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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Retrieval of Aerosol Properties from MODIS Terra, MODIS Aqua, and VIIRS SNPP: Calibration Focus

MODIS-DT Collection 6 - Aqua/Terra level 2, 3; entire record processed - "Trending" issues reduced - Still a 15% or 0.02 Terra vs Aqua offset. - Terra/Aqua convergence improved with C6+, but bias remains. - Other calibration efforts yield mixed results. VIIRS-­‐DT in development - VIIRS is similar, yet different then MODIS - With 50% wider swath, VIIRS has daily coverage - Ensures algorithm consistency with MODIS. - Currently: 20% NPP vs Aqua offset over ocean. - Only small bias (%) over land (2012-­‐2016) - Can VIIRS/MODIS create aerosol CDR? Calibration for MODIS - VIIRS continues to fundamentally important. It's not just Terra, or just Aqua, or just NPP-­‐VIIRS, I really want to push synergistic calibration.

VIIRS↗
NASA NTRS2016

Marine Boundary Layer Cloud Property Retrievals from High-Resolution ASTER Observations: Case Studies and Comparison with Terra MODIS

A research-level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. It is based on the operational MODIS algorithm. This paper documents the technical details of this algorithm and evaluates the retrievals for selected marine boundary layer cloud scenes through comparisons with the operational MODIS Data Collection 6 (C6) cloud product. The newly developed, ASTERspecific cloud masking algorithm is evaluated through comparison with an independent algorithm reported in Zhao and Di Girolamo (2006). To validate and evaluate the cloud optical thickness (tau) and cloud effective radius (r(sub eff)) from ASTER, the high-spatial-resolution ASTER observations are first aggregated to the same 1000m resolution as MODIS. Subsequently, tau(sub aA) and r(sub eff, aA) retrieved from the aggregated ASTER radiances are compared with the collocated MODIS retrievals. For overcast pixels, the two data sets agree very well with Pearson's product-moment correlation coefficients of R greater than 0.970. However, for partially cloudy pixels there are significant differences between r(sub eff, aA) and the MODIS results which can exceed 10 micrometers. Moreover, it is shown that the numerous delicate cloud structures in the example marine boundary layer scenes, resolved by the high-resolution ASTER retrievals, are smoothed by the MODIS observations. The overall good agreement between the research-level ASTER results and the operational MODIS C6 products proves the feasibility of MODIS-like retrievals from ASTER reflectance measurements and provides the basis for future studies concerning the scale dependency of satellite observations and three-dimensional radiative effects.

marine boundary layer↗
NASA NTRS2012

The Operational MODIS Cloud Optical and Microphysical Property Product: Overview of the Collection 6 Algorithm and Preliminary Results

Operational Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of cloud optical and microphysical properties (part of the archived products MOD06 and MYD06, for MODIS Terra and Aqua, respectively) are currently being reprocessed along with other MODIS Atmosphere Team products. The latest "Collection 6" processing stream, which is expected to begin production by summer 2012, includes updates to the previous cloud retrieval algorithm along with new capabilities. The 1 km retrievals, based on well-known solar reflectance techniques, include cloud optical thickness, effective particle radius, and water path, as well as thermodynamic phase derived from a combination of solar and infrared tests. Being both global and of high spatial resolution requires an algorithm that is computationally efficient and can perform over all surface types. Collection 6 additions and enhancements include: (i) absolute effective particle radius retrievals derived separately from the 1.6 and 3.7 !-lm bands (instead of differences relative to the standard 2.1 !-lm retrieval), (ii) comprehensive look-up tables for cloud reflectance and emissivity (no asymptotic theory) with a wind-speed interpolated Cox-Munk BRDF for ocean surfaces, (iii) retrievals for both liquid water and ice phases for each pixel, and a subsequent determination of the phase based, in part, on effective radius retrieval outcomes for the two phases, (iv) new ice cloud radiative models using roughened particles with a specified habit, (v) updated spatially-complete global spectral surface albedo maps derived from MODIS Collection 5, (vi) enhanced pixel-level uncertainty calculations incorporating additional radiative error sources including the MODIS L1 B uncertainty index for assessing band and scene-dependent radiometric uncertainties, (v) and use of a new 1 km cloud top pressure/temperature algorithm (also part of MOD06) for atmospheric corrections and low cloud non-unity emissivity temperature adjustments.

