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13
2000: A census of precipitation features in the Tropics using TRMM: Radar, ice scattering, and lightning observations
- J. Climate
"... An algorithm has been developed to identify precipitation features ($75 km2 in size) in two land and two ..."
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Cited by 65 (19 self)
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An algorithm has been developed to identify precipitation features ($75 km2 in size) in two land and two
Three years of TRMM precipitation features. Part I: Radar, radiometric, and lightning characteristics,
- Weather Rev.,
, 2005
"... ABSTRACT During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity, areal extent, a ..."
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Cited by 29 (10 self)
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ABSTRACT During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity, areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%, and remaining 97.6%. The set of "weakest/smallest" features composes 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate of 0.7 flashes (fl) min Ϫ1 . The greatest observed flash rate is 1351 fl min Ϫ1 ; the lowest brightness temperatures are 42 K (85 GHz) and 69 K (37 GHz). The largest precipitation feature covers 335 000 km 2 , and the greatest rainfall from an individual precipitation feature exceeds 2 ϫ 10 12 kg h Ϫ1 of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global Tropics and subtropics. Significant variability is known to exist between locations, seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land-ocean separation is made. The known differences in bulk lightning flash rates over land and ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.
2002: Reflectivity, ice scattering, and lightning characteristics of hurricane eyewalls and rainbands. Part I: Quantitative description
- Mon. Wea. Rev
"... Part I of this two-part paper treats Tropical Rainfall Measuring Mission (TRMM) radar, passive microwave, and lightning observations in hurricanes individually. This paper (Part II) examines relationships between these parameters (and implications of the relationships). Quantitative relationships be ..."
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Cited by 22 (4 self)
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Part I of this two-part paper treats Tropical Rainfall Measuring Mission (TRMM) radar, passive microwave, and lightning observations in hurricanes individually. This paper (Part II) examines relationships between these parameters (and implications of the relationships). Quantitative relationships between lightning occurrence and 85-GHz brightness temperature, 37-GHz brightness temperature, and radar reflectivity in the mixed phase region are established separately for hurricane eyewall regions, inner rainband regions, and outer rainband regions; other tropical oceanic regions; and tropical continental regions. When any of the brightness temperature or radar parameters are held constant as controls, lightning is more frequent in hurricane outer rainbands than elsewhere over tropical oceans, and more frequent over continents than even in the outer rainbands. Reflectivity profiles associated with specific brightness temperatures are presented, demonstrating a link between high-altitude ice phase precipitation and 85-GHz scattering and a link between lower-altitude precipitation and 37-GHz scattering. Based on the combination of radar, passive microwave, and lightning observations, it is proposed that supercooled cloud water occurs preferentially in outer rainbands compared to other tropical oceanic precipitation. The suspected microphysical differences produce only subtle differences in the remote sensing parameters other than lightning. 1.
2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the Tropics
"... The bulk radar reflectivity structures, 85- and 37-GHz brightness temperatures, and lightning characteristics of precipitating systems in tropical Africa, South America, the east Pacific, and west Pacific are documented using data from the Tropical Rainfall Measuring Mission (TRMM) satellite during ..."
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Cited by 20 (8 self)
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The bulk radar reflectivity structures, 85- and 37-GHz brightness temperatures, and lightning characteristics of precipitating systems in tropical Africa, South America, the east Pacific, and west Pacific are documented using data from the Tropical Rainfall Measuring Mission (TRMM) satellite during August, September, and October of 1998. The particular focus is on precipitation features [defined as a contiguous area $75 km2 with either a near-surface reflectivity $20 dBZ or an 85-GHz polarization-corrected temperature (PCT) # 250 K] with appreciable rainfall, which account for the bulk of the total rainfall and lightning flash density in their respective regions. Systems over the tropical continents typically have greater magnitudes of reflectivity extending to higher altitudes than tropical oceanic systems. This is consistent with the observation of stronger ice scattering signatures (lower 85- and 37-GHz PCT) in the systems over land. However, when normalized by reflectivity heights, tropical continental features consistently have higher 85-GHz PCT than tropical oceanic features. It is inferred that greater supercooled water contents aloft in the tropical continental systems contribute to this brightness temperature difference. Lightning (as detected by the Lightning Imaging Sensor) is much more likely in tropical continental features than tropical oceanic features with similar brightness temperatures or similar reflectivity heights. Vertical profiles
a: Physical and Microwave Radiative Properties of Precipitating Clouds. Part II: A Parametric ID Rain-Cloud Model for Use in Microwave Radiative Transfer Simulations
- J. Appl. Met
, 2001
"... ABSTRACT Using stringent criteria pertaining to rain-cloud optical thickness and horizontal extent, 3203 multichannel microwave observations of heavy, widespread tropical precipitation over ocean were selected from 9 months of global Special Sensor Microwave Imager (SSM/I) data. These observations ..."
