[세미나] 노유정 박사님

Improvement of Satellite Cloud Layer Products Using Multi-Sensor Observations and Numerical Model Simulations (다중 센서 관측과 수치모델 데이터를 결합한 위성 구름 연직층 산출물 개선에 관한 연구)

#### 노유정 박사님 (NOAA Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University) #### 2019년 9월 3일 (화) 16:00 #### 과학관 553호
#### Abstract

Estimating three‐dimensional cloud structures is important to aviation applications as well as numerical model development, but assigning cloud base from conventional passive radiometers is still challenging. Recently, we developed a statistical Cloud Base Height (CBH) algorithm constrained by Cloud Top Height (CTH) and Cloud Water Path (CWP) using active and passive sensor observations from NASA A-Train satellite data (CloudSat/CALIPSO and Aqua MODIS). In the algorithm, Cloud Geometric Thickness (CGT) is derived from a lookup table with two primary inputs of CTH and CWP and subtracted from CTH to generate CBH. The CBH information is used to improve Cloud Cover/Layers (CCL) products. The algorithm includes special accommodations for handling optically thin cirrus and deep convection. Numerical Weather Prediction model output is employed as supplementary data when satellite retrievals are not valid. The algorithm, now operational as part of the NOAA Enterprise Cloud Algorithms, has been successfully applied to both polar and geostationary satellite sensors including the Joint Polar Satellite System (JPSS) program satellites (S-NPP and NOAA-20) Visible Infrared Imaging Radiometer Suite (VIIRS) and GOES-16/17 Advanced Baseline Image (ABI). This study introduces the CBH/CCL algorithm and our ongoing efforts to improve nighttime retrievals and multilayer cloud cases.