Bryan A. Baum (PI, SSEC/UW-Madison); W. Paul Menzel (Co-I, SSEC/UW-Madison); Irina Gladkova (Co-I, City College of New York)

Title: Continuity of cloud top pressure and cloud infrared thermodynamic phase by combining CrIS and VIIRS Measurements

The goal of this effort is to provide cloud properties from merged CrIS and VIIRS data. While MODIS has four channels within the broad 15-µm CO₂ band, VIIRS has no infrared (IR) absorption channels. The lack of at least one IR absorption channel on VIIRS degrades the accuracy of the cloud top pressure/height and thermodynamic phase products. Fortunately, we have adopted a method to synthesize a high spatial resolution 13.3-µm CO₂ channel for VIIRS. The approach involves using the high spatial resolution VIIRS IR window channels in combination with a lower spatial resolution 13.3-µm channel derived using CrIS high spectral resolution measurements convolved with the MODIS 13.3-µm spectral response function. In the first year, we will transition to the SIPS (Science Investigator-led Processing System) existing software that builds a high spatial resolution 13.3-µm pseudo-channel for VIIRS that is similar to MODIS band 33. This pseudo-channel will be at the VIIRS pixel spatial resolution of 750 m, but will be limited to the portion of the VIIRS swath for which the CrIS data are available. Based on the availability of the 13.3-µm pseudo-channel, cloud top pressure/height/temperature will be derived using an optimal estimation method. Additionally, the MODIS IR cloud thermodynamic phase algorithm will be modified for use with the VIIRS. The software for deriving the pseudo-channel and both cloud algorithms will be transitioned into operations in the first year of this work, and the associated ATBDs will be prepared concurrently. The second part of our effort is to place the VIIRS/CrIS cloud properties into context with those from MODIS and other sensors. For this, the cloud products will be analyzed using the Space-Time Gridding (STG) method, which maps the data onto a common spatial grid using a specified list of filters. The benefit of the STG method is that each data set is filtered exactly the same way, which is important for comparing cloud climate data records coherently. Subsequently, weekly and monthly maps (the time element is flexible) will be built from the daily VIIRS gridded products and compared with those derived from MODIS.

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Eva Borbas (PI, SSEC/UW-Madison); Zhenglong Li (Co-I, CIMSS/UW-Madison)

Title: Continuity of EOS clear-sky infrared total precipitable water vapor product using a combination of VIIRS and CrIMSS measurements

We propose to provide total column water vapor properties from merged VIIRS infrared measurements and CrIMSS (CrIS plus ATMS) water vapor soundings to continue the depiction of global moisture at high spatial resolution started with MODIS. While MODIS has two channels within the 6.5-µm H₂O band and four channels within the 15-µm CO₂ band, VIIRS has no infrared (IR) absorption channels. However, the VIIRS IR windows at 8.6, 10.8 and 12-µm give some indication of low level moisture (which constitutes much of the total column amount) and we propose to complement this with CrIMSS column moisture determinations. This VIIRS/CrIMSS algorithm will follow the approach used for MODIS. A clear sky regression relationship will be established between total precipitable water vapor (TPW) and VIIRS IR window brightness temperatures (BTs) and CrIMSS water vapor soundings calculated from a global training radiosonde based profile data set. A high spatial resolution surface emissivity database will be used to help differentiate surface emission and atmospheric moisture absorption. CrIMSS is added in clear and partly cloudy regions to enhance the TPW depiction and to extend the coverage.

The goal of this effort is to provide TPW from merged VIIRS and CrIMSS water vapor data at 780 meter resolution, enhancing the capability to track atmospheric moisture gradients at spatial resolutions commensurate with moisture variability. The operational MODIS algorithm for retrieving temperature and moisture profiles along with TPW and Total Ozone from IR MODIS measurements is a clear sky synthetic regression retrieval method, called MOD07. In this method, a dataset of global radiosonde determined atmospheric temperature and moisture profiles are used to establish a regression relationship with radiative transfer forward model calculated synthetic MODIS clear sky BTs with assigned accuracy and surface properties; the calculated regression coefficients are then applied to real MODIS BT measurements. We propose to develop a VIIRS TPW algorithm that is similar to the MOD07 synthetic regression algorithm in terms of BT and viewing angle classifications, but because of the absence of water vapor absorption channels on VIIRS we will compensate by adding the CrIMSS water vapor sounding EDR products to the regression relation. The resulting relationship for TPW will then be applied to actual VIIRS IR channel measurements and CrIMSS sounding EDRs to produce a TPW product at full VIIRS spatial resolution.

