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- Author or Editor: Shoichi Shige x
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Abstract
The spectral latent heating (SLH) algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Part I of this study. The method uses PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). To assess its global application to TRMM PR data, the universality of the lookup tables from the TOGA COARE simulations is examined in this paper. Heating profiles are reconstructed from CRM-simulated parameters (i.e., PTH, precipitation rates at the surface and melting level, and rain type) and are compared with the true CRM-simulated heating profiles, which are computed directly by the model thermodynamic equation. CRM-simulated data from the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), South China Sea Monsoon Experiment (SCSMEX), and Kwajalein Experiment (KWAJEX) are used as a consistency check. The consistency check reveals discrepancies between the SLH-reconstructed and Goddard Cumulus Ensemble (GCE)-simulated heating above the melting level in the convective region and at the melting level in the stratiform region that are attributable to the TOGA COARE table. Discrepancies in the convective region are due to differences in the vertical distribution of deep convective heating due to the relative importance of liquid and ice water processes, which varies from case to case. Discrepancies in the stratiform region are due to differences in the level separating upper-level heating and lower-level cooling. Based on these results, improvements were made to the SLH algorithm. Convective heating retrieval is now separated into upper-level heating due to ice processes and lower-level heating due to liquid water processes. In the stratiform region, the heating profile is shifted up or down by matching the melting level in the TOGA COARE lookup table with the observed one. Consistency checks indicate the revised SLH algorithm performs much better for both the convective and stratiform components than does the original one. The revised SLH algorithm was applied to PR data, and the results were compared with heating profiles derived diagnostically from SCSMEX sounding data. Key features of the vertical profiles agree well—in particular, the level of maximum heating. The revised SLH algorithm was also applied to PR data for February 1998 and February 1999. The results are compared with heating profiles derived by the convective–stratiform heating (CSH) algorithm. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.
Abstract
The spectral latent heating (SLH) algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Part I of this study. The method uses PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). To assess its global application to TRMM PR data, the universality of the lookup tables from the TOGA COARE simulations is examined in this paper. Heating profiles are reconstructed from CRM-simulated parameters (i.e., PTH, precipitation rates at the surface and melting level, and rain type) and are compared with the true CRM-simulated heating profiles, which are computed directly by the model thermodynamic equation. CRM-simulated data from the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE), South China Sea Monsoon Experiment (SCSMEX), and Kwajalein Experiment (KWAJEX) are used as a consistency check. The consistency check reveals discrepancies between the SLH-reconstructed and Goddard Cumulus Ensemble (GCE)-simulated heating above the melting level in the convective region and at the melting level in the stratiform region that are attributable to the TOGA COARE table. Discrepancies in the convective region are due to differences in the vertical distribution of deep convective heating due to the relative importance of liquid and ice water processes, which varies from case to case. Discrepancies in the stratiform region are due to differences in the level separating upper-level heating and lower-level cooling. Based on these results, improvements were made to the SLH algorithm. Convective heating retrieval is now separated into upper-level heating due to ice processes and lower-level heating due to liquid water processes. In the stratiform region, the heating profile is shifted up or down by matching the melting level in the TOGA COARE lookup table with the observed one. Consistency checks indicate the revised SLH algorithm performs much better for both the convective and stratiform components than does the original one. The revised SLH algorithm was applied to PR data, and the results were compared with heating profiles derived diagnostically from SCSMEX sounding data. Key features of the vertical profiles agree well—in particular, the level of maximum heating. The revised SLH algorithm was also applied to PR data for February 1998 and February 1999. The results are compared with heating profiles derived by the convective–stratiform heating (CSH) algorithm. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the SLH algorithm in comparison with the CSH algorithm.
Abstract
A K-means clustering algorithm was used to classify Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) scenes within 1° square patches over the tropical (15°S–15°N) oceans. Three cluster centroids or “regimes” that minimize the Euclidean distance metric in a five-dimensional space of standardized variables were sought [convective surface rainfall rate; ratio of convective rain to total rain; and fractions of convective echo profiles with tops in three fixed height ranges (<5, 5–9, and >9 km)]. Independent cluster computations in adjacent ocean basins return very similar clusters in terms of PR echo-top distributions, rainfall, and diabatic heating profiles. The clusters consist of shallow convection (SHAL cluster), with a unimodal distribution of PR echo tops and composite diabatic heating rates of ∼2 K day−1 below 3 km; midlevel convection (MID-LEV cluster), with a bimodal distribution of PR echo tops and ∼5 K day−1 heating up to about 7 km; and deeper convection (DEEP cluster), with a multimodal distribution of PR echo tops and >20 K day−1 heating from 5 to 10 km. Each contributes roughly 20%–40% in terms of total tropical rainfall, but with MID-LEV clusters especially enhanced in the Indian and Atlantic sectors, SHAL relatively enhanced in the central and east Pacific, and DEEP most prominent in the western Pacific. While the clusters themselves are quite similar in rainfall and heating, specific cloud types defined according to the PR echo top and surface rainfall rate are less similar and exhibit systematic differences from one cluster to another, implying that the degree to which precipitation structures are similar decreases when one considers individual precipitating clouds as repeating tropical structures instead of larger-scale cluster ensembles themselves.
