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 Microphysical properties of frozen particles inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) polarimetric measurements

2017 ◽  
Vol 17 (4) ◽  
pp. 2741-2757 ◽  
Author(s):  
Jie Gong ◽  
Dong L. Wu
Keyword(s):  
Global Scale ◽  
Dual Frequency ◽  
Melting Layer ◽  
Bell Curve ◽  
Microphysical Properties ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
The Tropics ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract. Scattering differences induced by frozen particle microphysical properties are investigated, using the vertically (V) and horizontally (H) polarized radiances from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) 89 and 166 GHz channels. It is the first study on frozen particle microphysical properties on a global scale that uses the dual-frequency microwave polarimetric signals.From the ice cloud scenes identified by the 183.3 ± 3 GHz channel brightness temperature (Tb), we find that the scattering by frozen particles is highly polarized, with V–H polarimetric differences (PDs) being positive throughout the tropics and the winter hemisphere mid-latitude jet regions, including PDs from the GMI 89 and 166 GHz TBs, as well as the PD at 640 GHz from the ER-2 Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 campaign. Large polarization dominantly occurs mostly near convective outflow regions (i.e., anvils or stratiform precipitation), while the polarization signal is small inside deep convective cores as well as at the remote cirrus region. Neglecting the polarimetric signal would easily result in as large as 30 % error in ice water path retrievals. There is a universal bell curve in the PD–TBV relationship, where the PD amplitude peaks at  ∼  10 K for all three channels in the tropics and increases slightly with latitude (2–4 K). Moreover, the 166 GHz PD tends to increase in the case where a melting layer is beneath the frozen particles aloft in the atmosphere, while 89 GHz PD is less sensitive than 166 GHz to the melting layer. This property creates a unique PD feature for the identification of the melting layer and stratiform rain with passive sensors.Horizontally oriented non-spherical frozen particles are thought to produce the observed PD because of different ice scattering properties in the V and H polarizations. On the other hand, turbulent mixing within deep convective cores inevitably promotes the random orientation of these particles, a mechanism that works effectively in reducing the PD. The current GMI polarimetric measurements themselves cannot fully disentangle the possible mechanisms.

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.

The GPM Operational Radiometer Algorithm - Changes for 2021

2020 ◽  
Author(s):  
Christian Kummerow ◽  
Paula Brown
Keyword(s):  
A Priori ◽  
Microwave Radiometer ◽  
The United States ◽  
Passive Microwave ◽  
Dual Frequency ◽  
Brightness Temperatures ◽  
Precipitation Measurement ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Combined Algorithm

<p>The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States.  GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar’s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.  In May of 2017, GPM released Version 5 of its precipitation products starting with GMI and continuing with the constellation of radiometers.  The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.  Since this release, the Combined Algorithm hydrometeor profiles have shown good consistency with surface observations and computed brightness temperatures agree reasonably well with GMI observations in precipitating regions.  The same is true for MIRS profiles in non-precipitating regions.  Version 7 of the GPROF code will therefore make use of these operational products to construct it's a-priori databases.  This will allow continuous improvements in the a-priori database as these operational products are reprocessed with newer versions, while allowing the user community to better focus on the algorithm’s error covariance matrix and its validation.  Results from early versions of this algorithm will be presented.  In addition to creating an a-priori database that can be more directly updated with improvement to the raining and non-raining scenes, GPROF is also undertaking steps to improve the orographic representation of snow and a Neural Network based Convective/Stratiform classification of precipitation that will both help improve instantaneous correlations with in-situ observations.</p>

scholarly journals The Global Precipitation Measurement (GPM) Mission for Science and Society

2017 ◽  
Vol 98 (8) ◽  
pp. 1679-1695 ◽  
Author(s):  
Gail Skofronick-Jackson ◽  
Walter A. Petersen ◽  
Wesley Berg ◽  
Chris Kidd ◽  
Erich F. Stocker ◽  
...  
Keyword(s):  
Climate Models ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Current Status ◽  
Precipitation Data ◽  
Dual Frequency ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

scholarly journals Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency using Space-borne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

2021 ◽  
Author(s):  
Yasutaka Ikuta ◽  
Masaki Satoh ◽  
Masahiro Sawada ◽  
Hiroshi Kusabiraki ◽  
Takuji Kubota
Keyword(s):  
Cloud Microphysics ◽  
Japan Meteorological Agency ◽  
Dual Frequency ◽  
Microphysics Scheme ◽  
Precipitation Measurement ◽  
Single Column ◽  
Microwave Imager ◽  
Cloud Ice ◽  
Global Precipitation Measurement ◽  
Global Precipitation

AbstractIn this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

scholarly journals Microphysical Properties of Frozen Particles Inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) Polarimetric Measurements

2016 ◽  
Author(s):  
Jie Gong ◽  
Dong L. Wu
Keyword(s):  
Dual Frequency ◽  
Melting Layer ◽  
Bell Curve ◽  
Microphysical Properties ◽  
Precipitation Measurement ◽  
Wave Imaging ◽  
Microwave Imager ◽  
The Tropics ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Abstract. Scattering differences induced by frozen particle microphysical properties are investigated, using the vertically (V) and horizontally (H) polarized radiances from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) 89 and 166 GHz channels. It is the first study on global frozen particle microphysical properties that uses the dual-frequency microwave polarimetric signals. From the ice cloud scenes identified by the 183.3 ± 3 GHz channel brightness temperature (TB), we find that the scatterings of frozen particles are highly polarized with V-H polarimetric differences (PD) being positive throughout the tropics and the winter hemisphere mid-latitude jet regions, including PDs from the GMI 89 and 166 GHz TBs, as well as the PD at 640 GHz from the ER-2 Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 campaign. Large polarization dominantly occurs mostly near convective outflow region (i.e., anvils or stratiform precipitation), while the polarization signal is small inside deep convective cores as well as at the remote cirrus region. Neglecting the polarimetric signal would result in as large as 30 % error in ice water path retrievals. There is a universal "bell-curve" in the PD – TB relationship, where the PD amplitude peaks at ~ 10 K for all three channels in the tropics and increases slightly with latitude. Moreover, the 166 GHz PD tends to increase in the case where a melting layer is beneath the frozen particles aloft in the atmosphere, while 89 GHz PD is less sensitive than 166 GHz to the melting layer. This property creates a unique PD feature for the identification of the melting layer and stratiform rain with passive sensors. Horizontally oriented non-spherical frozen particles are thought to produce the observed PD because of different ice scattering properties in the V and H polarizations. On the other hand, changes in the ice microphysical habitats or orientation due to turbulence mixing can also lead to a reduced PD in the deep convective cores. The current GMI polarimetric measurements themselves cannot fully disentangle the possible mechanisms.

scholarly journals Evaluation of Global Precipitation Measurement Rainfall Estimates against Three Dense Gauge Networks

2018 ◽  
Vol 19 (3) ◽  
pp. 517-532 ◽  
Author(s):  
Jackson Tan ◽  
Walter A. Petersen ◽  
Gottfried Kirchengast ◽  
David C. Goodrich ◽  
David B. Wolff
Keyword(s):  
Land Surface ◽  
A Priori ◽  
Passive Microwave ◽  
Dual Frequency ◽  
Precipitation Measurement ◽  
Precipitation Estimates ◽  
Global Precipitation Measurement ◽  
Global Precipitation ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

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