Sarasota Climate: Monitoring Heat and Assessing Heat Vulnerability to Identify Locations for Heat Mitigation Efforts in Sarasota, Florida
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Will Hadley↗
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Overview of SatCORPS Satellite-Derived Cloud and Radiation Retrieval Coverage of ARM Domains
The ARM Climate Research Facility program maintains a number of climatically representative sites, which provide long-term cloud- and climate-monitoring records via ground-based instrumentation. These measurements provide a valuable data record over a localized region, but can be greatly enhanced by use of satellite monitoring. Satellite analyses over larger domains can provide parameters helpful for monitoring climate and evaluating models. The NASA/Langley Cloud group routinely derives such cloud and radiative parameters, from various geostationary and polar-orbiting satellite coverage over ARM sites; the group provides near-realtime analyses covering the 3 ARM fixed sites, as well as the GO-Amazon AMF site. This is accomplished by employing a suite of algorithms including VISST (Visible Infrared Solar Split-Window Technique), SIST (Solar Infrared Split-Window Technique), and SINT (Solar-infrared Infrared Near-Infrared Technique), now collectively called SatCORPS (Satellite Cloud Observations and Radiative Property retrieval System). An overview and catalog of SatCORPS-derived datasets processed for ASR, and available from both the ARM archive and the NASA/Langley Cloud group website, is provided. Specific improvements included in recently added datasets such as GO-Amazon and Azores are highlighted, including an improved cloud-detection mask, as well as improvements in derived Top-of-Atmosphere (TOA) SW albedoes and LW fluxes. New narrowband-to-broadband (NB-BB) fits and corrections for improved TOA fluxes are illustrated, including MTSAT-1/CERES Aqua NB-BB fits for the TWPICE field campaign, as well as new fits covering the Azores region which incorporate GERB TOA fluxes (Geostationery Earth Radiation Budget). Finally, validation of the reprocessed SatCORPS datasets is shown.
M M Khader↗
Bangladesh Agro-Climatic Environmental Monitoring Project
The Agro-Climatic Environmental Monitoring Project (ACEMP) is based on a Participating Agency Service Agreement (PASA) between the Agency for International Development (AID) and the National Oceanic and Atmospheric Administration (NOAA). In FY80, the Asia Bureau and Office of Federal Disaster Assistance (OFDA), worked closely to develop a funding mechanism which would meet Bangladesh's needs both for flood and cyclone warning capability and for application of remote sensing data to development problems. In FY90, OFDA provided for a High Resolution Picture Transmission (HRPT) receiving capability to improve their forecasting accuracy for cyclones, flooding and storm surges. That equipment is primarily intended as a disaster prediction and preparedness measure. The ACEM Project was designed to focus on the development applications of remote sensing technology. Through this Project, AID provided to the Bangladesh Government (BDG) the equipment, technical assistance, and training necessary to collect and employ remote sensing data made available by satellites as well as hydrological data obtained from data collection platforms placed in major rivers. The data collected will enable the BDG to improve the management of its natural resources.
Vermillion, C.↗
The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration
The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, are used to estimate the broadband fluxes between CERES observations. This requires stable and consistent cross-platform imager visible channel calibration. The CERES project utilizes deep convective clouds (DCC) as an invariant Earth target to both monitor the stability of sensors and for radiometric scaling. GSICS, an international collaboration, is also evaluating and implementing the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the AquaMODIS or the NOAA-20 VIIRS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical D CC-identified pixel-level reflectances, which are aggregated as probability density functions (PDF). By assuming the monthly PDF shape is otherwise consistent in time excepting shifts in reflectance caused by changes in the sensor calibration, the imager stability is monitored. Radiometric scaling is accomplished by ratioing the sensor pair DCC PDF reflectance values. The success of the DCC methodology relies on consistent PDF distributions. The goal of this study is to determine the impact of pixel resolution on the DCC reflectance distribution. Single SNPP-VIIRS 750-m and Landsat 8 OLI 30-m granules are aggregated to degrade the pixel resolution from the native level. The DCC pixels are identified using a BT threshold. Most of the brightest DCC pixels are also the coldest, although there are exceptions. It was found that increasing the BT threshold exponentially increased the number of darker pixels. The pixel resolution did not seem to impact the DCC reflectance PDF distribution for pixel resolutions less than 3 km, which suggests that imagers of varying pixel resolutions may be radiometrically scaled to each other using DCC targets.
