Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

Kidd, Chris; Matsui, Toshi; Ringerud, Sarah

doi:10.3390/rs13050947

Cited by 14 publications

(10 citation statements)

References 36 publications

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“…The PRPS is described in detail in [6,14]. A fundamental design of the PRPS is the independence from any dynamic ancillary datasets (e.g., atmospheric or surface temperature from model reanalysis and other satellite products); that is, the retrieval is based solely upon the satellite radiances linked to precipitation rates through a static a priori database and associated index file.…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…To generate the database, the orbital tracks of the DPR and the ATMS are first analyzed to find crossing locations that occur within 5 min of each other. These crossing points are then used to find coincident DPR and ATMS measurements (see Kidd et al [14]). The DPR observations are averaged over a 3 × 3 window, the center of which is coaligned with the ATMS footprint, thus providing a resolution of about 16.2 × 16.2 km (compared with 15.88 × 15.88 km best resolution of the ATMS at nadir and 17 × 17 km of the high-frequency channels of the TMS).…”

Section: Observational a Priori Databasementioning

confidence: 99%

“…With no correction, the retrieved values of precipitation will vary according to the scan position. Following the operational version of the PRPS retrieval scheme for Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie (SAPHIR) [14], a similar correction methodology is applied to the TMS retrievals. Since the scan angle is included in both the database and the observations, only the database entries that have the same or similar scan angles to that of the observations are considered in the retrieval.…”

Section: Scan Position Correctionmentioning

confidence: 99%

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Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

et al. 2022

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This paper describes the initial results of precipitation estimates from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter-wave Sounder (TMS) using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. The TMS instrument is a 12-channel cross-track scanning radiometer operating at frequencies of 91.655 to 204.8 GHz, providing similar resolutions to current passive microwave sounding instruments. These retrievals showcase the potential of the TMS instrument for precipitation retrievals. The PRPS has been modified for use with the TMS using a database based upon observations from current sounding sensors. The results shown here represent the initial postlaunch version of the retrieval scheme, as analyzed for the Pathfinder CubeSat launched on 30 June 2021. In terms of monthly precipitation estimates, the results fall within the mission specifications and are similar in performance to retrievals from other sounding instruments. At the instantaneous scale, the results are very promising.

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Section: Algorithm Overviewmentioning

confidence: 99%

Section: Observational a Priori Databasementioning

confidence: 99%

Section: Scan Position Correctionmentioning

confidence: 99%

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Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

et al. 2022

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“…Conically-scanning PMW sensors began in the late 1980's, with cross-track scanning PMW sounding instruments from the late 1990's onwards. The latter, although primarily designed for retrieving temperature and humidity (Mo 1995), have proved valuable in increasing the temporal sampling necessary for precipitation measurements (Kidd et al 2016(Kidd et al , 2021Bagaglini et al 2021). The launch of TRMM (Simpson et al 1988;Kummerow et al 1998) in 1997 facilitated multi-sensor retrievals through the inter-calibration of the then-available PMW sensors with the TRMM instruments.…”

Section: Current Precipitation Constellationmentioning

confidence: 99%

The Global Satellite Precipitation Constellation: Current Status and Future Requirements

Kidd

Huffman

Maggioni

et al. 2021

Bulletin of the American Meteorological Society

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To address the need to map precipitation on a global scale a collection of satellites carrying passive microwave (PMW) radiometers has grown over the last 20 years to form a constellation of about 10-12 sensors at any one time. Over the same period, a broad range of science and user communities has become increasingly dependent on the precipitation products provided by these sensors. The constellation presently consists of both conical and cross-track scanning precipitation-capable multi-channel instruments, many of which are beyond their operational and design lifetime but continue to operate through the cooperation of the responsible agencies. The Group on Earth Observations and the Coordinating Group for Meteorological Satellites (CGMS), among other groups, have raised the issue of how a robust, future precipitation constellation should be constructed. The key issues of current and future requirements for the mapping of global precipitation from satellite sensors can be summarised as providing: 1) sufficiently fine spatial resolutions to capture precipitation-scale systems and reduce the beam-filling effects of the observations; 2) a wide channel diversity for each sensor to cover the range of precipitation types, characteristics and intensities observed across the globe; 3) an observation interval that provides temporal sampling commensurate with the variability of precipitation; and 4) precipitation radars and radiometers in low inclination orbit to provide a consistent calibration source, as demonstrated by the first two spaceborne radar/radiometer combinations on the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission Core Observatory (CO). These issues are critical in determining the direction of future constellation requirements, while preserving the continuity of the existing constellation necessary for long-term climate-scale studies.

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“…In this context, satellite borne sensors, providing global observations, play a key role in estimating precipitation, while ground-based measurements provided by rain gauges and radars have limited coverage [6,[9][10][11]. Microwave (MW) sensors, in particular, are essential for the space-based precipitation measurements as, unlike infrared and visible instruments, directly respond to the absorption and scattering of cloud hydrometers (e.g., [2,[12][13][14][15][16]). Opaque channels around 183 GHz, for example, originally designed to retrieve water vapor distribution due to their different sensitivity to specific layers of the atmosphere [17][18][19], have shown great potential for precipitating cloud characterization and for precipitation retrieval.…”

Section: Introductionmentioning

confidence: 99%

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

et al. 2021

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This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

Cited by 14 publications

References 36 publications

Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

The Global Satellite Precipitation Constellation: Current Status and Future Requirements

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

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