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Cited by 8 (0 self)
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ABSTRACT Using stringent criteria pertaining to rain-cloud optical thickness and horizontal extent, 3203 multichannel microwave observations of heavy, widespread tropical precipitation over ocean were selected from 9 months of global Special Sensor Microwave Imager (SSM/I) data. These observations subsequently were found to be associated almost exclusively with stratiform rain areas in tropical cyclones. Because of the restrictions on optical thickness and spatial extent, the mean multichannel microwave brightness temperatures and their interchannel covariances are presumed to be determined primarily by the vertical microphysical structure of the rain clouds. The distribution of the above observations in seven-dimensional channel space is characterized concisely using principal component analysis. It is found that only three independent variables are sufficient to explain 97% of the variance in the correlation matrix. This result suggests that the radiometrically important microphysical properties of these rain clouds are strongly interdependent. The most significant eigenvector of the observation correlation matrix corresponds to variable scattering at high frequencies by ice aloft. Its spectral dependence is accurately given by 1.76 , where is the microwave frequency. This empirical result constrains the effective mean sizes of ice particles responsible for observed passive microwave scattering in rain clouds and provides a plausible empirical basis for accurately predicting the magnitude of scattering effects by ice at non-SSM/I microwave frequencies. There are also qualitative indications that this mode of brightness temperature variability is poorly correlated with surface rain rate in this study sample. The empirical results presented herein are expected to be of value for the validation and improvement of microphysical assumptions and optical parameterizations in forward microwave radiative transfer models. Companion papers describe the actual retrieval of effective rain-cloud microphysical properties from the observed multichannel radiances.
Using Opaque Microwave Bands
, 2004
"... This thesis describes the use of opaque microwave bands for global estimation of precipitation rate. An algorithm was developed for estimating instantaneous precip-itation rate for the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15, NOAA-16, and NOAA-17 satellites, and the Advanced Microwave ..."
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This thesis describes the use of opaque microwave bands for global estimation of precipitation rate. An algorithm was developed for estimating instantaneous precip-itation rate for the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15, NOAA-16, and NOAA-17 satellites, and the Advanced Microwave Sounding Unit and Humidity Sounder for Brazil (AMSU/HSB) aboard the NASA Aqua satellite. The algorithm relies primarily on channels in the opaque 54-GHz oxygen and 183-GHz water vapor resonance bands. Many methods for estimating precipitation rate using surface-sensitive microwave window channels have been developed by others. The algorithm involves a set of signal processing components whose outputs are fed into a neural net to produce a rain rate estimate for each 15-km spot. The signal processing components utilize techniques such as principal component analysis for characterizing groups of channels, spatial filtering for cloud-clearing brightness tem-
CORRECTING FOR PRECIPITATION EFFECTS IN SATELLITE-BASED PASSIVE MICROWAVE TROPICAL CYCLONE INTENSITY ESTIMATES
, 2005
"... Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments ..."
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Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this
CORRECTING FOR PRECIPITATION EFFECTS IN SATELLITE-BASED PASSIVE MICROWAVE TROPICAL CYCLONE INTENSITY ESTIMATES
, 2005
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406 JOURNAL OF HYDROMETEOROLOGY VOLUME 2 Multiscale Statistical Properties of a High-Resolution Precipitation Forecast
, 2000
"... Small-scale (less than �15 km) precipitation variability significantly affects the hydrologic response of a basin and the accurate estimation of water and energy fluxes through coupled land–atmosphere modeling schemes. It also affects the radiative transfer through precipitating clouds and thus rain ..."
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Small-scale (less than �15 km) precipitation variability significantly affects the hydrologic response of a basin and the accurate estimation of water and energy fluxes through coupled land–atmosphere modeling schemes. It also affects the radiative transfer through precipitating clouds and thus rainfall estimation from microwave sensors. Because both land–atmosphere and cloud–radiation interactions are nonlinear and occur over a broad range of scales (from a few centimeters to several kilometers), it is important that, over these scales, cloudresolving numerical models realistically reproduce the observed precipitation variability. This issue is examined herein by using a suite of multiscale statistical methods to compare the scale dependence of precipitation variability of a numerically simulated convective storm with that observed by radar. In particular, Fourier spectrum, structure function, and moment-scale analyses are used to show that, although the variability of modeled precipitation agrees with that observed for scales larger than approximately 5 times the model resolution, the model shows a falloff in variability at smaller scales. Thus, depending upon the smallest scale at which variability is considered to be important for a specific application, one has to resort either to very high resolution model runs (resolutions 5 times higher than the scale of interest) or to stochastic methods that can introduce the missing small-scale variability. The latter involve upscaling the model output to a scale approximately 5 times the model resolution and then stochastically downscaling it to smaller scales. The results of multiscale analyses, such as those presented herein, are key to the implementation of such stochastic downscaling methodologies. 1.
HARRIS AND FOUFOULA-GEORGIOU: CLOUD MODEL DOWNSCALING
"... Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval ..."
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Subgrid variability and stochastic downscaling of modeled clouds: Effects on radiative transfer computations for rainfall retrieval