DCC↗
The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration
The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, to estimate the broadband fluxes between CERES observations. This requires stable imager visible channel calibration, which the CERES project verifies by utilizing deep convective clouds (DCC) as an invariant Earth target. GSICS, which is an international collaboration, is also evaluating the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the Aqua-MODIS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical DCC-identified pixel-level reflectances, which are histogrammed to find the mode reflectance of the probability density function (PDF). The imager stability is monitored by tracking the monthly DCC PDF mode reflectance over time. Radiometric scaling is accomplished by ratioing the GEO and VIIRS DCC mode reflectance values. The PDF shape and mode dependency on sensor pixel resolution, which varies among sensors, is unknown. This study will characterize the impact of pixel resolution on the DCC PDFs by aggregating Landsat 30-m pixel reflectances into various coarser pixel resolutions ranging from 100-m to 4-km, and comparing the corresponding PDF statistics. This analysis will assist in improving the uncertainty in a DCC-based intercalibration between instruments with different pixel resolutions.
Conor Haney↗
Monitoring, analyzing, and modeling global climate
Diabatic heating rate estimates as residuals of the dry thermodynamic equation were generated for May 1, 1985 to December 1989 in pentad resolution. Published results show moderate correlations (approx. .6) between heating rate and outgoing long wave radiation for periods under 90 days in the tropics and many extratropical locations. Nine years of simulation with the Community Climate Model 1 (CCM1) using R15 and observed sea surface temperatures shows that the model retains significantly more heat at the surface and in the free atmosphere than does the actual earth system. A post-processor for the CCM1, with capabilities to produce simulated microwave sounding unit (MSU) brightness temperatures was written. Techniques were refined considerably and validation studies were carried out to verify the globally distributed free atmosphere temperature anomalies derived from MSU data. The precision is such that detailed, long-term climate monitoring is well within the capability of these data.
Christy, John R.↗
Stratospheric Ozone Intercomparison Campaign (STOIC) 1989: Overview
The NASA Upper Atmosphere Research Program organized a Stratospheric Ozone Intercomparison Campaign (STOIC) held in July-August 1989 at the Table Mountain Facility (TMF) of the Jet Propulsion Laboratory (JPL). The primary instruments participating in this campaign were several that had been developed by NASA for the Network for the Detection of Stratospheric Change: the JPL ozone lidar at TMF, the Goddard Space Flight Center trailer-mounted ozone lidar which was moved to TMF for this comparison, and the Millitech/LaRC microwave radiometer. To assess the performance of these new instruments, a validation/intercomparison campaign was undertaken using established techniques: balloon ozonesondes launched by personnel from the Wallops Flight Facility and from NOAA Geophysical Monitoring for Climate Change (GMCC) (now Climate Monitoring and Diagnostics Laboratory), a NOAA GMCC Dobson spectrophotometer, and a Brewer spectrometer from the Atmospheric Environment Service of Canada, both being used for column as well as Umkehr profile retrievals. All of these instruments were located at TMF and measurements were made as close together in time as possible to minimize atmospheric variability as a factor in the comparisons. Daytime rocket measurements of ozone were made by Wallops Flight Facility personnel using ROCOZ-A instruments launched from San Nicholas Island. The entire campaign was conducted as a blind intercomparison, with the investigators not seeing each others data until all data had been submitted to a referee and archived at the end of the 2-week period (July 20 to August 2, 1989). Satellite data were also obtained from the Stratospheric Aerosol and Gas Experiment (SAGE 2) aboard the Earth Radiation Budget Satellite and the Total Ozone Mapping Spectrometer (TOMS) aboard Nimbus 7. An examination of the data has found excellent agreement among the techniques, especially in the 20- to 40-km range. As expected, there was little atmospheric variability during the intercomparison, allowing for detailed statistical comparisons at a high level of precision. This overview paper summarizes the campaign and provides a 'road map' to subsequent papers in this issue by the individual instrument teams which will present more detailed analysis of the data and conclusions.
Margitan, J. J.↗
Intercalibration of Meteorological Satellite Imagers Using VIRS, ATSR-2 and MODIS
Global retrievals of surface, cloud and radiative properties from geostationary (GEO) and low-Earth-orbit (LEO) meteorological satellites require accurate calibration of their imagers. An accurate and consistent calibration increases the reliability and effectiveness of long-term monitoring of climate changes. More emphasis has been placed on calibrating the thermal infrared (IR) channel. The lack of on-board calibration in the visible (VIS) channel has prompted efforts to characterize the degradation of the VIS sensor using vicarious post-launch calibration techniques that measure bright stable desert targets from space and aircraft or using satellite-to-satellite normalizations. While such inter-calibrations are valuable and widely used, the lack of a well-characterized calibration reference source and the lengthy time delay between updates have minimized their effectiveness in climate monitoring. To address these shortcomings, this paper examines the use of research satellite imagers to provide stable calibration references for the visible (VIS, approximately 0.65 micrometers) channels and develops a method for rapid intercalibration of existing satellites. Calibration coefficients are determined for the Geostationary Operational Environmental Satellites (GOES-8 to GOES-10), Geostationary Meteorological Satellite (GMS-5), Meteosat-7, and the NOAA-14 Advanced Very High Resolution Radiometer (AVHRR). As a reference calibration source, this technique uses the self-calibrating sensors on the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Radiometers (VIRS) or the ERS-2 Along Track Scanning Radiometer (ATSR-2). GOES-8 is calibrated with VIRS and then its calibration is transferred to other GEO or LEO satellites. The absolute accuracy of this technique relies on the assumption that the on-board calibration is stable and well maintained. Minnis et al. assessed the VIRS calibration using comparisons with other self-calibrated satellite sensors including the broadband Clouds and Earth's Radiant Energy System (CERES) scanners, the ERS-2 Along Track Scanning Radiometer (ATSR-2), and the Terra Moderate-resolution Imaging Spectrometer (MODIS). Thus, the VIRS data can be confidently used as the initial reference source.
