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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Assessment of Terra MODIS On-Orbit Polarization Sensitivity Using Pseudoinvariant Desert Sites

The Moderate Resolution Imaging Spectroradiometer (MODIS) is currently flying on NASA's Earth Observing System Terra and Aqua satellites, launched in 1999 and 2002, respectively. MODIS reflective solar bands in the visible wavelength range are known to be sensitive to polarized light based on prelaunch polarization sensitivity tests. After about five years of on-orbit operations, it was discovered that the polarization sensitivity at short wavelengths had shown a noticeable increase. In this paper, we examine the impact of polarization on measured top-of-atmosphere (TOA) reflectance based on MODIS Collection-6 L1B over pseudo invariant desert sites. The standard polarization correction equation is used in combination with simulated at-sensor radiances using the second simulation of a satellite signal in the Solar Spectrum, Vector Radiative Transfer Code (6SV). We ignore the polarization contribution from the surface and a ratio approach is used for both 6SV-derived in put parameters and observed TOA reflectance. Results indicate that significant gain corrections up to 25% are required near the end of scan for the 412 and 443 nm bands. The polarization correction reduces the seasonal fluctuations in reflectance trends and mirror side ratios from 30% and 12% to 10% and 5%, respectively, for the two bands. Comparison of the effectiveness of the polarization correction with the results from the NASA Ocean Biology Processing Group shows a good agreement in the corrected reflectance trending results and their seasonal fluctuations.

Wu, Aisheng↗
NASA NTRS2017

Evaluation of the VIIRS BRDF, Albedo and NBAR Products Suite and an Assessment of Continuity with the Long Term MODIS Record

Bidirectional Reflectance Distribution Function (BRDF) model parameters, Albedo quantities, and Nadir BRDF Adjusted Reflectance (NBAR) products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), on the Suomi-NPP (National Polar-orbiting Partnership) satellite are evaluated with spatially representative in situ tower albedometer measurements and through comparison with the MODerate Resolution Imaging Spectroradiometer (MODIS) long term record. White Sky Albedo (WSA), Black Sky Albedo (BSA), NBAR, and Quality Assurance (QA) results show that the VIIRS algorithm is performing well at the global scale and is able to provide global data products comparable with the heritage MODIS (with an absolute bias of 0.0069 for shortwave broadband WSA) despite the spectral, angular, and effective spatial resolution differences between the two sensors. Both VIIRS and MODIS albedo are shown to agree well with in situ albedo measurements at a number of spatially representative sites. This evaluation provides confidence that the high quality, daily VIIRS BRDF model parameters, Albedo, and NBAR products will be able to provide the long term continuity required by the modeling and monitoring communities.

VIIRS↗
NASA NTRS2018

A Generic Approach for Inversion of Surface Reflectance over Land: Overview, Application and Validation Using MODIS and LANDSAT8 Data

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derive an explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS and LANDSAT data and its validation using the AERONET data.

validation↗
NASA NTRS2018

LaSRC (Land Surface Reflectance Code): Overview, Application and Validation Using MODIS, VIIRS, LANDSAT and Sentinel 2 Data's

This paper presents a generic approach developed to derive surface reflectance over land from a variety of sensors. This technique builds on the extensive dataset acquired by the Terra platform by combining MODIS and MISR to derivean explicit and dynamic map of band ratio's between blue and red channels and is a refinement of the operational approach used for MODIS and LANDSAT over the past 15 years. We will present the generic approach and the application to MODIS VIIRS, LANDSAT and Sentinel 2 data's and its validation using the AERONET data.

validation↗
NASA NTRS2020

Cross-Calibration of Terra and Aqua MODIS Using RadCalNet

The twin MODIS instruments onboard the Terra and Aqua spacecraft have been successfully operating for nearly two decades and providing complementary observations of the Earth’s land, ocean, and atmosphere. Although the two MODIS instruments view the entire Earth’s surface once every 2-3 days, simultaneous views between them are limited due to their varying orbits. Therefore, the intercomparison between these two instruments has been previously performed using a transfer instrument (such as AVHRR) or using lunar measurements normalized using a common model such as the USGS ROLO. In recent years RadCalNet, a CEOS initiative, has provided SI-traceable Top-of-Atmosphere (TOA) reflectances from a coordinated network of instrumented land-based sites. RadCalNet facilitates a unique mechanism to perform cross-calibration of instruments by minimizing the uncertainties associated with overpass time differences. In this work, the near-simultaneous TOA reflectance measurements from the Railroad Valley, US (RVUS) are used as a transfer to compare the on-orbit observations for the Terra and Aqua MODIS RSB. Near-nadir overpasses from January 2013 to January 2019 are processed and matched up with near-simultaneous RadCalNet measurements. Results show that the VIS/NIR bands agree to within 2% and the SWIR bands agree to within 5%. Also, discussed in this work are the future efforts that will be undertaken to expand this comparison to include other instruments, other sites, and both nadir- and off-nadir views after compensation for BRDF effects.

