Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics

2005 ◽  
Vol 133 (3) ◽  
pp. 543-566 ◽  
Author(s):  
Daniel J. Cecil ◽  
Steven J. Goodman ◽  
Dennis J. Boccippio ◽  
Edward J. Zipser ◽  
Stephen W. Nesbitt
Keyword(s):  
Brightness Temperature ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Reflectivity ◽  
Lightning Flash ◽  
Frequency Of Occurrence ◽  
Areal Extent ◽  
Flash Rate ◽  
Brightness Temperatures ◽  
Rate Minimum ◽  
Trmm Precipitation

Abstract During its first three years, the Tropical Rainfall Measuring Mission (TRMM) satellite observed nearly six million precipitation features. The population of precipitation features is sorted by lightning flash rate, minimum brightness temperature, maximum radar reflectivity, areal extent, and volumetric rainfall. For each of these characteristics, essentially describing the convective intensity or the size of the features, the population is broken into categories consisting of the top 0.001%, top 0.01%, top 0.1%, top 1%, top 2.4%, and remaining 97.6%. The set of “weakest/smallest” features composes 97.6% of the population because that fraction does not have detected lightning, with a minimum detectable flash rate of 0.7 flashes (fl) min−1. The greatest observed flash rate is 1351 fl min−1; the lowest brightness temperatures are 42 K (85 GHz) and 69 K (37 GHz). The largest precipitation feature covers 335 000 km2, and the greatest rainfall from an individual precipitation feature exceeds 2 × 1012 kg h−1 of water. There is considerable overlap between the greatest storms according to different measures of convective intensity. The largest storms are mostly independent of the most intense storms. The set of storms producing the most rainfall is a convolution of the largest and the most intense storms. This analysis is a composite of the global Tropics and subtropics. Significant variability is known to exist between locations, seasons, and meteorological regimes. Such variability will be examined in Part II. In Part I, only a crude land–ocean separation is made. The known differences in bulk lightning flash rates over land and ocean result from at least two differences in the precipitation feature population: the frequency of occurrence of intense storms and the magnitude of those intense storms that do occur. Even when restricted to storms with the same brightness temperature, same size, or same radar reflectivity aloft, the storms over water are considerably less likely to produce lightning than are comparable storms over land.

scholarly journals Passive Microwave Brightness Temperatures as Proxies for Hailstorms

2009 ◽  
Vol 48 (6) ◽  
pp. 1281-1286 ◽  
Author(s):  
Daniel J. Cecil
Keyword(s):  
Brightness Temperature ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Reflectivity ◽  
Passive Microwave ◽  
Tropical Rainfall ◽  
Brightness Temperatures

Abstract The Tropical Rainfall Measuring Mission (TRMM) satellite has been used to infer distributions of intense thunderstorms. Besides the lightning measurements from TRMM, the radar reflectivities and passive microwave brightness temperatures have been used as proxies for convective vigor. This is based on large graupel or hail lofted by strong updrafts being the cause of high–radar reflectivity values aloft and extremely low brightness temperatures. This paper seeks to empirically confirm that extremely low brightness temperatures are often accompanied by large hail at the surface. The three frequencies examined (85, 37, and 19 GHz) all show an increasing likelihood of hail reports with decreasing brightness temperature. Quantification is limited by the sparsity of hail reports. Hail reports are common when brightness temperatures are below 70 K at 85 GHz, 180 K at 37 GHz, or 230 K at 19 GHz.

Rain Retrieval from TMI Brightness Temperature Measurements Using a TRMM PR–Based Database

2006 ◽  
Vol 45 (3) ◽  
pp. 455-466 ◽  
Author(s):  
Nicolas Viltard ◽  
Corinne Burlaud ◽  
Christian D. Kummerow
Keyword(s):  
Brightness Temperature ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
High Standard ◽  
Brightness Temperatures ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Point To Point ◽  
Trmm Microwave Imager ◽  
Test Database

Abstract This study focuses on improving the retrieval of rain from measured microwave brightness temperatures and the capability of the retrieved field to represent the mesoscale structure of a small intense hurricane. For this study, a database is constructed from collocated Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the TRMM Microwave Imager (TMI) data resulting in about 50 000 brightness temperature vectors associated with their corresponding rain-rate profiles. The database is then divided in two: a retrieval database of about 35 000 rain profiles and a test database of about 25 000 rain profiles. Although in principle this approach is used to build a database over both land and ocean, the results presented here are only given for ocean surfaces, for which the conditions for the retrieval are optimal. An algorithm is built using the retrieval database. This algorithm is then used on the test database, and results show that the error can be constrained to reasonable levels for most of the observed rain ranges. The relative error is nonetheless sensitive to the rain rate, with maximum errors at the low and high ends of the rain intensities (+60% and −30%, respectively) and a minimum error between 1 and 7 mm h−1. The retrieval method is optimized to exhibit a low total bias for climatological purposes and thus shows a high standard deviation on point-to-point comparisons. The algorithm is applied to the case of Hurricane Bret (1999). The retrieved rain field is analyzed in terms of structure and intensity and is then compared with the TRMM PR original rain field. The results show that the mesoscale structures are indeed well reproduced even if the retrieved rain misses the highest peaks of precipitation. Nevertheless, the mesoscale asymmetries are well reproduced and the maximum rain is found in the correct quadrant. Once again, the total bias is low, which allows for future calculation of the heat sources/sinks associated with precipitation production and evaporation.