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Chris Elvidge (PI, NOAA/NESDIS/NGDC); Kimberly Baugh (Co-I, UC-Boulder); Feng Chi Hsu (Co-I; UC-Boulder); Mikhail Zhizhin (Co-I, UC-Boulder)

Title: VIIRS Nighttime Lights

The VIIRS instrument day/night band (DNB) collects global low light imaging data that have significant improvements over comparable data collected for 40 years by the DMSP Operational Linescan System. One of the prominent features of DNB data are detections of electric lighting present on the earth's surface. Most of these lights are from human settlements. VIIRS collects source data that could be used to generate monthly and annual science grade global radiance maps of human settlements with electric lighting. There are a substantial number of steps involved in producing a product that has been cleaned to exclude background noise, solar and lunar contamination, data degraded by cloud cover and features unrelated to electric lighting (e.g. fires, flares, volcanoes). The National Global Data Center (NGDC) proposes to develop the algorithms for the production of high quality global VIIRS nighttime lights. Products will be generated on monthly and annual increments and made available via an open access web site. There is a broad base of science users for VIIRS nighttime lights products, ranging from land use scientists, urban geographers, ecologists, carbon modelers, astronomers, demographers, economists, and social scientists.

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Bo-Cai Gao (PI, Naval Research Lab); Rong-Rong Li (Co-I, Naval Research Lab)

Title: Continuation of standard cirrus reflectance product from the EOS Terra and Aqua MODIS to Suomi-NPP VIIRS

The MODIS (Moderate Resolution Imaging Spectroradiometer) instruments on the Terra and Aqua spacecraft have a channel centered at 1.375 µm (Ch. 26) with a width of 30 nm for remote sensing of cirrus clouds from space. The cirrus reflectance data product has been generated for approximately 14 years from the Terra MODIS data and 12 years from the Aqua MODIS data at a NASA computing facility. This product has already been used in a variety of climate research and applications, such as for the study of tropical thin cirrus cloud effects on radiative forcing at the top of the atmosphere and the assessment of thin cirrus contamination effects in operational aerosol data products. The cirrus reflectance product is contained within a suite of EOS/MODIS standard cloud products. The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument currently on board the Suomi NPP (National Polar-orbiting Partnership) satellite has a similar channel centered at 1.378 micron (M9) with a width of 15 nm specifically designed for detecting thin cirrus clouds. The VIIRS M9 channel is narrower and significantly more sensitive for remote sensing of cirrus clouds than the MODIS channel 26. At present, there is no VIIRS cirrus reflectance data product produced at any of the NASA and NOAA sponsored computing facilities. We propose to build on our experience in developing the operational MODIS cirrus reflectance algorithm to produce a standard cirrus reflectance product from VIIRS data, and then to transfer the VIIRS version of the cirrus reflectance algorithm to the NASA Suomi NPP Atmosphere SIPS (Science Investigator-led Processing System) for routine processing. Because the VIIRS M9 channel has a higher signal to noise ratio and is nearly free of the out of band response effects found in the MODIS Channel 26, we expect that the quality of the proposed VIIRS cirrus reflectance data product would be superior to the current MODIS product. We will validate the VIIRS cirrus reflectance data product with co-located Lidar data collected with CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) on board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite platform.

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N. Christina Hsu (PI, NASA GSFC); Jaehwa Lee (Co-I, Univ. Maryland, College Park); Andrew Sayer (Co-I, Universities Space Research Association); Si-Chee Tsay (Co-I, NASA GSFC)

Title: Extending long-term aerosol data records from MODIS to VIIRS: Data continuity and enhancements to the Deep Blue algorithm

Many globally-important aerosol sources, notably of mineral dust and smoke aerosols, are found in semi-arid and arid regions. Thus, the ability to determine aerosol optical depth as well as particle type over these areas is particularly important. While the original MODIS operational retrieval algorithms were not able to provide aerosol properties over bright surfaces such as deserts, our previously-developed Deep Blue algorithm narrowed these gaps in MODIS aerosol products by performing retrievals over such surfaces. Deep Blue utilizes blue-wavelength measurements to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelengths. We recently successfully implemented (for both the MODIS and SeaWiFS sensors) an enhanced Deep Blue algorithm, extending spatial data coverage to most land areas, including both deserts and vegetated surfaces, to provide information on aerosol optical depth (AOD), Angstrom exponent, and dust absorption. Together with over-ocean aerosol retrievals developed by us and other groups, this closes some of the major remaining gaps in spatial coverage in aerosol datasets from MODIS- like sensors.
However, accurate estimates of long-term aerosol trends cannot be achieved without high-fidelity long-term aerosol Earth System Data Records (ESDRs), which rely on high-quality and consistent sensor calibration and retrieval algorithms applied across different satellite platforms. With the aging of the MODIS Terra and Aqua sensors, extending such long-term continuous ESDRs from the EOS-era MODIS to VIIRS is imperative. The intent of this proposal is to request funding for providing data continuity of aerosol data records by using a consistent aerosol retrieval algorithm from MODIS to VIIRS, to meet requirements for both short- and long-term aerosol applications such as air quality and climate studies.