Amit Angal↗
NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗
NASA NTRS2020

Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet(UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of>~0.4 and GEMS SSA values of<~0.95.Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

aerosol↗
NASA NTRS2021

Optimized MODIS Channel Selection to Improve the NASA CERES FluxByCloudType Product Fluxes

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project has provided the climate community more than a 20-year record of top of the atmosphere (TOA) reflected shortwave (SW) and emitted longwave (LW) fluxes. The fluxes used to monitor the Earth’s energy balance as well as for climate model validation and cloud feedback studies. One of the largest uncertainties in climate models is the response of clouds in feedback studies. To reduce this uncertainty requires more stringently validation of model generated fluxes by cloud-type. Rather than relying on radiative transfer model generated fluxes from observed cloud-type retrievals, the CERES FluxByCloudType (FBCT) product, relies on MODIS empirical narrowband to broadband relationships to convert the cloudy portions of the CERES footprints into fluxes. The overcast footprint and sub-footprint cloud layer fluxes are then stratified by 7 pressure layers and 6 optical depth bins and temporally averaged into daily and monthly regional cloud-type fluxes. The CERES project analyzed a total of 19 MODIS channels for use in the next generation FBCT product narrowband to broadband conversion. This was accomplished by comparing the narrowband to broadband RMS errors from all possible 5-channel combinations. The SW and LW optimized channels were selected by also considering analogous geostationary, MODIS and VIIRS imager channels as well as channel combinations that provided the lowest RMS errors across all surface types. The optimized channel combinations will be tested in the FBCT algorithm to investigate any cloud fraction, effective pressure, optical depth, PW, solar and view angle, surface type and regional narrowband to broadband dependencies.

CERES↗
NASA NTRS2022

MODIS TEB Electronic Crosstalk Correction Update and Impact on L1B Product Uncertainty

The MODIS instruments onboard the Terra and Aqua satellites have been in operation for over22 and 20 years, respectively. The instruments’ calibration accuracy has been maintained, even with instrument degradation. Electronic crosstalk in the thermal emissive bands (TEB) is a known issue with an increasing impact on the calibration and product. The Terra MODIS photovoltaic (PV) longwave infrared (LWIR) bands crosstalk corrections have been applied in Collection 6.1 (C6.1). However, the electronic crosstalk contamination for some detectors in the mid-wave infrared (MWIR) bands and the Aqua PVLWIR bands affect the Level-1B (L1B) product’s measurement accuracy and image quality. In Collection 7 (C7), crosstalk corrections for select detectors in the Terra and Aqua MWIR and Aqua PV LWIR bands are applied. The entire mission crosstalk coefficients for the select detectors and bands are derived from scheduled lunar observations and populated in the form of look-up tables (LUTs). The Aqua PV-LWIR bands exhibit similar downward crosstalk trends as the Terra PV-LWIR bands, especially in recent years. The crosstalk coefficients and their trends provide a guideline for the correction application. Earth measurement analyses before and after the correction provide contamination and correction assessments. It has been shown that the product quality is enhanced with the crosstalk correction applied in C7. For C7, the crosstalk coefficient uncertainty is derived from the fit residuals between the measured values and a linear fit over a three-year sliding window. The uncertainty propagation is modeled and applied in the total uncertainty calculation intheL1B product. The TEB electronic crosstalk LUTs have been processed over the entire Terra and Aqua MODIS missions. This paper presents the C7 crosstalk correction, as well as its assessment and uncertainty propagation algorithm to the TEB uncertainty.

