IASI 일차변분법 자료동화 시스템 내 구름 변수 산출 알고리즘 개선과 수치예보 정확도에 미치는 영향

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 지구환경과학부, 2020. 8. 손병주.The Unified Model (UM) data assimilation system incorporates a one-dimensional variational (1D-Var) analysis of cloud variables for hyperspectral infrared sounders that allows the assimilation of radiances in cloudy areas. For the Infrared Atmospheric Sounding Interferometer (IASI) radiance assimilation in the UM, a first guess pair of cloud top pressure (CTP) and cloud fraction (CF) is estimated using the minimum residual (MR) method, which simultaneously obtains CTP and CF by minimizing radiances difference between observation and model simulation. In this study, specific pairs of CTP and CF yielding the smallest 1D-Var temperature and humidity analysis error were found from the ECMWF short-range forecast based IASI simulated radiances and background states, and defined as optimum cloud parameters. Compared to the optimum results, it is noted that the MR method tends to overestimate cloud top height while underestimating cloud fraction. This fact necessitates an improved cloud retrieval for better 1D-Var analysis performance. An Artificial Neural Network (ANN) approach was taken to estimate CTP as close as possible to the optimum value, based on the hypothesis that CTP and CF closer to the optimum values will bring in better 1D-Var results. The ANN-based cloud retrievals indicated that CTP and CF biases and root mean square errors against the optimum values shown in the MR method are much reduced. The resultant 1D-Var analysis with new first guess based on the ANN method showed that the errors of temperature and moisture in the mid-troposphere are reduced, due to the use of larger volume of cloud-affected infrared radiances. Furthermore, the computational time can be substantially reduced as much as 1.85% by the ANN method, compared to the MR method. The evaluation of the ANN method in the UM global weather forecasting system demonstrated that it helps to use more infrared radiances in the cloudy-sky data assimilation. Although its impact on the UM global temperature and moisture forecasts was found to be near neutral, it has been demonstrated that the UM global precipitation forecasts and tropical cyclone forecast, which occur mostly around cloud regions, can be improved by the ANN method.기상청 현업 모델인 통합수치모델 (Unified Model) 내 자료동화 과정 중, 적외 초분광 센서인 IASI (Infrared Atmospheric Sounding Interferometer) 관측자료를 활용하는 방법으로는 구름 변수를 1D-Var 과정 내에서 산출하는 Cloudy 1D-Var 방법(Pavelin et al., 2008)이 사용되고 있다. Cloudy 1D-Var 방법에서는 산출된 구름변수(운정고도, 운량)를 이용해 구름지역을 탐지할 뿐만 아니라 자료동화에 사용되는 채널을 선정하기 때문에 운정고도와 운량을 정확하게 산출하는 것은 매우 중요하다. Cloudy 1D-Var 방법에서 구름 변수인 운정고도와 운량의 초기값은 minimum residual (MR) 방법(Eyre and Menzel, 1989)을 통해 관측 복사량과 모델 배경장이 만들어내는 복사량의 차이를 최소화 시키는 값으로 얻어진다. 본 연구에서는 ECMWF 단기예보장을 활용하여 IASI 모의 관측 복사량과 모델 배경장을 생산하였고, 이를 이용해 최종 온도 습도 분석장의 에러를 최소로 만드는 새로운 구름변수들을 찾아내어 이를 최적의 운정고도, 운량으로 정의하였다. 정의한 최적의 구름 변수를 기준으로 MR 방법이 구름의 고도를 상대적으로 상층으로 산출한다는 것을 확인하였고, 이로 인해 온습도 1D-Var 분석장의 에러를 최소화 시키지 못한다는 점을 확인하였다. 온습도 1D-Var 분석장을 개선하기 위해 최적의 구름변수와 가장 가까운 구름변수를 산출할 수 있는 방법을 모색하였고, IASI 적외 관측 복사량과 모델 배경장을 입력 자료로 하여 운정고도를 산출해내는 인공신경망(ANN; Artificial Neural Network) 모델을 개발하였다. 검증을 통해 ANN 모델에서 산출된 운정고도, 운량이 앞서 정의한 최적의 운정고도, 운량과 높은 상관관계를 갖는다는 것을 확인하였고, 구름에 의해 영향을 받은 더 많은 채널들이 자료동화 과정 내에 사용되는 것을 확인하였다. 이와 함께 기존 MR 방법을 사용했을 때 얻어진 1D-Var 온습도 분석장 결과와 비교해보았을 때 모든 층에서 온습도 분석장이 개선되는 것을 볼 수 있었고, 특히 중층에서 온도 에러가 10% 가량 줄어드는 것을 확인하였다. 개발한 ANN 모델을 이용하면 운정고도를 먼저 산출하고, 이를 이용해 운량을 계산한다는 점에서 계산시간을 기존 MR 방법의 1.85%로 줄이는 장점까지 얻을 수 있었다. 또한 새로 개발한 ANN 알고리즘을 실제 UM 내에도 적용시켜 보았는데, 이때도 새롭게 산출된 운정고도가 기존의 MR 방법을 통해 산출되었던 운정고도보다 상대적으로 낮게 산출되면서 더 많은 구름지역 IASI 적외 초분광 채널 정보가 자료동화 과정 내에 사용된다는 것을 확인할 수 있었다. 나아가 새롭게 개발한 ANN 방법이 수치예보 모델 초기장 및 예보장 정확도에 주는 영향도 살펴보았다. 전 지구적 온습도 초기장 및 예보 정확도에 미치는 영향은 미미하게 나타났지만, 주로 구름 지역 주변에서 구름을 동반하여 발생하는 날씨 현상인 강수 및 열대 저기압의 예보정확도가 ANN 방법을 사용함으로써 향상되는 것을 확인할 수 있었다.1. Introduction 1 2. Background and theory 6 2.1. IASI hyperspectral measurement 6 2.2. Theoretical background 10 2.3. Radiance simulation in Radiative Transfer Model 12 3. IASI 1D-Var assimilation 14 3.1. Retrieval of cloud top pressure and cloud fraction 14 3.2. 1D-Var analysis 20 4. Preparation of simulation dataset 21 4.1. ECMWF short-range forecast 21 4.2. Simulation of IASI radiances 25 4.3. Simulation of UM background profiles 32 5. Assessment of pre-developed methods with simulation dataset 35 5.1. Pre-developed cloudy-sky radiance assimilation 35 5.2. Assessment of the pre-developed assimilation method 37 6. Development of a new cloud parameters retrieval method 40 6.1. Definition of 'Optimum CTP' 40 6.2. Evaluation of original retrieval method 48 6.3. New retrieval method with an ANN approach 54 7. Assessment of ANN retrieval method in the 1D-Var analysis 58 7.1. Simulation Framework 58 7.2. Experiments with the UM NWP system 73 8. Impact study of ANN method on the UM forecast 83 8.1. Assessment of experiments in the UM NWP system 85 8.2. Impact on the precipitation forecast 92 8.3. Impact of tropical cyclone forecast 97 9. Summary and discussion 110 References 116 국문초록 121 감사의 글 124Docto

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