Results for “radiometer uncertainty” — Page 18

NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗

NASA NTRS2013

Improvements of VIIRS and MODIS Solar Diffuser and Lunar Calibration

Both VIIRS and MODIS instruments use solar diffuser (SD) and lunar observations to calibrate their reflective solar bands (RSB). A solar diffuser stability monitor (SDSM) is used to track the SD on-orbit degradation. On-orbit observations have shown similar wavelength-dependent SD degradation (larger at shorter VIS wavelengths) and SDSM detector response degradation (larger at longer NIR wavelengths) for both VIIRS and MODIS instruments. In general, the MODIS scan mirror has experienced more degradation in the VIS spectral region whereas the VIIRS rotating telescope assembly (RTA) mirrors have seen more degradation in the NIR and SWIR spectral region. Because of this wavelength dependent mirror degradation, the sensor's relative spectral response (RSR) needs to be modulated. Due to differences between the solar and lunar spectral irradiance, the modulated RSR could have different effects on the SD and lunar calibration. In this paper, we identify various factors that should be considered for the improvements of VIIRS and MODIS solar and lunar calibration and examine their potential impact. Specifically, we will characterize and assess the calibration impact due to SD and SDSM attenuation screen transmission (uncertainty), SD BRF uncertainty and onorbit degradation, SDSM detector response degradation, and modulated RSR resulting from the sensor's optics degradation. Also illustrated and discussed in this paper are the calibration strategies implemented in the VIIRS and MODIS SD and lunar calibrations and efforts that could be made for future improvements.

VIIRS↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

In Part I (Lei et. al, submitted to J. of Appl. Rem. Sens.),we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). In this paper we review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the SNPP and the NOAA-20 VIIRS, and the Aqua MODIS. Our results show that although the SNRs trend downwards, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS.

on-orbit calibration↗

NASA NTRS2020

SNPP VIIRS RSB on-orbit radiometric calibration algorithms Version 2.0 and the performances. Part II: The performances

In Part I (Lei et. al, submitted to J. of Appl. Rem. Sens.),we gave detailed reviews of the algorithms Version 2.0 used for the on-orbit radiometric calibration of the reflective solar bands (RSBs) of the first Visible Infrared Imaging Radiometer Suite instrument. These algorithms improve the accuracy of the measured on-orbit change factor of the solar diffuser bidirectional reflectance distribution function, the H-factor, and reveal that the H-factor is angle dependent. With the help of lunar observations and the improved H-factor, the algorithms give more accurate values for the RSB detector on-orbit F-factor changes (F-factor is a correction factor to the initially retrieved scene spectral radiance). In this paper we review the RSB radiometric calibration performances. We show the H-factor temporal trend, the estimated uncertainty of the retrieved H-factor, and the F-factor temporal trend. Additionally we show the detector signal-to-noise ratio (SNR), the estimated uncertainty of the top-of-the-atmosphere solar spectral reflectance, the reflectance temporal trend for the Libya 4 desert, and the differences in the reflectances among the SNPP and the NOAA-20 VIIRS, and the Aqua MODIS. Our results show that although the SNRs trend downwards, they exceed the requirements by large margins. The reflectances from the Libya 4 desert show that the SNPP VIIRS’ reflectance is higher than those of the Aqua MODIS and the NOAA-20 VIIRS

