Suomi NPP Research Articles

Retrospective analysis of Suomi NPP VIIRS radiometric bias for reflective solar bands due to operational calibration changes

ABSTRACTA comprehensive analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) radiometric bias relative to Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 data has been performed since early 2012 for selected reflective solar bands. The study suggests that VIIRS bias trends changes over time mainly due to calibration updates and anomalies. Results show nearly consistent biases of 1.7% for M5 (0.672 µm) and 2% for M7 (0.865 µm) throughout the mission. However, M1 (0.412 µm) and M4 (0.555 µm) biases are less consistent. While biases for both M1 and M4 fluctuates mostly around 0%, M1 shows most frequent short-term changes in bias trends, as high as 4%. When the bias trends are compared with VIIRS on- board-calibration-based gain trends, there exists a high correlation. In addition, the operational VIIRS data product of NOAA and the reprocessed NASA Land Product Evaluation and Test Element (PEATE) data were compared by trending the radiance ratio. The ratio trends show calibration differences that agree well with bias trends. The comparison of bias with F-factors and ratio trends indicates that the frequent changes observed in VIIRS bias trends are primarily caused by calibration updates and anomalies in VIIRS operational calibration. The study suggests that even though the operational VIIRS data archive meets the specification of ±2% radiometric uncertainty, reprocessing can improve data quality needed for rigorous scientific applications.

A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements

Different aspects of the operational constraints of remote sensing inverse problems are thoroughly investigated by simulation studies, using a deterministic method, namely regularized total least squares (RTLS). For demonstration purposes, water vapor profiles retrievals from simulated Suomi NPP Cross-track Infrared Souder (CrIS) hyperspectral measurements are considered. Synthetic CrIS radiances are generated using a line-by-line radiative transfer model (GENSPECT) with ~424 realistic radiosonde profiles and US 1976 standard atmosphere as inputs. These results are also compared with those from a prevalent stochastic method. Our findings show that the stochastic method, even with additional deterministic constraints (truncated singular value decomposition) applied on top of it, is often unable to produce useful retrieval results, i.e., posterior error is more than the a priori error. In contrast, RTLS is able to produce deterministically unique results according to the available information content in the measurements, which could result in a paradigm shift in operational satellite inversion.

Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites

Information on ice is important for shipping, weather forecasting, and climate monitoring. Historically, ice cover has been detected and ice concentration has been measured using relatively low-resolution space-based passive microwave data. This study presents an algorithm to detect ice and estimate ice concentration in clear-sky areas over the ocean and inland lakes and rivers using high-resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Orbiting Partnership (S-NPP) and on future Joint Polar Satellite System (JPSS) satellites, providing spatial detail that cannot be obtained with passive microwave data. A threshold method is employed with visible and infrared observations to identify ice, then a tie-point algorithm is used to determine the representative reflectance/temperature of pure ice, estimate the ice concentration, and refine the ice cover mask. The VIIRS ice concentration is validated using observations from Landsat 8. Results show that VIIRS has an overall bias of −0.3% compared to Landsat 8 ice concentration, with a precision (uncertainty) of 9.5%. Biases and precision values for different ice concentration subranges from 0% to 100% can be larger.

High-resolution tropospheric carbon monoxide profiles retrieved from CrIS and TROPOMI

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) instrument is the only satellite-borne sensor in operation that uses both thermal (TIR) and near-infrared (NIR) channels to estimate CO profiles. With more than 15 years (2000 to present) of validated multispectral observations, MOPITT provides the unique capability to separate CO in the lowermost troposphere (LMT, surface to 3 km (∼ 700 hPa)) from the free-tropospheric abundance. To extend this record, a new, hyper-spectral approach is presented here that will provide CO data products exceeding the capabilities of MOPITT by combining the short-wavelength infrared (SWIR, equivalent to the MOPITT NIR) channels from the TROPOspheric Monitoring Instrument (TROPOMI) to be launched aboard the European Sentinel 5 Precursor (S5p) satellite in 2016 and the TIR channels from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. We apply the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) retrieval algorithm to quantify the potential of this joint CO product. CO profiles are retrieved from a single-footprint, full-spectral-resolution CrIS transect over Africa on 27–28 August 2013 coincident with significant biomass burning. Comparisons of collocated CrIS and MOPITT CO observations for the LMT show a mean difference of 2.8 ± 24.9 ppb, which is well within the estimated measurement uncertainty of both sensors. The estimated degrees of freedom (DOF) for CO signals from synergistic CrIS–TROPOMI retrievals are approximately 0.9 in the LMT and 1.3 above the LMT, which indicates that the LMT CO can be distinguished from the free troposphere, similar to MOPITT multispectral observations (0.8 in the LMT, and 1.1 above the LMT). In addition to increased sensitivity, the combined retrievals reduce measurement uncertainty, with ∼ 15 % error reduction in the LMT. With a daily global coverage and a combined spatial footprint of 14 km, the joint CrIS–TROPOMI measurements have the potential to extend and improve upon the MOPITT multispectral CO data records for the coming decade.

