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- Author or Editor: Gail Skofronick-Jackson x
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Abstract
Profiles of the microphysical properties of clouds and rain cells are essential in many areas of atmospheric research and operational meteorology. To enhance the understanding of the nonlinear and underconstrained relationships between cloud and hydrometeor microphysical profiles and passive microwave brightness temperatures, estimations of cloud profiles for an anvil region, a convective region, and an updraft region of an oceanic squall were performed. The estimations relied on comparisons between radiative transfer calculations of incrementally estimated microphysical profiles and concurrent dual-altitude wideband brightness temperatures from the 22 February 1993 flight during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The wideband observations (10–220 GHz) are necessary for estimating cloud profiles reaching up to 20 km. The low frequencies enhance the rain and cloud water profiles, and the high frequencies are required to detail the higher-altitude ice microphysics. A microphysical profile was estimated for each of the three regions of the storm. Each of the three estimated profiles produced calculated brightness temperatures within ∼10 K of the observations. A majority of the total iterative adjustments were to the estimated profile’s frozen hydrometeor characteristics and were necessary to match the high-frequency calculations with the observations. This requirement indicates a need to validate cloud-resolving models using high frequencies. Some difficulties matching the 37-GHz observation channels on the DC-8 and ER-2 aircraft with the calculations simulated at the two aircraft heights (∼11 km and 20 km, respectively) were noted, and potential causes were presented.
Abstract
Profiles of the microphysical properties of clouds and rain cells are essential in many areas of atmospheric research and operational meteorology. To enhance the understanding of the nonlinear and underconstrained relationships between cloud and hydrometeor microphysical profiles and passive microwave brightness temperatures, estimations of cloud profiles for an anvil region, a convective region, and an updraft region of an oceanic squall were performed. The estimations relied on comparisons between radiative transfer calculations of incrementally estimated microphysical profiles and concurrent dual-altitude wideband brightness temperatures from the 22 February 1993 flight during the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The wideband observations (10–220 GHz) are necessary for estimating cloud profiles reaching up to 20 km. The low frequencies enhance the rain and cloud water profiles, and the high frequencies are required to detail the higher-altitude ice microphysics. A microphysical profile was estimated for each of the three regions of the storm. Each of the three estimated profiles produced calculated brightness temperatures within ∼10 K of the observations. A majority of the total iterative adjustments were to the estimated profile’s frozen hydrometeor characteristics and were necessary to match the high-frequency calculations with the observations. This requirement indicates a need to validate cloud-resolving models using high frequencies. Some difficulties matching the 37-GHz observation channels on the DC-8 and ER-2 aircraft with the calculations simulated at the two aircraft heights (∼11 km and 20 km, respectively) were noted, and potential causes were presented.
Abstract
A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter μ of a gamma size distribution and the melted-equivalent mass median diameter D 0, the particle density, the dielectric mixing formula, and the choice of complex index of refraction for ice. These properties are examined for selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz—consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles are found to be extremely sensitive to the D 0 of the size distribution. Similar large sensitivities are found for variations in the ice volume fraction whenever the geometric mass median diameter exceeds approximately ⅛th of the wavelength. At 94 GHz the two-way path-integrated attenuation is potentially large for dense/compact particles. The distribution parameter μ has a comparatively weak effect on any observable: less than 1–2 K in brightness temperature and a maximum of 2.7 dB (S band only) in the effective radar reflectivity. Reversal of the roles of ice and air in the Maxwell Garnett dielectric mixing formula leads to a substantial change in both microwave brightness temperature (~10 K) and radar reflectivity (approximately 2 dB across all frequencies). The choice of the complex index of refraction of ice can produce a 3%–4% change in the brightness temperature depression.
Abstract
A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter μ of a gamma size distribution and the melted-equivalent mass median diameter D 0, the particle density, the dielectric mixing formula, and the choice of complex index of refraction for ice. These properties are examined for selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz—consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles are found to be extremely sensitive to the D 0 of the size distribution. Similar large sensitivities are found for variations in the ice volume fraction whenever the geometric mass median diameter exceeds approximately ⅛th of the wavelength. At 94 GHz the two-way path-integrated attenuation is potentially large for dense/compact particles. The distribution parameter μ has a comparatively weak effect on any observable: less than 1–2 K in brightness temperature and a maximum of 2.7 dB (S band only) in the effective radar reflectivity. Reversal of the roles of ice and air in the Maxwell Garnett dielectric mixing formula leads to a substantial change in both microwave brightness temperature (~10 K) and radar reflectivity (approximately 2 dB across all frequencies). The choice of the complex index of refraction of ice can produce a 3%–4% change in the brightness temperature depression.
Abstract
Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.
Abstract
Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.
