GPM SSMIS on F18 (GPROF) Radiometer Precipitation Profiling L2 1.5 hours 12 km V08 (GPM_2AGPROFF18SSMIS) at GES DISC

Version 08 is the current version of the data set. Older versions will no longer be available and have been superseded by the current version.

The 2AGPROF (Goddard Profiling) algorithm retrieves consistent precipitation and related science fields from the following GMI and partner passive microwave sensors:
+ TMI (TRMM)
+ GMI, (GPM)
+ SSMI (DMSP F15), SSMIS (DMSP F16, F17, F18, F19)
+ AMSR2 (GCOM-W1)
+ MHS (NOAA 18,19)
+ MHS (METOP A,B)
+ ATMS (NPP)
+ SAPHIR (MT1)

This provides the bulk of the 3-hour coverage achieved by GPM. For each sensor, there are nearrealtime (NRT) products, standard products, and climate products. These differ only in the amount of data that are available within 3 hours, 48 hours, and 3 months of collection, as well as the ancillary data used. The NRT product uses GANAL forecast fields. Standard products use the GANAL analysis product, while the climate product uses ECMWF reanalysis in order to allow for consistent data records with earlier missions. These earlier data may be archived separately. The main strength of the product is the large sampling provided.

The GPM radiometer algorithms are Bayesian-type algorithms. These algorithms search an apriori database of potential rain profiles and retrieve a weighted average of these entries based upon the proximity of the observed brightness temperature (Tb) to the simulated Tb corresponding to each rain profile. By using the same a-priori database of rain profiles, with appropriate simulated Tb for each constellation sensor, the Bayesian method is completely parametric and thus well suited for GPM's constellation approach. The a-priori information will be supplied by the combined algorithm supplied by GPM's core satellite as soon after launch as feasible. Databases for V0 of the algorithm had to be constructed from various sources as described in the ATBD. The solution provides a mean rain rate as well as the vertical structure of cloud and precipitation hydrometeors and their uncertainty.

GPM Project generated these data at spatial sampling of 13 x 13 km (nominal at nadir).

本数据集当前版本为V08，旧版本已不再提供服务，由当前版本完全替代。

2AGPROF（Goddard Profiling，戈达德廓线）算法可从以下GMI及配套被动微波传感器中反演出一致性的降水及相关科学参量：
+ TMI（TRMM）
+ GMI（GPM）
+ SSMI（DMSP F15）、SSMIS（DMSP F16、F17、F18、F19）
+ AMSR2（GCOM-W1）
+ MHS（NOAA 18、19）
+ MHS（METOP A、B）
+ ATMS（NPP）
+ SAPHIR（MT1）

该算法可覆盖GPM任务绝大部分的3小时观测范围。针对每一款传感器，本数据集提供近实时（NRT）产品、标准产品以及气候产品三类。三类产品的差异仅体现在数据可用时效（采集后3小时、48小时及3个月内）以及所使用的辅助数据上。近实时产品采用GANAL预报场数据；标准产品采用GANAL分析场数据；而气候产品则采用欧洲中期天气预报中心（ECMWF）再分析数据，以确保与早期任务的数据记录保持一致性。此类早期数据可单独归档存储。本产品的核心优势在于其庞大的采样覆盖规模。

GPM辐射计反演算法属于贝叶斯（Bayesian）类算法。此类算法会遍历潜在降水廓线的先验数据库，并根据观测亮温（Tb）与各降水廓线对应的模拟亮温的匹配程度，对数据库中的条目进行加权平均，从而得到反演结果。通过采用统一的降水廓线先验数据库，并为各星座传感器配置适配的模拟亮温计算方式，该贝叶斯方法为完全参数化方法，因此非常适配GPM的卫星星座观测方案。先验信息将由GPM核心卫星搭载的联合算法在发射后尽快提供。该算法V0版本所需的数据库需按照算法理论基础文档（ATBD）中的描述，从多类数据源构建。反演结果可给出平均降水率，以及云与降水水凝物的垂直结构及其不确定性。

GPM项目生成的本数据集空间采样分辨率为13×13 km（星下点标称值）。