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- Author or Editor: Arunas Kuciauskas x
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Abstract
A small subset of midlatitude, midwinter precipitation events affecting the central Sierra Nevada are analyzed. The examples given are representative of 60% of the storm types documented during the past 4 yr of the Sierra Cooperative Pilot Project (SCPP). The structure of thee frontal systems is consistent with those observed in the United States Pacific Northwest and the British Isles.
Combining information from a vertically pointing microwave radiometer, conventional radar, satellite imagery, and detailed time cross sections of rawinsonde data, relationships are developed between these remote sensing devices and the onset of supercooled liquid water (SLW). For the storms described. the highest concentration of SLW occurs after passage of an upper jet with accompanying upper-level front or surface cold ana- and/or katafront. Thee frontal passages lead to decreasing cloud thickness, warming cloud tops, decreasing precipitation rate, and shallow embedded convection over the Sierra.
Discontinuities in cloud top temperature, rainbands, and decreasing echo height, associated with the passage of the upper jet and accompanying front, can be identified with satellite and radar several hours before affecting the Sierra Nevada. thus providing a prediction for the onset or increase in SLW. These relationships have application to wintertime cloud modification programs over the central Sierra.
Abstract
A small subset of midlatitude, midwinter precipitation events affecting the central Sierra Nevada are analyzed. The examples given are representative of 60% of the storm types documented during the past 4 yr of the Sierra Cooperative Pilot Project (SCPP). The structure of thee frontal systems is consistent with those observed in the United States Pacific Northwest and the British Isles.
Combining information from a vertically pointing microwave radiometer, conventional radar, satellite imagery, and detailed time cross sections of rawinsonde data, relationships are developed between these remote sensing devices and the onset of supercooled liquid water (SLW). For the storms described. the highest concentration of SLW occurs after passage of an upper jet with accompanying upper-level front or surface cold ana- and/or katafront. Thee frontal passages lead to decreasing cloud thickness, warming cloud tops, decreasing precipitation rate, and shallow embedded convection over the Sierra.
Discontinuities in cloud top temperature, rainbands, and decreasing echo height, associated with the passage of the upper jet and accompanying front, can be identified with satellite and radar several hours before affecting the Sierra Nevada. thus providing a prediction for the onset or increase in SLW. These relationships have application to wintertime cloud modification programs over the central Sierra.
Abstract
Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.
Abstract
Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.
Abstract
During the spring and summer months, the greater Caribbean region typically experiences pulses of moderate to heavy episodes of airborne African dust concentrations that originate over the Sahara Desert and propagate westward across the tropical North Atlantic basin. These dust episodes are often contained within the Saharan air layer (SAL), an elevated air mass (between 850–500 hPa) marked by very dry and warm conditions within the lowest levels. During its westward transport, the SAL’s distinct environmental characteristics can persist well into the Gulf of Mexico and southern United States. As a result, the Caribbean population is susceptible to airborne dust levels that often exceed healthy respiratory limits. One of the major responsibilities within the National Weather Service in San Juan, Puerto Rico (NWS-PR), is preparing the public within their area of responsibility (AOR) for such events. The Naval Research Laboratory Marine Meteorology Division (NRL-MMD) is sponsored by the National Oceanic and Atmospheric Administration (NOAA) to support the NWS-PR by providing them with an invaluable “one stop shop” web-based resource (hereafter SAL-WEB) that is designed to monitor these African dust events. SAL-WEB consists of near-real-time output generated from ground-based instruments, satellite-derived imagery, and dust model forecasts, covering the extent of dust from North Africa, westward across the Atlantic basin, and extending into Mexico. The products within SAL-WEB would serve to augment the Advanced Weather Interactive Processing System (AWIPS-II) infrastructure currently in operation at the NWS-PR. The goal of this article is to introduce readers to SAL-WEB, along with current and future research underway to provide improvements in African dust prediction capabilities.
