scholarly journals Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part III: Estimating Apparent Moisture Sink Profiles over Tropical Oceans

2008 ◽  
Vol 47 (2) ◽  
pp. 620-640 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Wei-Kuo Tao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Latent Heating ◽  
Tropical Ocean ◽  
Tropical Oceans ◽  
Cloud Resolving Model ◽  
Trmm Precipitation ◽  
Low Levels ◽  
Heating Profiles ◽  
The Western Pacific ◽  
Good Agreement

Abstract The spectral latent heating (SLH) algorithm was developed to estimate apparent heat source (Q1) profiles for the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in Parts I and II of this study. In this paper, the SLH algorithm is used to estimate apparent moisture sink (Q2) profiles. The procedure of Q2 retrieval is the same as that of heating retrieval except for using the Q2 profile lookup tables derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) utilizing a cloud-resolving model (CRM). The Q2 profiles were reconstructed from CRM-simulated parameters with the COARE table and then compared with CRM-simulated “true” Q2 profiles, which were computed directly from the water vapor equation in the model. The consistency check indicates that discrepancies between the SLH-reconstructed and CRM-simulated profiles for Q2, especially at low levels, are larger than those for Q1 and are attributable to moistening for the nonprecipitating region that SLH cannot reconstruct. Nevertheless, the SLH-reconstructed total Q2 profiles are in good agreement with the CRM-simulated ones. The SLH algorithm was applied to PR data, and the results were compared with Q2 profiles derived from the budget study. Although discrepancies between the SLH-retrieved and sounding-based profiles for Q2 for the South China Sea Monsoon Experiment (SCSMEX) are larger than those for heating, key features of the vertical profiles agree well. The SLH algorithm can also estimate differences of Q2 between the western Pacific Ocean and the Atlantic Ocean, consistent with the results from the budget study.

scholarly journals Shallow and Deep Latent Heating Modes over Tropical Oceans Observed with TRMM PR Spectral Latent Heating Data

2010 ◽  
Vol 23 (8) ◽  
pp. 2030-2046 ◽  
Author(s):  
Yukari N. Takayabu ◽  
Shoichi Shige ◽  
Wei-Kuo Tao ◽  
Nagio Hirota
Keyword(s):  
Large Scale ◽  
Deep Convection ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Radiative Heating ◽  
Latent Heating ◽  
Tropical Oceans ◽  
Trmm Precipitation ◽  
Subtropical Oceans ◽  
Cumulus Congestus

Abstract Three-dimensional distributions of the apparent heat source (Q1) − radiative heating (QR) estimated from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) utilizing the spectral latent heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated Q1 − QR averaged over the tropical oceans is estimated as ∼72.6 J s−1 (∼2.51 mm day−1) and that over tropical land is ∼73.7 J s−1 (∼2.55 mm day−1) for 30°N–30°S. It is shown that nondrizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems: deep systems and congestus. A rough estimate of the shallow-heating contribution against the total heating is about 46.7% for the average tropical oceans, which is substantially larger than the 23.7% over tropical land. Although cumulus congestus heating linearly correlates with SST, deep-mode heating is dynamically bounded by large-scale subsidence. It is notable that a substantial amount of rain, as large as 2.38 mm day−1 on average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that, even in the region with SSTs warmer than 28°C, large-scale subsidence effectively suppresses the deep convection, with the remaining heating by congestus clouds. The results support that the entrainment of mid–lower-tropospheric dry air, which accompanies the large-scale subsidence, is the major factor suppressing the deep convection. Therefore, a representation of the realistic entrainment is very important for proper reproduction of precipitation distribution and the resultant large-scale circulation.

Residual Diagnosis of Diabatic Heating from ERA-40 and NCEP Reanalyses: Intercomparisons with TRMM

2009 ◽  
Vol 22 (2) ◽  
pp. 414-428 ◽  
Author(s):  
Steven C. Chan ◽  
Sumant Nigam
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Tropical Rainfall Measuring Mission ◽  
Latent Heating ◽  
Maritime Continent ◽  
Precipitation Estimates ◽  
Trmm Precipitation ◽  
Heating Profiles ◽  
The Tropics ◽  
Heating Profile

Abstract Diabatic heating is diagnosed from the 40-yr ECMWF Re-Analysis (ERA-40) circulation as a residue in the thermodynamic equation. The heating distribution is compared with the heating structure diagnosed from NCEP and 15-yr ECMWF Re-Analysis (ERA-15) circulation and latent heating generated from Tropical Rainfall Measuring Mission (TRMM) observations using the convective–stratiform heating (CSH) algorithm. The ERA-40 residual heating in the tropics is found to be stronger than NCEP’s (and ERA-15), especially in July when its zonal–vertical average is twice as large. The bias is strongest over the Maritime Continent in January and over the eastern basins and Africa in July. Comparisons with precipitation indicate ERA-40 heating to be much more realistic over the eastern Pacific but excessive over the Maritime Continent, by at least 20% in January. Intercomparison of precipitation estimates from heating-profile integrals and station and satellite analyses reveals the TRMM CSH latent heating to be chronically weak by as much as a factor of 2! It is the low-side outlier among nine precipitation estimates in three of the four analyzed regions. No less worrisome is the inconsistency between the integral of the CSH latent heating profile in the tropics and the TRMM precipitation retrievals constraining the CSH algorithm (e.g., the 3A25 analysis). Confronting TRMM’s diagnosis of latent heating from local rainfall retrievals and local cumulus-model heating profiles with heating based on the large-scale assimilated circulation is a defining attribute of this study.

Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part IV: Comparisons of Lookup Tables from Two- and Three-Dimensional Cloud-Resolving Model Simulations

2009 ◽  
Vol 22 (20) ◽  
pp. 5577-5594 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Satoshi Kida ◽  
Wei-Kuo Tao ◽  
Xiping Zeng ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Latent Heating ◽  
3D Simulations ◽  
Toga Coare ◽  
Trmm Pr ◽  
Lookup Tables ◽  
Cloud Resolving Model ◽  
Heating Profiles ◽  
Maximum Heating ◽  
Melting Level

Abstract The spectral latent heating (SLH) algorithm was developed to estimate latent heating profiles for the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR). The method uses TRMM PR information (precipitation-top height, precipitation rates at the surface and melting level, and rain type) to select heating profiles from lookup tables (LUTs). LUTs 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) using a cloud-resolving model (CRM). The two-dimensional (2D) CRM was used in previous studies. The availability of exponentially increasing computer capabilities has resulted in three-dimensional (3D) CRM simulations for multiday periods becoming increasingly prevalent. In this study, LUTs from the 2D and 3D simulations are compared. Using the LUTs from 3D simulations results in less agreement between the SLH-retrieved heating and sounding-based heating for the South China Sea Monsoon Experiment (SCSMEX). The level of SLH-estimated maximum heating is lower than that of the sounding-derived maximum heating. This is explained by the fact that using the 3D LUTs results in stronger convective heating and weaker stratiform heating above the melting level than is the case if using the 2D LUTs. More condensate is generated in and carried from the convective region in the 3D model than in the 2D model, and less condensate is produced by the stratiform region’s own upward motion.

scholarly journals Cross Validation of TRMM PR Reflectivity Profiles Using 3D Reflectivity Composite from the Ground-Based Radar Network over the Korean Peninsula

2015 ◽  
Vol 16 (2) ◽  
pp. 668-687 ◽  
Author(s):  
Shinju Park ◽  
Sung-Hwa Jung ◽  
GyuWon Lee
Keyword(s):  
Korean Peninsula ◽  
Tropical Rainfall Measuring Mission ◽  
Melting Layer ◽  
Radar Network ◽  
Vertical Distributions ◽  
Trmm Precipitation ◽  
The Mean ◽  
Low Levels ◽  
Low Sensitivity ◽  
Attenuation Corrected

Abstract The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measures reflectivity downward from space and provides observations of the vertical distributions of precipitation over land as well as the ocean. It overpasses the southern part of the Korean Peninsula where (i) a dense network of operational S-band scanning radars is available and (ii) various types of precipitation occur. By utilizing a 3D reflectivity composite from the ground S-band radar (GR) observations, this paper shows a comparison of reflectivity profiles observed with both PR and GR focusing on their vertical structure. For four cases of widespread rain, visual and statistical analyses show that PR attenuation-corrected reflectivity agrees closely with reflectivity observed from the GR composite below the melting layer. Above and within the melting layer, PR is affected critically by its sensitivity while GR beam broadening at far ranges causes systematic differences in the PR–GR comparisons. For four cases of convective rain, PR underestimates the mean reflectivities by 1–3 dB compared with those from GR at low levels where precipitation attenuation is significant toward the ground. In these cases, the low sensitivity of PR results in a small number of matched points for weak echoes. Also, the PR–GR discrepancy for the convective case is more affected by time mismatching.

scholarly journals Relationships between Rain Characteristics and Environment. Part I: TRMM Precipitation Features and the Large-Scale Environment over the Tropical Pacific

2012 ◽  
Vol 140 (9) ◽  
pp. 2831-2840 ◽  
Author(s):  
Chie Yokoyama ◽  
Yukari N. Takayabu
Keyword(s):  
Large Scale ◽  
Tropical Rainfall Measuring Mission ◽  
Warm Pool ◽  
Intertropical Convergence Zone ◽  
The Other ◽  
Precipitation Radar ◽  
Trmm Precipitation ◽  
Highly Correlated ◽  
The Western Pacific ◽  
The Tropical Pacific

Abstract Differences in the characteristics of rain systems in the eastern Pacific (EP) intertropical convergence zone (ITCZ) and the western Pacific (WP) warm pool are quantitatively examined in relation to the large-scale environment. This study mainly uses precipitation feature (PF) data observed by the precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM). The PFs are classified into four types according to their areas and maximum heights. Rain from tall unorganized systems and very tall organized systems tends to be dominant in high-SST regions such as the WP. On the other hand, the EP has more rain from congestus and organized systems with moderate heights than the WP. It is shown that shallow rain from congestus and moderately deep rain from organized systems are highly correlated with shallow (1000–925 hPa) convergence fields with coefficients of 0.75 and 0.66, respectively. These relationships between characteristics of rain systems and the large-scale environment are robust through all seasons.

