scholarly journals Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part I: Development of a Model-Based Algorithm

2004 ◽  
Vol 43 (8) ◽  
pp. 1095-1113 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Wei-Kuo Tao ◽  
Daniel E. Johnson
Keyword(s):  
Latent Heating ◽  
Model Based ◽  
Trmm Pr ◽  
Heating Profiles

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 Assessing the Vertical Latent Heating Structure of the East Pacific ITCZ Using the CloudSat CPR and TRMM PR

2018 ◽  
Vol 31 (7) ◽  
pp. 2563-2577 ◽  
Author(s):  
L. Huaman ◽  
C. Schumacher
Keyword(s):  
Meridional Circulation ◽  
Deep Convection ◽  
Tropical Rainfall Measuring Mission ◽  
Hadley Cell ◽  
Convective Precipitation ◽  
Latent Heating ◽  
Shallow Convection ◽  
East Pacific ◽  
Trmm Pr ◽  
Heating Profiles

In the east Pacific (EP) intertropical convergence zone (ITCZ), Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) latent heating retrievals suggest a top-heavy structure; however, light precipitation and its associated low-level heating are underestimated by the PR. This study uses stratiform and deep convective precipitation from the TRMM PR and shallow precipitation from the more sensitive CloudSat radar to assess the seasonal latent heating structure in the EP ITCZ (130°–90°W) for 1998–2014. This study also uses reanalyses (MERRA-2, ERA-Interim, and NCEP–NCAR) to analyze the meridional circulation linked to variations in ITCZ heating. The TRMM/ CloudSat heating profiles suggest a distinct seasonality. During DJF, latent heating peaks at 800 hPa because of the predominance of shallow convection and rises to 700 hPa during MAM as the contribution from deep convective rain increases. During JJA and SON, stratiform precipitation increases and the latent heating has a double peak at 700 and 400 hPa. Additionally, the EP ITCZ heating has a meridional slope throughout most of the year as a result of the prevalence of shallow (deep) convection in the southern (northern) part of the ITCZ. While the reanalyses agree that the most bottom-heavy heating occurs in DJF and the most top-heavy heating occurs in JJA, they underestimate heating aloft compared to the satellite retrievals throughout the year and show varying ability in representing the shallow meridional circulation and deeper Hadley cell overturning.

scholarly journals The microphysical contributions to and evolution of latent heating profiles in two MC3E MCSs

2016 ◽  
Vol 121 (13) ◽  
pp. 7913-7935 ◽  
Author(s):  
P. J. Marinescu ◽  
S. C. van den Heever ◽  
S. M. Saleeby ◽  
S. M. Kreidenweis
Keyword(s):  
Latent Heating ◽  
Heating Profiles

Spectral Retrieval of Latent Heating Profiles from TRMM PR Data. Part II: Algorithm Improvement and Heating Estimates over Tropical Ocean Regions

2007 ◽  
Vol 46 (7) ◽  
pp. 1098-1124 ◽  
Author(s):  
Shoichi Shige ◽  
Yukari N. Takayabu ◽  
Wei-Kuo Tao ◽  
Chung-Lin Shie
Keyword(s):  
Convective Heating ◽  
Consistency Check ◽  
Tropical Ocean ◽  
Stratiform Region ◽  
Toga Coare ◽  
Trmm Pr ◽  
Lookup Tables ◽  
Upper Level ◽  
Heating Profiles ◽  
Melting Level

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.

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.

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.

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.

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