Multi-Instrument Inter-Calibration (MIIC) System

remote sensing

Article Multi-Instrument Inter-Calibration (MIIC) System

Chris Currey 1,*, Aron Bartle 2, Constantine Lukashin 1, Carlos Roithmayr 1 and James Gallagher 3

1 NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681, USA; constantine.lukashin-1@nasa.gov (C.L.); [email protected] (C.R.) 2 Mechdyne Corporation, Virginia Beach, VA 23462, USA; [email protected] 3 OPeNDAP, Inc., Butte, MT 59701, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-757-864-4691

Academic Editors: Richard Müller and Prasad S. Thenkabail Received: 21 September 2016; Accepted: 25 October 2016; Published: 3 November 2016

Abstract: In order to have confidence in the long-term records of atmospheric and surface properties derived from satellite measurements it is important to know the stability and accuracy of the actual radiance or reflectance measurements. Climate quality measurements require accurate calibration of space-borne instruments. Inter-calibration is the process that ties the calibration of a target instrument to a more accurate, preferably SI-traceable, reference instrument by matching measurements in time, space, wavelength, and view angles. A major challenge for any inter-calibration study is to find and acquire matched samples from within the large data volumes distributed across Earth science data centers. Typically less than 0.1% of the instrument data are required for inter-calibration analysis. Software tools and networking middleware are necessary for intelligent selection and retrieval of matched samples from multiple instruments on separate spacecraft. This paper discusses the Multi-Instrument Inter-Calibration (MIIC) system, a web-based software framework used by the Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder mission to simplify the data management mechanics of inter-calibration. MIIC provides three main services: (1) inter-calibration event prediction; (2) data acquisition; and (3) data analysis. The combination of event prediction and powerful server-side functions reduces the data volume required for inter-calibration studies by several orders of magnitude, dramatically reducing network bandwidth and disk storage needs. MIIC provides generic retrospective analysis services capable of sifting through large data volumes of existing instrument data. The MIIC tiered design deployed at large institutional data centers can help international organizations, such as Global Space Based Inter-Calibration System (GSICS), more efficiently acquire matched data from multiple data centers. In this paper we describe the MIIC architecture and services.

Keywords: inter-calibration; CLARREO; OPeNDAP

1. Introduction Global Space Based Inter-Calibration System (GSICS) is an international collaboration that recommends inter-calibration algorithms for space-borne instruments [1]. Most algorithms compare observations at Earth surface targets or simultaneous nadir overpasses (SNOs). The ﬁrst step of any inter-calibration process is to locate matched measurements from within large datasets distributed across multi-agency international data centers. The typical process involves months of time downloading large datasets from remote data centers onto terabytes of expensive disk space. The distributed MIIC system provides a more effective solution. MIIC is a tool that primarily supports retrospective analysis. The MIIC system communicates with remote servers based on the Open-source Project for Network Data Access Protocol (OPeNDAP) middleware [2] to sift through large data volumes of online storage. Custom MIIC functions running within remote OPeNDAP servers provide

Remote Sens. 2016, 8, 902; doi:10.3390/rs8110902 www.mdpi.com/journal/remotesensing Remote Sens. 2016, 8, 902 2 of 18 subset, filter, and statistics services. These functions are designed to work with any hierarchical data format (HDF) or network common data format (netCDF) file. The MIIC event prediction software uses orbital mechanics and instrument scan models to determine when an instrument is viewing a particular Earth surface site or aligned in time, space, and angle with another instrument observation. The current system can predict inter-calibration (IC) events for configured spacecraft instruments given the availability of spacecraft daily two-line element (TLE) files from Space-Track.org [3]. LEO-LEO and LEO-GEORemote IC events Sens. 2016 typically, 8, 902 vary between two and 28 min in duration depending on orbit2 of 18 properties. High-resolutionhierarchical instrument data format data (HDF) are or aggregated network commo to covern data aformat single (netCDF) event. Thefile. The MIIC MIIC system event can mine data fromprediction calibration software targets, uses such orbital as mechanics deep convective and instrument clouds scan (DCC) models and to homogeneousdetermine when surfacean sites, used for vicariousinstrument calibration. is viewing a particular Each inter-calibration Earth surface site planor aligned (ICPlan) in time, when space, executed and angle with performs another up to three instrument observation. The current system can predict inter-calibration (IC) events for configured operations:spacecraft (1) event instruments prediction; given (2) the data availability acquisition; of spacecraft and (3)daily data two-line analysis. element This (TLE) paper files from describes the functionalitySpace-Track.org and results [3]. of LEO-LEO these three and LEO-GEO services. IC events typically vary between two and 28 min in The CLARREOduration depending Pathfinder on orbit (CPF) properties. mission, High-resolution scheduled instrument for launch data to are the aggregated International to cover Space a Station (ISS) in 2020,single will event. provide The MIIC a highly system accurate can mine data SI-traceable from calibration calibration targets, standard such as deep in convective orbit to measure clouds spectral (DCC) and homogeneous surface sites, used for vicarious calibration. Each inter-calibration plan reflectance(ICPlan) between when 350 executed and 2300 performs nm. Theup to CPF threemission operations: will (1) demonstrateevent prediction; significant (2) data acquisition; improvements in the inter-calibrationand (3) data analysis. process This by usingpaper describes the most the accurate functionality spectral and results reflectance of these three reference services. spectrometer in space to date,The 0.3% CLARREO (2σ) accuracy, Pathfinder and (CPF) by mission, matching scheduled target for observations launch to the International at multiple Space scan Station angles using a two-axis(ISS) pointing in 2020, systemwill provide [4]. a Simultaneoushighly accurate SI-traceable nadir overpass calibration (SNO) standard comparisons in orbit to measure typically spectral only align reflectance between 350 and 2300 nm. The CPF mission will demonstrate significant improvements in observationsthe inter-calibration at nadir. CLARREO process by datausing willthe most inter-calibrate accurate spectral both reflectance the Clouds reference and spectrometer the Earth’s in Radiant Energy Systemspace to (CERES) date, 0.3% shortwave (2σ) accuracy, channel and by matching (0.3–5.0 targetµm) observations and the Visible at multiple Infrared scan angles Imaging using Radiometera (VIIRS) reflectedtwo-axis solarpointing bands. system Figure [4]. Simultaneous1 depicts thenadir free overpass flyer (SNO) CLARREO comparisons concept typically using only a uniquealign active pointing systemobservations to match at nadir. target CLARREO instrument data will views inter-ca nearlibrate orbit both crossingsthe Clouds inand support the Earth’s of inter-calibration.Radiant Energy System (CERES) shortwave channel (0.3–5.0 μm) and the Visible Infrared Imaging Radiometer The ISS CPF(VIIRS) instrument reflected solar will bands. provide Figure the 1 depicts same capabilities,the free flyer CLARREO exceptthe concept orbit using will a beunique at 400 active km altitude instead ofpointing 609 km. system Active to match pointing target increasesinstrument views inter-calibration near orbit crossings sampling in suppo byrt of a inter-calibration. factor of 100 compared to the currentThe ISS GSICS CPF instrument capabilities will provide [4]. The the same CPF capabili instrumentties, except swath the orbit width will be is at 70 400 km km at altitude 0.5 km spatial resolution.instead The CLARREOof 609 km. Active instrument pointing increases will serve inter- ascalibration a calibration sampling reference by a factor for of many100 compared low earth orbit to the current GSICS capabilities [4]. The CPF instrument swath width is 70 km at 0.5 km spatial (LEO) andresolution. geostationary The CLARREO earth orbit instrument (GEO) will instruments serve as a calibration in space. reference The CLARREOfor many low Pathfinderearth orbit mission will use the(LEO) Multi-Instrument and geostationary Inter-Calibrationearth orbit (GEO) instruments (MIIC) in system space. The toacquire CLARREO matched Pathfinder CERES mission and VIIRS observationswill foruse inter-calibrationthe Multi-Instrument with Inter-Calibration the CPF instrument. (MIIC) system The to acquire MIIC matched system CERES simplifies and VIIRS access to data from multipleobservations data centers for inter-calibration in support with of the inter-comparison CPF instrument. The studies MIIC system performed simplifies by access international to data Earth from multiple data centers in support of inter-comparison studies performed by international Earth science communitiesscience communities suchas such GSICS. as GSICS.

