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NASA NTRS1987

Simultaneous observations of the near-earth and distant geomagnetic tail during a substorm by ISEE-1, ISEE-3 and geostationary spacecraft

The structure of the geomagnetic tail during a substorm is investigated by combining plasma, magnetic field, and energetic particle data from the ISEE-3 spacecraft in the deep tail with similar near-earth observations from ISEE-1 and geostationary spacecraft. The observations can be interpreted in terms of the neutral-line model of substorms and indicate the formation of a closed-loop field region (plasmoid) following substorm onset, which is ejected down the tail. The plasmoid is observed to have a double-loop field strucure. This may be the result of a second substorm onset occurring about 25 min after the first, producing a further near-earth neutral line and closed field loop. During the substorm recovery phase, the substorm neutral line moves tailward to beyond 130 earth radii from earth by some 3 h after substorm onset.

Richardson, I. G.↗
NASA NTRSDate not supplied

The Convective Nature of the Tropical Cyclone Lifecycle via GLM and GPM Observations

This study examines the convective nature of the tropical cyclone (TC) lifecycle from tropical storm through extratropical transition. We analyze lightning observations collected from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in combination with coincident passive microwave and Ku-band radar observations collected from Global Precipitation Measurement (GPM) mission satellites. A unique aspect of this study, which spans the Atlantic basin hurricane seasons 2018-2020, is that it provides the first known total lightning observations in TCs throughout their extratropical (ET) transition. Analysis of Tropical Storm (TS), Category 1-2 hurricanes (CAT12), Category 3-5 hurricanes (CAT35), and ET time periods, which are grouped by storm-motion and shear-relative characteristics, show that lightning maxima generally occur regimes of down-motion (up-shear), consistent (inconsistent) with previous studies. Further analysis also breaks down time periods by geographic location (e.g., land, coast, ocean) and shear strength; shifting lightning patterns are observed with increasing shear. The lightning maxima are also generally collocated with minima in 37-GHz brightness temperature observations, which is indicative of precipitation-sized ice. DPR Ku-band reflectivity profiles from the GPM Precipitation Feature (PF) database exhibit distinct differences in depth and intensity for electrically active PFs vs those that are not. On average, PFs defined by a rain rate threshold are larger for hurricane strength ITPs (CAT12, CAT35) as compared to either TS or ET ITPs. This indicates that during the hurricane strength ITPs, PFs may be capturing the entire, symmetric rain shield.

hurricane↗
NASA NTRSDate not supplied

A NASA Airborne Lens Into Air Quality Field Studies of the Last Decade

Field studies add an enhanced perspective to our everyday observing system for air quality with the goals of better understanding the air we breathe and identifying solutions toward a healthier future. Over the last decade, over 10 air quality field studies were conducted around the US with other ranging internationally with support through large agency-led efforts down to the grass-roots collaborative style. This presentation will highlight how NASA airborne observations have fit as one piece of the integrated observing system for air quality during these field studies. Research topics to be discussed are centered around the idea of the transition to geostationary air quality satellite observations. Specific topics include how temporally and spatially resolved measurements help us learn about knowledge gaps in NOx emissions, satellite-proxies for surface air quality, as well as evaluating state-of-the-art chemical transport models and using the enhanced observations as tools for understand what models/satellites can and cannot resolve. Each research topic will aim to discuss how the field measurement strategies that made this work possible as well as specific challenges that still exist to take these results further. Lastly, this presentation will discuss strategies to be carried forward as well as new ones as a peek into future air quality airborne field work.

Laura Judd↗
NASA NTRS2019

Observation of Absorbing Aerosols Above Clouds over the South-East Atlantic Ocean from the Geostationary Satellite SEVIRI – Part 1: Method Description and Sensitivity

High temporal resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the South East Atlantic Ocean (SEAO). This paper presents a method developed to retrieve simultaneously aerosol and cloud properties in aerosol above cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud Aerosol Optical Thickness (AOT), the Cloud Optical Thickness (COT) and the Cloud droplet Effective Radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10% error on the humidity profile leads to an 18.5% bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00-15:00 UTC, an uncertainty of 40% is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17% between 09:00-15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6% on the COT and 3% on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time-series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above cloud events over the SEAO.