Platnick, Steven↗
NASA NTRS2014

Cloud Retrieval Intercomparisons Between SEVIRI, MODIS and VIIRS with CHIMAERA PGE06 Data Collection 6 Products

The Cross-platform HIgh resolution Multi-instrument AtmosphEric Retrieval Algorithms (CHIMAERA) system allows us to perform MODIS-like cloud top, optical and microphysical properties retrievals on any sensor that possesses a minimum set of common spectral channels. The CHIMAERA system uses a shared-core architecture that takes retrieval method out of the equation when intercomparisons are made. Here we show an example of such retrieval and a comparison of simultaneous retrievals done using SEVIRI, MODIS and VIIRS sensors. All sensor retrievals are performed using CLAVR-x (or CLAVR-x based) cloud top properties algorithm. SEVIRI uses the SAF_NWC cloud mask. MODIS and VIIRS use the IFF-based cloud mask that is a shared algorithm between MODIS and VIIRS. The MODIS and VIIRS retrievals are performed using a VIIRS branch of CHIMAERA that limits available MODIS channel set. Even though in that mode certain MODIS products such as multilayer cloud map are not available, the cloud retrieval remains fully equivalent to operational Data Collection 6.

cloud retrieval↗
NASA NTRS2020

The MODIS RSB calibration and look-up-table delivery process for Collection 6 and 6.1

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on-board the Aqua and Terra space-craft have provided valuable science data for the last 18 and 20 years, respectively. Each instrument is equipped with 36 spectral bands, 20 of which are reflective solar bands (RSBs). These bands cover a wavelength range of 0.4 – 2.2μm and are calibrated on-orbit using several on-board calibrators (OBCs), such as a solar diffuser (SD)and a solar diffuser stability monitor (SDSM), along with regularly-scheduled lunar observations through the space view (SV) port. The gain (1/m1) and response-verses-scan angle (RVS) are updated on a near-monthly basis and act as the primary look-up-tables (LUTs) for the RSB calibration. A set of separate uncertainty LUTs for each of the RSBs are also delivered regularly and incorporated into the Level 1B (L1B) product to generate a pixel-level Uncertainty Index (UI). In addition to the gain, RVS and uncertainty, there are several other LUTs associated with the reflective bands that are either updated less frequently or remain static. The accuracy of both the forward-predicted and historical RSB LUTs, which are derived by the MODIS Characterization Sup-port Team (MCST), is important in maintaining the quality and accuracy of the L1B and science products.To ensure a timely and accurate LUT update, MCST has established a comprehensive set of procedures. This paper provides an overview of the calibration process, along with the current LUT delivery process for the RSB sin Collection 6 (C6) and Collection 6.1 (C6.1). Improvements to be implemented in future collections are also discussed.

MODIS↗
NASA NTRS2021

Evaluation of Novel NASA MODIS and VIIRS Aerosol Products and Assessment of Smoke Height Boundary Layer Ratio During Extreme Smoke Events in the Western U.S.

We analyze new aerosol products from NASA satellite retrievals over the western U.S. during August 2013, with special attention to locally generated wildfire smoke and downwind plume structures. Aerosol optical depth (AOD) at 550 nm from MODIS (Terra and Aqua Collections 6 and 6.1) and VIIRS Deep Blue (DB) and MODIS (Terra and Aqua) Multi‐Angle Implementation of Atmospheric Correction (MAIAC) retrievals are evaluated against ground‐based AErosol RObotic NETwork (AERONET) observations. We find a significant improvement in correlation with AERONET and other metrics in the latest DB AOD (MODIS C6.1 r^2=0.75, VIIRS r^2=0.79) compared to MODIS C6 (r2=0.62). In general, MAIAC (r^2=0.84) and DB (MODIS C6.1 and VIIRS) present similar statistical evaluation metrics for the western U.S. and are useful tools to characterize aerosol loading associated with wildfire smoke. We also evaluate three novel NASA MODIS plume injection height (PIH) products, one from MAIAC and two from the Aerosol Single scattering albedo and layer Height Estimation (ASHE) (MODIS and VIIRS) algorithm. Both Terra and Aqua MAIAC PIHs statistically agree with ground‐based and satellite lidar observations near the fire source, as do ASHE, although the latter is sensitive to assumptions about aerosol absorption properties. We introduce a first‐order approximation Smoke Height Boundary Layer Ratio (SHBLR) to qualitatively distinguish between aerosol pollution within the planetary boundary layer and the free troposphere. We summarize the scope, limitations, and suggestions for scientific applications of surface level aerosol concentrations specific to wildfire emissions and smoke plumes using these novel NASA MODIS and VIIRS aerosol products.

Plume injection height↗
NASA NTRS2012

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470 nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004 yr−1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistentwith simulated results,with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

NDVI↗
NASA NTRS2011

Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed.

