Search Results
You are looking at 1 - 10 of 24 items for
- Author or Editor: F. Joseph Turk 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
The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.
Abstract
The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.
Abstract
A synthesis of remote sensing and in situ observations throughout the life cycle of Hurricane Dennis (2005) during the NASA Tropical Cloud Systems and Processes (TCSP) experiment is presented. Measurements from the ER-2 Doppler radar (EDOP), the Advanced Microwave Sounding Unit (AMSU), airborne radiometer, and flight-level instruments are used to provide a multiscale examination of the storm. The main focus is an episode of deep convective bursts (“hot towers”) occurring during a mature stage of the storm and preceding a period of rapid intensification (11-hPa pressure drop in 1 h 35 min). The vigorous hot towers penetrated to 16-km height, had maximum updrafts of 20 m s−1 at 12–14-km height, and possessed a strong transverse circulation through the core of the convection. Significant downdrafts (maximum of 10–12 m s−1) on the flanks of the updrafts were observed, with their cumulative effects hypothesized to result in the observed increases in the warm core. In one ER-2 overpass, subsidence was transported toward the eye by 15–20 m s−1 inflow occurring over a deep layer (0.5–10 km) coincident with a hot tower.
Fourier analysis of the AMSU satellite measurements revealed a large shift in the storm’s warm core structure, from asymmetric to axisymmetric, ∼12 h after the convective bursts began. In addition, flight-level wind calculations of the axisymmetric tangential velocity and inertial stability showed a contraction of the maximum winds and an increase in the stiffness of the vortex, respectively, after the EDOP observations.
The multiscale observations presented here reveal unique, ultra-high-resolution details of hot towers and their coupling to the parent vortex, the balanced dynamics of which can be generally explained by the axisymmetrization and efficiency theories.
Abstract
A synthesis of remote sensing and in situ observations throughout the life cycle of Hurricane Dennis (2005) during the NASA Tropical Cloud Systems and Processes (TCSP) experiment is presented. Measurements from the ER-2 Doppler radar (EDOP), the Advanced Microwave Sounding Unit (AMSU), airborne radiometer, and flight-level instruments are used to provide a multiscale examination of the storm. The main focus is an episode of deep convective bursts (“hot towers”) occurring during a mature stage of the storm and preceding a period of rapid intensification (11-hPa pressure drop in 1 h 35 min). The vigorous hot towers penetrated to 16-km height, had maximum updrafts of 20 m s−1 at 12–14-km height, and possessed a strong transverse circulation through the core of the convection. Significant downdrafts (maximum of 10–12 m s−1) on the flanks of the updrafts were observed, with their cumulative effects hypothesized to result in the observed increases in the warm core. In one ER-2 overpass, subsidence was transported toward the eye by 15–20 m s−1 inflow occurring over a deep layer (0.5–10 km) coincident with a hot tower.
Fourier analysis of the AMSU satellite measurements revealed a large shift in the storm’s warm core structure, from asymmetric to axisymmetric, ∼12 h after the convective bursts began. In addition, flight-level wind calculations of the axisymmetric tangential velocity and inertial stability showed a contraction of the maximum winds and an increase in the stiffness of the vortex, respectively, after the EDOP observations.
The multiscale observations presented here reveal unique, ultra-high-resolution details of hot towers and their coupling to the parent vortex, the balanced dynamics of which can be generally explained by the axisymmetrization and efficiency theories.
Abstract
Estimation of overland precipitation using observations from the radar and passive microwave radiometer sensors onboard the current Global Precipitation Measurement (GPM) and predecessor Tropical Rainfall Measuring Mission (TRMM) satellites is constrained by the underlying surface variability. The factors controlling the multichannel microwave surface emissivity and radar surface backscatter are related to surface properties such as soil type and vegetation properties that vary with location and time. Variability due to slowly varying seasonal changes can be considered when simulating radar reflectivities and radiometer equivalent blackbody brightness temperatures for use with precipitation retrieval algorithms. However, over certain surfaces, a more transient, dynamic surface change is manifested upon the onset of intermittent rain events. In these situations, a timely update of the surface state prior to each satellite overpass, together with knowledge of the associated variability in the emissivity and radar surface backscatter, may be useful to improve the performance of the overland precipitation retrieval algorithms. In this study, the potential for wide-swath surface backscatter observations from the Ku-band, dual-beam OceanSat-2 scatterometer (OSCAT) is examined as a surface reference for tracking previous-time precipitation. Over certain surfaces, it is shown that a time-change detection approach is useful to isolate the change in radar backscatter owing to previous 3-h rainfall accumulations from the more slowly varying background state. A practical use of this method would be the production of an ancillary previous-time precipitation map, which could be consulted by retrieval algorithms to select (or adjust the weighting of) candidate solutions that represent the most current surface conditions.