Characterization of geolocation accuracy of Suomi NPP Advanced Technology Microwave Sounder measurements

AbstractThe Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar‐orbiting Partnership satellite has 22 channels at frequencies ranging from 23 to 183 GHz for probing the atmospheric temperature and moisture under all weather conditions. As part of the ATMS calibration and validation activities, the geolocation accuracy of ATMS data must be well characterized and documented. In this study, the coastline crossing method (CCM) and the land‐sea fraction method (LFM) are utilized to characterize and quantify the ATMS geolocation accuracy. The CCM is based on the inflection points of the ATMS window channel measurements across the coastlines, whereas the LFM collocates the ATMS window channel data with high‐resolution land‐sea mask data sets. Since the ATMS measurements provide five pairs of latitude and longitude data for K, Ka, V, W, and G bands, respectively, the window channels 1, 2, 3, 16, and 17 from each of these five bands are chosen for assessing the overall geolocation accuracy. ATMS geolocation errors estimated from both methods are generally consistent from 40 cases in June 2014. The ATMS along‐track (cross‐track) errors at nadir are within ±4.2 km (±1.2 km) for K/Ka, ±2.6 km (±2.7 km) for V bands, and ±1.2 km (±0.6 km) at W and G bands, respectively. At the W band, the geolocation errors derived from both algorithms are probably less reliable due to a reduced contrast of brightness temperatures in coastal areas. These estimated ATMS along‐track and cross‐track geolocation errors are well within the uncertainty requirements for all bands.

Downscaling land surface temperatures at regional scales with random forest regression

Abstract Environmental monitoring with satellite data is facilitated by frequent observations at a fine spatial scale. As land surface temperature (LST) is one environmental key variable, we implemented a random forest (RF) regression approach to increase the spatial resolution of LST maps from ~ 1 km, routinely available in daily repetition from the Moderate Resolution Imaging Spectroradiometer (MODIS), to ~ 250 m. LST was downscaled based on its relationship to topographic variables derived from digital elevation data of the Shuttle Radar Topography Mission (SRTM), land cover data (MODIS product MCD12Q1), and surface reflectances in the visible red and near-infrared, which both are provided with the MODIS/Terra daily product MOD09GQ at ~ 250 m resolution. The approach was tested for a complex landscape in the Eastern Mediterranean, the Jordan River Region, with LST fields from aggregated Landsat-7 ETM + and MODIS (MODIS/Terra LST product MOD11A1) data; as reference at the finer scale, we used Landsat-7 derived LST data. For the ideal-case scenario with both degraded and reference values from the same sensor (Landsat-7 ETM +), root mean square errors (RMSE) of downscaled LSTs ranged from 1.02 K to 1.43 K for six different acquisition dates. When compared to the widely-adopted and in parallel applied TsHARP sharpening method, that is based on the relationship between NDVI and LST, downscaling accuracy with RF improved up to 19%. Applying the RF approach to MODIS LST products yielded RMSEs from 1.41 K to 1.92 K, whereas the TsHARP method and also a uniform disaggregation by resampling provided only slightly worse results. For the real MODIS LST product, downscaling with RF was affected by lower thermal contrasts in the image data that hindered an adequate training to reproduce temperature variations at the finer scale of ~ 250 m. In this context, we assume the LST product of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument (as a successor of MODIS onboard the SUOMI NPP platform) to be a better candidate for downscaling, as it provides a spatial resolution of ~ 750 m. One advantage of the RF approach is that predictor datasets can easily be adapted to data availability. In an extended RF approach with all Landsat spectral bands, downscaling results for formerly aggregated Landsat data improved distinctly and now ranged from 0.98 K to 1.33 K. This approach is also promising for the downscaling of real MODIS or VIIRS LST data as it may be combined with already available reflective data fusion models that are able to blend Landsat data and sensor data with a coarse spatial resolution (given spectral bandwidths corresponding to Landsat) to generate temporally dense synthetic Landsat time series.