Peers, Fanny↗
NASA NTRSDate not supplied

Deriving Broadband Radiances from Geostationary Satellite Visible Radiance Channels for Use in the CERES SYN1deg Product

The NASA CERES project was designed to monitor the Earth’s radiation budget, both at the regional and global scale. The CERES SSF1deg single satellite product provides TOA flux observations from Aqua, Terra, NPP, and NOAA-20. The SYN1deg product incorporates observations from Terra, Aqua, and geostationary (GEO) satellites. The GEO observations are included in the SYN1deg product to infer the regional diurnal cycle between CERES observations. GEO satellites throughout the CERES record have different narrowband spectral response functions and do not directly observe radiances over the broadband spectra of CERES instruments. Hence the emphasis of this study is improving the process of deriving broadband radiances from the myriad GEO visible channel observations. We present a new method for converting the narrowband radiances observed from GEO operational satellites. This study relies on radiative transfer model hyper-spectral radiances for many cloud and surface conditions under all angular conditions observed by GEO sensors. First, the hyper-spectral radiances from a radiative transfer model must be convolved with the spectral response function of the visible imager for each GEO sensor in the CERES record. For each angular bin and surface type, the clear-sky and multiple optical depth and phase cloudy-sky narrowband and broadband reflectance pairs are linearly regressed. This study found that, no matter how the scene conditions are stratified, for example the all-sky, clear-sky, ice or water clouds, they all provide similar linear regressions. This includes ocean glint conditions, where the clear-sky reflectance exceeds cloudy conditions and yet the relationship was found to be linear in the given angular bin. This infers that no GEO retrieved cloud information is necessary in the narrowband to broadband conversion process, which relies only on visible channel reflectance. Over land, most of the GEO visible channels do not observe the NIR vegetation reflectance. We intend to use land adjustment factors developed using the TOA reflectance difference between the real-time MODIS .86um and the .64um channel. We validate this approach by using coincident observational data between CERES and MODIS aboard Aqua and Terra as well as GEO and CERES observations .

CERES↗
NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗
NASA NTRS2019

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

cloud properties↗
NASA NTRSDate not supplied

Use of Aerosol Observations From Aircraft in Satellite Remote Sensing and Modelling Comparisons

Aerosol extensive and intensive properties vary throughout the world and remain one of the largest uncertainties in determining future climate both because of difficulties in their measurement and their modelled behaviors. Here we highlight the contributions of airborne sampling by the sunphotometer 4STAR (Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) from two different parts of the world; Korean peninsula and region during the KORUS-AQ experiment (KORean-US Air Quality in May-June 2016) and over the South East Atlantic during ORACLES (ObseRvations of CLouds above Aerosols and their intEractionS in August-September-October 2016, 2017, and 2018) field campaign. The high temporal and spatial resolution, afforded by airborne sampling during KORUS-AQ, are used to determine the variability of aerosol intensive and extensive properties, which is a metric than can be compared in between multiple different observations and models, even over varying surfaces and spanning multiple different aerosol emission sources. We show the consistency over spatial scales of the AOD (Aerosol Optical Depth), and the aerosol intensive properties (Angstrom exponent - AE, fine mode fraction - FMF) observed by 4STAR, GOCI (Geostationary Ocean Color Imager Yonsei aerosol retrieval v2), MERRA-2 reanalysis (Modern-Era Retrospective Analysis for Research and Applications, v2), and from airborne in situ aerosol optical measurements by LARGE (NASA Langley Aerosol Research Group Experiment). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km and is dependent on the prevailing meteorological conditions. AE and FMF are found to be more spatially variable than AOD during all of KORUS-AQ, even when accounting for potential sampling biases. This may indicate that microphysical processes like aerosol particle formation, growth, and coagulation impact the dominant aerosol size at shorter scales than their combined effect on the aerosol optical depth by the aerosol emission, transport, and removal. Averaging between measurements and model, the distance at which the correlation to itself is reduced by 15% is 65 km for AOD, and 22.7 km for AE. Vertically resolved measurement from an aircraft enable the direct measurements of aerosols overlying clouds. When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in AOD retrievals when clouds are underneath. The AOD above clouds measured by 4STAR during ORACLES are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS.

Aerosol↗
NASA NTRS2012

The United States' Next Generation of Atmospheric Composition and Coastal Ecosystem Measurements: NASA's Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission

The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95deg-100degW, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.

Atmospheric composition↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRSDate not supplied

Development of the GeoNEX Level 2G Products: Exploiting the Diurnal Variability of TOA Reflectance in Atmospheric Correction