Ning Lei↗

DOE OSTI2023

BeyondPlanck X. Bandpass and beam leakage corrections

We discuss the treatment of bandpass and beam leakage corrections in the Bayesian BeyondPlanck CMB analysis pipeline as applied to the Planck LFI measurements. As a preparatory step, we first apply three corrections to the nominal LFI bandpass profiles including removal of a known systematic effect in the ground measuring equipment at 61 GHz; smoothing of standing wave ripples; and edge regularization. The main net impact of these modifications is an overall shift in the 70 GHz bandpass of +0.6 GHz; we argue that any analysis of LFI data products, either from Planck or BeyondPlanck, should use these new bandpasses. In addition, we fit a single free bandpass parameter for each radiometer of the form Δ i = Δ 0 + $δ$ i , where Δ 0 represents an absolute frequency shift per frequency band and $δ$i is a relative shift per detector. The absolute correction is only fitted at 30 GHz with a full $\chi$ 2 -based likelihood, resulting in a correction of Δ 30 = 0.24 ± 0.03 GHz. The relative corrections are fitted using a spurious map approach, fundamentally similar to the method pioneered by the WMAP team, but without introducing many additional degrees of freedom. All bandpass parameters are sampled using a standard Metropolis sampler within the main BeyondPlanck Gibbs chain, and bandpass uncertainties are thus propagated to all other data products in the analysis. In total, we find that our bandpass model significantly reduces leakage effects. For beam leakage corrections, we adopt the official Planck LFI beam estimates without additional degrees of freedom, and only marginalize over the underlying sky model. We note that this is the first-time leakage from beam mismatch has been included for Planck LFI maps.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2005

Detection and Retrieval of Multi-Layered Cloud Properties Using Satellite Data

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.

Minnis, Patrick↗

NASA NTRS2008

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors: Theory - Part 1

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

Ganguly, Sangram↗

NASA NTRSDate not supplied

Remote Sensing of Hail from Space: Retrievals, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in the most globally consistent way. Passive-microwave algorithms leverage the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. These retrievals are used to construct global climatologies of severe hail. The Bang and Cecil (2019) retrievals has been applied to several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and used to construct [near] global climatologies of severe hail. This retrieval, and others, have been tested using Global Precipitation Measurement (GPM) Ku-band precipitation radar, to assess their effectiveness and regional variability. A successful retrieval and climatology are those that correspond tightly to radar reflectivity and give minimal appearance of regional biases, especially with latitude. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. There are ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗

NASA NTRSDate not supplied

Spaceborne Remote Sensing of Hail: Retrievals, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in the most globally consistent way. Passive-microwave algorithms leverage the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. These retrievals are used to construct global climatologies of severe hail. The Bang and Cecil (2019) retrievals has been applied to several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and used to construct [near] global climatologies of severe hail. This retrieval, and others, have been tested using Global Precipitation Measurement (GPM) Ku-band precipitation radar, to assess their effectiveness and regional variability. A successful retrieval and climatology are those that correspond tightly to radar reflectivity and give minimal appearance of regional biases, especially with latitude. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. There are ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗

NASA NTRS2010

Development of a Climate-Data Record (CDR) of the Surface Temperature of the Greenland Ice Sheet

Regional "clear sky" surface temperature increases since the early 1980s in the Arctic, measured using Advanced Very High Resolution Radiometer (AVHRR) infrared data, range from 0.57+/-0.02 deg C to 72+/-0.10 deg C per decade. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. An increase in melting of the ice sheet would accelerate sea-level rise, an issue affecting potentially billions of people worldwide. To quantify the ice-surface temperature (IST) of the Greenland Ice Sheet, and to provide an IST dataset of Greenland for modelers that provides uncertainties, we are developing a climate-data record (CDR) of daily "clear-sky" IST of the Greenland Ice Sheet, from 1982 to the present using AVHRR (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. Known issues being addressed in the production of the CDR are: time-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds, time-series of satellite IST do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with automatic-weather station data and with satellite-derived surface-temperature products and biases will be calculated.

Hall, Dorthy K.↗

NASA NTRS1991

The albedo, effective temperature, and energy balance of Neptune, as determined from Voyager data

Data from the Voyager infrared spectrometer and radiometer (IRIS) investigation are used in determining the albedo, effective temperature, and energy balance of Neptune. From broadband radiometric observations made at phase angles of 14 deg and 134 deg, together with measurements at intermediate phase angles from the literature, an orbital mean value of 0.290 +/-0.067 is obtained for the bolometric Bond albedo. This yields an equilibrium temperature Teq = 46.6 +/-1.1 K. From thermal spectra obtained over latitudes from pole to pole an effective temperature Teff = 59.3 +/-0.8 K is derived. This represents a substantial improvement over previously determined values. The energy balance of Neptune is therefore E = 2.61 +/-0.28, which is in agreement with previous results. The reduced uncertainty in this value is due to the improved determination of the effective temperature.