Abstract
Estimation of overland precipitation using observations from the radar and passive microwave radiometer sensors onboard the current Global Precipitation Measurement (GPM) and predecessor Tropical Rainfall Measuring Mission (TRMM) satellites is constrained by the underlying surface variability. The factors controlling the multichannel microwave surface emissivity and radar surface backscatter are related to surface properties such as soil type and vegetation properties that vary with location and time. Variability due to slowly varying seasonal changes can be considered when simulating radar reflectivities and radiometer equivalent blackbody brightness temperatures for use with precipitation retrieval algorithms. However, over certain surfaces, a more transient, dynamic surface change is manifested upon the onset of intermittent rain events. In these situations, a timely update of the surface state prior to each satellite overpass, together with knowledge of the associated variability in the emissivity and radar surface backscatter, may be useful to improve the performance of the overland precipitation retrieval algorithms. In this study, the potential for wide-swath surface backscatter observations from the Ku-band, dual-beam OceanSat-2 scatterometer (OSCAT) is examined as a surface reference for tracking previous-time precipitation. Over certain surfaces, it is shown that a time-change detection approach is useful to isolate the change in radar backscatter owing to previous 3-h rainfall accumulations from the more slowly varying background state. A practical use of this method would be the production of an ancillary previous-time precipitation map, which could be consulted by retrieval algorithms to select (or adjust the weighting of) candidate solutions that represent the most current surface conditions.
Abstract
Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.
Abstract
Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.
Estimating Nonraining Surface Parameters to Assist GPM Constellation Radiometer Precipitation AlgorithmsAbstract
The joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) is a constellation mission, centered upon observations from the core satellite dual-frequency precipitation radar (DPR) and its companion passive microwave (MW) GPM Microwave Imager (GMI). One of the key challenges for GPM is how to link the information from the single DPR across all passive MW sensors in the constellation, to produce a globally consistent precipitation product. Commonly, the associated surface emissivity and environmental conditions at the satellite observation time are interpolated from ancillary data, such as global forecast models and emissivity climatology, and are used for radiative transfer simulations and cataloging/indexing the brightness temperature (TB) observations and simulations within a common MW precipitation retrieval framework.
In this manuscript, the feasibility of an update to the surface emissivity state at or near the satellite observation time, regardless of surface type, is examined for purposes of assisting these algorithms with specification of the surface and environmental conditions. Since the constellation MW radiometers routinely observe many more nonprecipitating conditions than precipitating conditions, a principal component analysis is developed from the noncloud GMI–DPR observations as a means to characterize the emissivity state vector and to consistently track the surface and environmental conditions. The method is demonstrated and applied over known complex surface conditions to probabilistically separate cloud and cloud-free scenes. The ability of the method to globally identify “self-similar” surface locations from the TB observations without requiring any ancillary knowledge of geographical location or time is demonstrated.
Abstract
The joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) is a constellation mission, centered upon observations from the core satellite dual-frequency precipitation radar (DPR) and its companion passive microwave (MW) GPM Microwave Imager (GMI). One of the key challenges for GPM is how to link the information from the single DPR across all passive MW sensors in the constellation, to produce a globally consistent precipitation product. Commonly, the associated surface emissivity and environmental conditions at the satellite observation time are interpolated from ancillary data, such as global forecast models and emissivity climatology, and are used for radiative transfer simulations and cataloging/indexing the brightness temperature (TB) observations and simulations within a common MW precipitation retrieval framework.
In this manuscript, the feasibility of an update to the surface emissivity state at or near the satellite observation time, regardless of surface type, is examined for purposes of assisting these algorithms with specification of the surface and environmental conditions. Since the constellation MW radiometers routinely observe many more nonprecipitating conditions than precipitating conditions, a principal component analysis is developed from the noncloud GMI–DPR observations as a means to characterize the emissivity state vector and to consistently track the surface and environmental conditions. The method is demonstrated and applied over known complex surface conditions to probabilistically separate cloud and cloud-free scenes. The ability of the method to globally identify “self-similar” surface locations from the TB observations without requiring any ancillary knowledge of geographical location or time is demonstrated.