Tiejun Chang↗
NASA NTRS2022

Modis TEB Electronic Crosstalk Correction Update and Impact on L1B Product Uncertainty

The MODIS instruments on board the Terra and Aqua satellites have been in operation for over 22 and 20 years, respectively. The instruments’ calibration accuracy has been maintained, even with instrument degradation. Electronic crosstalk in the thermal emissive bands (TEB) is a known issue with an increasing impact on the calibration and product. The Terra MODIS photovoltaic (PV) longwave infrared (LWIR) bands crosstalk corrections have been applied in Collection 6.1 (C6.1). However, the electronic crosstalk contamination for some detectors in the mid-wave infrared(MWIR) bands and the Aqua PVLWIR bands affect the Level-1B(L1B) product’s measurement accuracy and image quality. In Collection 7 (C7), crosstalk corrections for select detectors in the Terra and Aqua MWIR and Aqua PV LWIR bands are applied. The entire mission crosstalk coefficients for the select detectors and bands are derived from scheduled lunar observations and populated in the form of look-up tables (LUTs). The Aqua PV-LWIR bands exhibit similar downward crosstalk trends as the Terra PV-LWIR bands, especially in recent years. The crosstalk coefficients and their trends provide a guideline for the correction application. Earth measurement analyses before and after the correction provide contamination and correction assessments. It has been shown that the product quality is enhanced with the crosstalk correction applied in C7. For C7, the crosstalk coefficient uncertainty is derived from the fit residuals between the measured values and a linear fit over a three-year sliding window. The uncertainty propagation is modeled and applied in the total uncertainty calculation intheL1B product. The TEB electronic cross talk LUT shave been processed over the entire Terra and Aqua MODIS missions. This paper presents the C7 crosstalk correction, as well as its assessment and uncertainty propagation algorithm to the TEB uncertainty.

Tiejun Chang↗
NASA NTRSDate not supplied

Creating Seasonal Climatologies of Aerosol Lidar Ratios Over Ocean Using MODIS AOD Constrained Retrievals and GOCART Model Simulations

The current CALIPSO algorithms assign one lidar ratio (i.e., extinction-to-backscatter ratio; LR) value globally for each of the seven tropospheric aerosol types. In a future data products release, the CALIPSO project aims to improve these algorithms through the development of regional and seasonal LR climatologies. In this work, aerosol LRs are inferred through CALIOP backscatter profiles constrained by collocated aerosol optical depth (AOD) from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data. This analysis is subsampled for those profiles that are cloud-free and contain only one CALIOP-classified aerosol type (e.g., marine). The CALIOP profiles are then collocated with aerosol volume fractions obtained through Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulations. In this poster, we show twelve-year (June 2006-August 2018) mean spatial distributions of inferred aerosol LRs for CALIOP-classified marine aerosols and how they correspond inversely with patterns of GOCART sea salt volume fraction (SSVF). Near land masses, smaller SSVFs and larger LRs are found (due to the influence of over-land aerosols). In the remote ocean regions (likely less impacted by over-land aerosols), larger SSVFs and smaller LRs are found. The developed relationship between the GOCART model SSVFs and MODIS AOD constrained LRs is used to create model-assisted seasonal LR maps. Additionally, we show maps of inferred LRs from constrained retrievals using the CALIPSO Ocean Derived Column Optical Depth (ODCOD) product and comparisons with those from the MODIS analyses. The technique demonstrated here benefits CALIPSO in the near-term, but similar methods can also be applied to the next generation space-based elastic backscatter lidars with collocated passive sensors, such as those of the upcoming NASA Atmosphere Observing System (AOS).

Travis Toth↗
DOE OSTI2022

Detection of surface water temperature variations of Mongolian lakes benefiting from the spatially and temporally gap-filled MODIS data

Lakes provide critical water resources for human activities and ecosystems, particularly in the Mongolian Plateau (MP), which is characterized by a dry climate and a harsh environment. As a region that is sensitive to anthropogenic warming, tracking lake surface water temperature (LSWT) changes in Mongolian lakes is crucial for understanding the consequences of a warming climate on lake ecosystems. However, the long-term monitoring of LSWT is restricted by the spatiotemporal gaps in the raw imagery of remote sensing-based land surface temperature (LST), e.g., the commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. This study applied an improved gap-filling method by utilizing the discrete cosine transform-based penalized least squares (DCT-PLS) strategy in the spatial domain combined with the linear interpolation (LI) algorithm in the temporal domain. The method was applied to fill gaps in the LSWT imagery of 12 representative lakes across MP. The randomly sampled high-quality MODIS LSWT values in the spatial and temporal domains were excavated as false data gaps and considered “virtual true” validation datasets. The spatial validation results showed that the estimated LSWT for all the lake cases were comparable with the “virtual true” LSWT values, with the average values of the coefficient of determination, mean absolute error, mean square error, and root mean square error being 0.98, 0.38 °C, 0.45 °C, and 0.59 °C, respectively. Meanwhile, the error of nighttime LSWT results was relatively lower than that of daytime LSWT. For temporal interpolation validation, the LI algorithm exhibited relatively better performance and could more objectively indicate the variation in LSWT. Benefiting from the spatially and temporally well-constrained data, we analyzed the interannual and intra-annual change characteristics of the LSWTs of the 12 lakes. The long-term variations of annual and seasonal mean LSWTs in the 12 selected lakes exhibited no evident trends in 2000–2020, while presented apparent interannual fluctuations. The slight changes in the average LSWTs of the 12 selected lakes were in excellent synchronization with the surrounding LST derived from the reanalysis datasets, confirming the widely reported phenomenon of “global warming hiatus” that occurred in the early 21st century. This study improves the understanding of the LSWT variations in Mongolian lakes in response to global climate change. It has the potential to provide an effective approach for monitoring LSWT changes in other large-scale studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CERES MODIS Cloud Product Retrievals for Edition 4--Part I: Algorithm Changes