Radiometric Inter-Calibration between Himawari-8 AHI and S-NPP VIIRS for the Solar Reflective Bands

The Advanced Himawari Imager (AHI) on-board Himawari-8, which was launched on 7 October 2014, is the first geostationary instrument housed with a solar diffuser to provide accurate onboard calibrated data for the visible and near-infrared (VNIR) bands. In this study, the Ray-matching and collocated Deep Convective Cloud (DCC) methods, both of which are based on incidently collocated homogeneous pairs between AHI and Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), are used to evaluate the calibration difference between these two instruments. While the Ray-matching method is used to examine the reflectance difference over the all-sky collocations with similar viewing and illumination geometries, the near lambertian collocated DCC pxiels are used to examine the difference for the median or high reflectance scenes. Strong linear relationships between AHI and VIIRS can be found at all the paired AHI and VIIRS bands. Results of both methods indicate that AHI radiometric calibration accuracy agrees well with VIIRS data within 5% for B1-4 and B6 at mid and high reflectance scenes, while AHI B5 is generally brighter than VIIRS by ~6%–8%. No apparent East-West viewing angle dependent calibration difference can be found at all the VNIR bands. Compared to the Ray-matching method, the collocated DCC method provides less uncertainty of inter-calibration results at near-infrared (NIR) bands. As AHI has similar optics and calibration designs to the GOES-R Advanced Baseline Imager (ABI), which is currently scheduled to launch in fall 2016, the on-orbit AHI data provides a unique opportunity to develop, test and examine the cal/val tools developed for ABI.

Comparison of the Calibration Algorithms and SI Traceability of MODIS, VIIRS, GOES, and GOES-R ABI Sensors

The radiometric calibration equations for the thermal emissive bands (TEB) and the reflective solar bands (RSB) measurements of the earth scenes by the polar satellite sensors, (Terra and Aqua) MODIS and Suomi NPP (VIIRS), and geostationary sensors, GOES Imager and the GOES-R Advanced Baseline Imager (ABI) are analyzed towards calibration algorithm harmonization on the basis of SI traceability which is one of the goals of the NOAA National Calibration Center (NCC). One of the overarching goals of NCC is to provide knowledge base on the NOAA operational satellite sensors and recommend best practices for achieving SI traceability for the radiance measurements on-orbit. As such, the calibration methodologies of these satellite optical sensors are reviewed in light of the recommended practice for radiometric calibration at the National Institute of Standards and Technology (NIST). The equivalence of some of the spectral bands in these sensors for their end products is presented. The operational and calibration features of the sensors for on-orbit observation of radiance are also compared in tabular form. This review is also to serve as a quick cross reference to researchers and analysts on how the observed signals from these sensors in space are converted to radiances.

Fast and Accurate Collocation of the Visible Infrared Imaging Radiometer Suite Measurements with Cross-Track Infrared Sounder

Given the fact that Cross-track Infrared Sounder (CrIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) are currently onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite and will continue to be carried on the same platform as future Joint Polar Satellite System (JPSS) satellites for the next decade, it is desirable to develop a fast and accurate collocation scheme to collocate VIIRS products and measurements with CrIS for applications that rely on combining measurements from two sensors such as inter-calibration, geolocation assessment, and cloud detection. In this study, an accurate and fast collocation method to collocate VIIRS measurements within CrIS instantaneous field of view (IFOV) directly based on line-of-sight (LOS) pointing vectors is developed and discussed in detail. We demonstrate that this method is not only accurate and precise from a mathematical perspective, but also easy to implement computationally. More importantly, with optimization, this method is very fast and efficient and thus can meet operational requirements. Finally, this collocation method can be extended to a wide variety of sensors on different satellite platforms.

