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Abstract
The objective of this paper is to establish a computationally efficient algorithm making use of the combination of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) observations. To set up the TMI algorithm, the retrieval databases developed in Part I served as input for different inversion techniques: multistage regressions and neural networks as well as Bayesian estimators. It was found that both Bayesian and neural network techniques performed equally well against PR estimates if all TMI channels were used. However, not using the 85.5-GHz channels produced consistently better results. This confirms the conclusions from Part I. Generally, regressions performed worse; thus they seem less suited for general application due to the insufficient representation of the nonlinearities of the TB–rain rate relation. It is concluded that the databases represent the most sensitive part of rainfall algorithm development.
Sensor combination was carried out by gridding PR estimates of rain liquid water content to 27 km × 44 km horizontal resolution at the center of gravity of the TMI 10.65-GHz channel weighting function. A liquid water dependent database collects common samples over the narrow swath covered by both TMI and PR. Average calibration functions are calculated, dynamically updated along the satellite track, and applied to the full TMI swath. The behavior of the calibration function was relatively stable. The TMI estimates showed a slight underestimation of rainfall at low rain liquid water contents (<0.1 g m−3) as well as at very high rainfall intensities (>0.8 g m−3) and excellent agreement in between. The biases were found to not depend on beam filling with a strong correlation to rain liquid water for stratiform clouds that may point to melting layer effects.
The remaining standard deviations between instantaneous TMI and PR estimates after calibration may be treated as a total retrieval error, assuming the PR estimates are unbiased. The error characteristics showed a rather constant absolute error of <0.05 g m−3 for rain liquid water contents <0.1 g m−3. Above, the error increases to 0.6 g m−3 for amounts up to 1 g m−3. In terms of relative errors, this corresponds to a sharp decrease from >100% to 35% between 0.05 and 0.5 g m−3. The database ambiguity, that is, the standard deviation of near-surface rain liquid water contents with the same radiometric signature, provides a means to estimate the contribution from the simulations to this error. In the range where brightness temperatures respond most sensitively to rainwater contents, almost the entire error originates from the ambiguity of signatures. At very low and very high rain rates (<0.05 and >0.7 g m−3) at least half of the total error is explained by the inversion process.
Abstract
The objective of this paper is to establish a computationally efficient algorithm making use of the combination of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) observations. To set up the TMI algorithm, the retrieval databases developed in Part I served as input for different inversion techniques: multistage regressions and neural networks as well as Bayesian estimators. It was found that both Bayesian and neural network techniques performed equally well against PR estimates if all TMI channels were used. However, not using the 85.5-GHz channels produced consistently better results. This confirms the conclusions from Part I. Generally, regressions performed worse; thus they seem less suited for general application due to the insufficient representation of the nonlinearities of the TB–rain rate relation. It is concluded that the databases represent the most sensitive part of rainfall algorithm development.
Sensor combination was carried out by gridding PR estimates of rain liquid water content to 27 km × 44 km horizontal resolution at the center of gravity of the TMI 10.65-GHz channel weighting function. A liquid water dependent database collects common samples over the narrow swath covered by both TMI and PR. Average calibration functions are calculated, dynamically updated along the satellite track, and applied to the full TMI swath. The behavior of the calibration function was relatively stable. The TMI estimates showed a slight underestimation of rainfall at low rain liquid water contents (<0.1 g m−3) as well as at very high rainfall intensities (>0.8 g m−3) and excellent agreement in between. The biases were found to not depend on beam filling with a strong correlation to rain liquid water for stratiform clouds that may point to melting layer effects.
The remaining standard deviations between instantaneous TMI and PR estimates after calibration may be treated as a total retrieval error, assuming the PR estimates are unbiased. The error characteristics showed a rather constant absolute error of <0.05 g m−3 for rain liquid water contents <0.1 g m−3. Above, the error increases to 0.6 g m−3 for amounts up to 1 g m−3. In terms of relative errors, this corresponds to a sharp decrease from >100% to 35% between 0.05 and 0.5 g m−3. The database ambiguity, that is, the standard deviation of near-surface rain liquid water contents with the same radiometric signature, provides a means to estimate the contribution from the simulations to this error. In the range where brightness temperatures respond most sensitively to rainwater contents, almost the entire error originates from the ambiguity of signatures. At very low and very high rain rates (<0.05 and >0.7 g m−3) at least half of the total error is explained by the inversion process.
