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NASA NTRSDate not supplied

TPSAS-NF1676L-33912-DND

The agricultural, renewable energy management, and science communities need global surface and top-of-atmosphere (TOA) radiative fluxes on a near real-time basis for uses such as building energy performance monitoring and seasonal crop yield modeling. The Clouds and Earth's Radiant Energy System (CERES) FLASHFlux (Fast Longwave and SHortwave radiative Flux) data products address this need by enhancing the speed of CERES processing using simplified calibration, averaging techniques and fast radiation parameterizations to produce global fluxes within a week of satellite observations. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Aqua and Terra observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1degree x 1degree gridded data that combines Aqua and Terra observations. The CERES FLASHFlux Version4A includes an update to process MODIS Collection 6.1 in an improved cloud retrieval algorithm, and an update to the CERES Edition 4 angular distribution models. We inter-compared FLASHFlux Version4A, FLASHFlux Version3C, and CERES EBAF (Energy Balanced and Filled) Top-of-Atmosphere fluxes to evaluate these improvements. We also compare both SSF overpass and TISA radiative flux products to a set of surface measurement sites that are globally distributed and assess agreement. FLASHFlux TISA and CERES EBAF are used in the analysis of TOA anomalies for the annual State of Climate report. This also poster highlights the methodology use to merge the two data products by correcting the bias in FLASHFlux TISA and inter-calibrate to CERES EBAF.

Parnchai K Sawaengphokhai↗

NASA NTRS2014

TPSAS-NF1676L-18357-DND

High-spectral resolution measurements (in the IR region) from space born infrared sensors can provide sufficient spectral information about cloud properties like cloud top pressure, cloud particle size and cloud optical thickness. Empirical orthogonal function (EOF) based regression scheme was used to retrieve cloud properties along with atmospheric state profiles. A channel based physical inversion scheme was also tried on cloud-contaminated radiances to improve retrieval accuracy achieved by regression scheme. Recently developed principle component (PC) based physical retrieval algorithm utilized the EOF transformation to compress both hyperspectral radiance spectra and atmospherical profiles into PC scores to greatly boost the computational speed while maintaining the information content. The accuracy of the (PC) based radiation transfer model (PCRTM) to simulate top-of the atmosphere(TOA) high-spectral resolution radiance spectra for both clear and cloudy sky conditions has been well tested by comparing with the other line-by-line radiation transfer model and using the observation data. The application of the PCRTM based physical inversion method on simulated IASI radiance under 'cloudy' conditions will be a good integrity test for the new algorithm.

X Liu↗

NASA NTRSDate not supplied

TPSAS-NF1676L-10574-DND

The Infrared Atmospheric Sounding Interferometer (IASI) aboard of MetOP-A was successfully launched on 19 October 2006 and has been providing excellent data for more than 3 years. The IASI spectra can be converted into proxy data for the NPOESS Crosstrack Infrared Sounder (CrIS) through a rigorous mathematical transformation by taking into account of instrument characteristics of both the IASI and the CrIS. The 15-channel AMSU temperature sounder, and the 5-channel and Microwave Humidity Sounder (MHS) aboard of MetOP-A can be used to generate proxy data for the NPOESS Advanced Technology Mircrowave Sounder (ATMS). The CrIS and ATMS make up the Cross-track Infrared and Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure profiles. We will describe the method used in generating the proxy data and show results of using the proxy data for evaluation of the NPOESS CrIMSS Environmental Data Record (EDR) retrieval algorithm.

Xu Liu↗

NASA NTRSDate not supplied

TPSAS-NF1676L-10898-DND

Xu Liu↗

NASA NTRSDate not supplied

Particle Shape, Size, and Composition from Satellites

The MISR Research Aerosol (RA) retrieval algorithm produces (A) aerosol amount (optical depth – AOD), (B) an aerosol size constraint (Angstrom Exponent) (C) fraction of non-spherical particles, and (D) particle light-absorption (SSA). Near the Camp fire source region the particles tend to be large (low Angstrom Exponent) and non-spherical particles, probably from burning in the town of Paradise. Aerosols are smaller and more spherical downwind, probably as the plume mixes with smoke from burning forest.

