Development of a precipitation-retrieval scheme for cross-track passive microwave sounding instruments

2022 ◽  
pp. 287-304
Author(s):  
Chris Kidd ◽  
Toshihisa Matsui ◽  
Sarah Ringerud
Keyword(s):  
Passive Microwave ◽  
Retrieval Scheme ◽  
Microwave Sounding ◽  
Cross Track

scholarly journals Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme

2015 ◽  
Vol 17 (1) ◽  
pp. 383-400 ◽  
Author(s):  
Chris Kidd ◽  
Toshihisa Matsui ◽  
Jiundar Chern ◽  
Karen Mohr ◽  
Chris Kummerow ◽  
...  
Keyword(s):  
Western Europe ◽  
The United States ◽  
Passive Microwave ◽  
The Arctic ◽  
Temporal Sampling ◽  
Physically Based ◽  
Retrieval Scheme ◽  
Cross Track ◽  
Global Precipitation ◽  
Mean Square Errors

Abstract The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

scholarly journals Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References

2008 ◽  
Vol 47 (12) ◽  
pp. 3170-3187 ◽  
Author(s):  
Xin Lin ◽  
Arthur Y. Hou
Keyword(s):  
United States ◽  
Tropical Rainfall Measuring Mission ◽  
Passive Microwave ◽  
Earth Observing System ◽  
Temporal Sampling ◽  
Trmm Pr ◽  
Microwave Imager ◽  
Cross Track ◽  
Microwave Sensors ◽  
Rainfall Estimates

Abstract This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.

scholarly journals The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

2016 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna Cinzia Marra ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Passive Microwave ◽  
Precipitation Radar ◽  
Water Vapor Absorption ◽  
The Neural Network ◽  
Trmm Pr ◽  
The Cross ◽  
Cross Track ◽  
Good Agreement

Abstract. The objective of this paper is to describe the development and evaluate the performance of a totally new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track ATMS radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered . The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area, the statistical analysis was carried out for a two-year (2013-2014) dataset of coincident observations, over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with general better estimate of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

scholarly journals The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

2021 ◽  
Author(s):  
Andrea Camplani ◽  
Daniele Casella ◽  
Paolo Sanò ◽  
Giulia Panegrossi
Keyword(s):  
Snow Cover ◽  
Classification Algorithm ◽  
Passive Microwave ◽  
Radiative Properties ◽  
Maximum Temperature ◽  
Satellite Measurements ◽  
Cold Surface ◽  
Surface Classification ◽  
Microwave Radiometers ◽  
Cross Track

AbstractThis paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.

scholarly journals The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU/MHS observations: description and application to European case studies

2015 ◽  
Vol 8 (2) ◽  
pp. 837-857 ◽  
Author(s):  
P. Sanò ◽  
G. Panegrossi ◽  
D. Casella ◽  
F. Di Paola ◽  
L. Milani ◽  
...  
Keyword(s):  
Neural Network ◽  
Case Studies ◽  
Heavy Rain ◽  
Passive Microwave ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Precipitation Pattern ◽  
Neural Network Approach ◽  
Cross Track

Abstract. The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

scholarly journals Beyond the Pixel: Using Patterns and Multiscale Spatial Information to Improve the Retrieval of Precipitation from Spaceborne Passive Microwave Imagers

2020 ◽  
Vol 37 (9) ◽  
pp. 1571-1591 ◽  
Author(s):  
Clément Guilloteau ◽  
Efi Foufoula-Georgiou
Keyword(s):  
Spatial Information ◽  
Quantitative Estimation ◽  
Passive Microwave ◽  
Temporal Organization ◽  
Brightness Temperatures ◽  
Retrieval Scheme ◽  
Statistical Relationships ◽  
Convective Cells ◽  
Convolution Filters ◽  
Observation Geometry

AbstractThe quantitative estimation of precipitation from orbiting passive microwave imagers has been performed for more than 30 years. The development of retrieval methods consists of establishing physical or statistical relationships between the brightness temperatures (TBs) measured at frequencies between 5 and 200 GHz and precipitation. Until now, these relationships have essentially been established at the “pixel” level, associating the average precipitation rate inside a predefined area (the pixel) to the collocated multispectral radiometric measurement. This approach considers each pixel as an independent realization of a process and ignores the fact that precipitation is a dynamic variable with rich multiscale spatial and temporal organization. Here we propose to look beyond the pixel values of the TBs and show that useful information for precipitation retrieval can be derived from the variations of the observed TBs in a spatial neighborhood around the pixel of interest. We also show that considering neighboring information allows us to better handle the complex observation geometry of conical-scanning microwave imagers, involving frequency-dependent beamwidths, overlapping fields of view, and large Earth incidence angles. Using spatial convolution filters, we compute “nonlocal” radiometric parameters sensitive to spatial patterns and scale-dependent structures of the TB fields, which are the “geometric signatures” of specific precipitation structures such as convective cells. We demonstrate that using nonlocal radiometric parameters to enrich the spectral information associated to each pixel allows for reduced retrieval uncertainty (reduction of 6%–11% of the mean absolute retrieval error) in a simple k-nearest neighbors retrieval scheme.

scholarly journals The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

2016 ◽  
Vol 9 (11) ◽  
pp. 5441-5460 ◽  
Author(s):  
Paolo Sanò ◽  
Giulia Panegrossi ◽  
Daniele Casella ◽  
Anna C. Marra ◽  
Francesco Di Paola ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Passive Microwave ◽  
Advanced Technology ◽  
Precipitation Radar ◽  
The Neural Network ◽  
Trmm Pr ◽  
The Cross ◽  
Cross Track ◽  
Good Agreement

Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5°  ×  0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

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