Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) requires improved accuracy in the retrieval of sea surface skin temperature (SSTS) from its Visible Infrared Imager Radiometer Suite (VIIRS) sensor over the capability to retrieve bulk sea surface temperature (SSTB) that has been demonstrated with currently operational National Oceanic and Atmospheric Administration (NOAA) satellites carrying the Advanced Very High Resolution Radiometer (AVHRR) sensor. Statistics show an existing capability to retrieve SSTB with a 1σ accuracy of about 0.8 K in the daytime and 0.6 K with nighttime data. During the NPOESS era, a minimum 1σ SSTS measurement uncertainty of 0.5 K is required during daytime and nighttime conditions, while 0.1 K is desired. Simulations have been performed, using PACEOS™ scene generation software and the multichannel sea surface temperature (MCSST) algorithms developed by NOAA, to better understand the implications of this more stringent requirement on algori...

Blueprints for near-future moderate resolution satellite-borne sensor systems such as the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) do not include plans to ensure that land surface observations are made from an early morning (AM) orbit (07:30-08:30 local time) as well as in the afternoon (PM), and current spaceborne sensors designed to sample the bidirectional reflectance distribution function (BRDF) achieve this by off-nadir viewing with a small range of solar zenith angles (SZA). This may limit the utility of current and future systems in important Earth observation applications such as estimating the fraction of absorbed photosynthetically-active radiation (fAPAR). The lack of observations at 50/spl deg/ < SZA < 70/spl deg/ may only be partly offset by the 09:30 orbit foreseen for EUMETSAT's METOP satellite, to carry a third generation Advanced Very High Resolution Radiometer (AVHRR). Here existing AVHRR data are used to examine the likely impact of inverting models with observations at a small range of Sun angles.

The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

The Suomi National Polar-orbiting Partnership (S-NPP) satellite, formally the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), provides a bridge between current and future low-Earth orbiting weather and environmental observation satellite systems. The NASA's NPP VIIRS Characterization Support Team (VCST) is designed to assess the long term geometric and radiometric performance of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the S-NPP spacecraft and to support NPP Science Team Principal Investigators (PI) for their independent evaluation of VIIRS Environmental Data Records (EDRs). This paper provides an overview of Suomi NPP VIIRS on-orbit calibration activities and examples of sensor initial on-orbit performance. It focuses on the radiometric calibration support activities and capabilities provided by the NASA VCST.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi NPP (S-NPP) and the NOAA-20 satellites that provides state-of-the-art Earth observations for ocean, land, aerosol, and cloud applications. VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR, or Level 1b products) are calibrated and produced independently by The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) VIIRS science teams. Multiple versions of S-NPP and NOAA-20 VIIRS SDRs are available to date. This study evaluates the long-term calibration stability, biases, and inter-channel consistency of S-NPP and NOAA-20 VIIRS SDRs generated by NOAA and NASA over Deep Convective Clouds (DCC) to support downstream applications, especially climate data record studies. Five VIIRS RSB SDRs were analyzed in this study: (1) NOAA version 2 S-NPP VIIRS reprocessed SDRs (NOAA-NPP-V2, 2012–2020), (2) NASA Collection 1 S-NPP VIIRS SDRs (NASA-NPP-C1, 2012–2021), (3) NASA Collection 2 S-NPP VIIRS SDRs (NASA-NPP-C2, 2012–2021), (4) NOAA constant F-factor calibrated NOAA-20 VIIRS SDRs (NOAA-N20-ConstF, 2018–2021), and (5) NASA Collection 2 NOAA-20 VIIRS SDRs (NASA-N20-C2, 2018–2021). The DCC time series analysis results indicate that the calibrations of the three S-NPP VIIRS RSB SDRs are generally stable, with trends within ±0.1%/year for all RSBs, except for M3–M4 (all three S-NPP SDRs) and I3 (NASA-NPP-C1 only). The calibration of NASA-NPP-C2 SDRs is more uniform at individual detector levels. NOAA-NPP-V2 and NASA-NPP-C1 SDRs exhibit non-negligible time-dependent detector level degradation in M1–M4 (up to 1.5% in 2020–2021), causing striping in the SDR imagery. The biases between NOAA and NASA S-NPP VIIRS RSB SDRs are from 0.1% to 2.4%. The calibrations of the two NOAA-20 VIIRS RSB SDRs are also generally stable, with trends within ±0.16%/year. Small downward trends were observed in the visible and near-infrared (VIS/NIR) bands, and small upward trends were observed in the shortwave infrared (SWIR) bands for both NOAA and NASA NOAA-20 SDRs. The biases between NOAA and NASA NOAA-20 VIIRS RSB SDRs are nearly constant over time and within ±0.2% for VIS/NIR bands and ±0.7% for SWIR bands. There exists large inter-satellite biases between S-NPP and NOAA-20 VIIRS SDRs, especially in the VIS/NIR bands (up to 4.5% for NOAA SDRs and up to 7% for NASA SDRs). In addition, the DCC reflectance of S-NPP VIIRS RSB spectral bands is more consistent with that of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) than that of NOAA-20. Bands M4 and M9 seem out of family in all five S-NPP and NOAA-20 RSB SDRs evaluated.

