and intensities of the tropical cyclones are closest to the subjective TC forecast and the wind field of the selected ensemble member will be adopted. This paper discussed on the pros and cons of the two methods based on verification for tropical cyclones that affected Hong Kong during the period from 2016 to 2018. Results using wind speed at offshore stations in Hong Kong as benchmark indicated that the errors in predicted wind speed using the two methods compared to actual observations are both smaller than those using the multi-model objective consensus approach. - 3/35 - 1. Introduction The Hong Kong Observatory (HKO) began to provide digital weather forecasts with fine spatial and temporal resolutions for the general public in 2010. The first generation of such forecasts, covering air temperature, wind direction and wind speed only, was based on outputs from a single numerical weather prediction (NWP) model with basic statistical correction techniques such as “Model Output Statistics” to account for systematic biases in the NWP direct model output (DMO) [1]. Since then, digital weather forecast services by the HKO have been enhanced progressively with an extension of forecast validity and inclusion of more weather elements. The current suite of digital weather forecasts, viz. “Automatic Regional Weather Forecast (ARWF) in Hong Kong & Pearl River Delta (PRD) Region”, provides hourly forecasts of air temperature, relative humidity, wind direction and wind speed for the next nine days at numerous stations in Hong Kong and a total of 240 grid boxes (around 10 km 10 km each) over the PRD region (see Figure 1 for the spatial domain). The ARWF is generated using a multi-model objective consensus technique, which involves (1) correcting the DMO from multiple global NWP models including the ECMWF, JMA, NCEP deterministic model, ensemble mean from the ECMWF Ensemble Prediction System and DMO from HKO’s non-hydrostatic mesoscale model Meso-NHM; and (2) combining the corrected forecasts from these - 4/35 - different models to give a “consensus forecast” with weights according to their past performance. While the multi-model objective consensus technique outperforms forecasts from individual models on average [2], there are occasions in which this technique can give unrealistic forecasts, especially when there are tropical cyclones over the western North Pacific or the South China Sea. Figure 2 gives an example which shows the ARWF wind field forecast for 21 UTC on 1 August 2016, when Tropical Cyclone (TC) Nida was expected to approach the Pearl River Estuary. While different NWP models all predicted that Nida would come very close to Hong Kong, their differences in the forecast positions of Nida led to the situation where the weighted “consensus” wind field appeared distorted compared as would be expected of a typical cyclonic wind field. In some other cases, it is possible for “consensus” forecasts to deviate from the subjective forecasts issued by the weather forecasters. For example, the NWP models may predict a TC to make landfall just east of Hong Kong while the forecasters may, after taking all available observations and forecasting aids into account, predict that it will make landfall to the west. In this case, the wind direction over Hong Kong as suggested by the “consensus” forecasts will be exactly opposite to that predicted by the forecasters. Large discrepancies between digital weather forecasts and subjective forecasts - 5/35 - issued by forecasters are undesirable as they may cause confusion to users. Yet with the large number of grid points and forecast validity time involved, it would be impractical to perform manual grid-by-grid adjustment. This paper explores two efficient methods to adjust automatic wind field forecasts in order to minimize their discrepancies with the subjective TC forecasts. The first method, “Generate Cyclone”, uses parameters of forecast intensity and wind distribution in the subjective forecast to generate a new wind field. The second method, “Model Wind Field Relocation”, selects a member from the NWP ensemble for which the forecast positions and intensities of the TCs are the closest to the subjective forecast. The wind field of the selected ensemble member will be adopted after adjustments. The two methods were tested for different TC cases in Hong Kong from 2016 to 2018 to evaluate their performance. This paper is organized as follows: Section 2 describes the algorithms for each adjustment method. The verification methodology is then briefly described in Section 3. Section 4 presents the overall verification results and discusses some interesting cases. Concluding remarks are given in Section 5. 2. Adjustment methods 2.1. Generate Cyclone - 6/35 - The idea of the “Generate Cyclone” method is to replace the TC wind fields using the consensus technique for each forecast hour with “bogus” wind fields constructed based on the modified Rankine vortex model [3] using parameters such as the forecast TC position, maximum winds and wind radii from the subjective forecast. The “bogus” wind fields are then further adjusted for frictional effects, semicircle effects for TC motion and “blended” with the background wind field. Similar algorithms have been used in the tool “TCMWindTool” of the software package Graphical Forecast Editor (GFE), a platform used in National Weather Services of the USA for manual adjustment of digital weather forecasts [4,5]. The detailed algorithms for each step are explained in the following sections. 2.1.1. Wind field construction For each forecast hour, a wind field is constructed based on the Rankine model, which specifies the variation of wind speed as a function of the distance from the centre of the TC [6]. We first estimate the radius of maximum winds (in km) by = 46.4exp (−0.0153 + 0.0169) (1) where and are the maximum winds of the TC (in m/s) and latitude (in degrees) of the centre respectively. Within , the wind speed at a distance from the TC centre is given by - 7/35 - = (2) Considering that many TCs often have asymmetric structure, a modified version of the Rankine model is adopted where the wind speed profile is allowed to be different over each quadrant of the TC. Within the same quadrant, with “known” values of strong, gale, storm and hurricane wind radii and , the wind speed profiles between such radii are given by = − (3) where and are wind speed at radii and resepectively. For example, if and are gale and storm wind radii respectively, and are the lower wind speed limits of gale and storm force winds. Beyond the strong wind radius , the decay in wind speed is given by = (4) where is the lower wind speed limit of strong force winds. An example wind speed profile based on (1) to (4) is shown in Figure 3. As for wind direction, a cyclonic field is adopted but with a deflection angle pointing towards the centre of the TC [7]: ⎧ 10 1 + if < ⎪ = (5) 20 + 25 − 1 if ≤ ≤ 1.2 ⎨ ⎪ ⎩ 25 if ≥ 1.2 For illustration, an example “bogus” wind field for TC Mangkhut in 2018 is - 8/35 - shown in Figure 4a. 2.1.2. Frictional adjustment As wind speed over land surface is lower than that over sea due to frictional effect, a land reduction factor needs to be applied to the wind speed values estimated using the modified Rankine model. Literature suggested a reduction factor of around 0.7 to 0.8 [8,9]. After testing the effects of different reduction factors on the test TC cases (results not shown), a factor of 0.7 was adopted for a land grid box, while for grid boxes with partial land cover, a factor ranging from 0.7 to 1 was used according to the percentage of land cover in each box. The wind direction was also deflected inwards (i.e. clockwise in the northern hemisphere) by a value between 0° and 15° according to the land cover percentage. Figure 4b shows the wind field for Mangkhut after adjustment for frictional effects. It can be seen that the wind speed over inland areas (e.g.

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