Nguyen, Louis↗
A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains
The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.
deep learning↗
NASA follow-on to the Bangladesh Agro-Climatic Environmental Monitoring Project
The NASA responsibility and activities for the follow-on to the original Agro-Climatic Environmental Monitoring Project (ACEMP) which was completed during 1987 is described. Five training sessions which comprise the NASA ACEMP follow-on are: Agrometeorology, Meteorology of Severe Storms Using GEMPAK, Satellite Oceanography, Hydrology, and Meteorology with TOVS. The objective of the follow-on is to train Bangladesh Government staff in the use of satellite data for remote sensing applications. This activity also encourages the scientific connection between NASA/Goddard Space Flight Center and The Bangladesh Space and Remote Sensing Organization (SPARRSO).
Vermillion, C.↗
Covid-19 Impact Monitoring for Climate Environment (Greenhouse Gases)
To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan’s Greenhouse gases Observing SATellite (GOSAT) in 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing and Tokyo. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 lockdowns. ESA, NASA and JAXA developed the dashboard jointly to monitor the climate impacts of COVID-19.
greenhouse gases↗
Monitoring Surface Climate With its Emissivity Derived From Satellite Measurements
Satellite thermal infrared (IR) spectral emissivity data have been shown to be significant for atmospheric research and monitoring the Earth fs environment. Long-term and large-scale observations needed for global monitoring and research can be supplied by satellite-based remote sensing. Presented here is the global surface IR emissivity data retrieved from the last 5 years of Infrared Atmospheric Sounding Interferometer (IASI) measurements observed from the MetOp-A satellite. Monthly mean surface properties (i.e., skin temperature T(sub s) and emissivity spectra epsilon(sub v) with a spatial resolution of 0.5x0.5-degrees latitude-longitude are produced to monitor seasonal and inter-annual variations. We demonstrate that surface epsilon(sub v) and T(sub s) retrieved with IASI measurements can be used to assist in monitoring surface weather and surface climate change. Surface epsilon(sub v) together with T(sub s) from current and future operational satellites can be utilized as a means of long-term and large-scale monitoring of Earth 's surface weather environment and associated changes.
Zhou, Daniel K.↗
Transitioning Earth Remote Sensing Data to Benefit Society: A Paradigm for a Center of Excellence
Over the past decade there has been a substantial increase in the number of Earth remote sensing satellites launched for research and operational usage and numerous others planned by the international community. These satellites have been used to varying degrees by their supporting agencies for weather and environmental monitoring, climate studies, disaster monitoring and response, and other humanitarian activities. While there are success stories on useful applications of remote sensing data, the broader use of these satellite assets by other organizations and entities has been limited for a number of reasons including lack of data services, data dissemination issues, and a general failure to engage the broader end user community with useful data access and knowledge of how to use the data and products. This paper describes some of these current limitations on the broader use of Earth remote sensing data by the international community and describes the concept of a general "Center of Excellence" to facilitate the development, transition, and utilization of these Earth remote sensing observations by the broader international community.
Transition to operations↗
Seasonal, Spectral, and Regional Characterization of Deep Convective Clouds Top-of-Atmosphere Bidirectional Reflectance for Improved Satellite Intercalibration
The NASA CERES project provides the scientific community with regional broadband fluxes designed for long-term climate monitoring. The CERES climate quality dataset requires that the CERES instrument, as well as the MODIS and VIIRS imager records to be radiometrically stable over time. Deep Convective Clouds (DCCs) are spectrally uniform, near-Lambertian natural diffusers offering high signal-to-noise ratio and stable radiometric response in the VIS-NIR spectrum. For shortwave infrared (SWIR) wavelengths greater than 1.2µm, the DCC response is significantly influenced by cloud particle size and atmospheric absorption. Previous studies improved the characterization of the SWIR band DCC radiance, by using channel specific monthly empirical BRDFs as well as using the probability density function mean statistic to track the SWIR band stability. Also, that the DCC radiance is greater over land than over ocean and that the TWP DCC radiance has the lowest tropical DCC radiance. This study confirms and improves upon the previous studies. The study stratified the tropics regionally into land and ocean domains and applied their respective ocean-only and land-only empirical monthly BRDFs and normalized the land DCC BRDF corrected radiances with their ocean counterpart. This approach provided the most stable DCC response. The DCC BRDF corrected radiance monthly standard error was 0.24%, 0.62%, 0.59%, and 0.43% for the 1.24µm, 1.37µm, 1.61µm, and 2.25µm SWIR bands, which reduced the standard error 20%, 13%, 24%, and 26%, respectively when compared with the all-surface approach. The same approach was attempted over the Tropical Western Pacific and found not to be an improvement over the tropical domain. Further stratification of the tropical domain will need to balance sufficient sampling while accounting for regional DCC radiance differences.