Pearl, J. C.↗

NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗

NASA NTRS2019

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

Tzallas, Vasileios↗

NASA NTRS2020

The MODIS TEBs Calibration and Look-up Table Delivery Process for Collections 6 and 6.1

MODIS is a cross-track, whisk-broom scanning imaging radiometer with a double-sided scan mirror that collects data in 36 spectral bands. Sixteen of the 36 MODIS spectral bands are Thermal Emissive Bands (TEBs) whose spectral wavelengths range from 3.5 μm to 14.4 μm. The TEB detectors are calibrated on a scan-by-scan basis using a quadratic calibration algorithm by observing both the MODIS on-board blackbody (BB) and a background space view reference. Blackbody warm-up/cool-down (WUCD) events are performed quarterly to track on-orbit changes associated with the TEB detectors' non-linearity. Following each WUCD, the calibration coefficients in the quadratic algorithm, and their associated contributions to the total uncertainty, are updated and delivered through separate look-up tables (LUTs) when all update criteria are met. Afterwards, the LUTs are incorporated into the Level 1B (L1B) product. Since the Terra MODIS mission began, a steady increase in electronic cross-talk has been observed for TEBs 27{30. Starting from Collection 6.1, an algorithm has been applied using correction coefficients derived from regularly-scheduled lunar observations, with the correction LUT update dependent on its impact on the current L1B product. The MODIS Characterization Support Team (MCST) has established a comprehensive set of procedures to assure timely and accurate LUT updates, and maintain the quality and accuracy of the L1B and science products. This paper provides an overview of the current calibration and LUT delivery process for the MODIS TEBs in Collections 6 and 6.1.

MODIS↗

NASA NTRS2012

Sensor Calibration Inter-Comparison Methodologies and Applications TO AVHRR, MODIS, AND VIIRS Observations

As more and more satellite observations become available to the science and user community, their on-orbit calibration accuracy and consistency over time continue to be an important and challenge issue, especially in the reflective solar spectral regions. In recent years, many sensor calibration inter-comparison methodologies have been developed by different groups and applied to a range of satellite observations, aiming to the improvement of satellite instrument calibration accuracy and data quality. This paper provides an overview of different methodologies developed for inter-comparisons of A VHRR and MODIS observations, and extends their applications to the Visible-Infrared Imaging Radiometer Suite (VIIRS) instrument. The first VIIRS was launched on-board the NPP spacecraft on October 28, 2011. The VIIRS, designed with MODIS heritage, collects data in 22 spectral bands from visible (VIS) to long-wave infrared (LWIR). Like both Terra and Aqua MODIS, the VIIRS on-orbit calibration is performed using a set of on-board calibrators (OBC), Methodologies discussed in this paper include the use of well-characterized ground reference targets, near simultaneous nadir overpasses (SNO), lunar observations, and deep convective clouds (DeC). Results from long-term A VHRR and MODIS observations and initial assessment of VIIRS on-orbit calibration are presented. Current uncertainties of different methodologies and potential improvements are also discussed in this paper.

Xiong, Xiaoxiong↗

NASA NTRS2009

A Climate-Data Record of the "Clear-Sky" Surface Temperature of the Greenland Ice Sheet