scholarly journals Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships

2013 ◽  
Vol 52 (1) ◽  
pp. 213-229 ◽  
Author(s):  
Weixin Xu ◽  
Robert F. Adler ◽  
Nai-Yu Wang
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Convective System ◽  
Specific Rate ◽  
Lightning Flash ◽  
Convective Rainfall ◽  
Light Rain ◽  
Flash Rate ◽  
Pixel Scale

AbstractThis study quantifies the relationships among lightning activity, convective rainfall, and associated cloud properties on both convective-system scale (or storm scale) and satellite-pixel scale (~5 km) on the basis of 13 yr of Tropical Rainfall Measuring Mission measurements of rainfall, lightning, and clouds. Results show that lightning frequency is a good proxy to separate storms of different intensity, identify convective cores, and screen out false convective-core signatures in areas of thick anvil debris. Significant correlations are found between storm-scale lightning parameters and convective rainfall for systems over the southern United States, the focus area of the study. Storm-scale convective rainfall or heavy-precipitation area has the best correlation (coefficient r = 0.75–0.85) with lightning-flash area. It also increases linearly with increasing lightning-flash rate, although correlations between convective/heavy rainfall and lightning-flash rate are somewhat weaker (r = 0.55–0.75). Statistics further show that active lightning and intense precipitation are not well collocated on the pixel scale (5 km); for example, only 50% of the lightning flashes are coincident with heavy-rain cores, and more than 20% are distributed in light-rain areas. Simple positive correlations between lightning-flash rate and precipitation intensity are weak on the pixel scale. Lightning frequency and rain intensity have positive probabilistic relationships, however: the probability of heavy precipitation, especially on a coarser pixel scale (~20 km), increases with increasing lightning-flash density. Therefore, discrete thresholds of lightning density could be applied in a rainfall estimation scheme to assign precipitation in specific rate categories.

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.

Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent

2017 ◽  
Vol 56 (7) ◽  
pp. 1867-1881 ◽  
Author(s):  
Andung Bayu Sekaranom ◽  
Hirohiko Masunaga
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Maritime Continent ◽  
Brightness Temperatures ◽  
Extreme Rain ◽  
Trmm Precipitation ◽  
Precipitation Analysis ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Rain Rates

AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.

scholarly journals Spatio-temporal Variability of Lightning Climatology and Its Association With Thunderstorm Indices Over India

2022 ◽  
Author(s):  
Unashish Mondal ◽  
Subrat Kumar Panda ◽  
Someshwar Das ◽  
Devesh Sharma
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Eastern Region ◽  
Lightning Flash ◽  
Time Duration ◽  
Jammu And Kashmir ◽  
Flash Rate ◽  
North Eastern ◽  
Very High Spatial Resolution ◽  
Spatio Temporal ◽  
Imaging Sensor

Abstract Lightning is an electrical discharge - a'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in three days’ span due to lightning events. In this work, Lightning Imaging Sensor (LIS) information from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1 X 0.1 degree has been utilized to create the climatology of India for 16 years from 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite low-resolution monthly time series (LRMTS) with 2.5-degree resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening (1400-1900 Hrs) time duration around 0.001 flashes/km2/hr. The highest lightning occurred in May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon (MAM) ranges from 0.248 – 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 – 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flashes mainly at North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu & Kashmir region. The CAPE and K Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated. This study also focused on finding of lightning hotspots region of India district wise and Rajouri district in Jammu and Kashmir got the highest lightning with 121 flashes/km2/yr.

scholarly journals What Are the Favorable Large-Scale Environments for the Highest-Flash-Rate Thunderstorms on Earth?