aerosol↗

NASA NTRS2020

Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88° S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r(exp 2)=−0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r(exp 2)=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

Michael Studinger↗

NASA NTRSDate not supplied

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

A recent, interdisciplinary survey of more than 40 international remote sensing experts reviewed the strengths and limitations of current space-based observational capabilities of the Arctic Boreal Zone (ABZ) and made recommendations for improving upon these current capabilities (Duncan et al., 2019). General recommendations focused on the complementarity of sub-orbital and satellite observations, the need for measurement continuity, and the importance of stakeholder engagement. The development of a comprehensive and robust sub-orbital portion of an ABZ observing network can act to fill some temporal gaps in satellite coverage, provide detail unobtainable from space, and is necessary for validation and interpretation of satellite data. A priority across all types of observations is the continuation, enhancement, and/or creation of long-term, multi-satellite, climate-quality, and self-consistent data records of ABZ components, such as surface temperature, energy fluxes, or sea ice extent and volume, for improved determination of trends. Long-term passive satellite observations currently represent the only feasible option for monitoring change of the ABZ at broad spatial scales required to address pressing science challenges. Active sensors are particularly attractive for the ABZ as they do not depend on reflected sunlight and so join passive microwave sensors in having a significant advantage over passive visible and infrared sensors in the low-light conditions that are typical of the ABZ for several months of the year. Application of consistent retrieval algorithms to multiple data sets, as well as careful characterization of satellite instruments and their temporal evolution, helps to ensure data quality and consistency. Among the needs ranked as “Most Important” are those associated with gaining a process-based understanding of the ABZ carbon cycle and hydrologic cycle (which includes sea level rise) as they have the potential to affect a large portion of Earth’s population.

Remote sensing↗

NASA NTRS2018

G-band FMCW Radar for Humidity Profiling Inside Boundary Layer Clouds

We are developing a G-band FMCW radar for remote sensing of vertical water vapor proﬁles and total column water vapor. The radar utilizes the frequency-dependent absorption near the 183 GHz water absorption line to obtain a differential signal from which we can derive range-resolved humidity measurements. This proof-of-concept instrument will eventually be tested from an airborne platform. In this report, we discuss recent measurements and a humidity proﬁle retrieval algorithm based on an absorption line shape ﬁtting model

Monje, Raquel↗

NASA NTRS2018

Assessing the Altimetric Measurement from CYGNSS Data

The Cyclone Global Navigation Satellite System (CYGNSS) mission was designed to study hurricane intensification by measuring wind speeds in tropical cyclones. However, the delay-Doppler maps (DDM) that are produced can be used to estimate the sea surface height (SSH) at the specular reflection point on the ocean surface. Proofof-concept studies that DDMs are suitable to solve for SSH have been recently reported (Clarizia et al., 2016; Mashburn et al., 2018), based on data acquired by the demonstration satellite experiment Tech Demo Sat - 1 (TDS-1) carrying a GNSS-R receiver similar to the ones onboard CYGNSS. Although the precision of each 1sec averaged SSH is considerably lower than that of the existing satellite altimeters, by virtue of the dense coverage and frequent revisit time exhibited by the constellation of 8 microsats, the error may be smoothed down considerably by optimal interpolation (Li et al., 2016). Hence the CYGNSS dataset presents a potential opportunity to sample the tropical oceans, and investigate the sensitivity of the SSH measurements to mesoscale eddies. Our objective is to analyze the CYGNSS data and test a suite of retrieval algorithms, including that of Mashburn et al., 2018 to obtain SSH, to understand the error sources and possible corrections when available. Because of the limited antenna gain of the CYGNSS instrument, the power SNR for general wind conditions is expected to be too low to accurately track the reflection point contribution, resulting in altimetry errors of the order of several meters. Other significant error sources are the CYGNSS satellites orbit knowledge, the ionosphere, the troposphere, the mean sea surface and the tides; some of these errors can be reduced by using models. The corrected SSH data will be assimilated into the ROMS high-resolution ocean model to investigate their ability to reproduce mesoscale eddies in the tropical oceans.