Figure 1. Free flyer concept using active pointing to match CLARREO scans (red) across the Suomi Figure 1. FreeNational flyer Polar-orbiting concept using Partnership active pointing (SNPP) tosa matchtellite instrument CLARREO scan scans width (red) (green). across CLARREO the Suomi National Polar-orbitingprovides Partnership reference (SNPP)SI-traceable satellite spectra instrument for operational scan sensors width that (green). cannot CLARREOachieve climate provides change reference SI-traceableaccuracy spectra directly. for operational sensors that cannot achieve climate change accuracy directly.

2. Background 2. Background Investigations to validate and track instrument calibration stability and accuracy once in orbit utilize Investigationsa wide range to of validatemethodologies and [1]. track The purpose instrument of this calibrationsection is to highlight stability the andtype inter-calibration accuracy once in orbit utilize a widestudies range that MIIC of methodologieswas designed to benefit. [1]. TheInter- purposecalibration ofstudies this typically section compare is to highlightcalibrated the type inter-calibration studies that MIIC was designed to benefit. Inter-calibration studies typically compare Remote Sens. 2016, 8, 902 3 of 18 calibrated radiance or reflectance from multiple instruments over similar spectral bands at the top-of-atmosphere (TOA). One such study performs a radiometric inter-comparison between the SNPP VIIRS and the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) using an extended simultaneous nadir overpass (SNO-x) technique to find homogeneous ocean and desert scenes at lower latitudes, an advantage over SNO, being typically limited to polar orbit crossings [5]. The MODIS calibration science team tracks the instrument response versus scan angle (RVS) change for both Aqua and Terra instruments using stable desert calibration sites recommended by the Committee on Earth Observation Satellites (CEOS). MIIC provides services to predict and mine observation data from any user-defined surface site for vicarious calibration studies. The CLARREO instrument uses active pointing control to match target instrument observations at various scan angles. CERES broadband measurements are converted to unfiltered radiances for inter-comparison with other instruments using a spectral correction algorithm to account for the different spectral responses of each instrument [6]. Shape and bandwidth differences of spectral response functions (SRF) must be accounted for when comparing radiances from separate instruments. The CERES project tracks calibration relative stability of multiple CERES instruments using vicarious techniques, including tropical mean and deep convective cloud comparisons referenced to the FM1 instrument [7]. CERES performs inter-satellite direct comparisons by commanding its two-axis gimbal pointing system to rotate in the azimuth direction so that observations from multiple instruments match near satellite orbit crossings. CERES performed the first commanded inter-satellite direct comparison with the Scanner for Radiation Budget (ScaRaB) radiometer during January and March 1999 [8]. Comparisons of shortwave radiances from two instruments require similar solar zenith and relative azimuth angles. This can be obtained by rotating the azimuth gimbal of one instrument such that the scan planes of both instruments are parallel. CERES performed similar Programmable Azimuth Plan Scan (PAPS) operations in March 2000 to compare CERES shortwave observations on both Tropical Rainfall Measuring Mission (TRMM) and Terra [9]. Acquisition of the requisite amount of samples for inter-calibration is a challenge due to variable clear-sky conditions, scene type, viewing geometry differences, and small surface site targets. The CLARREO Pathfinder instrument two-axis pointing system will significantly improve the sampling required for reflected solar instrument inter-calibration. Instrument teams spend a great deal of time validating calibration accuracy and stability by comparing measurements with other instruments. The CLARREO Pathfinder instrument will serve as a high-accuracy reflected solar reference to improve the calibration accuracy of many orbiting instruments. The most accurate inter-calibration techniques match reference and target observations in time, space, view and solar zenith angles, and wavelength [4]. CLARREO will match target observations in space, time, viewing geometry, and wavelength. Empirical models will be developed to account for imager band spectral response function differences and polarization sensitivity variability with scan angle. The algorithms used for CLARREO Pathfinder inter-calibration are documented in Roithmayr et al. [10]. The idea for the Multi-Instrument Inter-Calibration (MIIC) software framework was derived from the original CLARREO 2007 Decadal Survey mission concept [11]. The MIIC team received NASA funding from the Research Opportunities in Earth and Space Science (ROSES) Advancing Collaborative Connections for Earth System Science (ACCESS) program in 2011 and 2013 to develop software to improve access to satellite instrument data distributed across multiple data centers in support of inter-calibration. The MIIC design enables development of distributed collaborative systems to subset satellite instrument data efficiently for inter-calibration and comparisons of derived geophysical parameters. MIIC predicts, locates, and acquires data when multiple satellite instruments observe the same geographic scene at similar scan and solar angles within an allowable time difference. MIIC also collects data over user-defined surface sites to trend calibration stability. Observations are subset and filtered during acquisition using custom remote OPeNDAP server-side functions. The MIIC system enables a user to download only the matched data of interest without first downloading the entirety of both datasets for the time period in question. This reduces network traffic by several orders of magnitude. The MIIC data services have been tested using CERES, CALIPSO, CrIS, VIIRS, MODIS, Remote Sens. 2016, 8, 902 4 of 18 and GOES data. The current MIIC system is deployed at the NASA Langley Research Center (LaRC) Atmospheric Science Data Center (ASDC). Current plans are to use MIIC to access CERES and VIIRS data matched with the CLARREO Pathfinder Reflected Solar instrument for inter-calibration. Remote Sens. 2016, 8, 902 4 of 18

3. MIIC Architecture(LaRC) Atmospheric Science Data Center (ASDC). Current plans are to use MIIC to access CERES and VIIRS data matched with the CLARREO Pathfinder Reflected Solar instrument for inter-calibration. 3.1. Data System Architecture 3. MIIC Architecture MIIC is a multi-tiered web-based data system designed to meet the following objectives: 3.1. Data System Architecture • Support inter-calibration and inter-comparison science studies MIIC is a multi-tiered web-based data system designed to meet the following objectives: • Access matched measurements within large datasets distributed across multiple data centers • Support inter-calibration and inter-comparison science studies • Enable• developmentAccess matched of measurements distributed within collaborative large datasets data distributed systems across multiple data centers • Demonstrate• Enable the development beneﬁt of of customdistributed OPeNDAP collaborative server-side data systemsfunctions • • Provide web-basedDemonstrate the tools benefit to simplify of custom dataOPeNDAP access server-side for GSICS functions and instrument Cal-Val teams • Provide web-based tools to simplify data access for GSICS and instrument Cal-Val teams The MIICThe data MIIC system data system (Figure (Figure2) is 2) ais multi-tiereda multi-tiered architecture architecture that thatconsists consists of three of primary three tiers: primary tiers: client/userclient/user interface, interface, application, application, and and data. data.