We are developing a climate-data record (CDR of daily "clear-sky" ice-surface temperature (IST) of the Greenland Ice Sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) (1982 - present) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data (2000 - present) at a resolution of approximately 5 km. The CDR will be continued in the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite era. Two algorithms remain under consideration. One algorithm under consideration is based on the split-window technique used in the Polar Pathfinder dataset (Fowler et al., 2000 & 21007). Another algorithm under consideration, developed by Comiso (2006), uses a single channel of AVHRR data (channel 4) in conjunction with meteorological-station data to account for atmospheric effects and drift between AVHRR instruments. Known issues being addressed in the production of the CDR are: tune-series bias caused by cloud cover (surface temperatures can be different under clouds vs. clear areas) and cross-calibration in the overlap period between AVHRR instruments, and between AVHRR and MODIS instruments. Because of uncertainties, mainly due to clouds (Stroeve & Steffen, 1998; Wang and Key, 2005; Hall et al., 2008 and Koenig and Hall, submitted), time-series of satellite 1S'1" do not necessarily correspond to actual surface temperatures. The CDR will be validated by comparing results with automatic-,",eather station (AWS) data and with satellite-derived surface-temperature products. Regional "clear-sky" surface temperature increases in the Arctic, measured from AVHRR infrared data, range from 0.57+/-0.02 deg C (Wang and Key, 2005) to 0.72+/-0.10 deg C (Comiso, 2006) per decade since the early 1980s. Arctic warming has important implications for ice-sheet mass balance because much of the periphery of the Greenland Ice Sheet is already near 0 deg C during the melt season, and is thus vulnerable to rapid melting if temperatures continue to increase. References

Hall, D. K.↗

NASA NTRS2008

The Aerosol/Cloud/Ecosystems Mission (ACE)

The goals and measurement strategy of the Aerosol/Cloud/Ecosystems Mission (ACE) are described. ACE will help to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. Specifically, the goals of ACE are: 1) to quantify aerosol-cloud interactions and to assess the impact of aerosols on the hydrological cycle and 2) determine Ocean Carbon Cycling and other ocean biological processes. It is expected that ACE will: narrow the uncertainty in aerosol-cloud-precipitation interaction and quantify the role of aerosols in climate change; measure the ocean ecosystem changes and precisely quantify ocean carbon uptake; and, improve air quality forecasting by determining the height and type of aerosols being transported long distances. Overviews are provided of the aerosol-cloud community measurement strategy, aerosol and cloud observations over South Asia, and ocean biology research goals. Instruments used in the measurement strategy of the ACE mission are also highlighted, including: multi-beam lidar, multiwavelength high spectra resolution lidar, the ocean color instrument (ORCA)--a spectroradiometer for ocean remote sensing, dual frequency cloud radar and high- and low-frequency micron-wave radiometer. Future steps for the ACE mission include refining measurement requirements and carrying out additional instrument and payload studies.

Schoeberl, Mark↗

NASA NTRS2008

GPM Pre-Launch Algorithm Development for Physically-Based Falling Snow Retrievals

In this work we compare and correlate the long time series (Nov.-March) n~easurements of precipitation rate from the Parsivels and 2DVD to the passive (89, 150, 183+/-1, +/-3, +/-7 GHz) observations of NOAA's AMSU-B radiometer. There are approximately 5-8 AMSU-B overpass views of the CARE site a day. We separate the comparisons into categories of no precipitation, liquid rain and falling snow precipitation. Scatterplots between the Parsivel snowfall rates and AMSU-B brightness temperatures (TBs) did not show an exploitable relationship for retrievals. We further compared and contrasted brightness temperatures to other surface measurements such as temperature and relative humidity with equally unsatisfying results. We found that there are similar TBs (especially at 89 and 150 GHz) for cases with falling snow and for non-precipitating cases. The comparisons indicate that surface emissivity contributions to the satellite observed TB over land can add uncertainty in detecting and estimating falling snow. The newest results show that the cloud icc scattering signal in the AMSU-B data call be detected by computing clear air TBs based on CARE radiosonde data and a rough estimate of surface emissivity. That is the differences in computed TI3 and AMSU-B TB for precipitating and nonprecipitating cases are unique such that the precipitating versus ~lon-precipitating cases can be identified. These results require that the radiosonde releases are within an hour of the AMSU-B data and allow for three surface types: no snow on the ground, less than 5 cm snow on the ground, and greater than 5 cm on the ground (as given by ground station data). Forest fraction and measured emissivities were combined to calculate the surface emissivities. The above work and future work to incorporate knowledge about falling snow retrievals into the framework of the expected GPM Bayesian retrievals will be described during this presentation.

Gail Skofronick-Jackson↗