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Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and...
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Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (31 October 2017) | Viewed by 47208

Special Issue Editor

Dr. Zhong Liu

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems (CSISS), George Mason University; NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) Code 610.2, Greenbelt, MD 20771, USA
Interests: remote sensing; Precipitation research and applications; Earth Observing Data and Information Systems; interdisciplinary research; TRMM/GPM service development and user support

Special Issue Information

Dear Colleagues,

Precipitation is a critical variable in the Earth’s water cycle, as well as in other areas, such as agriculture sustainability, infectious diseases, etc. Launched on 27 November, 1997, the Tropical Rainfall Measuring Mission (TRMM) is a joint NASA-JAXA mission to measure four-dimensional rainfall and latent heating over vast and undersampled tropical and subtropical oceans and continents. The TRMM mission ended in April 2015 and TRMM data from the precipitation related instruments, such as the first space-borne precipitation radar (PR), the TRMM Microwave Imager (TMI), the Visible and Infrared Scanner (VIRS), have been collected over the 17 period. The NASA Goddard Earth Science (GES) Data and Information Services Center (DISC) archives and distributes TRMM precipitation products, ranging from Level-1 orbital raw data to Level-3 gridded data including multi-sensor, multi-satellite merged near-real-time and research products and their ancillary data products.  Over the years, a wide variety of research and applications using TRMM data such as ENSO, MJO, floods, droughts, etc. have been reported. This Special Issue of Climate provides an opportunity for researchers and application developers to publish their original articles about their latest advances and activities in TRMM-related research and applications around the world. In particular, we are soliciting articles in the following areas:

-Climate research and modeling (process studies, model development, etc.)
-Hydrological research and modeling (process studies, benchmarking, modeling development, etc.)
-Regional TRMM algorithm validation and enhancement. Regional comparison between TRMM and GPM (Global Precipitation Measurement) precipitation products.
-TRMM data applications (i.e., human health, water resources, food production, sustainability, short-term extreme events, etc.)
-TRMM value-added data and services

Zhong Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • TRMM
  • precipitation
  • rainfall
  • climate research
  • water cycle
  • applications

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Published Papers (6 papers)

