Abstract

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.

Highlights

  • The availability of data from the Advanced Technology Microwave Sounder (ATMS), a cross-track scanning radiometer currently onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite (and on the Joint Polar Satellite System (JPSS) series starting in 2017), represents an important step in short- and long-term weather forecasting and environmental monitoring

  • Puts of the Neural networks (NNs) in order to reduce its complexity, key aspects in any NN design. Another difference between Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) v2 and PNPR v1 algorithms is the result of the canonical correlation analysis (CCA) applied to the training database to find the linear combination of TBs (LCT) of selected channels best correlated with surface precipitation rate, to be used as additional input to the network

  • This paper describes the design of a new algorithm, PNPR v2, for estimation of precipitation on the ground for the cross-track ATMS radiometer and presents the results of a verification study where the instantaneous precipitation rate estimates available from TRMM and Global Precipitation Measurement (GPM) spaceborne radars are used as reference

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Summary

Introduction

The availability of data from the Advanced Technology Microwave Sounder (ATMS), a cross-track scanning radiometer currently onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite (and on the Joint Polar Satellite System (JPSS) series starting in 2017), represents an important step in short- and long-term weather forecasting and environmental monitoring. In the design of PNPR v2 important aspects in relation to the topics mentioned above, concerning the choice of the inputs, the number of networks used by the algorithm, and the database used in the training phase, have been thoroughly analyzed and will be presented in this paper Another important issue to consider is that PNPR v2 has been designed in the perspective of the full exploitation of the MW radiometers in the GPM constellation of satellites, and of the achievement of consistency (besides accuracy) of the retrievals from the different sensors.

The ATMS radiometer
Algorithm description
The training database
The neural network
Input selection
Sensitivity analysis
Dataset description
Comparison with TRMM-PR
Comparison with GPM-KuPR
Comparison with PNPR v1
Findings
Summary and conclusion
Full Text
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