GHRSST v2 Level 3U Global Skin Sea Surface Temperature from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite created by the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) (GDS version 2)

The ACSPO VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO VIIRS L2P product Data files are 10min granules in netcdf4 format compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 1GB/day. Fill values are reported at all invalid pixels, including pixels at >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.2deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010).

ACSPO VIIRS L3U（Level 3 Uncollated，即三级未整理）产品为ACSPO VIIRS L2P产品的网格化版本。其数据文件为时长10分钟的数据条带，采用netCDF4格式，符合全球高分辨率海表温度工作组（Group for High Resolution Sea Surface Temperature，GHRSST）数据规范2.0（GDS2）。每24小时内包含144个数据条带，单日总数据量达1GB。所有无效像元（包括距陆地5千米以上区域的像元）均会以填充值进行标记。针对每个有效水体像元（定义为海洋、近岸海域、湖泊或河流，且距陆地不超过5千米），将输出以下数据层：海表温度（Sea Surface Temperature，SST）、ACSPO晴空掩码（ACSPO Clear-Sky Mask，ACSM，作为l2p_flags的一部分集成于每个网格中，l2p_flags还包含昼夜、陆地、海冰、晨昏和太阳耀斑标记）、美国国家环境预报中心（National Centers for Environmental Prediction，NCEP）风速，以及ACSPO海表温度与参考值的差值（加拿大气象中心0.2°分辨率四级海表温度产品）。仅对质量等级（Quality Level，QL）为5的L2P海表温度数据进行网格化处理，因此所有有效海表温度数据均可供用户使用。根据GDS2规范，每个包含有效海表温度的像元中还将输出两个额外的传感器特定误差统计数据层：传感器特定误差统计（Sensor-Specific Error Statistics，SSES）的偏差与标准差。ACSPO VIIRS L3U产品已通过原位观测数据进行监测与验证（Xu与Ignatov，2014），验证工作基于SQUAM框架（Dash等，2010）。