scholarly journals A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner

2021 ◽  
Vol 13 (5) ◽  
pp. 2293-2306
Author(s):  
Lilu Sun ◽  
Yunfei Fu
Keyword(s):  
Hydrological Cycle ◽  
Tropical Rainfall Measuring Mission ◽  
Reanalysis Data ◽  
Radiation Budget ◽  
Atmospheric Parameters ◽  
Precipitation Radar ◽  
Near Surface ◽  
Tropical Rainfall ◽  
Gridded Dataset ◽  
Trmm Precipitation

Abstract. Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

scholarly journals A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of TRMM PR and VIRS

2021 ◽  
Author(s):  
Lilu Sun ◽  
Yunfei Fu
Keyword(s):  
Hydrological Cycle ◽  
Global Climate ◽  
Tropical Rainfall Measuring Mission ◽  
Reanalysis Data ◽  
Mean Value ◽  
Radiation Budget ◽  
Atmospheric Parameters ◽  
Reanalysis Dataset ◽  
Near Surface ◽  
Gridded Dataset

Abstract. Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) with the multi-channel cloud-top radiance measured by the Visible and Infrared Scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in the 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of the M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at http://doi.org/10.5281/zenodo.4458868 (Sun and Fu,2021).

scholarly journals Differences of Rainfall Estimates over Land by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI)—Dependence on Storm Height

2005 ◽  
Vol 44 (3) ◽  
pp. 367-383 ◽  
Author(s):  
Fumie A. Furuzawa ◽  
Kenji Nakamura
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Local Times ◽  
Precipitation Radar ◽  
Tropical Rainfall ◽  
Convective Rain ◽  
Brightness Temperatures ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Orbit Data

Abstract It is well known that precipitation rate estimation is poor over land. Using the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI), the performance of the TMI rain estimation was investigated. Their differences over land were checked by using the orbit-by-orbit data for June 1998, December 1998, January 1999, and February 1999, and the following results were obtained: 1) Rain rate (RR) near the surface for the TMI (TMI-RR) is smaller than that for the PR (PR-RR) in winter; it is also smaller from 0900 to 1800 LT. These dependencies show some variations at various latitudes or local times. 2) When the storm height is low (<5 km), the TMI-RR is smaller than the PR-RR; when it is high (>8 km), the PR-RR is smaller. These dependencies of the RR on the storm height do not depend on local time or latitude. The tendency for a TMI-RR to be smaller when the storm height is low is more noticeable in convective rain than in stratiform rain. 3) Rain with a low storm height predominates in winter or from 0600 to 1500 LT, and convective rain occurs frequently from 1200 to 2100 LT. Result 1 can be explained by results 2 and 3. It can be concluded that the TMI underestimates rain with low storm height over land because of the weakness of the TMI algorithm, especially for convective rain. On the other hand, it is speculated that TMI overestimates rain with high storm height because of the effect of anvil rain with low brightness temperatures at high frequencies without rain near the surface, and because of the effect of evaporation or tilting, which is indicated by a PR profile and does not appear in the TMI profile. Moreover, it was found that the PR rain for the cases with no TMI rain amounted to about 10%–30% of the total but that the TMI rain for the cases with no PR rain accounted for only a few percent of the TMI rain. This result can be explained by the difficulty of detecting shallow rain with the TMI.

“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 Madden–Julian Oscillation Recorded in Early Observations from the Tropical Rainfall Measuring Mission (TRMM)

2006 ◽  
Vol 63 (11) ◽  
pp. 2777-2794 ◽  
Author(s):  
Hirohiko Masunaga ◽  
Tristan S. L’Ecuyer ◽  
Christian D. Kummerow
Keyword(s):  
Hydrological Cycle ◽  
Critical Role ◽  
Tropical Rainfall Measuring Mission ◽  
Kelvin Waves ◽  
Madden Julian Oscillation ◽  
Kelvin Wave ◽  
Shallow Convection ◽  
Near Surface ◽  
Tropical Rainfall ◽  
Cross Correlation Analysis