Abstract
A K-means clustering algorithm was used to classify Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) scenes within 1° square patches over the tropical (15°S–15°N) oceans. Three cluster centroids or “regimes” that minimize the Euclidean distance metric in a five-dimensional space of standardized variables were sought [convective surface rainfall rate; ratio of convective rain to total rain; and fractions of convective echo profiles with tops in three fixed height ranges (<5, 5–9, and >9 km)]. Independent cluster computations in adjacent ocean basins return very similar clusters in terms of PR echo-top distributions, rainfall, and diabatic heating profiles. The clusters consist of shallow convection (SHAL cluster), with a unimodal distribution of PR echo tops and composite diabatic heating rates of ∼2 K day−1 below 3 km; midlevel convection (MID-LEV cluster), with a bimodal distribution of PR echo tops and ∼5 K day−1 heating up to about 7 km; and deeper convection (DEEP cluster), with a multimodal distribution of PR echo tops and >20 K day−1 heating from 5 to 10 km. Each contributes roughly 20%–40% in terms of total tropical rainfall, but with MID-LEV clusters especially enhanced in the Indian and Atlantic sectors, SHAL relatively enhanced in the central and east Pacific, and DEEP most prominent in the western Pacific. While the clusters themselves are quite similar in rainfall and heating, specific cloud types defined according to the PR echo top and surface rainfall rate are less similar and exhibit systematic differences from one cluster to another, implying that the degree to which precipitation structures are similar decreases when one considers individual precipitating clouds as repeating tropical structures instead of larger-scale cluster ensembles themselves.
Abstract
In this study, the spatial variability in precipitation at a 0.1° scale is investigated using long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar. Marked regional heterogeneities emerged for orographic rainfall on characteristic scales of tens of kilometers, high concentrations of small-scale systems (<10 km) over alpine areas, and sharp declines around mountain summits. In detecting microclimates, an additional concern is suspicious echoes observed around certain geographical areas with relatively low rainfall. A finescale land–river contrast can be extracted in the diurnal behavior of rainfall in medium-scale systems (10–100 km), corresponding to the course of the Amazon River. In addition, rainfall enhancement over small islands (0.1°–1°) was identified in terms of the storm scale. Even 0.1°-scale flat islands experience more rainfall than the adjacent ocean, primarily as a result of localized small or moderate systems. By contrast, compared with small islands, high-impact large-scale systems (>100 km) result in more rainfall over the adjacent ocean. Finescale hourly data represented the abrupt asymmetric fluctuation in rainfall across the coastline in the tropics and subtropics (30°S–30°N). Significant diurnal modulations in the rainfall due to large-scale systems are found over tropical offshore regions of vast landmasses but not over small islands or in the midlatitudes between 30° and 36°. Rainfall enhancement over small tropical islands is generated by abundant afternoon rainfall, which results from medium-scale storms that are regulated by the island size and inactivity of rainfall over coastal waters.
Abstract
In this study, the spatial variability in precipitation at a 0.1° scale is investigated using long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar. Marked regional heterogeneities emerged for orographic rainfall on characteristic scales of tens of kilometers, high concentrations of small-scale systems (<10 km) over alpine areas, and sharp declines around mountain summits. In detecting microclimates, an additional concern is suspicious echoes observed around certain geographical areas with relatively low rainfall. A finescale land–river contrast can be extracted in the diurnal behavior of rainfall in medium-scale systems (10–100 km), corresponding to the course of the Amazon River. In addition, rainfall enhancement over small islands (0.1°–1°) was identified in terms of the storm scale. Even 0.1°-scale flat islands experience more rainfall than the adjacent ocean, primarily as a result of localized small or moderate systems. By contrast, compared with small islands, high-impact large-scale systems (>100 km) result in more rainfall over the adjacent ocean. Finescale hourly data represented the abrupt asymmetric fluctuation in rainfall across the coastline in the tropics and subtropics (30°S–30°N). Significant diurnal modulations in the rainfall due to large-scale systems are found over tropical offshore regions of vast landmasses but not over small islands or in the midlatitudes between 30° and 36°. Rainfall enhancement over small tropical islands is generated by abundant afternoon rainfall, which results from medium-scale storms that are regulated by the island size and inactivity of rainfall over coastal waters.