This study develops a new atmospheric correction algorithm to generate the Level 2G products, in particular the gap-filled Surface Reflectance at 10-minute time steps, for the Geostationary-NASA Earth Exchange (GeoNEX) project. The algorithm is based on the MODIS MAIAC (Multi-Angle Implementation of Atmospheric Correction) framework but with significant modifications to exploit angular/temporal information from the diurnal variability of the GeoNEX L1G TOA (Top-of-Atmosphere) reflectance. The algorithm starts by evaluating the roughness/smoothness of the diurnal time series of the TOA reflectance. Because rapid changes in TOA reflectance are generally caused by passing clouds or shadows, rough segments of the time series are automatically filtered out while the smooth segments are further tested for brightness and temperature to identify clear-sky and snow-free observations. Next the algorithm runs the MAIAC RTM (Radiative Transfer Model) to retrieve the Ross-Thick-Li-Sparse (RTLS) BRDF model parameters and the daily-mean atmospheric optical depth (AOD) that allow the RTM to optimally simulate the observed diurnal variability of clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they are used as the prior information to predict the AOD level for the next days, while the subsequent clear-sky observations are used to make necessary adjustments to the RTLS parameters in an continuous fashion. This “prediction-analysis” cycle is then iterated to process the full time series of the L1G data, skipping only total-cloudy days or when surface snow is detected. We tested the algorithm over a list of selected AERONET sites. The retrieved results (the daily mean AOD and the RTLS parameters) reasonably agree with the ground-based measurements. Importantly, the results indicate that the diurnal cycles of surface reflectance are continuous functions of the illumination-view geometry. Thus we can use the retrieved RTLS model to accurately fill in data gaps on partial cloudy days. Also, our algorithm is totally independent from the traditional approaches based on the use of spectral band ratios between the shortwave infrared (e.g., 2.2µm) and the visible (e.g., 0.47µm and 0.64µm) bands. Our results thus demonstrate that the high-frequent diurnal geostationary observations contain unique information that helps us improve atmospheric correction of remote sensing data.

GeoNEX↗
NASA NTRS2018

Lightning Imaging Sensor on the International Space Station: Assessments and Results from First Year Operations

Over two decades, the NASA Marshall Space Flight Center, the University of Alabama in Huntsville, and their partners have demonstrated the effectiveness and value of space-based lightning observations as a remote sensing tool for Earth science research and applications, and, in the process, established a robust global lightning climatology. The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) provided global observations of tropical lightning for an impressive 17 years before that mission came to a close in April 2015. Now a space-qualified LIS, built as the flight spare for TRMM, has been installed on the International Space Station (ISS) for a minimum two year mission following its SpaceX launch on February 19, 2017. The LIS, flown as a hosted payload on the Department of Defense Space Test Program-Houston 5 (STP-H5) mission, was delivered to the ISS in the Dragon trunk and robotically installed in an Earth-viewing position on the outside of the ISS. Following successful activation and checkout, LIS has continuously observed the amount, rate, and radiant energy lightning within its field-of-view as it orbits the Earth. Placing LIS on the Space Station provides a great opportunity to not only extend the 17-year TRMM LIS record of tropical lightning measurements but also to expand that coverage to higher latitudes missed by the previous mission. Furthermore, this mission continues the important science focus to better understand the processes which cause lightning, as well as the connections between lightning and subsequent severe weather events. This understanding is a key to improving weather predictions and saving lives and property here in the United States and around the world. The LIS measurements, along with observations from the new Geostationary Lightning Mapper (GLM) operating on NOAA's newest weather satellites, the Geosynchronous Operational Environmental Satellite-16/17 (GOES-16/17), are being used to cross-validate both systems. An especially unique contribution from the ISS platform is the production of real-time lightning data, especially valuable for operational forecasting and warning applications over data sparse regions such as the oceans. Finally, LIS provides simultaneous and complementary observations with other ISS payloads such as the European Space Agency's Atmosphere-Space Interaction Monitor (ASIM) that is exploring the connection between thunderstorms and lightning with terrestrial gamma-ray flashes (TGFs). Leveraging TRMM's well-established processing and data handling assures that LIS data can be quickly delivered to users.

space-based lightning↗
NASA NTRSDate not supplied

A21I-1875: Leveraging TEMPO Formaldehyde Observations to Assess Surface Air Quality

Surface ozone (O3) pollution is detrimental to human health, crop yields, and ecosystem productivity. Ground-based ozone observations are sparse and surface ozone retrievals from space are challenging. The process of producing ozone from the oxidation of volatile organic compounds (VOCs) in the presence of nitrogen oxides (NOx) results in formaldehyde (HCHO), which can be observed from satellite and by surface-based remote sensing. From limited field campaign observations, column formaldehyde has been shown to exhibit a strong relationship with surface ozone and, therefore, could be an useful proxy for detecting unmonitored regions of elevated ozone pollution. The spatiotemporal characteristics of this relationship are explored using ground-based Pandora spectrometers and airborne remote sensing data with surface ozone monitors across the United States during recent field campaigns over major cities (TRACER-AQ and STAQS). We extend our analysis to take advantage of HCHO column observations from recently launched Tropospheric Emissions: Monitoring of Pollution (TEMPO), a geostationary satellite over North America. TEMPO observations show preliminary promise in mapping regions of elevated surface ozone from space using column HCHO. We demonstrate the use of column HCHO in assessing the strength of the surface monitoring network, and in detecting the extent of regional ozone exceedances.