Abstract
Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.
Abstract
Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.
Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
This study presents research in support of the design and implementation of a combined radar–radiometer algorithm to be used for precipitation retrieval during the Tropical Rainfall Measuring Mission (TRMM). The combined algorithm approach is expected to overcome various difficulties that arise with a radar-only approach, particularly related to estimates of path-integrated attenuation (PIA) along the TRMM radar beam. A technique is described for estimating PIA at the 13.8-GHz frequency of the TRMM precipitation radar (PR) from 10.7-GHz brightness temperature T B measurements obtained from the TRMM microwave imager. Because the PR measures at an attenuating frequency, an independent estimate of PIA is used to constrain the solution to the radar equation, which incorporates effects of attenuation propagation along a radar beam. Through the use of variational or probabilistic techniques, the independent PIA calculations provide a means to adjust for errors that accumulate in estimates of range-dependent rain rates at progressively increasing range positions from radar reflectivity vectors. The accepted radar approach for obtaining PIA from ocean-viewing radar reflectivity measurements is called the surface reference technique, a scheme based on the difference in ocean surface cross sections between cloud-free and raining radar pixels. This technique has encountered problems, which are discussed and analyzed with the aid of coordinated aircraft radar (Airborne Rain Mapping Radar) and radiometer (Advanced Microwave Precipitation Radiometer) measurements obtained during the west Pacific Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment in 1993. The derived relationship expressing 13.8-GHz PIAs as a function of 10.7-GHz T B ’s is based on statistical fitting of many thousands of radiative transfer (RTE) calculations in which the relevant physical and radiative parameters affecting transmission, absorption, and scattering in a raining column and the associated emission-scattering properties of the wind-roughened ocean surface are systematically varied over realistic range intervals. The results demonstrate that the T B –PIA relationship is stable, with a dynamic range up to about 8 dB. The RTE calculations are used to examine the relative merits of different viewing configurations of the radar and radiometer, and the associated uncertainty variance as the viewing configuration changes, since PIA uncertainty is an important control factor in the prototype TRMM combined algorithm.
Abstract
This study presents research in support of the design and implementation of a combined radar–radiometer algorithm to be used for precipitation retrieval during the Tropical Rainfall Measuring Mission (TRMM). The combined algorithm approach is expected to overcome various difficulties that arise with a radar-only approach, particularly related to estimates of path-integrated attenuation (PIA) along the TRMM radar beam. A technique is described for estimating PIA at the 13.8-GHz frequency of the TRMM precipitation radar (PR) from 10.7-GHz brightness temperature T B measurements obtained from the TRMM microwave imager. Because the PR measures at an attenuating frequency, an independent estimate of PIA is used to constrain the solution to the radar equation, which incorporates effects of attenuation propagation along a radar beam. Through the use of variational or probabilistic techniques, the independent PIA calculations provide a means to adjust for errors that accumulate in estimates of range-dependent rain rates at progressively increasing range positions from radar reflectivity vectors. The accepted radar approach for obtaining PIA from ocean-viewing radar reflectivity measurements is called the surface reference technique, a scheme based on the difference in ocean surface cross sections between cloud-free and raining radar pixels. This technique has encountered problems, which are discussed and analyzed with the aid of coordinated aircraft radar (Airborne Rain Mapping Radar) and radiometer (Advanced Microwave Precipitation Radiometer) measurements obtained during the west Pacific Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment in 1993. The derived relationship expressing 13.8-GHz PIAs as a function of 10.7-GHz T B ’s is based on statistical fitting of many thousands of radiative transfer (RTE) calculations in which the relevant physical and radiative parameters affecting transmission, absorption, and scattering in a raining column and the associated emission-scattering properties of the wind-roughened ocean surface are systematically varied over realistic range intervals. The results demonstrate that the T B –PIA relationship is stable, with a dynamic range up to about 8 dB. The RTE calculations are used to examine the relative merits of different viewing configurations of the radar and radiometer, and the associated uncertainty variance as the viewing configuration changes, since PIA uncertainty is an important control factor in the prototype TRMM combined algorithm.
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.