Abstract
An orographic/nonorographic rainfall classification scheme has been introduced for the operational algorithm of the Global Satellite Mapping of Precipitation (GSMaP) for passive microwave radiometers. However, problems of overestimations and false alarms of heavy orographic rainfall remain unresolved. This is because the current scheme selected lower constant thresholds of orographic rainfall conditions for global application and used values of orographically forced upward motion w derived from near-surface atmospheric data. This study improves the conceptual model of the warm-rain process for considering the strength of the upstream flow of the low-level troposphere. Under a weak upstream current, rain reaches the foothills of the windward mountain slope because of sufficient time for condensation and precipitation enhancement by the topography. Conversely, under a strong upstream current, precipitation enhancement occurs nearer to the mountain peak. This is because the upstream current flows so quickly that there is insufficient time for enhancement of precipitation over the foothills of the windward mountain slope. After implementing a variable threshold for w that depends on the mean horizontal low-level wind, the area of orographic enhancement of rain was detected reasonably well in cases of both strong and weak winds. To improve the accuracy of estimates of orographic rainfall, an adjustment to the rain estimation was introduced using a lower-frequency channel. The biases of the rainfall estimate for the adjusted scheme from the Tropical Rainfall Measuring Mission Precipitation Radar were improved for the cases considered here as well as for the Asian region of heavy orographic rainfall over land.
Abstract
An orographic/nonorographic rainfall classification scheme has been introduced for the operational algorithm of the Global Satellite Mapping of Precipitation (GSMaP) for passive microwave radiometers. However, problems of overestimations and false alarms of heavy orographic rainfall remain unresolved. This is because the current scheme selected lower constant thresholds of orographic rainfall conditions for global application and used values of orographically forced upward motion w derived from near-surface atmospheric data. This study improves the conceptual model of the warm-rain process for considering the strength of the upstream flow of the low-level troposphere. Under a weak upstream current, rain reaches the foothills of the windward mountain slope because of sufficient time for condensation and precipitation enhancement by the topography. Conversely, under a strong upstream current, precipitation enhancement occurs nearer to the mountain peak. This is because the upstream current flows so quickly that there is insufficient time for enhancement of precipitation over the foothills of the windward mountain slope. After implementing a variable threshold for w that depends on the mean horizontal low-level wind, the area of orographic enhancement of rain was detected reasonably well in cases of both strong and weak winds. To improve the accuracy of estimates of orographic rainfall, an adjustment to the rain estimation was introduced using a lower-frequency channel. The biases of the rainfall estimate for the adjusted scheme from the Tropical Rainfall Measuring Mission Precipitation Radar were improved for the cases considered here as well as for the Asian region of heavy orographic rainfall over land.
Abstract
The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation.
Significance Statement
The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.
Abstract
The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation.
Significance Statement
The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.
Abstract
An algorithm, the spectral latent heating (SLH) algorithm, has been developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission precipitation radar with a cloud-resolving model (CRM). Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were produced with numerical simulations of tropical cloud systems in the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. For convective and shallow stratiform regions, the lookup table refers to the precipitation-top height (PTH). For the anvil region, on the other hand, the lookup table refers to the precipitation rate at the melting level instead of PTH. A consistency check of the SLH algorithm was also done with the CRM-simulated outputs. The first advantage of this algorithm is that differences of heating profiles between the shallow convective stage and the deep convective stage can be retrieved. This is a result of the utilization of observed information, not only on precipitation type and intensity, but also on the precipitation depth. The second advantage is that heating profiles in the decaying stage with no surface rain can also be retrieved. This comes from utilization of the precipitation rate at the melting level for anvil regions.
Abstract
An algorithm, the spectral latent heating (SLH) algorithm, has been developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission precipitation radar with a cloud-resolving model (CRM). Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were produced with numerical simulations of tropical cloud systems in the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. For convective and shallow stratiform regions, the lookup table refers to the precipitation-top height (PTH). For the anvil region, on the other hand, the lookup table refers to the precipitation rate at the melting level instead of PTH. A consistency check of the SLH algorithm was also done with the CRM-simulated outputs. The first advantage of this algorithm is that differences of heating profiles between the shallow convective stage and the deep convective stage can be retrieved. This is a result of the utilization of observed information, not only on precipitation type and intensity, but also on the precipitation depth. The second advantage is that heating profiles in the decaying stage with no surface rain can also be retrieved. This comes from utilization of the precipitation rate at the melting level for anvil regions.