Estimates of Tropical Diabatic Heating Profiles: Commonalities and Uncertainties

2010 ◽  
Vol 23 (3) ◽  
pp. 542-558 ◽  
Author(s):  
Samson Hagos ◽  
Chidong Zhang ◽  
Wei-Kuo Tao ◽  
Steve Lang ◽  
Yukari N. Takayabu ◽  
...  
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Tropical Rainfall Measuring Mission ◽  
Upper Troposphere ◽  
Tropical Oceans ◽  
Field Campaigns ◽  
Heating Profiles ◽  
The Tropics ◽  
Rain Rates

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.

“Warm Rain” in the Tropics: Seasonal and Regional Distributions Based on 9 yr of TRMM Data

2009 ◽  
Vol 22 (3) ◽  
pp. 767-779 ◽  
Author(s):  
Chuntao Liu ◽  
Edward J. Zipser
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Precipitation Radar ◽  
Near Surface ◽  
Freezing Level ◽  
Tropical Oceans ◽  
Warm Rain ◽  
Seasonal And Diurnal Variations ◽  
Radar Echo ◽  
Trmm Precipitation ◽  
The Tropics

Abstract How much precipitation is contributed by warm rain systems over the tropics? What is the typical size, intensity, and echo top of warm rain events observed by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar over different regions of the tropics? What proportion of warm raining areas is actually attached to the edges of cold systems? Are there mesoscale warm raining systems, and if so, where and when do they occur? To answer these questions, a 9-yr TRMM precipitation feature database is used in this study. First, warm rain features in 20°S–20°N are selected by specifying precipitation features 1) with minimum infrared brightness temperature > 0°C, 2) with TRMM Precipitation Radar (PR) echo top below freezing level, or 3) without any ice-scattering signature in the microwave observations, respectively. Then, the geographical, seasonal, and diurnal variations of the rain volume inside warm rain features defined in these three ways are presented. The characteristics of warm rain features are summarized. Raining pixels with cloud-top temperature above 0°C contribute 20% of the rainfall over tropical oceans and 7.5% over tropical land. However, about half of the warm pixels over oceans and two-thirds of the warm pixels over land are attached to cold precipitation systems. A large amount of warm rainfall occurs over oceans near windward coasts during winter. Most of the warm rain systems have small size < 100 km2 and weak radar echo with a modal maximum near-surface reflectivity around 23 dBZ. However, mesoscale warm rain systems with strong radar echoes do occur in large regions of the tropical oceans, more during the nighttime than during daytime. Though the mean height of the warm precipitation features over oceans is lower than that over land, there is no significant regional difference in its size and intensity.

scholarly journals The Next-Generation Goddard Convective–Stratiform Heating Algorithm: New Tropical and Warm-Season Retrievals for GPM

2018 ◽  
Vol 31 (15) ◽  
pp. 5997-6026 ◽  
Author(s):  
Stephen E. Lang ◽  
Wei-Kuo Tao
Keyword(s):  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Shallow Convection ◽  
Heating Rates ◽  
Low Level ◽  
Precipitation Measurement ◽  
Cloud Resolving Model ◽  
Upper Level ◽  
Heating Profiles ◽  
Global Precipitation

The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dB Z values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.

scholarly journals Latent heating profiles from GOES-16 and its comparison to heating from NEXRAD and GPM

2021 ◽  
Author(s):  
Yoonjin Lee ◽  
Christian D. Kummerow ◽  
Milija Zupanski
Keyword(s):  
Temporal Resolution ◽  
Weather Forecasting ◽  
Tropical Rainfall Measuring Mission ◽  
Latent Heating ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Model Simulations ◽  
Forecast Models ◽  
Climate Analysis ◽  
Heating Profiles

Abstract. Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement (GPM), provide a continuous record of LH. However, with observations approximately 3 days apart, its temporal resolution is too coarse to be used to initiate convection in forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using it is that its data are only available over well observed land areas. This study suggests a method to derive LH from the Geostationary Operational-Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when coupled to a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions using model simulations coupled to a convective classification scheme and channel 14 (11.2 μm) brightness temperature. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from NEXRAD and one of Dual-frequency Precipitation Radar (DPR) products, Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. Overall, GOES LH appear to have the ability to mimic LH from radars, although the area identified as convective is roughly 25 % smaller than the current HRRR model, while the heating is correspondingly higher.

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