Data-intensive multispectral remote sensing of the nighttime Earth for environmental monitoring and emergency response

All Most of the remote sensing applications rely on the daytime visible and infrared images of the Earth surface. Increase in the number of satellites, their spatial resolution as well as the number of the simultaneously observed spectral bands ensure a steady growth of the data volumes and computational complexity in the remote sensing sciences. Recent advance in the night time remote sensing is related to the enhanced sensitivity of the on-board instruments and to the unique opportunity to observe “pure” emitters in visible infrared spectra without contamination from solar heat and reflected light. A candidate set of the night-time emitters observable from the low-orbiting and geostationary satellites include steady state and temporal changes in the city and traffic electric lights, fishing boats, high-temperature industrial objects such as steel mills, oil cracking refineries and power plants, forest and agricultural fires, gas flares, volcanic eruptions and similar catastrophic events. Current satellite instruments can detect at night 10 times more of such objects compared to daytime. We will present a new data-intensive workflow of the night time remote sensing algorithms for map-reduce processing of visible and infrared images from the multispectral radiometers flown by the modern NOAA/NASA Suomi NPP and the USGS Landsat 8 satellites. Similar radiometers are installed on the new generation of the US geostationary GOES-R satellite to be launched in 2016. The new set of algorithms allows us to detect with confidence and track the abrupt changes and long-term trends in the energy of city lights, number of fishing boats, as well as the size, geometry, temperature of gas flares and to estimate monthly and early flared gas volumes by site or by country. For real-time analysis of the night time multispectral satellite images with global coverage we need gigabit network, petabyte data storage and parallel compute cluster with more than 20 nodes. To meet the processing requirements, we have used the supercomputer at the Kurchatov Institute in Moscow.

Radiometric Stability Monitoring of the Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Reflective Solar Bands Using the Moon

The Suomi NPP (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) performs the scheduled lunar roll maneuver on a monthly basis. The lunar calibration coefficients and lunar F-factor are calculated by taking the ratio of the lunar observed radiance to the simulated radiance from the Miller and Turner (MT) lunar model. The lunar F-factor is also validated against that derived from the VIIRS Solar Diffuser (SD). The MT model-based lunar F-factors in general agree with SD F-factors. The Lunar Band Ratio (LBR) is also derived from two channel lunar radiances and is implemented in the National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration and Validation System (ICVS) to monitor the VIIRS long-term radiometric performance. The lunar radiances at pixels are summed for each of the VIIRS Reflective Solar Bands (RSBs) and normalized by the reference band M11 which has the most stable SD-based calibration coefficient. LBRs agree with the SD based F-factor ratios within one percent. Based on analysis with these two independent lunar calibration methods, SD-based and LBR-based calibrations show a lifetime consistency. Thus, it is recommended that LBR be used for both VIIRS radiometric calibration and lifetime stability monitoring.

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Agriculture residue (in the form of stubble) burning is commonly practised in the northern states of India along the Indian part of the Indo-Gangetic Plains (IGP), predominantly in the states of Punjab and Haryana. In the present study, we characterize spatio-temporal patterns of stubble burning in Punjab and Haryana states of India, using active fire data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership - Visible Infrared Imaging Radiometer Suite (Suomi NPP-VIIRS) for kharif cropping season (September- November) 2014. Analysis of active fire locations derived from MODIS and VIIRS during September-November 2014 suggest intense practice of stubble burning in the study area with total fire detections going up to 15,222 (MODIS) and 15,568 (VIIRS). Comparative analysis of MODIS and VIIRS active fire detections suggested that VIIRS is more sensitive with higher detection capability. Further, grid-based (5 × 5 km) analysis of fire patterns, viz. total fire detections, fire frequency and total fire intensity using temporal ( kharif season for 2004-2014) MODIS active fire locations suggest intense burning activity in the central and southern districts of Punjab during the study period. Temporal analysis of MODIS active fire locations (2004-2014) suggests a transition of decrease-to-increase in the total number of fire detections during October to November respectively, during 2004 through 2014.