Abstract
Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.
Abstract
Ground radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.
Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of PrecipitationAbstract
The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.
Abstract
The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.
Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical CyclonesAbstract
The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
Abstract
The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile information based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) observations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates especially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
Abstract
This paper presents a new scheme that classifies convective and stratiform (C/S) precipitation areas over oceans using microwave brightness temperature. In this scheme, data are first screened to eliminate nonraining pixels. For raining pixels, C/S indices are computed from brightness temperatures and their variability for emission (19 and 37 GHz) and scattering (85 GHz). Since lower-resolution satellite data generally contain mixtures of convective and stratiform precipitation, a probability matching method is employed to relate the C/S index to a convective fraction of precipitation area.
The scheme has been applied on synthetic data generated from a dynamical cloud model and radiative transfer computations to simulate the frequencies and resolutions of the Tropical Rainfall Measuring Mission (TRMM) Microwave (TMI) Imager as well as the Special Sensor Microwave/Imager (SSM/I). The results from simulated TMI data during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment agree very well with the ground-based radar classification maps. The classification accuracy degrades when SSM/I data is used, due largely to the lower spatial resolution of the SSM/I.
The successful launch of TRMM satellite in November 1997 has made it possible to test this scheme on actual TMI data. Preliminary results of TMI derived C/S classification compared with that from the first spaceborne precipitation radar has shown a very good agreement. Further verification and improvement of this scheme are under way.
Abstract
This paper presents a new scheme that classifies convective and stratiform (C/S) precipitation areas over oceans using microwave brightness temperature. In this scheme, data are first screened to eliminate nonraining pixels. For raining pixels, C/S indices are computed from brightness temperatures and their variability for emission (19 and 37 GHz) and scattering (85 GHz). Since lower-resolution satellite data generally contain mixtures of convective and stratiform precipitation, a probability matching method is employed to relate the C/S index to a convective fraction of precipitation area.
The scheme has been applied on synthetic data generated from a dynamical cloud model and radiative transfer computations to simulate the frequencies and resolutions of the Tropical Rainfall Measuring Mission (TRMM) Microwave (TMI) Imager as well as the Special Sensor Microwave/Imager (SSM/I). The results from simulated TMI data during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment agree very well with the ground-based radar classification maps. The classification accuracy degrades when SSM/I data is used, due largely to the lower spatial resolution of the SSM/I.
The successful launch of TRMM satellite in November 1997 has made it possible to test this scheme on actual TMI data. Preliminary results of TMI derived C/S classification compared with that from the first spaceborne precipitation radar has shown a very good agreement. Further verification and improvement of this scheme are under way.
Displays of multi-frequency passive microwave data from the Special Sensor Microwave/lmager (SSM/I) flying on the Defense Meteorological Satellite Program (DMSP) spacecraft are presented. Observed brightness temperatures at 85.5 GHz (vertical and horizontal polarizations) and 37 GHz (vertical polarization) are respectively used to “drive” the red, green, and blue “guns” of a color monitor. The resultant false-color images can be used to distinguish land from water, highlight precipitation processes and structure over both land and water, and detail variations in other surfaces such as deserts, snow cover, and sea ice. The observations at 85.5 Ghz also add a previously unavailable frequency to the problem of rainfall estimation from space. Examples of mesoscale squall lines, tropical and extra-tropical storms, and larger-scale land and atmospheric features as “viewed” by the SSM/I are shown.