FigureFigure 2. 2.MIIC MIIC datadata system system architecture. architecture.

The application tier controls the workflow for each inter-calibration task. The event prediction The applicationmodule predicts tier the controls time and location the workflow of event data for and each identifies inter-calibration the data files associated task. The with event each prediction module predictsevent. Instrument the time data and files location are processed of event on external data OPeNDAP and identifies servers thewithin data embedded files associated MIIC with server-side functions. The server-side functions subset and filter data within the event spatial each event.coverage. Instrument Data transmitted data files from are the processedOPeNDAP servers on external are cached OPeNDAP in the local data servers tier for within analysis. embedded MIIC server-sideThe application functions. server can The connect server-side to multiple functions OPeNDAP subset servers andphysically filter lo datacated withinat disparate the data event spatial coverage.centers. Data transmittedThe MIIC architecture from the uses OPeNDAP multiple HTTP servers REpresentational are cached State in Transfer the local (REST) data interfaces tier for analysis. The applicationto communicate server canbetween connect the distribu to multipleted components. OPeNDAP Daily two-line servers elements physically (TLEs) located (see Section at disparate 4.1) data needed for event prediction are acquired through a REST interface from Space-Track.org. Users centers. Theaccess MIIC the architectureMIIC services usesthrough multiple a web interface, HTTP REpresentational running in a browser, State or Transfervia external (REST) client interfaces to communicateapplication between programs the using distributed a REST API. components. Daily two-line elements (TLEs) (see Section 4.1) needed for eventThe predictiontiered software are stack acquired is implemented through in a RESTmultiple interface programming from languages Space-Track.org. that run on Users access the MIICseparate services servers. through The web-based a web interface, user interface running uses JavaScript, in a browser, the application or via tier external is written client in Java, application and the data tier is written in C++. Web pages are generated within the application tier. Interactive programsdynamic using a data REST plots API. are implemented using the Highcharts JavaScript library [12]. Standalone client The tieredapplications software use a stack REST isAPI implemented to submit inter-calib in multipleration programmingplans for execution. languages The application that run tier on separate servers. The web-based user interface uses JavaScript, the application tier is written in Java, and the data tier is written in C++. Web pages are generated within the application tier. Interactive dynamic data plots are implemented using the Highcharts JavaScript library [12]. Standalone client applications use a REST API to submit inter-calibration plans for execution. The application tier workflow managers sequence the event prediction, file location, collection, and analysis services. ICPlan state is updated Remote Sens. 2016, 8, 902 5 of 18 during execution and persisted in a PostgreSQL database. OPeNDAP Hyrax servers can be deployed at Earth science data centers or within the cloud, wherever instrument data are located. The MIIC system can easily be configured with additional OPeNDAP servers and data products. Each OPeNDAP server must include the MIIC plugin that contains server-side functions for histograms, filters, and spatial and spectral convolution. An analysis plugin using the Java implementation of the Abstract Interfaces for Data Analysis (JAIDA) library [13] generates histogram statistics on target and reference data retrieved by MIIC. Additional analysis plugins can be developed and easily added to the application tier. The MIIC software stack is extendable and can support many different use cases including multi-instrument data aggregation. Multi-tiered architectures for enterprise applications require scalable and secure network features. The application tier uses the Spring framework [14] to provide Java enterprise services within a lightweight Tomcat servlet container. Spring dependency injection simplifies reconfiguration for testing and prototyping. The Spring model-view-controller (MVC) framework handles all incoming requests for web pages, REST API requests, and dynamic JSON objects. Spring MVC handles crosscutting concerns in web requests, such as request authentication and authorization, session management, and necessary data transformations. We use the Hibernate framework [15] for object-relational mapping to persist data in a PostgreSQL database. The MIIC system consists of hardware deployed at the NASA LaRC Atmospheric Science Data Center. The application tier server communicates with two OPeNDAP servers with 10GE connectivity to the ASDC online disk storage (>6 PB). External OPeNDAP data servers have been deployed in the Amazon Web Service (AWS) and tested with connectivity to the MIIC application server. The MIIC web interface has been scanned by NASA network security and authorized for use on external-facing networks. Additional testing and software enhancements need to occur to ensure proper scalability and user support before MIIC services can be made public. The CLARREO Pathfinder mission will verify the MIIC architecture and enhance features by working closely with the CLARREO, CERES, and VIIRS Cal/Val teams. The CLARREO Pathfinder science data system will communicate through the MIIC REST API for inter-calibration services. MIIC software is primarily for large institutional data centers. The software is currently not open source but can be licensed to support future deployments.

3.2. Server-Side Functions The OPeNDAP open source software allows clients easy access to data over the network. The MIIC plugin is designed to execute functions within an OPeNDAP (Hyrax version) server. These functions subset and filter raw data, perform statistics (histograms), and mutate data by executing mathematical expressions on any combination of parameters within a file. The MIIC plugin is compatible with the OPeNDAP handlers that support the various flavors of HDF and netCDF. New data products can easily be supported using an XML-based configuration. MIIC tuple and profile server-side functions return filtered native resolution and averaged data respectively. The tuple function returns a 1D list of filtered observations for each requested data variable. Filtering is global in scope in that each filter modifies a mask applied to all requested variables. Multiple filters are chained together. The profile function performs histogram binning in multiple dimensions (1D-3D). Any variable can be used to define a histogram axis. Bin statistics for each profile parameter include count, average, and standard deviation. Visualizations of 1D and 2D profiles using HighCharts are shown in Section4. Data mutation is implemented using dynamic expressions of one or more data variables within the file. Dynamic expressions are implemented using the C++ Mathematical Expression Toolkit Library (ExprTk)[16]. For security reasons the dynamic expressions are defined within secure YAML Ain’t Markup Language (YAML) files hosted on each OPeNDAP server. Expressions can only be called by name within a URL command. The primary MIIC server-side functions include nested calls to other utility functions. MIIC OPeNDAP server-side commands use the following HTTP protocol URL syntax: Each command identifies the OPeNDAP server, data file, response type, e.g., .nc (NETCDF3), and query containing nested server-side function calls to any depth. Remote Sens. 2016, 8, 902 6 of 18