POTENTIAL ROLE OF DUAL-POLARIZATION RADAR IN THE VALIDATION OF SATELLITE PRECIPITATION MEASUREMENTS
Rationale and Opportunities
Dual-polarization weather radars have evolved significantly in the last three decades culminating in operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground-based remote sensing of rainfall microphysics, the study of precipitation evolution, and hydrometeor classification. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite-borne precipitation measurements and also serve as valuable tools in algorithm development. This paper presents the important role played by dual-polarization radar in validating spaceborne precipitation measurements. Examples of raindrop size distribution retrievals and hydrometeor-type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to spaceborne observations. During the Tropical Rainfall Measuring Mission (TRMM) program substantial advancement was made with ground-based polarization radars collecting unique observations in the tropics, which are noted. The scientific accomplishments of relevance to spaceborne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of the global precipitation measurement mission is also discussed.
Dual-polarization weather radars have evolved significantly in the last three decades culminating in operational deployment by the National Weather Service. In addition to operational applications in the weather service, dual-polarization radars have shown significant potential in contributing to the research fields of ground-based remote sensing of rainfall microphysics, the study of precipitation evolution, and hydrometeor classification. Microphysical characterization of precipitation and quantitative precipitation estimation are important applications that are critical in the validation of satellite-borne precipitation measurements and also serve as valuable tools in algorithm development. This paper presents the important role played by dual-polarization radar in validating spaceborne precipitation measurements. Examples of raindrop size distribution retrievals and hydrometeor-type classification are discussed. The quantitative precipitation estimation is a product of direct relevance to spaceborne observations. During the Tropical Rainfall Measuring Mission (TRMM) program substantial advancement was made with ground-based polarization radars collecting unique observations in the tropics, which are noted. The scientific accomplishments of relevance to spaceborne measurements of precipitation are summarized. The potential of dual-polarization radars and opportunities in the era of the global precipitation measurement mission is also discussed.
Abstract
Information about the vertical microphysical cloud structure is useful in many modeling and predictive practices. Radiometers and radars are used to observe hydrometeor properties. This paper describes an iterative retrieval algorithm that combines the use of airborne active and wideband (10–340 GHz) passive observations to estimate the vertical content and particle size distributions of liquid and frozen hydrometeors. Airborne radar and radiometer observations from the third Convection and Moisture Experiment (CAMEX-3) were used in the retrieval algorithm as constraints. Nadir profiles were estimated for 1 min each of flight time (approximately 12.5 km along track) for anvil, convective, and quasi-stratiform clouds associated with Hurricane Bonnie (August 1998). The physically based retrieval algorithm relies on high frequencies (≥150 GHz) to provide details on the frozen hydrometeors. Neglecting the high frequencies yielded acceptable estimates of the liquid profiles, but the ice profiles were poorly retrieved. The wideband observations were found to more than double the estimated frozen hydrometeor content as compared with retrievals using only 90 GHz and below. The convective and quasi-stratiform iterative retrievals quickly reached convergence. The complex structure of the frozen hydrometeors required the most iterations for convergence for the anvil cloud type. Nonunique profiles, within physical and theoretical bounds, were retrieved for thin anvil ice clouds. A qualitative validation using coincident in situ CAMEX-3 observations shows that the retrieved particle size distributions are well corroborated with independent measurements.
Abstract
Information about the vertical microphysical cloud structure is useful in many modeling and predictive practices. Radiometers and radars are used to observe hydrometeor properties. This paper describes an iterative retrieval algorithm that combines the use of airborne active and wideband (10–340 GHz) passive observations to estimate the vertical content and particle size distributions of liquid and frozen hydrometeors. Airborne radar and radiometer observations from the third Convection and Moisture Experiment (CAMEX-3) were used in the retrieval algorithm as constraints. Nadir profiles were estimated for 1 min each of flight time (approximately 12.5 km along track) for anvil, convective, and quasi-stratiform clouds associated with Hurricane Bonnie (August 1998). The physically based retrieval algorithm relies on high frequencies (≥150 GHz) to provide details on the frozen hydrometeors. Neglecting the high frequencies yielded acceptable estimates of the liquid profiles, but the ice profiles were poorly retrieved. The wideband observations were found to more than double the estimated frozen hydrometeor content as compared with retrievals using only 90 GHz and below. The convective and quasi-stratiform iterative retrievals quickly reached convergence. The complex structure of the frozen hydrometeors required the most iterations for convergence for the anvil cloud type. Nonunique profiles, within physical and theoretical bounds, were retrieved for thin anvil ice clouds. A qualitative validation using coincident in situ CAMEX-3 observations shows that the retrieved particle size distributions are well corroborated with independent measurements.
Abstract
The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earth’s surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near–real time to about 100,000 for all “official” gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)–available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earth’s surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.
Abstract
The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the de facto standard for precipitation information across Earth’s surface for hydrometeorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth owing to temporal sampling resolutions, periods of operation, data latency, and data access. Numbers of gauges range from a few thousand available in near–real time to about 100,000 for all “official” gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 and 3,000 m2. For comparison, the center circle of a soccer pitch or tennis court is about 260 m2. Although each gauge should represent more than just the gauge orifice, autocorrelation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC)–available gauge is independent and represents a surrounding area of 5-km radius, this represents only about 1% of Earth’s surface. The situation is further confounded for snowfall, which has a greater measurement uncertainty.
Abstract
Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.
Abstract
Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166 GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.
Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval EvaluationAbstract
This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.
Abstract
This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.
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