Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature

Mahesh, C.; Prakash, Satya; Sathiyamoorthy, V.; Gairola, R. M.

doi:10.1016/j.atmosres.2011.09.003

Cited by 23 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…However, some techniques such as principal component analysis (PCA) are applied to the selected channels in order to reduce the number of inputs, reduce the complexity of the NN, and to reduce the noise (e.g., to filter out the signal due to the background surface) (Chen and Staelin, 2003;Staelin, 2008a, 2010;Blackwell and Chen, 2005). Special functions of TBs already proposed for rainfall retrieval (Kidd, 1998;Ferraro and Marks, 1995;Grody, 1991), such as the polarization corrected temperature (PCT 85 ) and the scattering index, have also been considered as the NN inputs (Sarma et al, 2008;Mahesh et al, 2011). Some geographical and meteorological parameters (e.g., surface type, surface height, season, latitude) are often considered as auxiliary input data in order to reduce the ambiguity intrinsic to the PMW precipitation retrievals based only on observed TBs (e.g., Panegrossi et al, 1998;Kummerow et al, 2011;You and Liou, 2012;You et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Therefore, retrieving precipitation information from microwave observations has attracted considerable attention for both global and regional applications [17,18]. Since the 1980s, several methods have been developed to delineate rain areas on the basis of multi-channel passive microwave observations.…”

Section: Introductionmentioning

confidence: 99%

“…Since the 1990s, several types of ANN have been developed to retrieve rain information from satellite observations. For example, Hsu et al [27] proposed an ANN-based precipitation retrieval algorithm, called precipitation estimation from remotely-sensed information using artificial neural networks (PERSIANN), to estimate rain rate with visible and infrared data; Mahesh, Prakash, Sathiyamoorthy and Gairola [17] estimated the rain rate over land and oceans using ANN with the SI and PCT derived from microwave observations as the input and the radar-derived precipitation data as the output. Previous studies showed the remarkable capability of ANN to illustrate the complicated relationship between the satellite observations and precipitation and indicated that it is a promising approach to accurately delineate rain areas.…”

Section: Introductionmentioning

confidence: 99%

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Gonsamo

et al. 2014

Remote Sensing

View full text Add to dashboard Cite

False alarm and misdetected precipitation are prominent drawbacks of high-resolution satellite precipitation datasets, and they usually lead to serious uncertainty in hydrological and meteorological applications. In order to provide accurate rain area delineation for retrieving high-resolution precipitation datasets using satellite microwave observations, a probabilistic neural network (PNN)-based rain area delineation method was developed with rain gauge observations over the Yangtze River Basin and three parameters, including polarization corrected temperature at 85 GHz, difference of brightness temperature at vertically polarized 37 and 19 GHz channels (termed as TB37V and TB19V, respectively) and the sum of TB37V and TB19V derived from the observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The PNN method was validated with independent samples, and the performance of this method was compared with dynamic cluster K-means method, TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product and the threshold method used in the Scatter Index (SI), a widely used microwave-based precipitation retrieval algorithm. Independent validation indicated that the PNN method can provide more reasonable rain areas than the other three methods. OPEN ACCESSRemote Sens. 2014, 6 12119 Furthermore, the precipitation volumes estimated by the SI algorithm were significantly improved by substituting the PNN method for the threshold method in the traditional SI algorithm. This study suggests that PNN is a promising way to obtain reasonable rain areas with satellite observations, and the development of an accurate rain area delineation method deserves more attention for improving the accuracy of satellite precipitation datasets.

show abstract

“…Zhao et al [27] used both SI and PCT to monitor the high rainfall events over China on the basis of linear regression between rain rate and combination of SI and PCT using TMI observation. Mahesh et al [28] used both SI and PCT to estimate the rainfall using artificial neural network. Recently, heavy rainfall events during cyclonic cases over Indian ocean using SI and PCT were studied by Mishra [29].…”

Section: Introductionmentioning

confidence: 99%

Study of Rainfall from TRMM Microwave Imager Observation over India

Mishra

Kumar

2012

ISRN Geophysics

View full text Add to dashboard Cite

This paper presents a technique to estimate precipitation over Indian land • N, 65-99• E) at 0.25• spatial grid using tropical rainfall measuring mission (TRMM) microwave imager (TMI) observations. It adopts the methodology recently developed by Mishra (2012) to monitor the rainfall over the land portion. Regional scattering index (SI) developed for Indian region and polarization corrected temperature (PCT) have been utilized in this study. These proxy rain variables (i.e., PCT and SI) are matched with rainfall from precipitation radar (PR) to relate rain rate with PCT, SI, and their combination. Retrieval techniques have been developed using nonlinear relationship between rain and proxy variables. The results have been compared with the observations (independent of training data set) from PR. Results have also been validated with the observations from automatic weather station (AWS) rain gauges. It is observed from the validation results that nonlinear algorithm using single variable SI underestimates the low rainfall rates (below 20 mm/h) but overestimates the high rain rates (above 20 mm/h). On the other hand, algorithm using PCT overestimates the high rain rates (above 25 mm/h). Validation results with rain gauges show a CC of 0.68 and RMSE of 4.76 mm when both SI and PCT are used.

show abstract

Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature

Cited by 23 publications

References 22 publications

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

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

Delineation of Rain Areas with TRMM Microwave Observations Based on PNN

Study of Rainfall from TRMM Microwave Imager Observation over India

Contact Info

Product

Resources

About