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Spatio-Temporal Characterization of Agriculture Residue Burning in Punjab and Haryana, India, Using MODIS and Suomi NPP VIIRS Data

Remote sensing and petrological observations on the 2012–2013 fissure eruption at Tolbachik volcano, Kamchatka: Implications for reconstruction of the eruption chronology

Abstract We present a reconstruction of the chronological sequence of events that took place during the first days of the 2012–2013 Tolbachik fissure eruption using petrological data and remote sensing methods. We were forced to use this approach because bad weather conditions did not allow direct observations during the first two days of the eruption. We interpreted infrared images from the scanning radiometer VIIRS Suomi NPP and correlated the output with the results of the geochemical study, including comparison of the ash, deposited at the period from 27 to 29 November, with the samples of lava and bombs erupted from the Menyailov and Naboko vents. We argue that the compositional change observed in the eruption products (the decrease of SiO 2 concentration and K 2 O/MgO ratio, increase of MgO concentration and Mg#) started approximately 24 h after the eruption began. At this time the center of activity moved to the southern part of the fissure, where the Naboko group of vents was formed; therefore, this timeframe also characterizes the timing of the Naboko vent opening. The Naboko group of vents remained active until the end of eruption in September 2013.

Suomi NPP VIIRS reflective solar band on-orbit radiometric stability and accuracy assessment using desert and Antarctica Dome C sites

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument flying aboard Suomi NPP satellite. It is a follow on mission for NOAA series AVHRR and NASA MODIS. The radiometric stability and accuracy of VIIRS are critical to make its data useful for weather and climate applications. This study is focused on analyzing VIIRS radiometric performance using well established calibration sites and through the inter-comparison with other satellite instruments such as AQUA MODIS and Landsat 8 OLI. The paper analyzes stability of VIIRS reflective solar bands over more than two years by using sites such as Libya 4, Sudan 1 and Dome C. VIIRS absolute radiometric accuracy is quantified using AQUA MODIS over the above mentioned sites. For VIIRS band M11 (2.1μm), since there is no matching MODIS band pair, this study uses matching SWIR band from Landsat 8 OLI as a reference to quantify the absolute calibration accuracy of M11. The study shows that VIIRS moderate resolution reflective solar bands are stable with better than 0.5% for most of the bands with uncertainty on the order of 1%. After accounting the spectral differences, the absolute radiometric bias estimated through VIIRS and MODIS inter-calibration is within 2% for bands M1–5 and M10 and about 3% for bands M7–8. Similarly, M11 bias estimated using VIIRS and OLI inter-comparison is 5.4%.

Detector-level spectral characterization of the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite long-wave infrared bands M15 and M16.

The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data record (SDR) product achieved validated maturity status in March 2014 after roughly two years of on-orbit characterization (S-NPP spacecraft launched on 28 October 2011). During post-launch analysis the VIIRS Sea Surface Temperature (SST) Environmental Data Record (EDR) team observed an anomalous striping pattern in the daytime SST data. Daytime SST retrievals use the two VIIRS long-wave infrared bands: M15 (10.7μm) and M16 (11.8μm). To assess possible root causes due to detector-level spectral response function (SRF) effects, a study was conducted to compare the radiometric response of the detector-level and operational-band averaged SRFs of VIIRS bands M15 and M16. The study used simulated hyperspectral blackbody radiance data and clear-sky ocean hyperspectral radiances under different atmospheric conditions. It was concluded that the SST product is likely impacted by small differences in detector-level SRFs and that if users require optimal radiometric performance, detector-level processing is recommended for both SDR and EDR products. Future work should investigate potential SDR product improvements through detector-level processing in support of the generation of Suomi NPP VIIRS climate quality SDRs.

Gauss–Seidel limb scattering (GSLS) radiative transfer model development in support of the Ozone Mapping and Profiler Suite (OMPS) limb profiler mission

Abstract. The Gauss–Seidel limb scattering (GSLS) radiative transfer (RT) model simulates the transfer of solar radiation through the atmosphere and is imbedded in the retrieval algorithm used to process data from the Ozone Mapping and Profiler Suite (OMPS) limb profiler (LP), which was launched on the Suomi NPP satellite in October 2011. A previous version of this model has been compared with several other limb scattering RT models in previous studies, including Siro, MCC++, CDIPI, LIMBTRAN, SASKTRAN, VECTOR, and McSCIA. To address deficiencies in the GSLS radiance calculations revealed in earlier comparisons, several recent changes have been added that improve the accuracy and flexibility of the GSLS model, including 1. improved treatment of the variation of the extinction coefficient with altitude, both within atmospheric layers and above the nominal top of the atmosphere; 2. addition of multiple-scattering source function calculations at multiple solar zenith angles along the line of sight (LOS); 3. introduction of variable surface properties along the limb LOS, with minimal effort required to add variable atmospheric properties along the LOS as well; 4. addition of the ability to model multiple aerosol types within the model atmosphere. The model improvements 1 and 2 are verified by comparison to previously published results (using standard radiance tables whenever possible), demonstrating significant improvement in cases for which previous versions of the GSLS model performed poorly. The single-scattered radiance errors that were as high as 4% in earlier studies are now generally reduced to 0.3%, while total radiance errors generally decline from 10% to 1–3%. In all cases, the tangent height dependence of the GSLS radiance error is greatly reduced.

