scholarly journals Comparative Strengths of SCaMPR Satellite QPEs with and without TRMM Ingest versus Gridded Gauge-Only Analyses

2013 ◽  
Vol 14 (1) ◽  
pp. 153-170 ◽  
Author(s):  
Yu Zhang ◽  
Dong-Jun Seo ◽  
David Kitzmiller ◽  
Haksu Lee ◽  
Robert J. Kuligowski ◽  
...  
Keyword(s):  
False Alarm ◽  
Tropical Rainfall Measuring Mission ◽  
Detection Rates ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Quantitative Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract This paper assesses the accuracy of satellite quantitative precipitation estimates (QPEs) from two versions of the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm relative to that of gridded gauge-only QPEs. The second version of SCaMPR uses the QPEs from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and Microwave Imager as predictands whereas the first version does not. The assessments were conducted for 22 catchments in Texas and Louisiana against National Weather Service operational multisensor QPE. Particular attention was given to the density below which SCaMPR QPEs outperform gauge-only QPEs and effects of TRMM ingest. Analyses indicate that SCaMPR QPEs can be competitive in terms of correlation and CSI against sparse gauge networks (with less than one gauge per 3200–12 000 km2) and over 1–3-h scale, but their relative strengths diminish with temporal aggregation. In addition, the major advantage of SCaMPR QPEs is its relatively low false alarm rates, whereas gauge-only QPEs exhibit better skill in detecting rainfall—though the detection skill of SCaMPR QPEs tends to improve at higher rainfall thresholds. Moreover, it was found that ingesting TRMM QPEs help mitigate the positive overall bias in SCaMPR QPEs, and improve the detection of moderate–heavy and particularly wintertime precipitation. Yet, it also tends to elevate the false alarm rate, and its impacts on detection rates can be slightly negative for summertime storms. The implications for adoption of TRMM and Global Precipitation Measurement (GPM) QPEs for NWS operations are discussed.

scholarly journals Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

2019 ◽  
Vol 11 (6) ◽  
pp. 697 ◽  
Author(s):  
Fenglin Xu ◽  
Bin Guo ◽  
Bei Ye ◽  
Qia Ye ◽  
Huining Chen ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Accurate Estimation ◽  
Detection Capability ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Daily Precipitation Data ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Precipitation Events

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC > 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (>50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

scholarly journals Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

2020 ◽  
Author(s):  
Lisa Milani ◽  
Mark S. Kulie ◽  
Daniele Casella ◽  
Pierre E. Kirstetter ◽  
Giulia Panegrossi ◽  
...  
Keyword(s):  
Ad Hoc ◽  
A Priori ◽  
Precipitable Water ◽  
The United States ◽  
Lake Effect ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Quantitative Precipitation Estimates ◽  
Global Precipitation

AbstractThis study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

scholarly journals SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

2018 ◽  
Vol 10 (8) ◽  
pp. 1278 ◽  
Author(s):  
Jean-François Rysman ◽  
Giulia Panegrossi ◽  
Paolo Sanò ◽  
Anna Marra ◽  
Stefano Dietrich ◽  
...  
Keyword(s):  
False Alarm ◽  
Classification Scheme ◽  
Retrieval Algorithm ◽  
False Alarm Ratio ◽  
Water Path ◽  
Precipitation Measurement ◽  
Snow Water ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

scholarly journals Performance Evaluation of Version 5 (V05) of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) over the Tianshan Mountains of China

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1139 ◽  
Author(s):  
Min Yang ◽  
Zhongqin Li ◽  
Muhammad Naveed Anjum ◽  
Yayu Gao
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Tianshan Mountains ◽  
Spatiotemporal Variability ◽  
Precipitation Measurement ◽  
Satellite Retrievals ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Better Than

This study evaluated the performance of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) version 5 (V05) Early-run and Final-run (IMERG-E and IMERG-F, respectively) products over the Tianshan Mountains. For comparison, the accuracies of two Tropical Rainfall Measuring Mission (TRMM) products (3B42RT and 3B42V7) were also analyzed. Performance of the satellite-based precipitation products (SPPs) was analyzed at daily to annual scales from April 2014 to October 2017. Results showed that: (1) IMERG-F and 3B42V7 performed better than IMERG-E and 3B42RT in the characterization of spatiotemporal variability of precipitation; (2) Precipitation estimates from IMERG-F were in the best overall agreement with the gauge-based data, followed by IMERG-E and 3B42V7 on all temporal scales; (3) IMERG-E and 3B42RT products were failed to provide accurate precipitation amounts, whereas IMERG-F and 3B42V7 were able to provide accurate precipitation estimates with the lowest relative biases (4.98% and −1.71%, respectively) and RMSE (0.58 mm/day and 0.76 mm/day, respectively); (4) The enhancement from the IMERG Early-run to the Final-run to capture the moderate to heavy precipitation events was not evident; (5) On seasonal scale, IMEGR-F performed better than all other SPPs, particularly during the spring season with negligible bias (0.28%). It was deduced that IMERG-F was capable of replacing TRMM products.

