Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere ...over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR sounders, and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature, or altitude), cloud thermodynamic phase, and cloud radiative and bulk microphysical properties (optical depth or emissivity, effective particle radius, and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. The monthly gridded database presented here facilitates further assessments, climate studies, and the evaluation of climate models.
Tornadoes in the Southeast and central U.S. are episodically accompanied by smoke from biomass burning in central America. Analysis of the 27 April 2011 historical tornado outbreak shows that adding ...smoke to an environment already conducive to severe thunderstorm development can increase the likelihood of significant tornado occurrence. Numerical experiments indicate that the presence of smoke during this event leads to optical thickening of shallow clouds while soot within the smoke enhances the capping inversion through radiation absorption. The smoke effects are consistent with measurements of clouds and radiation before and during the outbreak. These effects result in lower cloud bases and stronger low‐level wind shear in the warm sector of the extratropical cyclone generating the outbreak, two indicators of higher probability of tornadogenesis and tornado intensity and longevity. These mechanisms may contribute to tornado modulation by aerosols, highlighting the need to consider aerosol feedbacks in numerical severe weather forecasting.
Key Points
First time smoke influence on tornado severity is shown for a real case study
A new mechanism on how smoke can influence tornadoes is presented
We show that aerosol effects should be considered in severe weather forecasts
Abstract
The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like ...measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric Global Forecast System (GFS) and SST Reynolds daily high-resolution blended SST (DSST) fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.
The Advanced Very High Resolution Radiometer Kalluri, S.; Cao, C.; Heidinger, A. ...
Bulletin of the American Meteorological Society,
02/2021, Letnik:
102, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The Advanced Very High Resolution Radiometers (AVHRR), which have been flying on National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting weather satellites since 1978, provide the ...longest global record of Earth observations from a visible–infrared imager. Experience gained through AVHRRs has been integral to the development of the new-generation sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and associated data processing algorithms in the United States, as well as a similar class of sensor by space agencies around the world. Over four decades of data have been vital for studying Earth and its change. The MetOp-C satellite that was successfully launched in 2018 carries the last AVHRR. This article reviews the contributions of AVHRR in building a continuous global data record over the last 40 years on the occasion of its last launch.
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 km 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 km to 0.6 km. 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.
The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG (Toward Improving Coastal Fog ...Prediction) field campaign. The C-FOG campaign was designed to advance understanding of fog formation, development, and dissipation over coastal environments to improve predictability. The project took place along coastlines and open water environments of eastern Canada (Nova Scotia, and the Island of Newfoundland) during August−October of 2018 where environmental conditions play an important role for late season fog formation. During the C-FOG field campaign, coastal instruments were mainly located at the Ferryland supersite, Newfoundland, with two main sites, and five satellite sites, as well as on the Research Vessel Hugh R. Sharp. Key in-situ measurement instruments included microphysical, meteorological, radiation, and aerosol sensors. A fog spectral probe was used for measuring droplet spectra from 1–50 µm at the Ferryland supersite. A laser precipitation monitor with 100 µm to 10 mm size range and an optical particle counter with 0.3–17 µm at 16 spectral channels provided information for fog and drizzle discrimination. Remote sensing platforms, e.g. profiling microwave radiometer, ceilometer, microwave rain radar, lidar, meteorological towers, tethered balloons, and GOES-R products for fog coverage, and droplet size and liquid water path) were used to evaluate fog over horizontal and vertical dimensions. Results suggest that effective radius, phase, liquid water path, and liquid water content values obtained from GOES-R and the profiling microwave radiometer are comparable to ground-based in-situ observations. It is concluded that integration of observations and nowcasting products may help improve short-term local fog predictions.
This paper demonstrates how the availability of specific infrared channels impacts the ability of two future meteorological satellite imagers to estimate cloud‐top pressure. Both of the imagers are ...planned for launch by the United States, one for a geostationary platform and the other for a polar‐orbiting platform. The geostationary imager, the Advanced Baseline Imager (ABI), will be flown first on the GOES‐R platform. In addition to the split window channels at 11 and 12 μm, it has one spectral channel located at 13.3 μm where there is relatively strong absorption of H2O and CO2. The polar‐orbiting imager, called the Visible/Infrared Imager Radiometer Suite (VIIRS) and flown on the National Polar‐Orbiting Environmental satellite Suite (NPOESS), has spectral channels in window regions only. The lack of an absorbing channel on VIIRS is shown to have negative consequences for the inference of cloud‐top pressure. This paper investigates the impact on the ability of a satellite imager such as VIIRS to confidently estimate cloud‐top pressure due to the absence of infrared absorption channels. The solution space is defined as the depth of the atmospheric layer in which a cloud can be placed where the calculated top‐of‐atmosphere radiances match the measurements used in the cloud‐top pressure retrieval. For optically thin cirrus, the channels used by the operational VIIRS algorithm provide a solution space of over 200 hPa. However, the inclusion of the single CO2 channel at 13.3 μm on the ABI narrows the solution space to under 30 hPa. Our imager‐based analysis is performed using Moderate Resolution Imaging Spectroradiometer (MODIS) data, which provides the relevant channel information with sufficient spatial resolution and radiometric accuracy. Additional results are provided using data from the current GOES and POES imagers. Active lidar data from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation/Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIPSO/CALIOP) observations are used to provide cloud boundaries for verification.