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Robert Levy (PI, NASA GSFC); Falguni Patadia (Co-I, Morgan State University); Lorraine Remer (Co-I, UMBC); Jun Wang (Co-I, Univ. Nebraska-Lincoln); Norman Loeb (Collaborator, NASA LaRC); Steve Platnick (Collaborator, NASA GSFC); Richard Kleidman (SAIC); Shana Mattoo (SAIC)

Title: A consistent dark-target aerosol data record created from MODIS, VIIRS, and beyond

For the last dozen years, NASA’s EOS sensors have provided geophysical products, including the MODIS dark-target aerosol products used for research and routine applications. However, a dozen years is not long enough for determining climate change. Therefore, as we transition from EOS into successor programs such as NPP/JPSS, aerosol data continuity will be required to answer the question, are aerosols increasing or decreasing? NPP-VIIRS has been orbiting on S-NPP since late 2011, and NOAA (through IDPS) is delivering an operational aerosol optical depth (AOD) product. Even though the IDPS VIIRS product is modeled on the traditional MODIS product there are many unavoidable differences introduced by differences in the sensors themselves and choices made during algorithm development. These differences result in subtle differences in the AOD products. When separately compared to sunphotometer (AERONET) data, globally, both datasets show similar accuracies. However, there are significant regional differences and the current IDPS VIIRS product cannot be used to extend MODIS’s regional aerosol data record. By modifying the MODIS dark-target algorithm for use with VIIRS radiances, we can produce a VIIRS dataset more statistically similar MODIS historical one. Use of a common algorithm leads to a more consistent data set, and the potential for stitching together a MODIS VIIRS climate data record. Our proposal team has developed the successful MODIS aerosol product, and will repeat this success for VIIRS. There are issues, including calibration, Level 1 (Sensor Data Record) availability, and having access to an environment (e.g. SIPS) where the VIIRS data can be processed robustly. However, many of these problems can be mitigated, and the remainder can be characterized through validation procedures.

We intend to produce a MODIS-like product both in content and in structure to satisfy users who have invested infrastructure in accessing the MODIS aerosol product and need to continue their own products. For example, the CERES surface flux retrieval requires knowledge about the aerosol properties, which are in turn provided by retrievals from imager data in the field of view. MODIS provides constraints on Terra and Aqua, and VIIRS is used for S-NPP. Changes in aerosol retrieval approach will have a direct impact on surface radiation budget, which is important for understanding changes on surface energy budget, hydrology, etc. A consistent aerosol retrieval applied to VIIRS would limit discontinuities related to the CERES flux records. Likewise, a consistent MODIS and VIIRS product is necessary for such applications as assimilation within forecast models and interpretation with regard to climate assessments. During the MODIS era, we have become accustomed to a consistent time series of aerosol properties, and as we transition to VIIRS that consistency must continue. We propose to lead the community forward by producing a multi-sensor, multi-decadal, continuous time-series of aerosol properties adequate to answer global and regional climate questions, and compatible with the community’s existing infrastructure built over the past 14 years.

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Steven Platnick (PI, NASA GSFC); Steve Ackerman (Co-I, UW-Madison); Andrew Heidinger (Co-I; NOAA/NESDIS/STAR); Robert Holz (Co-I, SSEC/UW-Madison); Robert Levy (Collaborator, NASA GSFC)

Title: Development of VIIRS L2 cloud and L3 gridded Atmosphere Team products for NASA research and EOS data record continuity

