Search Results
You are looking at 1 - 10 of 13 items for
- Author or Editor: Kim Richardson x
- Refine by Access: All Content x
Abstract
Using data from the GOES-8–9 imager, this paper discusses the potential for consistent, around-the-clock image products that can trace the movement and evolution of low, stratiform clouds. In particular, the paper discusses how bispectral image sequences based on the shortwave (3.9 μm) and longwave (10.7 μm) infrared channels can be developed for this purpose. These sequences can be animated to produce useful loops. The techniques address several problems faced by operational forecasters in the tracking of low clouds. Low clouds are often difficult or impossible to detect at night because of the poor thermal contrast with the background on infrared images. During the day, although solar reflection makes low, stratiform clouds bright on GOES visible images, it is difficult to distinguish low clouds from adjacent ground snowcover or dense cirrus overcasts. The shortwave infrared channel often gives a superior delineation of low clouds on images because water droplets produce much higher reflectances than ice clouds or ground snowcover. Combined with the longwave channel, the shortwave channel can be used to derive products that can distinguish low clouds from the background at any time of day or night. The first case study discusses cloud properties as observed from the shortwave channels from the polar-orbiting Advanced Very High Resolution Radiometer, as well as GOES-9, and applies a correction to produce shortwave reflectance. A second case study illustrates the use of the GOES-8 shortwave channel to observe the aftermath of a spring snowstorm in the Ohio Valley. Finally, the paper discusses a red–blue–green color combination technique to build useful forecaster products.
Abstract
Using data from the GOES-8–9 imager, this paper discusses the potential for consistent, around-the-clock image products that can trace the movement and evolution of low, stratiform clouds. In particular, the paper discusses how bispectral image sequences based on the shortwave (3.9 μm) and longwave (10.7 μm) infrared channels can be developed for this purpose. These sequences can be animated to produce useful loops. The techniques address several problems faced by operational forecasters in the tracking of low clouds. Low clouds are often difficult or impossible to detect at night because of the poor thermal contrast with the background on infrared images. During the day, although solar reflection makes low, stratiform clouds bright on GOES visible images, it is difficult to distinguish low clouds from adjacent ground snowcover or dense cirrus overcasts. The shortwave infrared channel often gives a superior delineation of low clouds on images because water droplets produce much higher reflectances than ice clouds or ground snowcover. Combined with the longwave channel, the shortwave channel can be used to derive products that can distinguish low clouds from the background at any time of day or night. The first case study discusses cloud properties as observed from the shortwave channels from the polar-orbiting Advanced Very High Resolution Radiometer, as well as GOES-9, and applies a correction to produce shortwave reflectance. A second case study illustrates the use of the GOES-8 shortwave channel to observe the aftermath of a spring snowstorm in the Ohio Valley. Finally, the paper discusses a red–blue–green color combination technique to build useful forecaster products.
Abstract
Cloud-top verification is inherently difficult because of large uncertainties in the estimates of observed cloud-top height. Misplacement of cloud top associated with transmittance through optically thin cirrus is one of the most common problems. Forward radiative models permit a direct comparison of predicted and observed radiance, but uncertainties in the vertical position of clouds remain. In this work, synthetic brightness temperatures are compared with forecast cloud-top heights so as to investigate potential errors and develop filters to remove optically thin ice clouds. Results from a statistical analysis reveal that up to 50% of the clouds with brightness temperatures as high as 280 K are actually optically thin cirrus. The filters successfully removed most of the thin ice clouds, allowing for the diagnosis of very specific errors. The results indicate a strong negative bias in midtropospheric cloud cover in the model, as well as a lack of land-based convective cumuliform clouds. The model also predicted an area of persistent stratus over the North Atlantic Ocean that was not apparent in the observations. In contrast, high cloud tops associated with deep convection were well simulated, as were mesoscale areas of enhanced trade cumulus coverage in the Sargasso Sea.
Abstract
Cloud-top verification is inherently difficult because of large uncertainties in the estimates of observed cloud-top height. Misplacement of cloud top associated with transmittance through optically thin cirrus is one of the most common problems. Forward radiative models permit a direct comparison of predicted and observed radiance, but uncertainties in the vertical position of clouds remain. In this work, synthetic brightness temperatures are compared with forecast cloud-top heights so as to investigate potential errors and develop filters to remove optically thin ice clouds. Results from a statistical analysis reveal that up to 50% of the clouds with brightness temperatures as high as 280 K are actually optically thin cirrus. The filters successfully removed most of the thin ice clouds, allowing for the diagnosis of very specific errors. The results indicate a strong negative bias in midtropospheric cloud cover in the model, as well as a lack of land-based convective cumuliform clouds. The model also predicted an area of persistent stratus over the North Atlantic Ocean that was not apparent in the observations. In contrast, high cloud tops associated with deep convection were well simulated, as were mesoscale areas of enhanced trade cumulus coverage in the Sargasso Sea.
