Availability of High-Quality TRMM Ground Validation Data from Kwajalein, RMI: A Practical Application of the Relative Calibration Adjustment Technique

Abstract Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.

Download Full-text

Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1700.1 ◽

2005 ◽

Vol 22 (4) ◽

pp. 365-380 ◽

Cited By ~ 125

Author(s):

David B. Wolff ◽

D. A. Marks ◽

E. Amitai ◽

D. S. Silberstein ◽

B. L. Fisher ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Marshall Islands ◽

Error Statistics ◽

Tropical Rainfall ◽

Nasa Goddard Space ◽

Ground Validation ◽

Microwave Imager ◽

Rain Rates ◽

Good Agreement

Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent validation techniques show good agreement between gauge-measured and radar-estimated rainfall. A comparison of the NASA GV products and those developed independently by the University of Washington for a subset of data from the Kwajalein Atoll site also shows good agreement. A comparison of NASA GV rain intensities to satellite retrievals from the TRMM Microwave Imager (TMI), precipitation radar (PR), and Combined (COM) algorithms is presented, and it is shown that the GV and satellite estimates agree quite well over the open ocean.

Download Full-text

Assessing the Relative Performance of Microwave-Based Satellite Rain-Rate Retrievals Using TRMM Ground Validation Data

Journal of Applied Meteorology and Climatology ◽

10.1175/2008jamc2127.1 ◽

2009 ◽

Vol 48 (6) ◽

pp. 1069-1099 ◽

Cited By ~ 41

Author(s):

David B. Wolff ◽

Brad L. Fisher

Keyword(s):

Rain Rate ◽

Flash Flood ◽

Distribution Functions ◽

Relative Performance ◽

Marshall Islands ◽

Climate Data ◽

Validation Data ◽

Surface Precipitation ◽

Ground Validation ◽

Microwave Imager

Abstract Spaceborne microwave sensors provide critical rain information used in several global multisatellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs 4 yr (2003–06) of satellite data to assess the relative performance and skill of the Special Sensor Microwave Imager [SSM/I (F13, F14, and F15], Advanced Microwave Sounding Unit [AMSU-B (N15, N16, and N17)], Advanced Microwave Scanning Radiometer for Earth Observing System [AMSR-E (Aqua)], and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from the TRMM Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ), and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain-rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB were further stratified into ocean, land, and coast categories using a 0.25° terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating–observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm h−1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data were analyzed on monthly scales. These results signal to developers of global rainfall products, such as the TRMM Multisatellite Precipitation Analysis (TMPA) and the Climate Data Center’s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates to provide the highest-quality estimates in their products.

Download Full-text

Evaluation of Radar Rainfall Products: Lessons Learned from the NASA TRMM Validation Program in Florida

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1968.1 ◽

2006 ◽

Vol 23 (11) ◽

pp. 1492-1505 ◽

Cited By ~ 11

Author(s):

Eyal Amitai ◽

David A. Marks ◽

David B. Wolff ◽

David S. Silberstein ◽

Brad L. Fisher ◽

...

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Product Evaluation ◽

Radar Data ◽

Lessons Learned ◽

Limiting Factors ◽

Radar Rainfall ◽

Precipitation Measurement ◽

Ground Validation ◽

Global Precipitation Measurement ◽

Global Precipitation

Abstract Evaluation of the Tropical Rainfall Measuring Mission (TRMM) satellite observations is conducted through a comprehensive ground validation (GV) program. Since the launch of TRMM in late 1997, standardized instantaneous and monthly rainfall products are routinely generated using quality-controlled ground-based radar data adjusted to the gauge accumulations from four primary sites. As part of the NASA TRMM GV program, effort is being made to evaluate these GV products. This paper describes the product evaluation effort for the Melbourne, Florida, site. This effort allows us to evaluate the radar rainfall estimates, to improve the algorithms in order to develop better GV products for comparison with the satellite products, and to recognize the major limiting factors in evaluating the estimates that reflect current limitations in radar rainfall estimation. Lessons learned and suggested improvements from this 8-yr mission are summarized in the context of improving planning for future precipitation missions, for example, the Global Precipitation Measurement (GPM).