To reduce the Earth's radiation budget uncertainty related to cloud types' changes, and better understand the climate constraints resulting from long‐term clouds' variability, frequent and finer ...(than actually existing) observations are necessary. This is one of the aims of future satellite programs such as the Global Change Observation Mission‐Climate (GCOM‐C) satellite, to be launched by the Japan Aerospace Exploration Agency (JAXA). To facilitate the transition from past to future observations, the actual state of climate variables (e.g., cloud types) needs to be evaluated. This evaluation is attempted in the present work with the analysis of long‐term cloud types' distribution and amounts. The data set used for this study is 25 years (1982–2006) of global daytime cloud properties observed by the National Oceanic and Atmospheric Administration‐Advanced Very‐High‐Resolution Radiometer (NOAA‐AVHRR) satellites sensors. Though various calibrations have been applied on NOAA‐AVHRR data, the effects of the orbit drift experienced by these satellites need to be corrected. A signal processing decomposition method allowing the filtering of the cloud types' amount trend affected by the orbit drift is used to perform the necessary corrections. The results obtained show a quantifiable improvement of the cloud amount estimation and trends of the individual NOAA satellites initial observations, at the global and regional scales. The corrected global cloud amount shows a slight decrease in its linear trend. The driving factors of this trend are the decrease in mid and low clouds overwhelming the increase in high clouds (+0.04% cloud amount/yr). A comparison with other cloud climatology studies such as the International Cloud Satellite Climatology Project (ISCCP) data set shows that the global cloud decrease noticed in NOAA‐AVHRR's data is smaller. And, contrary to the NOAA‐AVHRR's data, the driving force of the ISCCP linear trend is a sharp decrease in low clouds (−0.20% cloud amount/yr).
Key Points
Cloud types amounts long‐term trends
The challenge of using satellite observations to retrieve aerosol properties in a cloudy environment is to prevent contamination of the aerosol signal from clouds, while maintaining sufficient ...aerosol product yield to satisfy specific applications. We investigate aerosol retrieval availability at different instrument pixel resolutions using the standard MODIS aerosol cloud mask applied to MODIS data and supplemented with a new GOES-R cloud mask applied to GOES data for a domain covering North America and surrounding oceans. Aerosol product availability is not the same as the cloud free fraction and takes into account the techniques used in the MODIS algorithm to avoid clouds, reduce noise and maintain sufficient numbers of aerosol retrievals. The inherent spatial resolution of each instrument, 0.5×0.5 km for MODIS and 1×1 km for GOES, is systematically degraded to 1×1, 2×2, 1×4, 4×4 and 8×8 km resolutions and then analyzed as to how that degradation would affect the availability of an aerosol retrieval, assuming an aerosol product resolution at 8×8 km. The analysis is repeated, separately, for near-nadir pixels and those at larger view angles to investigate the effect of pixel growth at oblique angles on aerosol retrieval availability. The results show that as nominal pixel size increases, availability decreases until at 8×8 km 70% to 85% of the retrievals available at 0.5 km, nadir, have been lost. The effect at oblique angles is to further decrease availability over land but increase availability over ocean, because sun glint is found at near-nadir view angles. Finer resolution sensors (i.e., 1×1, 2×2 or even 1×4 km) will retrieve aerosols in partly cloudy scenes significantly more often than sensors with nadir views of 4×4 km or coarser. Large differences in the results of the two cloud masks designed for MODIS aerosol and GOES cloud products strongly reinforce that cloud masks must be developed with specific purposes in mind and that a generic cloud mask applied to an independent aerosol retrieval will likely fail.
The Visible Infrared Imager Radiometer Suite (VIIRS) is a high-resolution Earth imager of the United States National Polar-orbiting Operational Environmental Satellite System (NPOESS). VIIRS has its ...heritage in three sensors currently collecting imagery of the Earth-the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, and the Operational Linescan Sensor. The first launch of the VIIRS sensor is on NASA's NPOESS Preparatory Project (NPP). Data collected by VIIRS will provide products to a variety of users, supporting applications from real-time to long-term climate change timescales. VIIRS has been uniquely designed to satisfy this full range of requirements. Cloud masks derived from the automated analyses of VIIRS data are critical data products for the NPOESS program. In this paper, the VIIRS cloud mask (VCM) performance requirements are highlighted, along with the algorithm developed to satisfy these requirements. The expected performance of the VCM algorithm is established using global synthetic cloud simulations and manual cloud analyses of VIIRS proxy imagery. These results show the VCM analyses will satisfy the performance expectations of products created from it, including land and ocean surface products, cloud microphysical products, and automated cloud forecast products. Finally, minor deficiencies that remain in the VCM algorithm logic are identified along with a mitigation plan to resolve each prior to NPP launch or shortly thereafter.
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