Evaluation of the performance of Suomi NPP VIIRS top of canopy vegetation indices over AERONET sites

The Suomi NPP VIIRS Vegetation Index (VI) Environment Data Record (EDR) includes the Top Of the Atmosphere Normalized Difference Vegetation Index (TOA NDVI) and the Top Of the Canopy Enhanced Vegetation Index (TOC EVI). TOC NDVI will be included in the VI EDR suite for the upcoming JPSS-1 and JPSS-2 missions. Currently, the VIIRS VI suite is undergoing extensive Calibration and Validation (Cal/Val) activities to quantify product performance and to provide guidance for algorithm improvement. In this study we utilized AErosol RObotic NETwork (AERONET) based Surface Reflectance (SR) match-up data sets. Match-ups are pairs of VIIRS SR and SR derived from atmospheric correction of VIIRS TOA reflectances using AERONET ground measurements (aerosol and water vapor parameters) and ancillary data. Atmospheric correction is performed with the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code, the same code used in the operational VIIRS atmospheric correction. Daily time series January 1, 2013–March 31, 2014 of 101×101pixel window match-up subsets at 375m VIIRS Imagery resolution, centered at the selected AERONET site locations were used. The overall objective of the study was to characterize the performance of the VIIRS TOC VIs (TOC EVI and TOC NDVI) over AERONET sites as a function of accuracy of inputs of atmospheric correction algorithm (aerosol, water vapor and others) and quality control screening (cloud and snow masks). We performed three types of analyses: (1) analysis of overall performance of VI product over all sites, (2) analysis of time series over select sites representative of vegetation types, and (3) sensitivity analysis of VI to cloud, aerosols and snow contamination. Over the period of time series average Accuracy, Precision and Uncertainty statistics were 0.009, 0.035, and 0.038, respectively, for TOC NDVI and −0.004, 0.015, and 0.016, respectively, for TOC EVI. Those statistics were derived based on screening to retain only confidently clear, snow free, non-urban pixels. The reason for the substantial difference in the performance of VIs is a different sensitivity of VIs to residual atmospheric contamination. According to our study VIIRS cloud mask performed reasonably. However, Aerosol Optical Thickness (AOT) was substantially overestimated in the vicinity of clouds, which resulted in overcorrection of VIIRS visible channels and ultimately overestimation of TOC NDVI. TOC EVI, on the other hand, remained largely unaffected, as an atmospherically resistant index. TOC EVI was also found to be less sensitive to residual snow contamination. Results of this study indicate the need to develop VI-specific quality control, which effectively screens data based on index sensitivity to atmospheric contamination. Monitoring VIIRS VIs over AERONET sites has been automated with a web-based STAR JPSS VI Monitor.

Stratospheric and mesospheric concentric gravity waves over tropical cyclone Mahasen: Joint AIRS and VIIRS satellite observations

We report on the first simultaneous spaceborne observations of concentric gravity wave patterns in the stratosphere and mesosphere over the Indian Ocean excited by Tropical Cyclone Mahasen. On the nights of 13–14 May 2013, concentric ring patterns in nightglow were observed in close-proximity to Mahasen by the Day/Night Band (DNB) of the Visible/Infrared Imager/Radiometer Suite (VIIRS) on the Suomi NPP satellite. The waves exhibited horizontal wavelengths of 40–60km. On 13 May 2013, long concentric waves of ~500km wavelength were also seen west of India, far away (~1500km) from their estimated center near Mahasen. Concentric gravity waves in the stratosphere were observed nearly simultaneously by the Atmospheric Infrared Sounder on the Aqua satellite. These multi-level observations provide a clearer picture of the complex three-dimensional structure of tropical cyclone-generated gravity waves than a single instrument alone.