Abstract
Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (≥100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day−1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling.
Abstract
Observations of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) over 16 yr yielded hundreds of large precipitation systems (≥100 km) for each 0.1° grid over major rainy regions. More than 90% of the rainfall was attributed to large systems over certain midlatitude regions such as La Plata basin and the East China Sea. The accumulation of high-impact snapshots reduced the significant spatial fluctuation of the rain fraction arising from large systems and allowed the obtaining of sharp images of the geographic rainfall pattern. Widespread systems were undetected over low-rainfall areas such as regions off Peru. Conversely, infrequent large systems brought a significant percentage of rainfall over semiarid tropics such as the Sahel. This demonstrated an increased need for regional sampling of extreme phenomena. Differences in data collected over a period of 16 yr were used to examine sampling adequacy. The results indicated that more than 10% of the 0.1°-scale sampling error accounted for half of the TRMM domain even for a 10-yr data accumulation period. Rainfall at the 0.1° scale was negatively biased in the first few years for over more than half of the areas because of a lack of high-impact samples. The areal fraction of the 0.1°-scale climatology with a 50% accuracy exceeded 95% in the ninth year and in the fifth year for those areas with rainfall >2 mm day−1. A monotonic increase in the degree of similarity of finescale rainfall to the best estimate with an accuracy of 10% illustrated the need for further sampling.
Abstract
This study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.
Abstract
This study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.
Abstract
Four Tropical Rainfall Measuring Mission (TRMM) datasets of latent heating were diagnosed for signals in the Madden–Julian oscillation (MJO). In all four datasets, vertical structures of latent heating are dominated by two components—one deep with its peak above the melting level and one shallow with its peak below. Profiles of the two components are nearly ubiquitous in longitude, allowing a separation of the vertical and zonal/temporal variations when the latitudinal dependence is not considered. All four datasets exhibit robust MJO spectral signals in the deep component as eastward propagating spectral peaks centered at a period of 50 days and zonal wavenumber 1, well distinguished from lower- and higher-frequency power and much stronger than the corresponding westward power. The shallow component shows similar but slightly less robust MJO spectral peaks. MJO signals were further extracted from a combination of bandpass (30–90 day) filtered deep and shallow components. Largest amplitudes of both deep and shallow components of the MJO are confined to the Indian and western Pacific Oceans. There is a local minimum in the deep components over the Maritime Continent. The shallow components of the MJO differ substantially among the four TRMM datasets in their detailed zonal distributions in the Eastern Hemisphere. In composites of the heating evolution through the life cycle of the MJO, the shallow components lead the deep ones in some datasets and at certain longitudes. In many respects, the four TRMM datasets agree well in their deep components, but not in their shallow components and in the phase relations between the deep and shallow components. These results indicate that caution must be exercised in applications of these latent heating data.
Abstract
Four Tropical Rainfall Measuring Mission (TRMM) datasets of latent heating were diagnosed for signals in the Madden–Julian oscillation (MJO). In all four datasets, vertical structures of latent heating are dominated by two components—one deep with its peak above the melting level and one shallow with its peak below. Profiles of the two components are nearly ubiquitous in longitude, allowing a separation of the vertical and zonal/temporal variations when the latitudinal dependence is not considered. All four datasets exhibit robust MJO spectral signals in the deep component as eastward propagating spectral peaks centered at a period of 50 days and zonal wavenumber 1, well distinguished from lower- and higher-frequency power and much stronger than the corresponding westward power. The shallow component shows similar but slightly less robust MJO spectral peaks. MJO signals were further extracted from a combination of bandpass (30–90 day) filtered deep and shallow components. Largest amplitudes of both deep and shallow components of the MJO are confined to the Indian and western Pacific Oceans. There is a local minimum in the deep components over the Maritime Continent. The shallow components of the MJO differ substantially among the four TRMM datasets in their detailed zonal distributions in the Eastern Hemisphere. In composites of the heating evolution through the life cycle of the MJO, the shallow components lead the deep ones in some datasets and at certain longitudes. In many respects, the four TRMM datasets agree well in their deep components, but not in their shallow components and in the phase relations between the deep and shallow components. These results indicate that caution must be exercised in applications of these latent heating data.
Abstract
This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.
Abstract
This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.