4. MIIC Services and Results MIIC provides event prediction, data acquisition, and analysis web services.

4.1. Event Prediction Services The MIIC event prediction service finds collocated, near-coincident measurements from separate spacecraft instruments with similar viewing zenith, solar zenith, and relative azimuth angles. The MIIC event prediction algorithms [10] support both LEO-LEO and LEO-GEO inter-comparisons. The prediction service also locates data over user-defined Earth surface sites. The algorithm uses the open sourceRemote orbit Sens. 2016 propagator, 8, 902 SGP4 [17] software and two-line elements (TLEs)6 of 18 provided by Space-Track.org. A two-line element contains a list of orbital elements of an Earth-orbiting object for 4. MIIC Services and Results a given point in time. Predictions can be performed for any requested time period, days to years, as long MIIC provides event prediction, data acquisition, and analysis web services. as the satellite TLEs are available. The TLE ASCII text format is the de facto standard for distribution of Earth-orbiting4.1. Event orbital Prediction state Services vectors of satellites in the Earth-centered inertial coordinate system. The MIIC predictionThe serviceMIIC event is prediction fast and service efficient finds collocated, since datanear-coincident files are measurements not processed from separate to determine event spacecraft instruments with similar viewing zenith, solar zenith, and relative azimuth angles. The MIIC locations. Eventsevent are prediction predicted algorithms using [10] support two-line both LEO-LEO elements, and LEO-GEO orbit propagation inter-comparisons. software, The prediction and instrument scan models. LEOservice instruments also locates data over are user-defined assumed Earth to be surf cross-trackace sites. The algorithm scanners. uses the The open event source orbit predictor outputs propagator SGP4 [17] software and two-line elements (TLEs) provided by Space-Track.org. A two-line a series of latitude-longitudeelement contains a boundinglist of orbital boxeselements that of an define Earth-orbiting Earth object regions for a containinggiven point in matchedtime. data from one or more spacecraft.Predictions can Predicted be performed instrument for any requested scan time times period, that days transectto years, as eachlong as eventthe satellite box TLEs are used to access data files from onlineare available. repositories. The TLE ASCII text format is the de facto standard for distribution of Earth-orbiting orbital state vectors of satellites in the Earth-centered inertial coordinate system. The MIIC prediction service The currentis fast MIIC and efficient LEO-LEO since data event files are prediction not processed process to determine is event depicted locations. in Events Figure are predicted3 and defined in [ 10]. The event opportunitiesusing two-lineare elements, based orbit on propagation the time software, when and the instrument reference scan models. instrument LEO instruments (P) field are of view (FOV) assumed to be cross-track scanners. The event predictor outputs a series of latitude-longitude bounding pointing vectorsboxes are that within define theEarth blue regions tent containing structure matched constructed data from one with or more the targetspacecraft. instrument Predicted location at A and fictitious locationsinstrument scan at A+ timesand that transectA− corresponding each event box are used to user-definedto access data files from maximum online repositories. allowable observation The current MIIC LEO-LEO event prediction process is depicted in Figure 3 and defined in [10]. time difference.The The event time opportunities difference are based is tunable on the time and when is the typically reference instrument given a ( valueP) field of between view (FOV) two and 10 min for scene reflectancepointing tovectors remain are within comparable. the blue tent structure As long constructed as P is with within the target the instrument tent structure location at its observations are within theA allowable and fictitious time locations difference at A+ and with A− correspondingA. Orbit inclination to user-defined angles, maximum altitude allowable differences, and observation time difference. The time difference is tunable and is typically given a value between two time of year affectand 10 the min frequencyfor scene reflectance of events. to remain The comparable. number As long of inter-calibrationas P is within the tent structure events its may vary from one month to theobservations next. The are outputwithin the longitude-latitude allowable time difference event with A boundary. Orbit inclination is trimmed angles, altitude to include only the differences, and time of year affect the frequency of events. The number of inter-calibration events region that containsmay vary matching from one month observations to the next. The based output on longitude-latitude viewing zenith, event relative boundary azimuth,is trimmed to and solar zenith angle settings.include The current only the MIICregion that event contains prediction matching algorithmobservations based assumes on viewing that zenith, both relative target and reference azimuth, and solar zenith angle settings. The current MIIC event prediction algorithm assumes that instruments areboth cross-track target and reference scanners. instruments are cross-track scanners.

Figure 3. MIIC LEO-LEO event prediction tent structure. Figure 3. MIIC LEO-LEO event prediction tent structure. Table 1 shows the effect of spacecraft orbit altitude differences on frequency of events. The event prediction algorithm requires that each satellite orbit be at a different altitude so that one spacecraft Table1 showspasses the under effect the other. of spacecraft Satellites at the orbit same alti altitudetude essentially differences never cross. on There frequency are approximately of events. The event 40 potential events per month for ISS versus SNPP instruments, compared to only 11 potential events prediction algorithmper year for requires METOP-A that versus each SNPP satellite instruments. orbit For LEO-LEO be at a inter-comparisons different altitude a user can so set that the one spacecraft passes under theevent other. prediction Satellites parameters at defined the same in Table altitude 2. Nominal essentially event prediction never settings cross. for daytime There events are approximately are Δθ < 15°, Δφ < 30°, θ < 75°, and twin < 2.5 min. 40 potential events per month for ISS versus SNPP instruments, compared to only 11 potential events per year for METOP-A versus SNPP instruments. For LEO-LEO inter-comparisons a user can set the event prediction parameters deﬁned in Table2. Nominal event prediction settings for daytime events ◦ ◦ ◦ are ∆θv < 15 , ∆ϕv < 30 , θs < 75 , and twin < 2.5 min. Remote Sens. 2016, 8, 902 7 of 18 Remote Sens. 2016, 8, 902 7 of 18 Table 1. Orbit altitude and frequency of opportunities for several LEO spacecraft pairs.

Table 1. Orbit altitude and frequencySC2 of opportunities for several LEO spacecraft pairs. SC1 Altitude SC1 SC2 Crossing SC1 SC2 Altitude (km)SC1 Altitude SC2 Altitude(Orbits/Day)SC1 (Orbits/Day)SC2 CrossingPeriod (Days) SC1 SC2 (km) (km) (km) (Orbits/Day) (Orbits/Day) Period (Days) Aqua 705 Envisat 800 14.57 14.32 4.0 Aqua Aqua705 705SNPP Envisat 824 80014.57 14.57 14.32 14.19 4.0 2.6 Aqua 705 SNPP 824 14.57 14.19 2.6 METOP-AMETOP-A 820 820 SNPP SNPP824 824 14.22 14.22 14.19 14.19 33.3 33.3 ISS ISS 404 404 SNPP SNPP 824 824 15.55 15.55 14.19 14.19 0.74 0.74

TableTable 2. 2. User-specifiedUser-speciﬁed LEO-LEO LEO-LEO event event prediction prediction parameters. parameters.