Displays of multi-frequency passive microwave data from the Special Sensor Microwave/lmager (SSM/I) flying on the Defense Meteorological Satellite Program (DMSP) spacecraft are presented. Observed brightness temperatures at 85.5 GHz (vertical and horizontal polarizations) and 37 GHz (vertical polarization) are respectively used to “drive” the red, green, and blue “guns” of a color monitor. The resultant false-color images can be used to distinguish land from water, highlight precipitation processes and structure over both land and water, and detail variations in other surfaces such as deserts, snow cover, and sea ice. The observations at 85.5 Ghz also add a previously unavailable frequency to the problem of rainfall estimation from space. Examples of mesoscale squall lines, tropical and extra-tropical storms, and larger-scale land and atmospheric features as “viewed” by the SSM/I are shown.
Abstract
Precipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (N d ), and cloud-base rain rate (R cb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. The cloud-base rain rate R cb has a positive correlation with LWP/N d , and the increase of R cb becomes larger as LWP/N d increases. However, the increase of R cb with respect to LWP/N d becomes more gradual in regions with larger N d , which indicates the relation is moderated by N d . These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and N d . The sensitivity of the autoconversion rate to N d is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on R cb. The upper limit of the dependency of autoconversion rate on N d is assessed from the relation between R cb and N d , since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.
Abstract
Precipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (N d ), and cloud-base rain rate (R cb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. The cloud-base rain rate R cb has a positive correlation with LWP/N d , and the increase of R cb becomes larger as LWP/N d increases. However, the increase of R cb with respect to LWP/N d becomes more gradual in regions with larger N d , which indicates the relation is moderated by N d . These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and N d . The sensitivity of the autoconversion rate to N d is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on R cb. The upper limit of the dependency of autoconversion rate on N d is assessed from the relation between R cb and N d , since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.
Abstract
This paper explores changes in the principal components of observed energy budgets across the tropical Pacific in response to the strong 1998 El Niño event. Multisensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), and precipitation radar (PR) instruments aboard TRMM are used to quantify changes in radiative and latent heating in the east and west Pacific in response to the different phases of the El Niño–Southern Oscillation. In periods of normal east–west SST gradients there is substantial heating in the west Pacific and cooling in the east, implying strong eastward atmospheric energy transport. During the active phase of the El Niño, both the east and west Pacific tend toward local radiative–convective equilibrium resulting in their temporary energetic decoupling. It is further demonstrated that the response of these regions to ENSO-induced SST variability is directly related to changes in the characteristics of clouds and precipitation in each region. Through quantitative analysis of the radiative and latent heating properties of shallow, midlevel, and deep precipitation events and an equivalent set of nonprecipitating cloud systems, times of reduced atmospheric heating are found to be associated with a shift toward shallow and midlevel precipitation systems and associated low-level cloudiness. The precipitation from such systems is typically less intense, and they do not trap outgoing longwave radiation as efficiently as their deeper counterparts, resulting in reduced radiative and latent heating of the atmosphere. The results also suggest that the net effect of precipitating systems on top-of-the-atmosphere (TOA) fluxes and the efficiency with which they heat the atmosphere and cool the surface exhibit strong dependence on their surroundings. The sensitivity of cloud radiative impacts to the atmospheric and surface properties they act to modify implies the existence of strong feedbacks whose representation may pose a significant challenge to the climate modeling community.
Abstract
This paper explores changes in the principal components of observed energy budgets across the tropical Pacific in response to the strong 1998 El Niño event. Multisensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Visible and Infrared Scanner (VIRS), and precipitation radar (PR) instruments aboard TRMM are used to quantify changes in radiative and latent heating in the east and west Pacific in response to the different phases of the El Niño–Southern Oscillation. In periods of normal east–west SST gradients there is substantial heating in the west Pacific and cooling in the east, implying strong eastward atmospheric energy transport. During the active phase of the El Niño, both the east and west Pacific tend toward local radiative–convective equilibrium resulting in their temporary energetic decoupling. It is further demonstrated that the response of these regions to ENSO-induced SST variability is directly related to changes in the characteristics of clouds and precipitation in each region. Through quantitative analysis of the radiative and latent heating properties of shallow, midlevel, and deep precipitation events and an equivalent set of nonprecipitating cloud systems, times of reduced atmospheric heating are found to be associated with a shift toward shallow and midlevel precipitation systems and associated low-level cloudiness. The precipitation from such systems is typically less intense, and they do not trap outgoing longwave radiation as efficiently as their deeper counterparts, resulting in reduced radiative and latent heating of the atmosphere. The results also suggest that the net effect of precipitating systems on top-of-the-atmosphere (TOA) fluxes and the efficiency with which they heat the atmosphere and cool the surface exhibit strong dependence on their surroundings. The sensitivity of cloud radiative impacts to the atmospheric and surface properties they act to modify implies the existence of strong feedbacks whose representation may pose a significant challenge to the climate modeling community.
