Global Millimeter-Wave Precipitation Retrievals Trained With a Cloud-Resolving Numerical Weather Prediction Model, Part I: Retrieval Design

Surussavadee, Chinnawat; Staelin, D. H.

doi:10.1109/tgrs.2007.908302

Cited by 79 publications

(48 citation statements)

References 28 publications

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“…In Di Tommaso et al (2009) the rain rate retrieval procedure is based on an extensive set of regression curves between TB differences ( 17 , 37 , and between 89 and 150 GHz) and surface rainfall rate in various atmospheric and surface conditions. The third approach is based on the use of NNs (Hall et al, 1999;Staelin et al, 1999;Sorooshian et al, 2000;Chen and Staelin, 2003;Hong et al, 2004;Blackwell and Chen, 2005;Sussuravadee and Staelin, 2007, 2008a, b, 2009Krasnopolsky et al, 2008;Leslie et al, 2008). This approach originates from the consideration that an exact relation between surface rain rate and observed brightness temperatures is nonlinear and difficult to evaluate, as precipitation is one of the most difficult of all atmospheric variables to retrieve.…”

Section: P Sanò Et Al: the Passive Microwave Neural Network Precipimentioning

confidence: 99%

“…Particularly, the principal components (PCs) were not significant as expected (especially for the retrieval of weak and stratiform precipitation), probably because of the a priori condition of using a single network for all background surfaces. In fact, different PCs would have been selected as optimal (in terms of different TB combinations and different order) depending on the type of surface (as obtained in Surussavadee and Staelin, 2008a).…”

Section: The Neural Networkmentioning

confidence: 99%

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

et al. 2015

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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 cloudradiation 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.

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Section: P Sanò Et Al: the Passive Microwave Neural Network Precipimentioning

confidence: 99%

Section: The Neural Networkmentioning

confidence: 99%

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

et al. 2015

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“…NNs have been used in precipitation retrieval -precipitation being one of the most difficult of all atmospheric variables to retrieve -because of the opportunities offered by their ability to learn and generalize (Hsu et al, 1997;Hall et al, 1999;Staelin et al, 1999;Sorooshian et al, 2000;Chen and Staelin, 2003;Hong et al, 2004;Surussavadee and Staelin, 2007, 2008a, 2010Bellerby, 2007;Krasnopolsky et al, 2008;Leslie et al, 2008;Mahesh et al, 2011). However, it should be mentioned that the use of NNs involves the training phase with a large representative database, often obtained from cloud-resolving model simulations.…”

Section: Introductionmentioning

confidence: 99%

“…In PMW precipitation retrieval separate NN algorithms are usually proposed depending on the type of surface (i.e., land or sea) to discriminate between the different precipitation emission signatures relative to background (e.g., Surussavadee and Staelin, 2008a). Separate NN algorithms are also proposed to deal separately with stratiform and convective precipitation (e.g Sarma et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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 precipitationsensing 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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“…We adopt the AMSU precipitation retrieval algorithm that was developed by Surussavadee and Staelin (2008a) at the Massachusetts Institute of Technology (MIT). This algorithm is based on a neural network trained with simulated T B at AMSU frequencies.…”

Section: The Mw Algorithm For Instantaneous Rainfall Retrieval From Smentioning

confidence: 99%

Lightning-based propagation of convective rain fields

Dietrich

Casella

Paola

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements -as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.

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Global Millimeter-Wave Precipitation Retrievals Trained With a Cloud-Resolving Numerical Weather Prediction Model, Part I: Retrieval Design

Cited by 79 publications

References 28 publications

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

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

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

Lightning-based propagation of convective rain fields

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