EP Parameter Description EP Parameter Description razmax (Δφ) Maximum allowable difference in relative azimuth angles ϕ ) razmaxΔt (∆ v TimeMaximum increment allowable (seconds) difference for orbit in propagation relative azimuth steps angles (default = 1) ∆t Time increment (seconds) for orbit propagation steps (default = 1) referenceSwath Swath width (degrees) of reference instrument referenceSwath Swath width (degrees) of reference instrument targetSwathtargetSwath Swath Swath width width (degrees) (degrees) of oftarget target instrument instrument szmaxszmax (θ (θ)s ) MaximumMaximum allowable allowable solar solar zenith zenith value value for for any any observation observation twintwin Maximum Maximum allowable allowable time time differen differencece (minutes) (minutes) between between observations observations θ ) vzmaxvzmax (Δθ (∆)v MaximumMaximum allowable allowable difference difference in inviewing viewing zenith zenith angles angles

AA predicted predicted LEO-GEOLEO-GEO event event is depictedis depicted in Figurein Figure4 for Aqua4 for MODISAqua MODIS vs. GOES-13. vs. GOES-13. The rectangles The rectanglesdemarcate demarcate daytime eventsdaytime with events solar with zenith solar angle zenith < 75 angle◦, and < 75°, viewing and viewing zenith and zenith relative and relative azimuth azimuthangle differences angle differences within 10 ◦withinand 20 ◦10°respectively. and 20° respectively. For this case weFor are this searching case we for are simulated searching MODIS for simulatedpointing vectorsMODIS withinpointing a ±vectors55◦ scan within angle a co-aligned55° scan angle with theco-aligned GEO instrument with the GEO pointing instrument vectors. pointingSimulated vectors. instrument Simulated pixels instrument have 1 km resolution.pixels have For 1 km each resolution. LEO simulated For each pixel, LEO corresponding simulated pixel, GEO correspondingviewing zenith, GEO solar viewing zenith, zenith, and relative solar zenith, azimuth and angles relative are azimuth calculated. angles Purple are pixelscalculated. depict Purple where pixelsLEO anddepict GEO where viewing LEO zenith and GEO angles viewing differ byzenith less thanangles 10 differ◦. Light by blue less pixelsthan 10°. correspond Light blue to relativepixels correspondazimuth angles to relative differences azimuth that angles are less differences than 20◦ that. Dark are blue less than pixels 20°. indicate Dark blue when pixels both indicate conditions when are bothmet. conditions The geographic are met. bounding The geographic boxes identify bounding regions boxes that contain identify matching regions observations.that contain Inmatching a future observations.software release In a the future dark software blue pixel release locations the willdark be blue exported pixel tolocations a file to will simplify be exported the spatial to matchinga file to simplifyhandled the outside spatial of matching MIIC. There handled are five outside LEO orbitsof MIIC. that There intersect are thefive GOES-13LEO orbits FOV that with intersect footprints the GOES-13that meet FOV filter with criteria. footprints The time that coverage meet filter and crit geographiceria. The time coordinates coverage for and each geographic rectangle arecoordinates passed to forthe each MIIC rectangle data acquisition are passed service. to the MIIC data acquisition service.

FigureFigure 4. 4. LEO-GEOLEO-GEO event event prediction, prediction, Aqua Aqua MODIS MODIS vs. vs. GOES-13, GOES-13, 1 1January January 2011, 2011, daytime. daytime.

The CLARREO event prediction algorithms are integrated into the MIIC software. Users may The CLARREO event prediction algorithms are integrated into the MIIC software. Users may run predictions and show event locations on Google Maps using the MIIC web interface. This is a run predictions and show event locations on Google Maps using the MIIC web interface. This is powerful feature for studying sampling coverage prior to actually acquiring the data. Figure 5 shows a powerful feature for studying global coverage prior to acquiring the data. Figure5 shows a Mercator a single daytime event over Antarctica for SNPP VIIRS versus Aqua MODIS, and a full month of satellite view of a full month of predicted daytime events for ISS versus Aqua. Each red box depicts predicted daytime events for ISS versus Aqua. the geographic spatial coverage for a single inter-calibration event.

Remote Sens. 2016, 8, 902 8 of 18

Figure 5.FigureMIIC 5. event MIIC event prediction prediction web web interface, interface, ISS ISS versus versus Aqua, Aqua, one month one monthpredicts for predicts January for 2015; January 2015; twin = 10 min, vzmax = 180°, szmax = 75°. twin = 10 min, vzmax = 180◦, szmax = 75◦. 4.2. Data Acquisition Services 4.2. Data AcquisitionMIIC intelligently Services selects, acquires, and aggregates matched instrument data from data center online archives. The MIIC data acquisition controller uses the event definitions contained in the MIIC intelligently selects, acquires, and aggregates matched instrument data from data center ICPlan to acquire data over the network using the OPeNDAP network protocol [2]. Only instrument online archives.data within The each MIIC event datalatitude acquisition‐longitude bounding controller box uses are acquired. the event The definitions controller uses contained the in the ICPlan topredicted acquire event data time over range the networkto select the using appropriate the OPeNDAP files from networkthe MIIC‐compatible protocol [OPeNDAP2]. Only instrument data withinservers. each The controller event latitude-longitude processes events in parallel. bounding ICPlan metadata box are and acquired. status are stored The in controller the MIIC uses the database. Custom MIIC OPeNDAP server‐side functions perform filtering, spectral and spatial predicted event time range to select the appropriate files from the MIIC-compatible OPeNDAP servers. convolution, and histogram analysis within the remote servers. This combination of event prediction The controllerand server processes‐side functions events eliminate in parallel. the need ICPlan to transfer metadata large volumes and status of data are files stored in their in entirety, the MIIC database. Custom MIICthus reducing OPeNDAP data center server-side and user functionsnetwork bandwidth perform and filtering, disk storage spectral consumption. and spatial convolution, and histogram analysisThe controller within builds the HTTP remote commands servers. based This on combinationthe desired collection of event strategy prediction and server and‐side server-side function. There are three main collection modes: surface site, LEO‐LEO, and LEO‐GEO. Data can be functions eliminate the need to transfer large volumes of data files in their entirety, thus reducing data returned as flat arrays, multi‐dimensional arrays, or averaged data in 1D–3D histogram grids. Spatial center andand user spectral network convolution bandwidth server‐side and functions disk storage have been consumption. implemented but only support a few Thespecific controller instruments. builds MIIC HTTP services commands are designed based to be on generic, the desired i.e., work collection on any data strategy type and anddata server-side function.product. There The are data three collector main locates collection files using modes: a catalog surface search mechanism site, LEO-LEO, built on andtop of LEO-GEO. the Hyrax Data can Thematic Real‐Time Environmental Distributed Data Services (THREDDS) handler. Data are be returned as flat arrays, multi-dimensional arrays, or averaged data in 1D–3D histogram grids. returned from the OPeNDAP servers in NetCDF3. OPeNDAP servers must be configured to accept Spatial andPOST spectral commands convolution since many of server-sidethe MIIC URL functionsrequests are havetoo large been for GET implemented commands and but must only support a few specificbe contained instruments. within the MIIC HTTP services POST message are designed body. The toMIIC be tiered generic, design i.e., prevents work onoverloading any data type and data product.OPeNDAP The servers. data collectorThe OPeNDAP locates Lightweight files using Frontend a catalog Servlet search (OLFS) mechanismconfiguration limits built the on top of the number of simultaneous HTTP requests based on the number of Back‐end Server (BES) processes Hyrax Thematic Real-Time Environmental Distributed Data Services (THREDDS) handler. Data are available. The current LaRC ASDC deployment has two back‐end servers, each providing 32 BES returnedprocesses from the (one OPeNDAP per CPU core). servers The MIIC in controller NetCDF3. within OPeNDAP the application servers tier manages must bean event configured queue to accept POST commandsto fairly share since available many HTTP of therequest MIIC slots URL among requests all users. are too large for GET commands and must be containedMIIC within subsets the and HTTP filters data POST on remote message servers body. without The first MIIC having tiered to transmit design the entire prevents dataset overloading collection. One month of Aqua L1B MODIS and GOES‐13 imager data consists of approximately 9672 OPeNDAP servers. The OPeNDAP Lightweight Frontend Servlet (OLFS) configuration limits the files (1.4 TB). The event prediction algorithm finds near‐coincident observations with similar viewing numberconditions of simultaneous to reduce HTTPthe number requests of files based transmitted on the by number a factor of of 22. Back-end Server‐side Server equal‐ (BES)angle processes available.gridding The current (2DProfile) LaRC reduces ASDC the data deployment by an additional has two factor back-end of 34. The servers, final matched each gridded providing 32 BES processesMODIS/GOES (one per CPU‐13 samples core). Theare contained MIIC controller in 808 files within (1.8 GB). the This application is consistent tier with manages recommended an event queue GSICS LEO/GEO inter‐calibration algorithms that typically require less than 0.1% of the total data to fairly share available HTTP request slots among all users. volume. We have demonstrated similar data volume savings using server‐side spatial and spectral MIIC subsets and filters data on remote servers without first having to transmit the entire dataset collection. One month of Aqua L1B MODIS and GOES-13 imager data consists of approximately 9672 files (1.4 TB). The event prediction algorithm finds near-coincident observations with similar viewing conditions to reduce the number of files transmitted by a factor of 22. Server-side equal-angle gridding (2DProfile) reduces the data by an additional factor of 34. The final matched gridded MODIS/GOES-13 samples are contained in 808 files (1.8 GB). This is consistent with recommended GSICS LEO/GEO inter-calibration algorithms that typically require less than 0.1% of the total data volume. We have demonstrated similar data volume savings using server-side spatial and spectral convolution algorithms to compare L1B SCanning Imaging Absorption SpectroMeter for Atmospheric Remote Sens. 2016, 8, 902 9 of 18