Abstract
Images of the 85-GHz frequency from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) spacecraft are routinely viewed by forecasters for tropical cyclone analyses. These images are valued because of their ability to observe tropical cyclone structure and to locate center positions. Images of lower-frequency SSM/I channels, such as 37 GHz, have poor quality due to the coarse spatial resolution, and therefore 85 GHz has become the de facto analysis standard. However, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in 1997, has much better spatial resolution for all channels than the SSM/I. Although originally designed to investigate precipitation for climate research, real-time images are now sent into tropical cyclone forecast offices, and posted to Web pages of the Naval Research Laboratory and the Fleet Numerical Meteorology and Oceanography Center, both in Monterey, California. TMI images of 37 GHz have a number of properties that make them useful complements to images of 85 GHz. They have the capacity to detect low-level circulation centers, which are sometimes unseen at 85 GHz. Also, because the 37-GHz channel generally senses atmospheric layers much nearer to the surface than 85 GHz, parallax error is less, allowing more accurate fixes. This paper presents several case studies comparing the two TMI frequencies and offers some forecasting guidelines for when to use each.
Abstract
Images of the 85-GHz frequency from the Special Sensor Microwave Imager (SSM/I) aboard the Defense Meteorological Satellite Program (DMSP) spacecraft are routinely viewed by forecasters for tropical cyclone analyses. These images are valued because of their ability to observe tropical cyclone structure and to locate center positions. Images of lower-frequency SSM/I channels, such as 37 GHz, have poor quality due to the coarse spatial resolution, and therefore 85 GHz has become the de facto analysis standard. However, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in 1997, has much better spatial resolution for all channels than the SSM/I. Although originally designed to investigate precipitation for climate research, real-time images are now sent into tropical cyclone forecast offices, and posted to Web pages of the Naval Research Laboratory and the Fleet Numerical Meteorology and Oceanography Center, both in Monterey, California. TMI images of 37 GHz have a number of properties that make them useful complements to images of 85 GHz. They have the capacity to detect low-level circulation centers, which are sometimes unseen at 85 GHz. Also, because the 37-GHz channel generally senses atmospheric layers much nearer to the surface than 85 GHz, parallax error is less, allowing more accurate fixes. This paper presents several case studies comparing the two TMI frequencies and offers some forecasting guidelines for when to use each.
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.
Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location.
Tropical cyclone (TC) monitoring requires the use of multiple satellites and sensors to accurately assess TC location and intensity. Visible and infrared (vis/IR) data provide the bulk of TC information, but upper-level cloud obscurations inherently limit this important dataset during a storm's life cycle. Passive microwave digital data and imagery can provide key storm structural details and offset many of the vis/IR spectral problems. The ability to view storm rainbands, eyewalls, impacts of shear, and exposed low-level circulations, whether it is day or night, makes passive microwave data a significant tool for the satellite analyst. Passive microwave capabilities for TC reconnaissance are demonstrated via a near-real-time Web page created by the Naval Research Laboratory in Monterey, California. Examples are used to illustrate tropical cyclone monitoring. Collocated datasets are incorporated to enable the user to see many aspects of a storm's organization and development by quickly accessing one location.
Abstract
Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.
Abstract
Global monitoring of tropical cyclones (TC) is enhanced by the unique capabilities provided by the day–night band (DNB), a sensor included on the Visible Infrared Imaging Radiometer Suite (VIIRS) flying on board the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The DNB, a low-light visible–near-infrared-band passive radiometer, can leverage unconventional (i.e., nonsolar) sources of visible light illumination such as moonlight to infer storm structure at night. The DNB provides an unprecedented capability to resolve moonlit clouds at high resolution, offering numerous potential benefits to both operational TC analysts and researchers developing new methods of monitoring TCs occurring within the largely data-void tropical oceanic basins. DNB digital data provide significant enhancements over older nighttime visible data from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) by leveraging accurate calibration, high sensitivity, and sub-kilometer-scale imagery that covers 2–3 times the moon’s lunar cycle than the OLS. By leveraging these attributes, DNB data can enable the use of automated objective applications instead of subjective image interpretation. Here, the authors detail ways in which critical information about TC structure, location, intensity changes, shear environment, lightning, and other characteristics can be extracted when the DNB data are used in isolation or in a multichannel approach with coincident infrared (IR) channels.
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.
Abstract
Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.
Abstract
Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.