Continuity of Level-2 (L2) cloud data records between MODIS and VIIRS is problematic because of the absence of key spectral channels on VIIRS (CO₂-slicing, water vapor) and a significant change in the spectral location for a key shortwave infrared band used for cloud microphysical retrievals. We propose to further develop a suite of L2 algorithms, designed and tested by our team with previous NPP funding, which is applied to the spectral channels common to the two instruments. The code is based on the MODIS Collection 6 algorithms for cloud mask (MOD35) and cloud optical/microphysical properties (MOD06), and the GOES-R Algorithm Working Group (AWG) algorithm for cloud-top properties. Since the algorithms can be run with similar spectral observations from both MODIS and VIIRS, they provide the basis for establishing a continuous cloud data record across the two instruments. We also propose to develop cloud-top property retrievals from an Atmosphere PEATE algorithm that uses CrIS to generate MODIS-like CO₂ and water vapor channels that, when combined with VIIRS channels, creates an observational data record closer to that available from MODIS alone. Alternatively, it can serve as the basis for continuing a combined Aqua MODIS/AIRS data record. The MODIS Atmosphere Team experience is that Level-3 (L3) retrieval statistics can be extremely sensitive to aggregation choices. We propose to develop a L3 VIIRS product, that at a minimum, includes aggregation choices and statistical datasets that provide compatibility with MODIS Atmosphere Team products (MOD08). In support of broader NPP Atmosphere Team needs, the effort will include MOD08-consistent aerosol and cloud datasets. We will also develop a merged VIIRS aerosol and cloud sampled L2 product (similar to the MODATML2 product) that can be used as a basis for users to develop research-specific gridded products. The proposed product development is responsive to solicitation section 2.2.1, Data Products for EOS Continuity. Based on solicitation definitions, development schedules are nominally Type 2 (VIIRS-only L2 product) and Type 3 (VIIRS/CrIS product, joint aerosol and cloud L3 and merged L2 product).

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Jun Wang (PI, University of Nebraska-Lincoln); Yang Liu (Co-I, Emory University); Robert Levy (Co-I, NASA GSFC); James J. Szykman (Co-I, EPA ORD), Jing Zeng (UN-Lincoln); Robert Holz (Collaborator, SSEC/UW-Madison); Lina Balluz and Chaoyang Li (Collaborators, National Centor for Environmental Health, CDC); Kay Yang and Nickolay Krotkov (Collaborators, NASA GSFC)

Title: Evaluate and enhance Suomi-NPP products for air quality and public health applications

We propose to evaluate and enrich the utility of SNPP data for applied science research. Following the SNPP’s science strategy to continue the NASA’s EOS data record, and to avoid future possible discontinuity of using NASA’s data in air quality and public health applications, our proposed work has the following two components.

(1) PM2.5 air quality applications. We will (a) evaluate and improve the application of the (MODIS-type if possible) VIIRS aerosol product for the operational monitoring of PM2.5 air quality in Remote Sensing Information Gateway (RSIG) at the U.S. Environmental Protection Agency (EPA), (b) subsequently transfer RSIG’s PM2.5 estimates to the Environmental Public Health Tracking Network (EPHTN) at the Centers for Disease Control and Prevention (CDC), and (c) evaluate and improve VIIRS-RSIG PM2.5 estimates for enhanced spatial predictions in on-going EPA-CDC EPHTN efforts, currently using CMAQ model output and filter-based PM2.5 observations from EPA- AQS. The PM2.5 derivation will build upon the team’s expertise in this area and associated published methods by extending the use of aerosol vertical profiles from chemistry transport models from GEOS-Chem to regional models, WRF-Chem and WRF-CMAQ. We will also compare and contrast the differences among the use of AODs from MODIS Collection-6 algorithm, MODIS-type VIIRS algorithm, and existing VIIRS algorithm (maintained by NOAA) for air quality applications and surface PM2.5 estimates, and evaluate the feasibility of multi-sensor multi-product and multi-model ensemble approach (including the use of Bayesian downscaler model) for estimating surface PM2.5. To assess uncertainty and validate the PM2.5 estimate, we will use the existing ground-based PM2.5 data and other EOS (such as CALIOP) data in addition to well suited validation data sets from the NASA DISCOVER-AQ Earth Venture-1 Mission along with two new Satellite-to-Air Quality VALidations (SAQ-VAL) sites located in the mid-atlantics region.

(2) Public heath (skin-cancer-related) applications. We will incorporate OMPS-based estimates of surface UVB irradiance and erythemal doses into the CDC’s EPHTN, and apply them in both public heath advisory and skin cancer research. By integrating ground observations (AERONET), atmospheric chemical transport model (GEOS-Chem) simulations, VIIRS AOD, OMPS-based estimate of O₃, and aerosol index, we will estimate surface UV irradiance and erythemal doses from OMPS, and thereby continue the TOMS and OMI surface UV product since 1970s. The surface UV irradiance and erythemal doses data are highly needed in the skin-cancer-related research that often relies on the model-based data. After evaluating the accuracy of the our UVB product with ground measurements, we will spatially match UVB exposure doses to 3,100 U.S. counties, and their association with county-level melanoma incidences reported by CDC and National Cancer Institute (NCI) will be studied (through the leverage of existing projects).