Download Full-text

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2008jtecha1175.1 ◽

2009 ◽

Vol 26 (5) ◽

pp. 857-875 ◽

Cited By ~ 33

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.

Download Full-text

Toward Improving Ice Water Content and Snow-Rate Retrievals from Radars. Part II: Results from Three Wavelength Radar–Collocated In Situ Measurements and CloudSat–GPM–TRMM Radar Data

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0164.1 ◽

2018 ◽

Vol 57 (2) ◽

pp. 365-389 ◽

Cited By ~ 15

Author(s):

Andrew Heymsfield ◽

Aaron Bansemer ◽

Norman B. Wood ◽

Guosheng Liu ◽

Simone Tanelli ◽

...

Keyword(s):

Mass Flux ◽

Tropical Rainfall Measuring Mission ◽

Radar Data ◽

Validation Data ◽

Precipitation Measurement ◽

Ice Water Content ◽

Global Precipitation Measurement ◽

Ice Water ◽

Global Precipitation

AbstractTwo methods for deriving relationships between the equivalent radar reflectivity factor Ze and the snowfall rate S at three radar wavelengths are described. The first method uses collocations of in situ aircraft (microphysical observations) and overflying aircraft (radar observations) from two field programs to develop Ze–S relationships. In the second method, measurements of Ze at the top of the melting layer (ML), from radars on the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and CloudSat satellites, are related to the retrieved rainfall rate R at the base of the ML, assuming that the mass flux through the ML is constant. Retrievals of R are likely to be more reliable than S because far fewer assumptions are involved in the retrieval and because supporting ground-based validation data are available. The Ze–S relationships developed here for the collocations and the mass-flux technique are compared with those derived from level 2 retrievals from the standard satellite products and with a number of relationships developed and reported by others. It is shown that there are substantial differences among them. The relationships developed here promise improvements in snowfall-rate retrievals from satellite-based radar measurements.

Download Full-text

Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1827.1 ◽

2006 ◽

Vol 23 (1) ◽

pp. 23-37 ◽

Cited By ~ 32

Author(s):

Christian Kummerow ◽

Wesley Berg ◽

Jody Thomas-Stahle ◽

Hirohiko Masunaga

Keyword(s):

A Priori ◽

Simple Algorithm ◽

Tropical Rainfall Measuring Mission ◽

Radar Data ◽

Global Scale ◽

Time And Space ◽

Validation Data ◽

Brightness Temperatures ◽

Time Domains ◽

Imperfect Knowledge

Abstract While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world’s oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25% uncertainty that cannot be detected by a radiometer alone. Of this value, 20% is attributed to changes in cloud morphology and microphysics, while 5% is a result of changes in the rain/no-rain thresholds. All but 2%–3% of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined.

Download Full-text

Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-13-0107.1 ◽

2014 ◽

Vol 71 (4) ◽

pp. 1353-1370 ◽

Cited By ~ 3

Author(s):

Sabrina Gentile ◽

Rossella Ferretti ◽

Frank Silvio Marzano

Keyword(s):

High Resolution ◽

Conceptual Model ◽

Vertical Structure ◽

Mesoscale Model ◽

Tropical Rainfall Measuring Mission ◽

Sea Breeze ◽

Radar Data ◽

Full Life Cycle ◽

Resolution Model ◽

High Resolution Model

Abstract One event of a tropical thunderstorm typically observed in northern Australia, known as Hector, is investigated using high-resolution model output from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) observations from a ground-based weather radar located in Berrimah (Australia) and data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The analysis is carried out by tracking the full life cycle of Hector from prestorm stage to the decaying stage. In both the prestorm stage, characterized by nonprecipitating cells, and the triggering stage, when the Hector storm is effectively initiated, an analysis is performed with the aid of high-spatial-and-temporal-resolution MM5 output and the Berrimah ground-based radar imagery. During the mature (“old”) stage of Hector, considering the conceptual model for tropical convection suggested by R. Houze, TRMM Microwave Imager satellite-based data were added to ground-based radar data to analyze the storm vertical structure (dynamics, thermodynamics, and hydrometeor contents). Model evaluation with respect to observations (radar reflectivity and TRMM data) suggests that MM5 performed fairly well in reproducing the dynamics of Hector, providing support to the assertion that the strength of convection, in terms of vertical velocity, largely contributes to the vertical distribution of hydrometeors. Moreover, the stages of the storm and its vertical structure display good agreement with Houze’s aforementioned conceptual model. Finally, it was found that the most important triggering mechanisms for this Hector event are topography, the sea breeze, and a gust front produced by previous convection.