Data records based on remotely sensed, operational measurements of the ocean comprise 10 of the 38 user-validated data requirements levied on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and are drivers of the design and implementation strategy for two of its principal sensors: the Visible-Infrared Imager Radiometer Suite (VIIRS) and the Microwave Imager/Sounder (MIS). The NPOESS Preparatory Project (NPP) will deliver the VIIRS subset of these NPOESS ocean observations for demonstration and risk reduction after its planned launch in early 2011. This paper focuses on current best estimates of the performance of the system in delivering these ocean products. Test data and associated performance assessments from ongoing NPP VERS environmental testing and design definition activities associated with the MIS sensor provide additional insight into expected performance of the system. This insight translates to performance estimates that continue to meet or exceed comparable heritage operational systems and satisfy key operational threshold requirements for ocean observations.

ABSTRACTTwo approaches could be utilized to derive the vegetation health indices from recently launched Visible Infrared Imaging Radiometer Suite (VIIRS) sensor with the goal to maintain the consistency with the long-term Advanced Very High Resolution Radiometer (AVHRR) vegetation health products. One approach is to convert VIIRS Normalized Difference Vegetation Index (NDVI)/brightness temperature (BT) to AVHRR, as the first version. The other is to derive long-term pixel-based VIIRS climatology without converting at NDVI/BT level. The second version of products, derived from the new VIIRS pseudo long-term climatology as presented in this article, can trace the signature of the long-term AVHRR vegetation health products quite well, in terms of histogram, correlation coefficients, time series, and so on. The extended products with higher resolutions also perform as expected, indicating that the constructed long-term high-resolution climatology is reasonable. The third version of the products, based on VIIRS real long-term climatology, still needs many years to develop.

The NPP is a joint-agency mission intending to serve the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO) and the National Aeronautics and Space Administration (NASA) and their user communities. The NPP provides the earth science community with data continuity for systematic measurements in the disciplines of terrestrial and oceanic productivity, and atmospheric temperature and humidity profiles. The NPP also provides the IPO and its users a risk reduction demonstration of capabilities for critical NPOESS instruments, their associated algorithms, and NPOESS command, control, communications, and interface data processing systems, prior to the first NPOESS launch, thereby allowing lessons learned from NPP to be incorporated in to NPOESS.

