Abstract
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.
Highlights
IntroductionIn 2016, the European Centre for Medium-Range Weather Forecasts (ECMWF) implemented the Copernicus Climate Change Service (C3S), on behalf of the European
To assess the precipitation classification module (PCM) performance, the ACC, Heideke skill score (HSS), Probability of detection (POD) and False alarm rate (FAR) scores were computed as a function of the detection threshold δ, meaning that Dual-frequency Precipitation Radar (DPR) estimates above δ denoted precipitating conditions
Notice that the variations of δ do not change the proportion of the predicted positives/negatives, to balance the effect of introducing fictitious false alarms by increasing δ, the various indices were computed on the reduced population given by those pixels with DPR rate either equal to 0 mm/h or greater than δ
Summary
In 2016, the European Centre for Medium-Range Weather Forecasts (ECMWF) implemented the Copernicus Climate Change Service (C3S), on behalf of the European. Union, aimed at producing a new set of Essential Climate Variables (ECVs, variables that critically contribute to the characterization of the Earth’s climate) from observations (https://climate.copernicus.eu/c3s312b-essential-climate-variable-products-derivedobservations, accessed on 12 February 2021). Lot 1 contains precipitation as an essential climatic variable. Precipitation plays a crucial role in the global hydrological and energy cycles and in many activities, such as agriculture, management of water resources and natural hazards, weather and hydrological predictions. Accurate global measurements of precipitation are important for these reasons and for understanding the natural variability of the Earth’s climate [1,2,3,4,5,6,7,8,9]
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