The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24-μm channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO₂-channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced more accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product. View Full-Text

47 OTHER INSTRUMENTATION↗
NASA NTRS2004

Performance Expectations for Future Moderate Resolution Visible and Infrared Space Instruments Based on AIRS and MODIS In-Flight Experience

Lessons learned from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) projects highlight areas where further technology development is needed to address future land, ocean and atmospheric measurement needs. Although not established as requirements at this time, it is anticipated that scientists will expect improvements in the areas of spatial, spectral, radiometric, polarimetric, temporal and calibration performance for future sensors. This paper addresses each of these performance areas and provides lessons learned from MODIS and AIRS. We also present expectations in performance of the system based on information from NASA Instrument Incubator Program and industry reports. Tradeoffs are presented vs orbit altitude (LEO, ME0 and GEO) and provide a 'systems' perspective to future measurement concepts.

Infrared↗
NASA NTRS2013

Retrieval of Aerosol Optical Depth Under Thin Cirrus from MODIS: Application to an Ocean Algorithm

A strategy for retrieving aerosol optical depth (AOD) under conditions of thin cirrus coverage from the Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. We adopt an empirical method that derives the cirrus contribution to measured reflectance in seven bands from the visible to shortwave infrared (0.47, 0.55, 0.65, 0.86, 1.24, 1.63, and 2.12 μm, commonly used for AOD retrievals) by using the correlations between the top-of-atmosphere (TOA) reflectance at 1.38 micron and these bands. The 1.38 micron band is used due to its strong absorption by water vapor and allows us to extract the contribution of cirrus clouds to TOA reflectance and create cirrus-corrected TOA reflectances in the seven bands of interest. These cirrus-corrected TOA reflectances are then used in the aerosol retrieval algorithm to determine cirrus-corrected AOD. The cirrus correction algorithm reduces the cirrus contamination in the AOD data as shown by a decrease in both magnitude and spatial variability of AOD over areas contaminated by thin cirrus. Comparisons of retrieved AOD against Aerosol Robotic Network observations at Nauru in the equatorial Pacific reveal that the cirrus correction procedure improves the data quality: the percentage of data within the expected error +/-(0.03 + 0.05 ×AOD) increases from 40% to 80% for cirrus-corrected points only and from 80% to 86% for all points (i.e., both corrected and uncorrected retrievals). Statistical comparisons with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals are also carried out. A high correlation (R = 0.89) between the CALIOP cirrus optical depth and AOD correction magnitude suggests potential applicability of the cirrus correction procedure to other MODIS-like sensors.

cirrus correction↗
NASA NTRS2014

Comparison of Near-Surface Air Temperatures and MODIS Ice-Surface Temperatures at Summit, Greenland (2008-2013)

We have investigated the stability of the MODerate resolution Imaging Spectroradiometer (MODIS) infrared-derived ice surface temperature (IST) data from Terra for use as a climate quality data record. The availability of climate quality air temperature data (TA) from a NOAA Global Monitoring Division observatory at Greenlands Summit station has enabled this high temporal resolution study of MODIS ISTs. During a 5 year period (July 2008 to August 2013), more than 2500 IST values were compared with 3-minute average TA values derived from the 1-minute data from NOAAs primary 2 m air temperature sensor. These data enabled an expected small offset between air and surface temperatures at this the ice sheet location to be investigated over multiple annual cycles.

Greenland↗
NASA NTRS2014

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds.

radiative forcing↗