Prajjwal Rawat↗
NASA NTRS1988

Very long baseline interferometry observations using the tracking and data relay satellite as an orbiting radio telescope

An antenna in geostationary orbit was used for VLBI observations at 2.3 GHz, in combination with ground antennas in Australia and Japan. 23 of the 25 observed sources were detected on orbiter-ground baselines, with baseline lengths as large as 2.15 earth diameters. Brightness temperatures between 10 to the 12th K and 4 x 10 to the 12th K were measured for 10 sources.

Linfield, R. P.↗
NASA NTRSDate not supplied

The Impact of Drifting Orbits on the Monthly Regional TOA Flux Assuming Constant Meteorology

The NASA Clouds and the Earth's Radiant Energy System (CERES) gridded Single Scanner Footprint (SSF1deg) product provides TOA SW and LW monthly 1° regional all-sky fluxes, which are used to monitor the Earth’s energy balance. The CERES long-term climate data record relies on Terra and Aqua satellite sun-synchronous orbits that are maintained at 10:30 and 1:30 local equator crossing times (LECT), respectively. The Terra and Aqua satellites are expected to drift outside of their respective LECT during mid 2022. Both Terra and Aqua will drift over several years towards sunrise and sunset, respectively, and eventually will be deorbited. The CERES SSF1deg product monthly regional fluxes are based on the well calibrated and stable CERES observed fluxes and are temporally interpolated assuming constant meteorology between measurements to resolve the regional diurnal flux cycle to obtain a daily averaged flux. The drifting orbits may impact the monthly regional TOA flux over regions with systematic diurnal cycles, because the observations will shift in local time. The CERES project would like to determine the maximum Terra and Aqua LECT time shift before the monthly regional fluxes are diurnally impacted and become unreliable for long-term climate monitoring. To determine the impact of the drifting orbits on the SSF1deg monthly regional fluxes,15-minute Geostationary Earth Radiation Budget (GERB) broadband observed fluxes over the Meteosat geostationary satellite domain (±60° in longitude and latitude) are used as proxy CERES observations. The drifting orbit sampling pattern is achieved by simply incrementing the observation time by steps of 15 minutes from the CERES footprint time. For each 15-minute time interval, the CERES observed fluxes are replaced by the GERB observed fluxes. The-15 minute incremented monthly regional fluxes based on constant meteorology are compared to the reference 10:30 and 1:30 LECT fluxes. Regions with systematic diurnal cycles, include morning maritime stratus, where the clouds dissipate during the morning, and land afternoon convection, where clouds increase in the afternoon will impact the regional flux differences. Based on January and July 2010 GERB data, even a 15-minute LECT change caused regional monthly flux differences that would impact long term regional trend analysis. Results will be shown at the conference.

David Doelling↗
NASA NTRSDate not supplied

The Orbital Drift Impact on the Monthly Regional TOA Flux Assuming Constant Meteorology

The NASA Clouds and the Earth's Radiant Energy System (CERES) gridded Single Scanner Footprint (SSF1deg) product provides TOA SW and LW monthly 1° regional all-sky fluxes, which are used to monitor the Earth’s energy balance. The CERES long-term climate data record relies on Terra and Aqua satellite sun-synchronous orbits that are maintained at 10:30 and 1:30 local equator crossing times (LECT), respectively. The Terra and Aqua satellites are expected to drift outside of their respective LECT during mid 2022. Both Terra and Aqua will drift over several years towards sunrise and sunset, respectively, and eventually will be deorbited. The CERES SSF1deg product monthly regional fluxes are based on the well calibrated and stable CERES observed fluxes and are temporally interpolated assuming constant meteorology between measurements to resolve the regional diurnal flux cycle to obtain a daily averaged flux. The drifting orbits may impact the monthly regional TOA flux over regions with systematic diurnal cycles, because the observations will shift in local time. The CERES project would like to determine the maximum Terra and Aqua LECT time shift before the monthly regional fluxes are diurnally impacted and become unreliable for long-term climate monitoring. To determine the impact of the drifting orbits on the SSF1deg monthly regional fluxes,15-minute Geostationary Earth Radiation Budget (GERB) broadband observed fluxes over the Meteosat geostationary satellite domain (±60° in longitude and latitude) are used as proxy CERES observations. The drifting orbit sampling pattern is achieved by simply incrementing the observation time by steps of 15 minutes from the CERES footprint time. For each 15-minute time interval, the CERES observed fluxes are replaced by the GERB observed fluxes. The-15 minute incremented monthly regional fluxes based on constant meteorology are compared to the reference 10:30 and 1:30 LECT fluxes. Regions with systematic diurnal cycles, include morning maritime stratus, where the clouds dissipate during the morning, and land afternoon convection, where clouds increase in the afternoon will impact the regional flux differences. Based on January and July 2010 GERB data, even a 15-minute LECT change caused regional monthly flux differences that would impact long term regional trend analysis. Results will be shown at the conference.

D R Doelling↗