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>

scholarly journals Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

2018 ◽  
Vol 31 (10) ◽  
pp. 3731-3754 ◽  
Author(s):  
Abishek Adhikari ◽  
Chuntao Liu ◽  
Mark S. Kulie
Keyword(s):  
Reanalysis Data ◽  
Global Distribution ◽  
Precipitation Radar ◽  
Near Surface ◽  
Level Data ◽  
Seasonal And Diurnal Variations ◽  
Surface Reflectivity ◽  
Global Precipitation Mission ◽  
Snow Precipitation ◽  
Global Precipitation

Abstract Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately. To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

scholarly journals Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar

2021 ◽  
Vol 13 (22) ◽  
pp. 4690
Author(s):  
Merhala Thurai ◽  
Viswanathan Bringi ◽  
David Wolff ◽  
David A. Marks ◽  
Patrick N. Gatlin ◽  
...  
Keyword(s):  
Size Distribution ◽  
Drop Size ◽  
Drop Size Distribution ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Distribution Moments ◽  
The Us ◽  
Rain Drop ◽  
Global Precipitation Mission ◽  
Global Precipitation

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.

scholarly journals Direct Validation of TRMM/PR Near Surface Rain over the Northeastern Indian Subcontinent Using a Tipping Bucket Raingauge Network

SOLA ◽  
2017 ◽  
Vol 13 (0) ◽  
pp. 157-162 ◽  
Author(s):  
Toru Terao ◽  
Fumie Murata ◽  
Yusuke Yamane ◽  
Masashi Kiguchi ◽  
Azusa Fukushima ◽  
...  
Keyword(s):  
Indian Subcontinent ◽  
Near Surface ◽  
Trmm Pr

Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM

2015 ◽  
Vol 72 (2) ◽  
pp. 623-640 ◽  
Author(s):  
Weixin Xu ◽  
Steven A. Rutledge
Keyword(s):  
Mesoscale Convective System ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Convective System ◽  
Precipitation Radar ◽  
Convective Systems ◽  
Trmm Pr ◽  
Mesoscale Convective ◽  
Trmm Precipitation

Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.

“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 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 Comparisons of Reflectivities from the TRMM Precipitation Radar and Ground-Based Radars

2009 ◽  
Vol 26 (5) ◽  
pp. 857-875 ◽  
Author(s):  
Jianxin Wang ◽  
David B. Wolff
Keyword(s):  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Horizontal Displacement ◽  
Marshall Islands ◽  
Precipitation Radar ◽  
Convective Rain ◽  
Ground Validation ◽  
Trmm Precipitation ◽  
Cartesian Coordinate ◽  
Global Precipitation

Abstract Given the decade-long and highly successful Tropical Rainfall Measuring Mission (TRMM), it is now possible to provide quantitative comparisons between ground-based radars (GRs) and the spaceborne TRMM precipitation radar (PR) with greater certainty over longer time scales in various tropical climatological regions. This study develops an automated methodology to match and compare simultaneous TRMM PR and GR reflectivities at four primary TRMM Ground Validation (GV) sites: Houston, Texas (HSTN); Melbourne, Florida (MELB); Kwajalein, Republic of the Marshall Islands (KWAJ); and Darwin, Australia (DARW). Data from each instrument are resampled into a three-dimensional Cartesian coordinate system. The horizontal displacement during the PR data resampling is corrected. Comparisons suggest that the PR suffers significant attenuation at lower levels, especially in convective rain. The attenuation correction performs quite well for convective rain but appears to slightly overcorrect in stratiform rain. The PR and GR observations at HSTN, MELB, and KWAJ agree to about ±1 dB on average with a few exceptions, whereas the GR at DARW requires +1 to −5 dB calibration corrections. One of the important findings of this study is that the GR calibration offset is dependent on the reflectivity magnitude. Hence, it is proposed that the calibration should be carried out by using a regression correction rather than by simply adding an offset value to all GR reflectivities. This methodology is developed to assist TRMM GV efforts to improve the accuracy of tropical rain estimates, but can also be applied to the proposed Global Precipitation Measurement and other related activities over the globe.

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