Platnick, Steven E.↗
NASA NTRS2008

Assessment of MODIS Scan Mirror Reflectance Changes On-Orbit

Since launch, the NASA EOS Terra and Aqua MODIS have operated successfully for more than 8 and 6 years, respectively. MODIS collects data using a two-sided scan mirror over a large scan angular range. The scan mirror is made of a polished, nickel-plated beryllium base coated with high purity silver, which is then over-coated with the Denton proprietary silicon monoxide and silicon dioxide mixture. The scan mirror's reflectance was characterized pre-launch using its witness samples, and the response versus scan angle was measured at the sensor system level. In this study, we present an assessment of MODIS scan mirror on-orbit degradation by examining changes of spectral band response over each sensor's mission lifetime. Results show that the scan mirror's optical properties for both Terra and Aqua MODIS have experienced significant degradation since launch in the VIS spectral region, which is mirror side dependent as well as scan angle dependent. In general, the mirror degradation is more severe for Terra MODIS than Aqua MODIS, especially during recent years. For Terra MODIS, the degradation rate is noticeably different between the mirror sides. On the other hand, there has been little mirror side dependent difference for Aqua MODIS.

Xiong, Xiaoxiong↗
NASA NTRS2011

Impact of Sensor Degradation on the MODIS NDVI Time Series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Wang, Dongdong↗
NASA NTRS2019

Response Versus Scan-Angle Assessment of MODIS Reflective Solar Bands in Collection 6.1 Calibration

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Aqua and Terra satellites have been operated for nearly two decades, producing high-quality earth observation data sets suitable for a broad range of scientific studies regarding the earth’s land, ocean, and atmospheric processes. The high radiometric accuracy of MODIS reflective solar band (RSB) calibration has also served as benchmark measurements for on-orbit cross-calibration studies. As the two MODIS instruments have operated well beyond their design lifespan of six years, the measurements from the onboard calibrators alone become inadequate to characterize the sensor’s response at all scan angles, as evinced by long-term drifts observed at certain scan positions of the Aqua-MODIS 0.64- and 0.86-μm bands in Collection 6 (C6) data set. The latest MODIS Level 1B C6.1 data set incorporates earth-view response trending from invariant desert sites as supplemental inputs to characterize the scan-angle calibration dependencies for all RSB. This article presents a deep convective cloud (DCC)-based calibration approach for an independent evaluation of the MODIS RSB response versus scan-angle (RVS) performance in C6.1. The long-term calibration stability and RVS differences in C6.1 have been significantly improved for Aqua-MODIS RSB. The observed RVS differences of more than 2% in Aqua-MODIS C6 bands 1 and 2 have been reduced to within 1% in C6.1. Some RSBs of Terra-MODIS have suffered temporal drifts up to ~2% and calibration shifts up to 3%, particularly around 2016 when the Terra satellite entered into safe mode. The DCC approach has been found very effective in tracking the on-orbit RVS changes over time.

Clouds and the Earth’s Radiant Energy System (CERE↗
NASA NTRS2011

Improvements in Night-Time Low Cloud Detection and MODIS-Style Cloud Optical Properties from MSG SEVIRI

The MODIS cloud optical properties algorithm (MOD06IMYD06 for Terra and Aqua MODIS, respectively) slated for production in Data Collection 6 has been adapted to execute using available channels on MSG SEVIRI. Available MODIS-style retrievals include IR Window-derived cloud top properties, using the new Collection 6 cloud top properties algorithm, cloud optical thickness from VISINIR bands, cloud effective radius from 1.6 and 3.7Jlm and cloud ice/water path. We also provide pixel-level uncertainty estimate for successful retrievals. It was found that at nighttime the SEVIRI cloud mask tends to report unnaturally low cloud fraction for marine stratocumulus clouds. A correction algorithm that improves detection of such clouds has been developed. We will discuss the improvements to nighttime low cloud detection for SEVIRI and show examples and comparisons with MODIS and CALIPSO. We will also show examples of MODIS-style pixel-level (Level-2) cloud retrievals for SEVIRI with comparisons to MODIS.

Wind, Galina (Gala)↗
NASA NTRS2014

Cross-Calibration of Earth Observing System Terra Satellite Sensors MODIS and ASTER

The Advanced Spaceborne Thermal Emissive and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectrometer (MODIS) are two of the five sensors onboard the Earth Observing System's Terra satellite. These sensors share many similar spectral channels while having much different spatial and operational parameters. ASTER is a tasked sensor and sometimes referred to a zoom camera of the MODIS that collects a full-earth image every one to two days. It is important that these sensors have a consistent characterization and calibration for continued development and use of their data products. This work uses a variety of test sites to retrieve and validate intercalibration results. The refined calibration of Collection 6 of the Terra MODIS data set is leveraged to provide the up-to-date reference for trending and validation of ASTER. Special attention is given to spatially matching radiance measurements using prelaunch spatial response characterization of MODIS. Despite differences in spectral band properties and spatial scales, ASTER-MODIS is an ideal case for intercomparison since the sensors have nearly identical views and acquisitions times and therefore can be used as a baseline of intercalibration performance of other satellite sensor pairs.