Voronovich, Alexander↗

NASA NTRS2021

Depletion & Recovery of the O3 Layer Observed from Space Over the Past 50 Years of the Anthropocene

This year we are celebrating the 50th anniversary of the launch of the backscatter UV (BUV) instrument on NASA’s Nimbus-4 satellite. Since then 10 more advanced instruments, but based on the same basic measurement principle, have been launched on polar orbiting NASA and NOAA satellites. The datasets produced by these instruments are unique for they have captured the period before the onset of the decline of the ozone layer due to manmade chemicals, the declining period, and the present slow recovery period. This period includes the rapid development of the iconic anthropocene era feature called the “Ozone Hole” . However, stitching together data from 11 separate satellite instruments to create a homogeneous record has presented many challenges. The first two NASA instruments suffered considerable degradation after launch that required the development of novel techniques to characterize their calibration. The next 7 instruments were launched on NOAA’s polar orbiting weather satellites that were not designed for long-term monitoring of climate variables. In particular, they didn’t have the station keeping capabilities needed to maintain a stable equator crossing time. Drifting orbits not only stress the retrieval algorithm and the calibration system of UV instruments but also make the interpretation of data difficult because the upper stratospheric ozone has systematic and complex diurnal variabilities that are comparable to the anthropogenic signal one wants to extract. We will highlight our recent efforts in untangling this knot. Finally, we will discuss the results from the OMPS limb scattering instrument, a recent addition to this constellation, that has been operating on the Suomi NPP satellite since October 2011. It is helping us capture how the ozone layer in the lower stratosphere and troposphere is evolving in response to the climate change and rising chemical pollutants in some parts of the world, while the upper stratosphere slowly recovers due to phaseout of the chemicals that initiated the decline of the ozone layer in the late 70s.

P Bhartia↗

NASA NTRSDate not supplied

SAGE III on ISS: Mission Update and Science Milestone Results and Future Plans

The Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS) has been collecting data on the stratosphere almost continuously for 4 years since the first light milestone was achieved on March 17, 2017. In that time, the SAGE III/ISS data products have withstood robust assessments both internal and external, using comparisons to data sets both in-situ and spaceborne, and incorporated improvements into the retrieval algorithm and the science data products themselves. With the new v5.2 release of the SAGE III/ISS science data product algorithm, improvements made to the aerosol and water vapor products have produced better correlations with both MLS and balloonsonde data. At this 4-year milestone, we will look back on the last 4 years of data collection highlighting SAGE III/ISS ozone and aerosol data from 2017-2020 in a 6-minute video highlighting the SAGE III mission. An overview of mission operations and the challenges of operating a science mission on the ISS will highlight lessons learned and the mission science objectives completed during the first three years of the mission. The future goals for the SAGE III/ISS mission, as we continue operations and science collection, will round out the presentation.

Marilee M Roell↗

NASA NTRS2019

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

Hylke E. Beck↗

NASA NTRS2019

Surface Polarized Reflectance Analysis For Aerosol Remote Sensing

We study the Earth surface polarized reflectance using data collected by a space-based lidar. Accurate modelling of the surface reflectance supports retrieval algorithm development for the current and future Earth Science missions. Strong polarization of the laser light from Cloud-Aerosol Transport System (CATS) instrument, operated in 2015-2017, and nighttime measurements yield higher signal-to-noise ratio for polarization compared to the previous analysis of reflected, initially unpolarized, solar light.