CHartography (SCIAMACHY) data (5287 spectral bands, 240–1748 nm, 30 km × 240 km) with L1B MODIS band 1 (0.65 µm, 1 km). Spectral convolution of MODIS Band 1 (0.65 µm) relative spectral response (RSR) values with hyperspectral SCIAMACHY data reduces the 5287 spectral values to one simulated reﬂectance value. Spatial convolution of 1 km MODIS pixels within the 30 km × 240 km SCIAMACHY footprints accounts for a reduction factor of 7000 at nadir. These two examples show orders of magnitude savings in data transmission. Powerful event prediction and server-side processing simplify data accessibility and enable researchers to focus more on analysis tasks.

4.2.1. LEO-LEO Data Acquisition Figure6 shows the full-resolution data acquired for the matched LEO-LEO event, SNPP VIIRS versus Aqua MODIS, over Antarctica for 3 January 2015. The top image shows SNPP VIIRS reflectance band I1, 0.64 µm; the bottom image shows Aqua MODIS reflectance band 1, 0.62–0.67 µm. Scale and offset metadata parameters within the HDF products are automatically applied to the data within MIIC server-side functions. MODIS reflectance data are stored within the data product as ρcosθs, where ρ is reflectance and θs is solar zenith angle. To match VIIRS reflectance (ρ) the solar zenith angle correction is removed using a server-side expression (divide by cosθs). Application of metadata parameters and data mutation expressions are enabled in MIIC system data product configuration files. For this scene, the mean difference of matched grid cells in reflectance is 0.0053 (1.1% of the scene mean reflectance of 0.478). MIIC acquires matched data from multiple instruments and performs quality control-type analyses of the data. The image in Figure6 is generated using the MIIC interactive web analysis plugin (discussed in Section 4.3). The CLARREO Pathfinder mission will use MIIC to acquire target instrument data for LEO-LEO inter-calibration.

4.2.2. LEO-GEO Data Acquisition Figure7 shows full-resolution data acquired for a single matched LEO-GEO event. The top image shows Aqua CERES shortware flux; the bottom image depicts GOES-13 shortwave albedo. Both images are generated from data acquired over the GOES-East Imager Southern Hemisphere Scan Sector region (20◦–40◦S, 80◦–40◦W) on 30 January 2014. The source data are contained in 10 files (458.6 MB); 27.9 MB of data are retrieved within this region and stored in the application tier cache. Data are binned by longitude and latitude for inter-comparison. The image in Figure7 is generated using the MIIC interactive web analysis plugin. Histogram profile statistics include the mean value, standard deviation, and count for each selected parameter (in this case shortwave flux and albedo) for each latitude-longitude bin.

4.2.3. Surface Site Data Acquisition The surface site acquisition mode is useful for vicarious calibration studies. Figure8 shows VIIRS I1 reflectance data acquired over North Africa for the month of January 2014. The VIIRS data are filtered for M15 brightness temperature greater than 300 ◦K within the tuple server-side function to eliminate cloudy pixels from acquisition. The entire North African region is too inhomogeneous for inter-calibration since dispersion, defined as σ/x, is 21.7% even after cloud filtering. Homogeneous desert sites are typically very small, less than 50 km in diameter. The bright region around 25N, 13E is a recommended CEOS reference site (Libya-1). The CEOS Infrared and Visible Optical Sensors (IVOS) working group has endorsed a set of globally-distributed reference standard test sites for the post-launch calibration of space-based optical imaging sensors [1]. CEOS recommends eight instrumented sites and six pseudo-invariant calibration sites (PICS) [18]. The instrumented sites are primarily used for field campaigns and inter-comparison of instrument radiometric biases. A pseudo-invariant site is a location on the Earth’s surface that is very stable, both temporally and spatially, over long periods of time. These sites, depicted in Figure9, are used to analyze the stability of instrument calibrations and perform inter-calibrations. The MIIC system can mine satellite instrument data from any of these regions. Remote Sens. 2016, 8, 902 10 of 18 Remote Sens. 2016, 8, 902 10 of 18

FigureFigure 6.6. Single matched matched LEO-LEO LEO-LEO event event for for VIIRS VIIRS (top (top) and) andMODIS MODIS (bottom (bottom), 3 January), 3 January 2015 over 2015 Antarctica. over Antarctica. The mean The difference mean difference between MODIS between and MODIS VIIRS reflectance and VIIRS reﬂectanceis 0.0053 (σ is = 0.00530.0177) (forσ = matched 0.0177) forgrid matched cells of size grid 0.3°. cells of size 0.3◦.