scholarly journals Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

2014 ◽  
Vol 31 (9) ◽  
pp. 1902-1921 ◽  
Author(s):  
Ji-Hye Kim ◽  
Mi-Lim Ou ◽  
Jun-Dong Park ◽  
Kenneth R. Morris ◽  
Mathew R. Schwaller ◽  
...  
Keyword(s):  
Korean Peninsula ◽  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
International Partnerships ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR − PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between −2.0 and +1.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.

scholarly journals TRMM Microwave Imager (TMI) Alignment and Along-Scan Bias Corrections

2018 ◽  
Vol 35 (7) ◽  
pp. 1457-1470 ◽  
Author(s):  
Rachael Kroodsma ◽  
Stephen Bilanow ◽  
Darren McKague
Keyword(s):  
Processing System ◽  
Tropical Rainfall Measuring Mission ◽  
Temperature Bias ◽  
Data Product ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Trmm Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Bias Corrections

AbstractThe Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) has been updated to a final version following the decommissioning of the TRMM satellite in April 2015. The updates are based on increased knowledge of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI updates. This paper discusses two aspects of the TMI data product that have been reanalyzed and updated: alignment and along-scan bias corrections. The TMI’s pointing accuracy is significantly improved over prior PPS versions, which used at-launch alignment values. A TMI instrument mounting offset is discovered as well as new alignment offsets for the two TMI feedhorns. The original TMI along-scan antenna temperature bias correction is found to be generally accurate over ocean, but a scene temperature-dependent correction is needed to account for edge-of-scan obstruction. These updates are incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI.

The GPM Combined Algorithm

2016 ◽  
Vol 33 (10) ◽  
pp. 2225-2245 ◽  
Author(s):  
Mircea Grecu ◽  
William S. Olson ◽  
Stephen Joseph Munchak ◽  
Sarah Ringerud ◽  
Liang Liao ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Environmental Data ◽  
Dual Frequency ◽  
Precipitation Measurement ◽  
Deconvolution Technique ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Combined Algorithm

AbstractIn this paper, the operational Global Precipitation Measurement (GPM) mission combined radar–radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss–Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals.

scholarly journals TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suite

2018 ◽  
Vol 35 (6) ◽  
pp. 1181-1199 ◽  
Author(s):  
E. F. Stocker ◽  
F. Alquaied ◽  
S. Bilanow ◽  
Y. Ji ◽  
L. Jones
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Initial Step ◽  
Dual Frequency ◽  
Precipitation Radar ◽  
Precipitation Measurement ◽  
Trmm Precipitation ◽  
Microwave Imager ◽  
Using Data ◽  
Trmm Microwave Imager ◽  
Global Precipitation

AbstractThe National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.

scholarly journals Benefits of the Successive GPM Based Satellite Precipitation Estimates IMERG–V03, –V04, –V05 and GSMaP–V06, –V07 Over Diverse Geomorphic and Meteorological Regions of Pakistan

2018 ◽  
Vol 10 (9) ◽  
pp. 1373 ◽  
Author(s):  
Frédéric Satgé ◽  
Yawar Hussain ◽  
Marie-Paule Bonnet ◽  
Babar Hussain ◽  
Hernan Martinez-Carvajal ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Global Scale ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Potential Benefits ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Insight Into ◽  
Statistical Metrics

Launched in 2014, the Global Precipitation Measurement (GPM) mission aimed at ensuring the continuity with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 that has provided unprecedented accuracy in Satellite Precipitation Estimates (SPEs) on the near-global scale. Since then, various SPE versions have been successively made available from the GPM mission. The present study assesses the potential benefits of the successive GPM based SPEs product versions that include the Integrated Multi–Satellite Retrievals for GPM (IMERG) version 3 to 5 (–v03, –v04, –v05) and the Global Satellite Mapping of Precipitation (GSMaP) version 6 to 7 (–v06, –v07). Additionally, the most effective TRMM based SPEs products are also considered to provide a first insight into the GPM effectiveness in ensuring TRMM continuity. The analysis is conducted over different geomorphic and meteorological regions of Pakistan while using 88 precipitations gauges as the reference. Results show a clear enhancement in precipitation estimates that were derived from the very last IMERG–v05 in comparison to its two previous versions IMERG–v03 and –v04. Interestingly, based on the considered statistical metrics, IMERG–v03 provides more consistent precipitation estimate than IMERG–v04, which should be considered as a transition IMERG version. As expected, GSMaP–v07 precipitation estimates are more accurate than the previous GSMaP–v06. However, the enhancement from the old to the new version is very low. More generally, the transition from TRMM to GPM is successful with an overall better performance of GPM based SPEs than TRMM ones. Finally, all of the considered SPEs have presented a strong spatial variability in terms of accuracy with none of them outperforming the others, for all of the gauges locations over the considered regions.

Sign in / Sign up

Export Citation Format

Share Document