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Research

7004 KiB  
Article
Assessing Radiometric Stability of the 17-Plus-Year TRMM Microwave Imager 1B11 Version-8 (GPM05) Brightness Temperature Product
by Ruiyao Chen, Faisal Alquaied and W. Linwood Jones
Climate 2017, 5(4), 92; https://doi.org/10.3390/cli5040092 - 7 Dec 2017
Cited by 3 | Viewed by 3927
Abstract
The NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has produced a 17-plus-year time-series of calibrated microwave radiances that have remarkable value for investigating the effects of the Earth’s climate change over the tropics. Recently, the Global Precipitation Measurement (GPM) Inter-Satellite Radiometric [...] Read more.
The NASA Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has produced a 17-plus-year time-series of calibrated microwave radiances that have remarkable value for investigating the effects of the Earth’s climate change over the tropics. Recently, the Global Precipitation Measurement (GPM) Inter-Satellite Radiometric Calibration (XCAL) Working Group have performed various calibration and corrections that yielded the legacy TMI 1B11 Version 8 (also called GPM05) brightness temperature product, which will be released in late 2017 by the NASA Precipitation Processing System. Since TMI served as the radiometric transfer standard for the TRMM constellation microwave radiometer sensors, it is important to document its accuracy. In this paper, the various improvements applied to TMI 1B11 V8 are summarized, and the radiometric calibration stability is evaluated by comparisons with a radiative transfer model and by XCAL evaluations with the Global Precipitation Measuring Microwave Imager during their 13-month overlap period. Evaluation methods will be described and results will be presented, which demonstrate that TMI has achieved a radiometric stability level of a few deciKelvin over almost two decades. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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10473 KiB  
Article
Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling
by Mauro Rossi, Dalia Kirschbaum, Daniela Valigi, Alessandro Cesare Mondini and Fausto Guzzetti
Climate 2017, 5(4), 90; https://doi.org/10.3390/cli5040090 - 3 Dec 2017
Cited by 29 | Viewed by 11726
Abstract
Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the [...] Read more.
Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database “Verifica Rischio Frana”, VRF) are compared with the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) products. For the purpose, we couple point gauge and satellite rainfall estimates at individual grid cells, evaluating the correlation between gauge and satellite data in different morpho-climatological conditions. We then analyze the statistical distributions of both rainfall data types and the rainfall events derived from them. Results show that satellite data underestimates ground data, with the largest differences in mountainous areas. Power-law models, are more appropriate to correlate gauge and satellite data. The gauge and satellite-based products exhibit different statistical distributions and the rainfall events derived from them differ. In conclusion, satellite rainfall cannot be directly compared with ground data, requiring local investigation to account for specific morpho-climatological settings. Results suggest that satellite data can be used for forecasting landslides, only performing a local scaling between satellite and ground data. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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2912 KiB  
Article
Application of Satellite-Based Precipitation Estimates to Rainfall-Runoff Modelling in a Data-Scarce Semi-Arid Catchment
by Peshawa M. Najmaddin, Mick J. Whelan and Heiko Balzter
Climate 2017, 5(2), 32; https://doi.org/10.3390/cli5020032 - 11 Apr 2017
Cited by 24 | Viewed by 6874
Abstract
Rainfall-runoff modelling is a useful tool for water resources management. This study presents a simple daily rainfall-runoff model, based on the water balance equation, which we apply to the 11,630 km2 Lesser Zab catchment in northeast Iraq. The model was forced by [...] Read more.
Rainfall-runoff modelling is a useful tool for water resources management. This study presents a simple daily rainfall-runoff model, based on the water balance equation, which we apply to the 11,630 km2 Lesser Zab catchment in northeast Iraq. The model was forced by either observed daily rain gauge data from four stations in the catchment or satellite-derived rainfall estimates from two TRMM Multi-satellite Precipitation Analysis (TMPA) data products (TMPA-3B42 and 3B42RT) based on the Tropical Rainfall Measuring Mission (TRMM) from 2003 to 2014. As well as using raw TMPA data, we used a bias-correction method to adjust TMPA values based on rain gauge data. The uncorrected TMPA data products underestimated observed mean catchment rainfall by −10.1% and −10.7%. Corrected data also slightly underestimated gauged rainfall by −0.7% and −1.6%, respectively. Nash-Sutcliffe Efficiency (NSE) and Pearson’s Correlation Coefficient (r) for the model fit with the observed hydrograph were 0.75 and 0.87, respectively, for a calibration period (2010–2011) using gauged rainfall data. Model validation performance (2012–2014) was best (highest NSE and r; lowest RMSE and bias) using the corrected 3B42 data product and poorest when driven by uncorrected 3B42RT data. Uncertainty and equifinality were also explored. Our results suggest that TRMM data can be used to drive rainfall-runoff modelling in semi-arid catchments, particularly when corrected using rain gauge data. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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27827 KiB  
Article
Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India
by Akhilesh S. Nair and J. Indu
Climate 2017, 5(1), 2; https://doi.org/10.3390/cli5010002 - 11 Jan 2017
Cited by 46 | Viewed by 8109
Abstract
Error characterization is vital for the advancement of precipitation algorithms, the evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) has been a benchmark for successive Global Precipitation Measurement (GPM) [...] Read more.
Error characterization is vital for the advancement of precipitation algorithms, the evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) has been a benchmark for successive Global Precipitation Measurement (GPM) based products. This has given way to the evolution of many multi-satellite precipitation products. This study evaluates the performance of the newly released multi-satellite Multi-Source Weighted-Ensemble Precipitation (MSWEP) product, whose temporal variability was determined based on several data products including TMPA 3B42 RT. The evaluation was conducted over India with respect to the IMD-gauge-based rainfall for pre-monsoon, monsoon, and post monsoon seasons at daily scale for a 35-year (1979–2013) period. The rainfall climatology is examined over India and over four geographical extents within India known to be subject to uniform rainfall. The performance evaluation of rainfall time series was carried out. In addition to this, the performance of the product over different rainfall classes was evaluated along with the contribution of each class to the total rainfall. Further, seasonal evaluation of the MSWEP products was based on the categorical and volumetric indices from the contingency table. Upon evaluation it was observed that the MSWEP products show large errors in detecting the higher quantiles of rainfall (>75th and > 95th quantiles). The MSWEP precipitation product available at a 0.25° × 0.25° spatial resolution and daily temporal resolution matched well with the daily IMD rainfall over India. Overall results suggest that a suitable region and season-dependent bias correction is essential before its integration in hydrological applications. While the MSWEP was observed to perform well for daily rainfall, it suffered from poor detection capabilities for higher quantiles, making it unsuitable for the study of extremes. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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2525 KiB  
Article
Evaluation of TRMM 3B42 V7 Rainfall Product over the Oum Er Rbia Watershed in Morocco
by Hamza Ouatiki, Abdelghani Boudhar, Yves Tramblay, Lionel Jarlan, Tarik Benabdelouhab, Lahoucine Hanich, M. Rachid El Meslouhi and Abdelghani Chehbouni
Climate 2017, 5(1), 1; https://doi.org/10.3390/cli5010001 - 4 Jan 2017
Cited by 71 | Viewed by 9228
Abstract
In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall [...] Read more.
In arid and semi-arid areas, rainfall is often characterized by a strong spatial and temporal variability. These environmental factors, combined with the sparsity of the measurement networks in developing countries, constitute real constraints for water resources management. In recent years, several spatial rainfall measurement sources have become available, such as TRMM data (Tropical Rainfall Measurement Mission). In this study, the TRMM 3B42 Version 7 product was evaluated using rain gauges measurements from 19 stations in the Oum-Er-Bia (OER) basin located in the center of Morocco. The relevance of the TRMM product was tested by direct comparison with observations at different time scales (daily, monthly, and annual) between 1998 and 2010. Results show that the satellite product provides poor estimations of rainfall at the daily time scale giving an average Pearson correlation coefficient (r) of 0.2 and average Root Mean Square Error (RMSE) of 10 mm. However, the accuracy of TRMM rainfall is improved when temporally averaged to monthly time scale (r of 0.8 and RMSE of 28 mm) or annual time scale (r of 0.71 and RMSE of 157 mm). Moreover, improved correlation with observed data was obtained for data spatially averaged at the watershed scale. Therefore, at the monthly and annual time scales, TRMM data can be a useful source of rainfall data for water resources monitoring and management in ungauged basins in semi-arid regions. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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3700 KiB  
Article
A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input
by Robert M. Parinussa, Richard A. M. De Jeu, Robin Van der Schalie, Wade T. Crow, Fangni Lei and Thomas R. H. Holmes
Climate 2016, 4(4), 50; https://doi.org/10.3390/cli4040050 - 12 Oct 2016
Cited by 20 | Viewed by 5855
Abstract
Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth’s surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1–2 [...] Read more.
Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth’s surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1–2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E’s day- and night-time observations. Full article
(This article belongs to the Special Issue Using Tropical Rainfall Measuring Mission (TRMM) Data in Research and Applications)
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