Abstract A satellite data analysis is performed to explore the Madden–Julian oscillation (MJO) focusing on the potential roles of the equatorial Rossby (ER) and Kelvin waves. Measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) are analyzed in the frequency–wavenumber domain to identify and ultimately filter primary low-frequency modes in the Tropics. The space–time spectrum of deep-storm fraction estimated by PR and VIRS exhibits notable Kelvin wave signals at wavenumbers 5–8, a distinct MJO peak at wavenumbers 1–7 and periods of about 40 days, and a signal corresponding to the ER wave. These modes are separately filtered to study the individual modes and possible relationship among them in the time–longitude space. In 10 cases analyzed here, an MJO event is often collocated with a group of consecutive Kelvin waves as well as an intruding ER wave accompanied with the occasional onset of a stationary convective phase. The spatial and temporal relationship between the MJO and Kelvin wave is clearly visible in a lag composite diagram, while the ubiquity of the ER wave leads to a less pronounced relation between the MJO and ER wave. A case study based on the Geostationary Meteorological Satellite (GMS) imagery together with associated dynamic field captures the substructure of the planetary-scale waves. A cross-correlation analysis confirms the MJO-related cycle that involves surface and atmospheric parameters such as sea surface temperature, water vapor, low clouds, shallow convection, and near-surface wind as proposed in past studies. The findings suggest the possibility that a sequence of convective events coupled with the linear waves may play a critical role in MJO propagation. An intraseasonal radiative–hydrological cycle inherent in the local thermodynamic conditions could be also a potential factor responsible for the MJO by loosely modulating the envelope of the entire propagation system.

Validation of satellite-borne precipitation radars by raingauges and disdrometers over the northeastern Indian subcontinent

2021 ◽  
Author(s):  
Fumie Murata ◽  
Toru Terao ◽  
Yusuke Yamane ◽  
Masashi Kiguchi ◽  
Azusa Fukushima ◽  
...  
Keyword(s):  
Indian Subcontinent ◽  
Monsoon Season ◽  
Precipitation Radar ◽  
Near Surface ◽  
Trmm Pr ◽  
Rain Drop ◽  
Distribution Parameters ◽  
Trmm Precipitation ◽  
Global Precipitation Mission ◽  
Global Precipitation

<p>The near surface rain (NSR) dataset of the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) and the Global Precipitation Mission (GPM) Dual Precipitation Radar (DPR) was validated using around 40 tipping bucket raingauges installed over the northeastern Indian subcontinent, and disdrometers in the Meghalaya Plateau, India. The comparison during 2006-2014 showed significant overestimation of TRMM PR in Assam and Bengal plains during pre-monsoon season (March to May), and significant underestimation of TRMM PR over the Indian subcontinent during monsoon season (June to September). Whereas, the comparison during 2014-2019 showed significant overestimation of GPM DPR over only Meghalaya during monsoon season. The validation of rain-drop size distribution parameters: Dm and Nw showed positive correlation between GPM DPR derived values and Parsivel disdrometers observed ones, while unrealistic concentration of Nw on 30-40 dB was derived by GPM DPR. In the southern slope of the Meghala Plateau, NSR of TRMM PR at Cherrapunji, where is known as the heaviest rainfall station, on the plateau observed smaller rainfall than that in the adjacent valley. However, newly installed raingauges in the valley showed rather less rainfall than that on the plateau. The validity of the satellite derived rainfall distribution over the complicated terrain are discussed.</p>

Scaling of tropical rainfall as observed by TRMM precipitation radar

2008 ◽  
Vol 88 (3-4) ◽  
pp. 337-354 ◽  
Author(s):  
Mekonnen Gebremichael ◽  
Witold F. Krajewski ◽  
Thomas M. Over ◽  
Yukari N. Takayabu ◽  
Phillip Arkin ◽  
...  
Keyword(s):  
Precipitation Radar ◽  
Tropical Rainfall ◽  
Trmm Precipitation
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