The Advanced Baseline Imager (ABI) is the primary instrument onboard GOES-R for imaging Earth’s weather, climate,
and environment and will be used for a wide range of applications related to weather, oceans, land, climate, and hazards
(fires, volcanoes, hurricanes, and storms that spawn tornados). It will provide over 65% of all the mission data products
currently defined. ABI views the Earth with 16 different spectral bands, including two visible channels, four nearinfrared
channels and ten infrared channels at 0.5, 1, and 2 km spatial resolutions respectively. For most of the
operational ABI retrieval algorithms, the collocated/co-registered radiance dataset are at 2 km resolution for all of the
bands required. This requires down-scaling of the radiance data from 0.5 or 1 km to 2 km for ABI visible and near-IR
bands (2 or 1, 3 & 5 respectively), the reference of 2 km is the nominal resolution at the satellite sub-point. In this paper,
the spatial resolution characteristic of the ABI fixed grid level1b radiance data is discussed. An optimum interpolation
algorithm which has been developed for the ABI multiple channel radiance down-scaling processing is present.
Satellite observation collocation algorithms are generally used to spatially match observations or products from different
satellite systems. The spatially matched and integrated satellite datasets are commonly used in integrated retrievals,
satellite instrument inter-calibration and satellite observation validation. Instrument physical based collocation
algorithms are developed at NOAA/NESDIS/STAR to support the development of the satellite observation integration
system. The algorithms are applied within the Geostationary satellite & Polar satellite (GEO-LEO) integration system
for IASI/SEVRI and will applied in the future CrIS/GOES-R observation integration system. In this paper, the details of
the algorithms for IASI/SEVERI and AIRS/SEVIRI collocation are described and some results for both are presented.
Since late 1980s there have been a series of satellite-borne microwave (MW) radiometers operated for remote sensing of water-related parameters in particular for rainfall observation among them SSMII on DMSP TMI on TRMM and AMSR-E on AQUA are well known instruments. A lot of retrieval schemes have been published for operational and research purposes. Inter-comparison of different retrieval algorithms and their products is an important task for their reliable application with enough accuracy. In this paper we will compare some algorithms for SSM/I products over West Pacific area. The methodology is mainly focusing on the comparison of retrieved hourly rain rate at the spatially and temporally collocated area statistical results and case study will be given.
Development and testing of the IASI processing and distribution system is currently ongoing at NOAA/NESDIS/ORA. Level 1C data for 8461 channels will be available to NESDIS/NOAA from EUMETSAT shortly after MetOp 1 launch (currently scheduled for October 2005). Prior to launch, a simulation system will provide pseudo near-real time data for system testing and refinement. This will allow for a smooth and immediate system transition to the actual data processing when it becomes available. The ingested EUMETSAT level1C data will be subset both spectrally and spatially and then placed into BUFR format for a number of products including: (1) Level 1C (calibrated, apodized, and navigated) brightness temperatures, (2) cloud-cleared radiances, and (3) PCA reconstructed radiances. The subset level 1C data will be delivered within three hours of observation. System validation will consist of comparing the products to collocated radiosonde observations and model forecasts.
Spaceborne microwave (MW) remote sensing of rainfall distribution with multi-channel radiometers has been proved as a powerful tool in past decade, in particular with DMSP's SSM/I data. Similar instruments but with different channel combinations, such as ADEOS-II/AMSR are being developed. Although there have been several retrieval schemes used for research and operational application, improvement of retrieval accuracy is still an important subject. As part of a NASDA project for ADEOS-II/AMSR retrieval algorithm development and Chinese National High-Tech R and D Program for Space Development, we proposed two kinds of retrieval algorithms for rainfall rate over ocean with SSM/I data. The first kind of algorithm is based on the probability pairing method, in which three different retrieval rain indices composed with different combination of SSM/I's channels are used as pairing indices with surface rainfall rate data provided by NASDA, Japan. Three different empirical rainfall rate--rain index relationship are produced. The second kind of algorithm is based on the method of self organization feature mapping (SOM), a kind of artificial neural network. SSM/I data and co-located surface rainfall data are put into SOM and clustering procedure is self-trained. After training, 154 clustering centers are formed and for each cluster a regressive relationship between retrieved rainfall rate and SSM/I data is established. In this paper, these two kinds of algorithm are briefly reviewed with their developments and validation. Their respective advantages and limitations are discussed.