Abstract
Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.
Abstract
Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.
Abstract
Regional and temporal variability in the vertical and horizontal characteristics of tropical precipitating clouds are investigated using the Precipitation Radar (PR) and the Visible and Infrared Scanner (VIRS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The present study focuses on the three oceanic regions (west, central, and east Pacific) together with two continental regions for comparison and the two separate time periods (February 1998 and February 2000) under different phases of the El Niño–Southern Oscillation (ENSO) in order to examine regional and ENSO-related variations. The height spectrums of storms are investigated in terms of radar echo-top height and infrared brightness temperature. The variability in the spectrum clearly correlates with the large-scale circulation and its ENSO-related change. On the basis of the height spectrum, storm systems are classified into the four categories of shallow, cumulus congestus, deep stratiform, and deep convective. The deep stratiform and deep convective categories, both of which have very cold cloud tops, are differentiated by radar echo-top heights so that deep convective systems are accompanied with an appreciable amount of large frozen particles aloft. While shallow events are dominant in the probability of occurrence over relatively cold oceans, deep convective systems take their place for warmer sea surface temperatures (SSTs). The turnover occurs at the SST threshold of 28°–29°C for all the oceanic regions and years investigated except the west Pacific in 2000, for which deep convective systems prevail over the entire range of SST. Rain correlation-scale length (RCSL) and cloud correlation-scale length (CCSL) are introduced as statistical indicators of the horizontal scale of storms. While the RCSL is 8–18 km for shallow- and cumulus congestus–type clouds without significant regional and temporal variations, the RCSL and CCSL associated with deep stratiform and deep convective systems consistently exceed 100 km and exhibit a systematic variability. The RCSL and CCSL in the central and east Pacific, particularly, increase significantly in the El Niño year.
Abstract
Regional and temporal variability in the vertical and horizontal characteristics of tropical precipitating clouds are investigated using the Precipitation Radar (PR) and the Visible and Infrared Scanner (VIRS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The present study focuses on the three oceanic regions (west, central, and east Pacific) together with two continental regions for comparison and the two separate time periods (February 1998 and February 2000) under different phases of the El Niño–Southern Oscillation (ENSO) in order to examine regional and ENSO-related variations. The height spectrums of storms are investigated in terms of radar echo-top height and infrared brightness temperature. The variability in the spectrum clearly correlates with the large-scale circulation and its ENSO-related change. On the basis of the height spectrum, storm systems are classified into the four categories of shallow, cumulus congestus, deep stratiform, and deep convective. The deep stratiform and deep convective categories, both of which have very cold cloud tops, are differentiated by radar echo-top heights so that deep convective systems are accompanied with an appreciable amount of large frozen particles aloft. While shallow events are dominant in the probability of occurrence over relatively cold oceans, deep convective systems take their place for warmer sea surface temperatures (SSTs). The turnover occurs at the SST threshold of 28°–29°C for all the oceanic regions and years investigated except the west Pacific in 2000, for which deep convective systems prevail over the entire range of SST. Rain correlation-scale length (RCSL) and cloud correlation-scale length (CCSL) are introduced as statistical indicators of the horizontal scale of storms. While the RCSL is 8–18 km for shallow- and cumulus congestus–type clouds without significant regional and temporal variations, the RCSL and CCSL associated with deep stratiform and deep convective systems consistently exceed 100 km and exhibit a systematic variability. The RCSL and CCSL in the central and east Pacific, particularly, increase significantly in the El Niño year.