MODIS↗
NASA NTRS2016

On-Orbit Performance and Calibration Improvements For the Reflective Solar Bands of Terra and Aqua MODIS

Moderate Resolution Imaging Spectroradiometer (MODIS) is the keystone instrument for NASAs EOS Terra and Aqua missions, designed to extend and improve heritage sensor measurements and data records of the land, oceans and atmosphere. The reflective solar bands (RSB) of MODIS covering wavelengths from 0.41 micrometers to 2.2 micrometers, are calibrated on-orbit using a solar diffuser (SD), with its on-orbit bi-directional reflectance factor (BRF) changes tracked using a solar diffuser stability monitor (SDSM). MODIS is a scanning radiometer using a two-sided paddle-wheel mirror to collect earth view (EV) data over a range of (+/-)55 deg. off instrument nadir. In addition to the solar calibration provided by the SD and SDSM system, lunar observations at nearly constant phase angles are regularly scheduled to monitor the RSB calibration stability. For both Terra and Aqua MODIS, the SD and lunar observations are used together to track the on-orbit changes of RSB response versus scan angle (RVS) as the SD and SV port are viewed at different angles of incidence (AOI) on the scan mirror. The MODIS Level 1B (L1B) Collection 6 (C6) algorithm incorporated several enhancements over its predecessor Collection 5 (C5) algorithm. A notable improvement was the use of the earth-view (EV) response trends from pseudo-invariant desert targets to characterize the on-orbit RVS for select RSB (Terra bands 1-4, 8, 9 and Aqua bands 8, 9) and the time, AOI, and wavelength-dependent uncertainty. The MODIS Characterization Support Team (MCST) has been maintaining and enhancing the C6 algorithm since its first update in November, 2011 for Aqua MODIS, and February, 2012 for Terra MODIS. Several calibration improvements have been incorporated that include extending the EV-based RVS approach to other RSB, additional correction for SD degradation at SWIR wavelengths, and alternative approaches for on-orbit RVS characterization. In addition to the on-orbit performance of the MODIS RSB, this paper also discusses in detail the recent calibration improvements implemented in the MODIS L1B C6.

Angal, Amit↗
NASA NTRS2021

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products from MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03),central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals thanMOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereasMOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

MAIAC↗
NASA NTRS2017

Validation of Quasi-Invariant Ice Cloud Radiative Quantities with MODIS Satellite-Based Cloud Property Retrievals

Similarity relations applied to ice cloud radiance calculations are theoretically analyzed and numerically validated. If t(1v) and t(1vg) are conserved where t is optical thickness, v the single-scattering albedo, and g the asymmetry factor, it is possible that substantially different phase functions may give rise to similar radiances in both conservative and non-conservative scattering cases, particularly in the case of large optical thicknesses. In addition to theoretical analysis, this study uses operational ice cloud optical thickness retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Collection5 (C5) and Collection 6 (C6) cloud property products to verify radiative similarity relations. It is found that, if the MODIS C5 and C6 ice cloud optical thickness values are multiplied by their respective (1wg)factors, the resultant products referred to as the effective optical thicknesses become similar with their ratio values around unity. Furthermore, the ratios of the C5 and C6 ice cloud effective optical thicknesses display an angular variation pattern similar to that of the corresponding ice cloud phase function ratios. The MODIS C5 and C6 values of ice cloud similarity parameter, defined as [(1w)(1(exp. 1/2)wg)]12, also tend to be similar.

Radiative transfer↗
NASA NTRS2019

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗
NASA NTRS2016

Validation of AIRS V6 Surface Temperature over Greenland with GCN and NOAA Stations

This work compares the temporal and spatial characteristics of the AIRSAMSU (Atmospheric Infrared Sounder Advanced Microwave Sounding Unit A) Version 6 and MODIS (Moderate resolution Imaging Spectroradiometer) Collection 5 derived surface temperatures over Greenland. To estimate uncertainties in space-based surface temperature measurements, we re-projected the MODIS Ice Surface Temperature (IST) to 0.5 by 0.5 degree spatial resolution. We also re-gridded AIRS Skin Temperature (Ts) into the same grid but classified with different cloud conditions and surface types. These co-located data sets make intercomparison between the two instruments relatively straightforward. Using this approach, the spatial comparison between the monthly mean AIRS Ts and MODIS IST is in good agreement with RMS 2K for May 2012. This approach also allows the detection of any long-term calibration drift and the careful examination of calibration consistency in the MODIS and AIRS temperature data record. The temporal correlations between temperature data are also compared with those from in-situ measurements from GC-Net (GCN) and NOAA stations. The coherent time series of surface temperature evident in the correlation between AIRS Ts and GCN temperatures suggest that at monthly time scales both observations capture the same climate signal over Greenland. It is also suggested that AIRS surface air temperature (Ta) can be used to estimate the boundary layer inversion.

AIRS↗