Download Full-text

Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding

Computational Intelligence and Neuroscience ◽

10.1155/2021/4296247 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Senhong Wang ◽

Jiangzhong Cao ◽

Fangyuan Lei ◽

Qingyun Dai ◽

Shangsong Liang ◽

...

Keyword(s):

State Of The Art ◽

Graph Embedding ◽

Multiple Views ◽

Time Cost ◽

Practical Application ◽

Real World Data ◽

High Quality ◽

Free Graph ◽

Anchor Points ◽

Almost All

A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data. However, real-world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi-view clustering models do not execute incomplete multi-view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high-quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter-free graph fusion mechanism which directly coalesces the view-wise graphs. To this end, the proposed method can not only deal with complete multi-view clustering well but also be easily extended to incomplete multi-view cases. Experimental results clearly show that our algorithm surpasses some state-of-the-art competitors in clustering ability and time cost.

Download Full-text

Análisis comparativo de una metaheurística en base a algoritmo genético vs un método de ramificación y corte para un caso de entrega y recolección con restricciones de ventana de horario

ENERO-JUNIO 2021 - Revista Innovaciones de Negocios ◽

10.29105/rinn1.2-4 ◽

2017 ◽

Vol 1 (2) ◽

Author(s):

Fabian López

Keyword(s):

Time Window ◽

Time Frame ◽

Branch And Cut ◽

Pickup And Delivery ◽

Np Hard ◽

Practical Application ◽

High Quality ◽

Method Of Solution ◽

Time Window Constraints ◽

Computational Resources

Palabras claves: Algoritmos genéticos, logística de ruteo, metaheuristicas, secuenciaciónResumen. En la solución de problemas combinatorios, es importante evaluar el costo-beneficio entre la obtención de soluciones de alta calidad en detrimento de los recursos computacionales requeridos. El problema planteado es para el ruteo de un vehículo con entrega y recolección de producto y con restricciones de ventana de horario. En la práctica, dicho problema requiere ser atendido con instancias de gran escala (nodos ≥100). Existe un fuerte porcentaje de ventanas de horario activas (≥90%) y con factores de amplitud ≥75%. El problema es NP-hard y por tal motivo la aplicación de un método de solución exacta para resolverlo en la práctica, está limitado por el tiempo requerido para la actividad de ruteo. Se propone un algoritmo genético especializado, el cual ofrece soluciones de buena calidad (% de optimalidad aceptables) y en tiempos de ejecución computacional que hacen útil su aplicación en la práctica de la logística. Para comprobar la eficacia de la propuesta algorítmica se desarrolla un diseño experimental el cual hará uso de las soluciones óptimas obtenidas mediante un algoritmo de ramificación y corte sin límite de tiempo. Los resultados son favorables.Key words: Genetic algorithms, routing logistics, metaheuristics, schedulingAbstract. In an attempt to sovle the combinatorics problems, it is important to evaluate the costbenefit ratio between obtaining solutions of high quality and the loss of the computational resources required. The problem presented is for the routing of a vehicle with pickup and delivery of products with time window constraints. This problem requires instances of great scale (nodes≥100). A strong active time window percentage exists (≥90%) with factors of amplitude ≥75%. The problem is NP-hard and hence, the application of an exact method of solution, is limited by the time frame required for routing activity. A specialized genetic algorithm is proposed, which offers solutions of high precision and in computational times that makes its practical application useful. An experimental design is developed with good results that makes use of optimum solutions obtained by means of branch and cut algorithm without time limit.

Download Full-text