Remote Sens. 2016, 8, 902 11 of 18 Remote Sens. 2016, 8, 902 11 of 18

Figure 7. SingleFigure matched 7. Single LEO-GEO matched event LEO-GEO acquired event from acquired Aqua fromCERES Aqua (top CERES) and GOES-East (top) and GOES-EastImager (bottom Imager), 30 (bottom January), 302014 January over South 2014 overAmerica. South America.

Remote Sens. 2016, 8, 902 12 of 18 Remote Sens. 2016, 8, 902 12 of 18

FigureFigure 8. Surface 8. Surface site site acquisition, acquisition, North North Africa Africa (20–30N, (20◦–30 4–20E);◦N, VIIRS 4◦–20 band◦E); I1 VIIRS reflectance, band August I1 reflectance, 2014; server August‐side cloud 2014; filter server-side M15 BT > 300 cloud K; scene filter reflectance M15 BT dispersion > 300 K; sceneσ⁄ = 21.7%. reflectance dispersion σ/x = 21.7%. Remote Sens. 2016, 8, 902 13 of 18

Figure 9. The North Africa surface above is a mixture of rock and desert and is non‐homogeneous in reflectance. The bright homogeneous region in the center of this image is CEOS site Libya‐1. Figure 9. The North Africa surface above is a mixture of rock and desert and is non-homogeneous in Figure 9. The North Africa surface abovereflectance. is a mixture The bright ofrock homogeneou and deserts region and isin non-homogeneousthe center of this image in reﬂectance.is CEOS site Libya-1. The bright homogeneous region in the center of this image is CEOS site Libya-1. Smith [19] accurately describes the challenges involved with analyzing multiple instrument data sets over small test sites for calibration and validation analyses. Statistical analyses require acquisition of many data samples from multiple surface sites over long time periods (years). Access to calibrated observation data for specific sites is not always straightforward. Acquiring and analyzing large volumes of data is time consuming, especially if the data of interest are located at a remote data center. The size of data products is usually very large compared to the area of interest, hundreds of megabytes of information typically need to be acquired per event. Download limits and local storage are problematic for the average user. Users must deal with multiple file formats. Acquisition of matched co-incident observations from multiple spacecraft is even more challenging. The MIIC system simplifies these data management challenges.

4.3. Analysis Services The MIIC analysis plugin architecture allows users to develop data analysis routines in Java. Plugins run on the web server in the context of an executing inter-calibration plan. Users can select from available analysis plugins to define an analysis task. Analysis tasks are automatically run once the event data have been collected. The following are the key analysis features: • Analysis tasks may access all data retrieved by an ICPlan • Analysis tasks may be chained together • Analysis tasks may accept user input in a format defined by the plugin • Statistics calculations are memory intensive • Statistics are generated using JAIDA AIDA is a set of abstract interfaces and formats for representing common data analysis objects used in the High Energy Physics community. The most important of these interfaces are histogram, profile, and tuple. JAIDA is the Java implementation of AIDA and part of the FreeHEP library [13]. All MIIC analyses are based on JAIDA. Interactive plots of MIIC analyses are generated using the interactive HighCharts JavaScript library. Analysis results are exported to file for further offline analysis. Figure 10 shows the trend analysis of nine years of CERES unfiltered shortwave radiance data, 2006–2014, collected using the MIIC surface site acquisition mode over the Libya-1 calibration site. The plot shows a 1DProfile calculated within the MIIC Analysis Plugin. Data are filtered for solar zenith (<60°) and viewing zenith (<30°) during acquisition within the tuple server-side function. The CERES data show the seasonal cycle and excellent long-term calibration stability. MIIC excels at acquiring data for time series analysis.

Remote Sens. 2016, 8, 902 13 of 18

FiuSmith [19] accurately describes the challenges involved with analyzing multiple instrument data sets over small test sites for calibration and validation analyses. Statistical analyses require acquisition of many data samples from multiple surface sites over long time periods (years). Access to calibrated observation data for specific sites is not always straightforward. Acquiring and analyzing large volumes of data is time consuming, especially if the data of interest are located at a remote data center. The size of data products is usually very large compared to the area of interest, hundreds of megabytes of information typically need to be acquired per event. Download limits and local storage are problematic for the average user. Users must deal with multiple ﬁle formats. Acquisition of matched co-incident observations from multiple spacecraft is even more challenging. The MIIC system simpliﬁes these data management challenges.

• Analysis tasks may access all data retrieved by an ICPlan • Analysis tasks may be chained together • Analysis tasks may accept user input in a format deﬁned by the plugin • Statistics calculations are memory intensive • Statistics are generated using JAIDA

AIDA is a set of abstract interfaces and formats for representing common data analysis objects used in the High Energy Physics community. The most important of these interfaces are histogram, profile, and tuple. JAIDA is the Java implementation of AIDA and part of the FreeHEP library [13]. All MIIC analyses are based on JAIDA. Interactive plots of MIIC analyses are generated using the interactive HighCharts JavaScript library. Analysis results are exported to file for further offline analysis. Figure 10 shows the trend analysis of nine years of CERES unfiltered shortwave radiance data, 2006–2014, collected using the MIIC surface site acquisition mode over the Libya-1 calibration site. The plot shows a 1DProfile calculated within the MIIC Analysis Plugin. Data are filtered for solar zenith (<60◦) and viewing zenith (<30◦) during acquisition within the tuple server-side function. The CERES data show the seasonal cycle and excellent long-term calibration stability. MIIC excels at acquiring data for time series analysis. El Niño is an anomalous, yet periodic, warming of the central and eastern equatorial Pacific Ocean. Every two to seven years this patch of ocean warms for six to 18 months. Figure 11 shows 13 years of L2 CERES sea surface temperature (SST) observations collected over the Nino 3 (5◦N–5◦S, 150◦W–90◦W) region using MIIC and compared with the NOAA monthly ocean time series climate indices published at the NOAA Climate Prediction Center [20]. The CERES trend results are exported from the MIIC 1DProfile analysis and analyzed in an external Python program since the NOAA data are not accessible to MIIC. The mean difference in temperature between the two time series is 0.17 ◦C. The NOAA and CERES time series derived from different instrument observations are strongly correlated (R = 0.99535). The MIIC web-based tiered collection of software simplifies acquisition of matched data for inter-comparison scientific studies. The MIIC analysis plugin architecture provides a mechanism for implementing common analyses and visualizations for initial assessment of matched observation data. The MIIC analysis plugin is not intended to compete with many of the excellent standalone analysis packages that already exist. Figure 12 show how well MODIS band 1 matches VIIRS band I1 in reflectance for various solar zenith and viewing zenith angles. The 2DProfiles contain grid cells ~0.25◦ in solar zenith–viewing zenith angular grid space. The difference plot capability allows users to identify anomalies or significant trends in the data. The scene contains a mixture of ocean, clouds, and snow and ice over Antarctica on 3 January 2015. Additional error analyses will be performed outside of MIIC to analyze observations better matched in viewing geometry and scene type. For optimum inter-calibration results observations need to match in space, time, wavelength, and view angles. Remote Sens. 2016, 8, 902 14 of 18

Remote Sens. 2016, 8, 902 14 of 18 Remote Sens. 2016, 8, 902 14 of 18

Figure 10. MIIC Analysis Plugin Results, CERES SW unfiltered radiance over Libya-1; nine years of CERES data collected and analyzed using MIIC; 1233 events detected, and 6361 CERES observations

are processed; data are filtered for θ < 60° and θ < 30°.