2020 ◽  
Vol 77 (5) ◽  
pp. 1583-1612 ◽  
Author(s):  
Nana Liu ◽  
Chuntao Liu ◽  
Baohua Chen ◽  
Edward Zipser
Keyword(s):  
Wind Shear ◽  
Large Scale ◽  
Convective Available Potential Energy ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
Lightning Flash ◽  
Tropical Regions ◽  
Flash Rate ◽  
South Central ◽  
Sahel Region

Abstract A 16-yr Tropical Rainfall Measuring Mission (TRMM) convective feature (CF) dataset and ERA-Interim data are used to understand the favorable thermodynamic and kinematic environments for high-flash-rate thunderstorms globally as well as regionally. We find that intense thunderstorms, defined as having more than 50 lightning flashes within a CF during the ~90-s TRMM overpassing time share a few common thermodynamic features over various regions. These include large convective available potential energy (>1000 J kg−1), small to moderate convection inhibition (CIN), and abundant moisture convergence associated with low-level warm advection. However, each region has its own specific features. Generally, thunderstorms with high lightning flash rates have greater CAPE and wind shear than those with low flash rates, but the differences are much smaller in tropical regions than in subtropical regions. The magnitude of the low- to midtropospheric wind shear is greater over the subtropical regions, including the south-central United States, Argentina, and southwest of the Himalayas, than tropical regions, including central Africa, Colombia, and northwest Mexico, with the exception of Sahel region. Relatively, favorable environments of high-flash-rate thunderstorms in the tropical regions are characterized by higher CAPE, lower CIN, and weaker wind shear compared to the high-flash-rate thunderstorms in the subtropical regions, which have a moderate CAPE and CIN, and stronger low to midtropospheric wind shear.

scholarly journals Electrically Active Convection in Tropical Easterly Waves and Implications for Tropical Cyclogenesis in the Atlantic and East Pacific

2013 ◽  
Vol 141 (2) ◽  
pp. 542-556 ◽  
Author(s):  
Kenneth D. Leppert ◽  
Walter A. Petersen ◽  
Daniel J. Cecil
Keyword(s):  
Brightness Temperature ◽  
Tropical Cyclones ◽  
Tropical Rainfall Measuring Mission ◽  
Meridional Wind ◽  
Tropical Cyclogenesis ◽  
Lightning Flash ◽  
Reanalysis Dataset ◽  
Easterly Waves ◽  
East Pacific ◽  
Environmental Prediction

Abstract In this study, the authors investigated the characteristics of tropical easterly wave convection and the possible implications of convective structure on tropical cyclogenesis and intensification over the Atlantic Ocean and the east Pacific Ocean. Easterly waves were partitioned into northerly, southerly, trough, and ridge phases based on the 700-hPa meridional wind from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. Waves were subsequently divided according to whether they did or did not develop tropical cyclones (i.e., developing and nondeveloping, respectively), and developing waves were further subdivided according to development location. Finally, composites as a function of wave phase and category were created using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager, Precipitation Radar (PR), and Lightning Imaging Sensor as well as infrared (IR) brightness temperature data from the NASA global-merged IR brightness temperature dataset. Results suggest that the convective characteristics that best distinguish developing from nondeveloping waves vary according to where developing waves spawn tropical cyclones. For waves that develop a cyclone in the Atlantic basin, coverage by IR brightness temperatures ≤240 and ≤210 K provide the best distinction between developing and nondeveloping waves. In contrast, several variables provide a significant distinction between nondeveloping waves and waves that develop cyclones over the east Pacific as these waves near their genesis location including IR threshold coverage, lightning flash rates, and low-level (<4.5 km) PR reflectivity. Results of this study may be used to help develop thresholds to better distinguish developing from nondeveloping waves and serve as another aid for tropical cyclogenesis forecasting.

scholarly journals TRMM LIS Climatology of Thunderstorm Occurrence and Conditional Lightning Flash Rates*

2015 ◽  
Vol 28 (16) ◽  
pp. 6536-6547 ◽  
Author(s):  
Daniel J. Cecil ◽  
Dennis E. Buechler ◽  
Richard J. Blakeslee
Keyword(s):  
East Coast ◽  
Eastern China ◽  
Central Africa ◽  
Tropical Rainfall Measuring Mission ◽  
Lightning Flash ◽  
Conditional Mean ◽  
Flash Rate ◽  
Central United States ◽  
Total Lightning ◽  
Imaging Sensor

Abstract The Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite has previously been used to build climatologies of mean lightning flash rate across the global tropics and subtropics. This new work explores climatologies of thunderstorm occurrence as seen by LIS and the conditional mean flash rates when thunderstorms do occur. The region where thunderstorms are seen most often by LIS extends slightly farther east in central Africa than the corresponding region with the highest total mean annual flash rates. Presumably this reflects a difference between more frequent thunderstorm initiation in the east and upscale growth as storms move westward. There are some differences between locations with the greatest total lightning flash counts and those where thunderstorms occur most often. The greatest conditional mean flash rates—considering only those TRMM orbits that do have lightning in a given grid box—are found in subtropical regions. The highest values are in Argentina, with the central United States, Pakistan, eastern China, and the east coast of Australia also having particularly high values.

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