Earth surface polarized reflectance↗

NASA NTRSDate not supplied

166 GHz Ice Scattering Signal in Cold-Season Precipitating Cloud Systems

Surface snowfall can be produced by a variety of forcing mechanisms that are accompanied by different in-cloud physical processes and microphysical composition. For instance, shallow cumuliform snowfall forced by marine cold air outbreaks shows distinct geographic and seasonal signatures and plays in important role in annual snowfall production in many regions worldwide. Coincident CloudSat-CALIPSO observations confirm that oceanic regions exhibiting frequent shallow cumuliform snow are typically composed of mixed-phase cloud and precipitation particles and display passive microwave brightness temperature behavior that is dominated by cloud liquid water emission, while combined Global Precipitation Measurement (GPM) radiometer and surface radar studies of intense shallow cumuliform events indicate significant ice particle scattering signals at the highest GPM radiometer frequencies. Passive microwave imager channels in the 37 to 89 GHz range have long been used to detect precipitation-sized ice particle scattering signatures in clouds. Dual polarization plays an important role in distinguishing between scattering signatures and other background variability. The GPM Microwave Imager (GMI) is the first satellite radiometer to possess dual-polarized channels at 166 GHz. These high-frequency channels are more susceptible to emission and attenuation by cloud liquid water and water vapor than lower-frequency channels, but are also more sensitive to ice scattering. This research studies the ice scattering information contained in the 166 GHz channels compared to the historically used 85/89 GHz channels on previous sensors. A 166 GHz scattering index (SI) will be developed to understand how its correlation with non-precipitating ice scattering at higher altitudes also affects the 166 GHz scattering signature in cold-season precipitating cloud systems. The 166 GHz SI will be used to possibly isolate precipitation scattering signature for different snowfall modes (shallow convective vs. deep stratiform). The supercooled liquid water affecting shallow convective systems and drastically reducing the scattering signal could be detected and quantified, providing additional information for convective snow detection and quantification in solid precipitation retrieval algorithms.

Lisa Milani↗

NASA NTRSDate not supplied

Deriving Climate Change Signal from Hyperspectral Sounders Using Spectral Fingerprinting Method

Hyperspectral observations from satellite-based sensors provide high information content for the Earth’s atmospheric temperature, water vapor and trace gas vertical profiles. We have developed a radiometrically consistent spectral fingerprinting method to derive climate change signals from Aqua AIRS/AMSU and S-NPP CrIS/ATMS data. The climate variables include temperature and water vapor profiles, cloud, trace gases, and surface skin temperature. The radiative kernels obtained via a single field of view physical retrieval algorithm under all-sky conditions. A key component to this work is a Principal Component-based Radiative Transfer Model (PCRTM). It is 4 orders of magnitude faster than a line-by-line radiative transfer model while keeping a similar accuracy (0.03 K RMS errors with close to zero bias). The PCRTM includes multiple scattering of clouds and non-thermodynamics equilibrium of CO2 in the RT calculations. Instead of quantifying the radiometric differences between AIRS/AMSU and CrIS/ATMS measurements directly using Simultaneous Nadir Overpass (SNO) or Double Difference Technique (DDT), we use the radiometric consistent fingerprinting scheme to derive two sets of space-time averaged anomalies from the Level 1 data of AIRS/AMSU and CrIS/ATMS. The derived anomalies in geophysical space will form a long-term, stable, and continuous climate data record. We can further infer the causes of any offset or drift by studying the differences between two overlapping data sets. For example, the offset in surface skin temperature anomaly time series will most likely caused by the Blackbody temperature calibration errors of the sounder instruments.

climate↗

NASA NTRSDate not supplied

Fostering Open Science Inclusiveness for Interdisciplinary Users of Earth Observations

The term Open Science is subject to a variety of interpretations because of a key (and useful) ambiguity in the meaning of “Open”. Open in the sense of Transparency enables more trust in science research by making the details of the scientific process visible and accessible to anyone. “Open” in the sense of Inclusiveness enables more scientists from other disciplines to participate in research in a given discipline, thus producing more interdisciplinary research. Data Systems can play a major role in enabling Open (Inclusive) Science by making it easier for users from other disciplines to work with data within a given discipline. This is challenging for Earth Observation datasets, most of which are the product of advanced instrumentation and sophisticated, specialized variable retrieval algorithms and code. Serving the “extra-disciplinary”communities begins with simple things, like accessible, readable data documentation with adequate scaffolding. But just as important is provisioning Analysis-Ready data that does not require expert pre-processing. Disciplines also often have dominant toolsets, such as R in the biomass community or GIS in many applications communities. Ensuring that EO data are easy to use in the tools favored in other communities will enable more interdisciplinary research. Ideally, interdisciplinary research also benefits from scientists with different domain expertise. Platforms and frameworks that facilitate frictionless collaboration with discipline experts, together with capacity building efforts in those external disciplines also improve the inclusiveness aspect of Open Science. In short, Open Science is at root a way of thinking about how users from diverse discipline can best access and use data and services from a particular discipline.