Prathana Khakurel↗
In-flight pointing accuracy assessment and GNC commissioning overview for the dual-spinning SMAP (Soil Moisture Active Passive) spacecraft
NASA’s new SMAP (Soil Moisture Active Passive) spacecraft is a radar and radiometer- based climate monitoring mission that, for an earth-orbiting satellite, presented an uncommonly large engineering challenge for the spacecraft designers at NASA’s Jet Propulsion Laboratory. The primary engineering challenge of this mission was to design a three-axis stabilized dual-spinning spacecraft with the largest spinning flexible mesh reflector of any known spacecraft. This paper reports on the attitude control performance of this duel-spinning conicalscanning system during the first 18 months of science operations, and provides an overview of the Guidance, Navigation, and Control (GNC) subsystem performance for this climate monitoring asset.
Sung, Tina S.↗
The Need for Better Monitoring of Climate Change in the Middle and Upper Atmosphere
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Juan A. Añel↗
Remote Sensing of Cloud Top Height from SEVIRI: Analysis of Eleven Current Retrieval Algorithms
The role of clouds remains the largest uncertainty in climate projections. They influence solar and thermal radiative transfer and the earth's water cycle. Therefore, there is an urgent need for accurate cloud observations to validate climate models and to monitor climate change. Passive satellite imagers measuring radiation at visible to thermal infrared (IR) wavelengths provide a wealth of information on cloud properties. Among others, the cloud top height (CTH) - a crucial parameter to estimate the thermal cloud radiative forcing - can be retrieved. In this paper we investigate the skill of ten current retrieval algorithms to estimate the CTH using observations from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). In the first part we compare ten SEVIRI cloud top pressure (CTP) data sets with each other. The SEVIRI algorithms catch the latitudinal variation of the CTP in a similar way. The agreement is better in the extratropics than in the tropics. In the tropics multi-layer clouds and thin cirrus layers complicate the CTP retrieval, whereas a good agreement among the algorithms is found for trade wind cumulus, marine stratocumulus and the optically thick cores of the deep convective system. In the second part of the paper the SEVIRI retrievals are compared to CTH observations from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) instruments. It is important to note that the different measurement techniques cause differences in the retrieved CTH data. SEVIRI measures a radiatively effective CTH, while the CTH of the active instruments is derived from the return time of the emitted radar or lidar signal. Therefore, some systematic differences are expected. On average the CTHs detected by the SEVIRI algorithms are 1.0 to 2.5 kilometers lower than CALIOP observations, and the correlation coefficients between the SEVIRI and the CALIOP data sets range between 0.77 and 0.90. The average CTHs derived by the SEVIRI algorithms are closer to the CPR measurements than to CALIOP measurements. The biases between SEVIRI and CPR retrievals range from −0.8 kilometers to 0.6 kilometers. The correlation coefficients of CPR and SEVIRI observations vary between 0.82 and 0.89. To discuss the origin of the CTH deviation, we investigate three cloud categories: optically thin and thick single layer as well as multi-layer clouds. For optically thick clouds the correlation coefficients between the SEVIRI and the reference data sets are usually above 0.95. For optically thin single layer clouds the correlation coefficients are still above 0.92. For this cloud category the SEVIRI algorithms yield CTHs that are lower than CALIOP and similar to CPR observations. Most challenging are the multi-layer clouds, where the correlation coefficients are for most algorithms between 0.6 and 0.8. Finally, we evaluate the performance of the SEVIRI retrievals for boundary layer clouds. While the CTH retrieval for this cloud type is relatively accurate, there are still considerable differences between the algorithms. These are related to the uncertainties and limited vertical resolution of the assumed temperature profiles in combination with the presence of temperature inversions, which lead to ambiguities in the CTH retrieval. Alternative approaches for the CTH retrieval of low clouds are discussed.
Hamann, U.↗
California Methane Monitoring for Climate Action and Public Safety
No abstract available
Duren, Riley↗