Abstract
During the spring and summer months, the greater Caribbean region typically experiences pulses of moderate to heavy episodes of airborne African dust concentrations that originate over the Sahara Desert and propagate westward across the tropical North Atlantic basin. These dust episodes are often contained within the Saharan air layer (SAL), an elevated air mass (between 850–500 hPa) marked by very dry and warm conditions within the lowest levels. During its westward transport, the SAL’s distinct environmental characteristics can persist well into the Gulf of Mexico and southern United States. As a result, the Caribbean population is susceptible to airborne dust levels that often exceed healthy respiratory limits. One of the major responsibilities within the National Weather Service in San Juan, Puerto Rico (NWS-PR), is preparing the public within their area of responsibility (AOR) for such events. The Naval Research Laboratory Marine Meteorology Division (NRL-MMD) is sponsored by the National Oceanic and Atmospheric Administration (NOAA) to support the NWS-PR by providing them with an invaluable “one stop shop” web-based resource (hereafter SAL-WEB) that is designed to monitor these African dust events. SAL-WEB consists of near-real-time output generated from ground-based instruments, satellite-derived imagery, and dust model forecasts, covering the extent of dust from North Africa, westward across the Atlantic basin, and extending into Mexico. The products within SAL-WEB would serve to augment the Advanced Weather Interactive Processing System (AWIPS-II) infrastructure currently in operation at the NWS-PR. The goal of this article is to introduce readers to SAL-WEB, along with current and future research underway to provide improvements in African dust prediction capabilities.
Under the auspices of the National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Integrated Program Office (IPO), the Naval Research Laboratory (NRL) has developed “NexSat” (www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html)—a public-access online demonstration over the continental United States (CONUS) of near-real-time environmental products highlighting future applications from the Visible/Infrared Imager/Radiometer Suite (VIIRS). Based on a collection of operational and research-grade satellite observing systems, NexSat products include the detection, enhancement, and where applicable, physical retrieval of deep convection, low clouds, light sources at night, rainfall, snow cover, aircraft contrails, thin cirrus layers, dust storms, and cloud/aerosol properties, all presented in the context of value-added imagery. The purpose of NexSat is threefold: 1) to communicate the advanced capabilities anticipated from VIIRS, 2) to present this information in near–real time for use by forecasters, resource managers, emergency response teams, civic planners, the aviation community, and various government agencies, and 3) to augment the NRL algorithm development multisensor/model-fusion test bed for accelerated transitions to operations during the NPOESS era. This paper presents an overview of NexSat, highlighting selected products from the diverse meteorological phenomenology over the CONUS.
Under the auspices of the National Polar-orbiting Operational Environmental Satellite System's (NPOESS) Integrated Program Office (IPO), the Naval Research Laboratory (NRL) has developed “NexSat” (www.nrlmry.navy.mil/nexsat_pages/nexsat_home.html)—a public-access online demonstration over the continental United States (CONUS) of near-real-time environmental products highlighting future applications from the Visible/Infrared Imager/Radiometer Suite (VIIRS). Based on a collection of operational and research-grade satellite observing systems, NexSat products include the detection, enhancement, and where applicable, physical retrieval of deep convection, low clouds, light sources at night, rainfall, snow cover, aircraft contrails, thin cirrus layers, dust storms, and cloud/aerosol properties, all presented in the context of value-added imagery. The purpose of NexSat is threefold: 1) to communicate the advanced capabilities anticipated from VIIRS, 2) to present this information in near–real time for use by forecasters, resource managers, emergency response teams, civic planners, the aviation community, and various government agencies, and 3) to augment the NRL algorithm development multisensor/model-fusion test bed for accelerated transitions to operations during the NPOESS era. This paper presents an overview of NexSat, highlighting selected products from the diverse meteorological phenomenology over the CONUS.
GOES ABI Detection of Thin Cirrus over LandAbstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.
Abstract
This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.
Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” BandAbstract
We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.
Abstract
We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.