Christopher Lynnes↗

NASA NTRSDate not supplied

High Latitude Considerations in the Latest GPCP monthly and daily products (V3.1)

The Global Precipitation Climatology Project (GPCP) product is a popular combined satellite-gauge precipitation data set in which the long-term standards of consistency and homogeneity is underlined. Here we discuss various high latitude analysis considered in the recently released GPCP V3.1 monthly and daily products. Satellite data are used over land and ocean and obtained from the Special Sensor Microwave Imager (SSMI), Special Sensor Microwave Imager/Sounder (SSMIS), geostationary imagers and polar orbiting infrared sounders. GPCP uses the Global Precipitation Climatology Centre (GPCC) over land, as its in situ component, but prior to combination with satellite data GPCC estimates are adjusted for gauge undercatch. Advanced sensors aboard the Tropical Rainfall Measuring Mission (TRMM), CloudSat, and Global Precipitation Measurement (GPM) mission have enabled more accurate estimation of rain and snowfall rates in recent years. Started with GPCP V3.1 these observations are integrated into GPCP through the development of the Tropical Combined Climatology (TCC) used at lower latitudes and the Merged CloudSat, TRMM, and GPM (MCTG) climatology used over the extra tropics and higher latitudes. Improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation are used outside 60ºN-S, where inside this zone the Goddard Profiling (GPROF) algorithm retrievals from SSMI/SSMIS is used to calibrate geostationary IR based precipitation estimate at monthly scale. The Gravity Recovery and Climate Experiment (GRACE) mass change observations are used to determine snowfall accumulations over frozen land and arctic basins and to assess gauge undercatch corrections. Observations of snow on sea ice from NASA’s Operation IceBridge (OIB) flights are utilized as an additional tool for snowfall assessment over sea ice. GPCP V3.1 has higher spatial resolution (0.5ox0.5o) than earlier versions (2.5ox2.5o) over both land and ocean, going back to 1983. Version 3 Daily product uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates, where available (initially restricted to 60°N-S), as well as rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate. GPCP V3.1 shows about 6% increase in global oceanic precipitation and about 4.5% increase over global land and ocean compared to the previous version (V2.3), some major changes occur over the ocean and around 40oS and 60 oS. We will discuss other important changes of GPCP V3.1, compared to the earlier versions, and our future plans. Through this presentation we will also discuss that while ACCP will provide key information about precipitation, synergistic use of other Earth observing systems (e.g., mass change; recognized as a designated mission in 2017 decadal survey) can also help refine precipitation analysis, especially in high latitude and cold regions.

Ali Behrangi↗

NASA NTRS2022

Satellite Precipitation Measurements: What Have We Learnt About Cloud-Precipitation Processes From Space?

Precipitation is one of the fundamental elements that define global and regional climatology. Precipitation systems consist of a broad spectrum of three-dimensional structures in which microphysical processes interact with macro-scale processes in the cloud system and the ambient environment that prescribe the evolution of the system. Since 1970s, satellite observations of precipitation have been a fundamental tool in quantifying this complex interaction. They have first quantified the frequency and intensity of global precipitation, including remote areas over open oceans and polar regions, thus providing today’s precipitation climatology. More recently, satellite observations of cloud and precipitation have been exploited for understanding the physical mechanisms governing precipitation systems. A subset of these studies also provided observation-based metrics to probe physical processes operating in cloud-precipitation systems and to apply them as diagnostic measures for evaluating the representation of the processes in numerical models for better projections of future climate. In this chapter, we first review the theoretical basis of precipitation remote sensing from space and describe how it is practically applied in satellite missions. In the first part of the chapter, an historical overview of the satellite missions is described, summarising the instruments and retrieval algorithms developed in the missions. In the second part, we introduce a set of studies discussing the fundamental mechanisms behind precipitation formation, highlighting what we have learnt to date on cloud-precipitation processes from satellite observations.

Precipitation↗