The retrieval of the distribution of aerosol properties and determination of trends in their temporal variation can only be achieved by the use of long-term satellite measurements. The Advanced Very High Resolution Radiometer (AVHRR) carried on board the National Oceanic and Atmospheric Administration (NOAA) and the EUMETSAT Meteorological Operational Satellite (Metop) polar orbiting satellites offers more than 35 years of satellite data since 1978 which makes it worthwhile to explore their use for the analysis of aerosols on a daily basis. So in this paper we applied the algorithm for the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) to data from AVHRR Channel 1 on the NOAA-12 and NOAA-14 satellites using a model for the earth-atmosphere system which couples an atmospheric radiative transfer model with the Ross - Thick - Li-sparse bidirectional reflectance factor (BRF) model. Preliminary results show that LABITS provides good results over China for different surface type. Comparing our results with the with SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) aerosol product AOD products shows good agreement. The algorithm has the potential to retrieve global AOD over land for long time series of NOAA AVHRR data going back to the 1980s, which are urgently needed for studies on aerosol climatology and global climate change.

The operational Moderate-Resolution Imaging Spectroradiometer (MODIS) products for cloud properties such as cloud-top pressure (CTP), effective cloud amount (ECA), cloud particle size (CPS), cloud optical thickness (COT), and cloud phase (CP) have been available for users globally. An approach to retrieve COT is investigated using MODIS infrared (IR) window spectral bands (8.5 mm, 11mm, and 12 mm). The COT retrieval from MODIS IR bands has the potential to provide microphysical properties with high spatial resolution during night. The results are compared with those from operational MODIS products derived from the visible (VIS) and near-infrared (NIR) bands during day. Sensitivity of COT to MODIS spectral brightness temperature (BT) and BT difference (BTD) values is studied. A look-up table is created from the cloudy radiative transfer model accounting for the cloud absorption and scattering for the cloud microphysical property retrieval. The potential applications and limitations are also discussed. This algorithm can be applied to the future imager systems such as Visible/Infrared Imager/Radiometer Suite (VIIRS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R.

Systematic corrections of AVHRR image composites for temporal studies

In the framework of the Satellite Application Facility on Climate Monitoring (CM‐SAF) an algorithm was developed to retrieve Cloud Physical Properties (CPP) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (METEOSAT−8) and the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) satellites. This paper presents the CPP algorithm and determines if SEVIRI can be used together with AVHRR to build a consistent and accurate data set of cloud optical thickness (COT) and cloud liquid water path (CLWP) over Europe for climate research purposes. After quantifying the differences in 0.6 and 1.6 μm operational calibrated reflectances of SEVIRI and AVHRR, a recalibration procedure is proposed to normalize and absolutely calibrate these reflectances. The effects of recalibration, spatial resolution, and viewing geometry differences on the SEVIRI and AVHRR cloud property retrievals are evaluated. The intercomparison of 0.6 and 1.6 μm operationally calibrated reflectances indicates ∼6 and ∼26% higher reflectances for SEVIRI than for AVHRR. These discrepancies result in retrieval differences between AVHRR and SEVIRI of ∼8% for COT and ∼60% for CLWP. Owing to recalibration these differences reduce to ∼5%, while the magnitude of the median COT and CLWP values of AVHRR decrease ∼2 and ∼60% and the SEVIRI values increase ∼10 and ∼55%, respectively. The differences in spatial resolution and viewing geometry slightly influence the retrieval precision. Thus the CPP algorithm can be used to build a consistent and high‐quality data set of SEVIRI and AVHRR retrieved cloud properties for climate research purposes, provided the instrument reflectances are recalibrated, preferably guided by the satellite operators.

In the next decade, weather forecasters, climate researchers, and decision-makers will rely on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) to meet many of their needs for remotely-sensed, Earth science data and information. NPOESS spacecraft will be launched into two orbital planes beginning in 2013 to provide significantly improved operational capabilities and benefits to satisfy critical civil and national security requirements for space-based, remotely sensed environmental data.

The Integrated Program Office (IPO) is developing a ground system architecture for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) because requirements cannot be accommodated by current technology. Increased data rates, increased processing complexity, and more stringent latency requires another approach. We discuss the rationale behind the challenging requirements, in which direction the requirements are evolving, and how the NPOESS ground system architecture might be related to the NASA Vision of a Web of Sensors.

Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research.