El Niño is an anomalous, yet periodic, warming of the central and eastern equatorial Pacific Ocean. Every two to seven years this patch of ocean warms for six to 18 months. Figure 11 shows 13 years of L2 CERES sea surface temperature (SST) observations collected over the Nino 3 (5°N–5°S, 150°W–90°W) region using MIIC and compared with the NOAA monthly ocean time series climate indices published at the NOAA Climate Prediction Center [20]. The CERES trend results are exported from the MIIC 1DProfile analysis and analyzed in an external Python program since the NOAA data are not accessible to MIIC. The mean difference in temperature between the two time series is 0.17 °C. Figure 10. MIIC Analysis Plugin Results, CERES SW unfiltered radiance over Libya-1; nine years of Figure 10. MIIC AnalysisThe Plugin NOAA Results, and CERES CERES SW unﬁlteredtime series radiance derived over from Libya-1; different nine years instrument of CERES observations data collected are and strongly analyzed using MIIC; 1233 events CERES data collected and analyzed using MIIC; 1233◦ events detected,◦ and 6361 CERES observations detected, and 6361 CEREScorrelated observations (R are = 0.99535). processed; data are ﬁltered for θs < 60 and θv < 30 . are processed; data are filtered for θ < 60° and θ < 30°.

Figure 11. MIIC collected CERESFigure SST 11. observations MIIC collected (red) CERES vs. NOAA SST observations monthly time (red series) vs. (blue) NOAA for monthly Nino 3the time Central series Tropical(blue) for Paciﬁc (5◦N–5◦S, 150◦E–90◦W); January 2003–July 2015; R = 0.99535,Nino di3 fthe f = Central−0.170 Tropical◦C, σ = 0.217 Pacific◦C. (5N–5S, 150E–90W); January 2003–July 2015; R = 0.99535, = −0.170 °C, σ = 0.217 °C.

The MIIC web-based tiered collection of software simplifies acquisition of matched data for inter-comparison scientific studies. The MIIC analysis plugin architecture provides a mechanism for implementing common analyses and visualizations for initial assessment of matched observation data. The MIIC analysis plugin is not intended to compete with many of the excellent standalone analysis packages that already exist. Figure 12 show how well MODIS band 1 matches VIIRS band I1 in reflectance for various solar zenith and viewing zenith angles. The 2DProfiles contain grid cells ~0.25° in solar zenith–viewing zenith angular grid space. The difference plot capability allows users Figure 11. MIIC collected CERES SST observations (red) vs. NOAA monthly time series (blue) for to identifyNino 3 anomaliesthe Central orTropical significant Pacific trends (5N–5S, in 150E–90W);the data. The January scene 2003–July contains 2015;a mixture R = 0.99535, of ocean, clouds, = and −snow0.170 °C,and σ ice= 0.217 over °C. Antarctica on 3 January 2015. Additional error analyses will be performed outside of MIIC to analyze observations better matched in viewing geometry and scene type. For optimumThe MIICinter-calibration web-based results tiered observations collection ofneed software to match simplifies in space, time,acquisition wavelength, of matched and view data angles. for inter-comparison scientific studies. The MIIC analysis plugin architecture provides a mechanism for implementing common analyses and visualizations for initial assessment of matched observation data. The MIIC analysis plugin is not intended to compete with many of the excellent standalone analysis packages that already exist. Figure 12 show how well MODIS band 1 matches VIIRS band I1 in reflectance for various solar zenith and viewing zenith angles. The 2DProfiles contain grid cells ~0.25° in solar zenith–viewing zenith angular grid space. The difference plot capability allows users to identify anomalies or significant trends in the data. The scene contains a mixture of ocean, clouds, and snow and ice over Antarctica on 3 January 2015. Additional error analyses will be performed outside of MIIC to analyze observations better matched in viewing geometry and scene type. For optimum inter-calibration results observations need to match in space, time, wavelength, and view angles.

RemoteRemote Sens.Sens. 2016,, 88,, 902 902 1515 of of 18 18

Figure 12. Cont.

Remote Sens. 2016, 8, 902 16 of 18 Remote Sens. 2016, 8, 902 16 of 18

Figure 12. Mixed scene (ice, clouds, ocean) over Antarctica showing reflectance from VIIRS (top) and MODIS (middle) plotted against the solar zenith angle (x-axis) and Figure 12. Mixed scene (ice, clouds, ocean) over Antarctica showing reflectance from VIIRS (top) and MODIS (middle) plotted against the solar zenith angle (x-axis) viewing zenith (y-axis). For this scene the average reflectance difference is 0.0014 (0.29% of the average scene reflectance). The difference plot shows the average reflectance and viewing zenith (y-axis). For this scene the average reflectance difference is 0.0014 (0.29% of the average scene reflectance). The difference plot shows the average difference of 0.0075 (1.56%) for grid cells based on the viewing zenith and solar zenith angles. reflectance difference of 0.0075 (1.56%) for grid cells based on the viewing zenith and solar zenith angles.

Remote Sens. 2016, 8, 902 17 of 18

5. Conclusions The Multi-Instrument Inter-Calibration (MIIC) system is deployed at the NASA LaRC Atmospheric Science Data Center with access to more than 6 PB of online data. MIIC can access data from both local and remote OPeNDAP servers using powerful custom server-side functions. The MIIC system simplifies data access to support inter-calibration. MIIC will support the NASA CLARREO Pathfinder mission by predicting and acquiring time-, space-, and angle-matched target instrument data (CERES and VIIRS) with the accurate CLARREO Reflected Solar spectrometer. MIIC is a distributed web-based system that predicts, locates, and acquires matched event data housed within multiple data centers. This combination of event prediction and powerful server-side functions reduces the data volume required for inter-calibration studies by several orders of magnitude, dramatically reducing network bandwidth and disk storage needs. MIIC software can be deployed at large institutional data centers to ease access to satellite instrument data in support of scientific analysis.

Acknowledgments: This research has been supported by the NASA Advancing Collaborative Connections for Earth System Science (ACCESS) Program and the NASA CLARREO project. The CERES and CALIPSO datasets were obtained from the NASA Langley Atmospheric Sciences Data Center. VIIRS and MODIS data were obtained from the GSFC Level 1 and Atmosphere Archive and Distribution System (LAADS). In the near future MIIC software will be available from the NASA Earth Observing System Data and Information System (EOSDIS) Earthdata Code Collaborative repository. Author Contributions: Chris Currey analyzed the data and wrote this manuscript. Aron Bartle developed the MIIC software. Carlos Roithmayr developed the event prediction algorithms. Constantine Lukashin defined the histogram analysis features. Conflicts of Interest: The authors declare no conflict of interest.

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© 2016 by the US Government; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).