CloudSat-based assessment of GPM Microwave Imager snowfall observation capabilities

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60◦N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water,and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166∆TB)and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW \u3e 3.6 kg·m−2, IWP \u3e 0.24 kg·m−2 over land, and SIC \u3e 57%, TPW \u3e 5.1 kg·m−2 over sea). The complex combined 166∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms

SLALOM: An all-surface snow water path retrieval algorithm for the GPM microwave imager

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI

The Cloud dynamics and radiation database (CDRD) approach for precipitation retrieval by means of satellite based microwave radiometry

Questa tesi affronta alcuni aspetti della stima delle precipitazioni attraverso misure a microonde effettuate da satellite. Sebbene le tecniche di telerilevamento con radiometri a microonde per la stima delle precipitazioni si siano sviluppate notevolmente negli ultimi anni, ulteriori sviluppi sono apparsi necessari ed alcuni aspetti di queste tecniche costituiscono temi di ricerca attualmente in corso. L’attività svolta in questa tesi ha riguardato, in primo luogo, la costruzione e lo sviluppo dell’algoritmo BAMPR (Bayesian Algorithm for Microwave-based Precipitation Retrieval), basato sulla teoria di Bayes, per la stima della precipitazione attraverso l’elaborazione dei dati (temperature di brillanza) forniti dai radiometri SSM/I, SSMIS e AMSR-E. La prima parte della tesi descrive l’algoritmo completo realizzato, richiamando la teoria di Bayes e descrivendo anche le tecniche di “screening” utilizzate per il corretto trattamento dei dati dei vari “pixel”. Particolare attenzione è stata data inoltre alla descrizione della base di dati usata (CRD - Cloud Radiation Database) e ai test effettuati su di essa. La seconda parte dell’attività ha riguardato l’introduzione, nella procedura di stima della precipitazione, di variabili dinamiche e termodinamiche (“dynamical tags”) da associare alle temperature di brillanza (Cloud Dynamics and Radiation Database (CDRD) approach). Questo sviluppo dell’algoritmo è mirato alla riduzione dei problemi di “ambiguità” (o “non unicità”) che rappresentano attualmente un grave limite della metodologia di stima. I risultati ottenuti con il “nuovo” BAMPR, con l’introduzione delle variabili dinamiche e termodinamiche, in due applicazioni a perturbazioni meteorologiche verificatesi sul Lazio, sono descritti nella seconda parte della tesi. In tale parte è infine presentato un confronto della nuova metodologia sviluppata con le prestazioni dell’algoritmo NESDIS (NOAA).In this thesis we have investigated some important issues regarding the retrieval of precipitation from satellite-based microwave measurements. Although microwave radiometry retrieval techniques for the estimation of rainfall have advanced considerably over the past years, further developments are still necessary and some aspects of these techniques are currently being investigated in research activities. The activity we have carried out in this thesis concerned first the implementation and the development of the complete BAMPR (Bayesian Algorithm for Microwave-based Precipitation Retrieval) algorithm, based on the Bayesian estimation theory, for the SSM/I, SSMIS and AMSR-E data (brightness temperatures) processing. The complete algorithm is described in the first part of the thesis, together with the screening procedures we have selected for the correct processing of pixels. The characteristics of the database used (Cloud Radiation Database), and some tests we have performed on it are also presented. The activity was then focused on the introduction of the “dynamical tags” in the retrieval procedure of BAMPR, to be combined with brightness temperatures (Cloud Dynamics and Radiation Database (CDRD) approach). This development we have carried out on the algorithm is aimed at reducing the “ambiguity” or the “non-uniqueness” of the database that is a severe limit for retrieval methodology. The results obtained with the “new” BAMPR algorithm, using three “dynamical tags”, in two case studies over Lazio, are described in the second part of the thesis. A comparison between the “new” BAMPR algorithm and the operational algorithm NESDIS of NOAA is also presented

A Machine Learning Snowfall Retrieval Algorithm for ATMS

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF)

Chapter 12 - Recent advances and challenges in satellite-based snowfall detection and estimation

5noThe use of satellites for snowfall continuous monitoring and quantification on a global scale is fundamental to quantify water resources, and for understanding feedback mechanisms and interconnections in hydrology and climate. Spaceborne active and passive microwave sensors are particularly tailored to provide consistent measurements of falling snow thanks to their ability to penetrate clouds. However, remote sensing of snowfall remains among the most challenging tasks in precipitation science. Indeed, current state-of-the-art satellite products show large discrepancies in snowfall climatologies. The goal of this chapter is to provide an overview of the most recent studies dedicated to satellite-based snowfall detection and quantification by highlighting, in particular: (1) potentials and limitations of the most advanced spaceborne microwave sensors used for snowfall observation; (2) the main issues and challenges encountered in satellite-based retrieval of falling snow; and (3) recent advancements made in the scientific community in the attempt to overcome some of these issues. Concluding remarks include recommendations on future observation and retrieval strategies to improve detection and quantification of falling snow, with particular focus on higher latitudes.reservedmixedPanegrossi, Giulia; Casella, Daniele; Sanò, Paolo; Camplani, Andrea; Battaglia, AlessandroPanegrossi, Giulia; Casella, Daniele; Sanò, Paolo; Camplani, Andrea; Battaglia, Alessandr

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

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

A Machine Learning Snowfall Retrieval Algorithm for ATMS

This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM–Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks—NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF)

A 4-Year Climatological Analysis Based on GPM Observations of Deep Convective Events in the Mediterranean Region

Since early March 2014, the NASA/JAXA Global Precipitation Measurement Core- Observatory (GPM-CO) satellite has allowed analysis of precipitation systems around the globe, thanks to the capabilities of the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). In this work, we demonstrate how GPM-CO measurements obtained from 4 years of observations over the Mediterranean area can be used as an extremely effective tool to study the main climatological characteristics of the most intense Mediterranean storm structures. DPR and GMI-based Precipitation Features (PFs) parameters are used as proxies of the vertical structure and microphysical properties of these events, and their statistical distribution is analyzed to identify extremes. The analysis of annual, seasonal and geographical distribution of the identified deep convective systems highlights substantial differences in their diurnal cycle and in the distribution between land-sea and summer-winter. There is a general shift of the convective systems from the south (mostly over the sea) in the cold season, to the north (mostly over land) in the warm season. The analysis shows also that the inferred convective intensity is not always related to heavy precipitation. Known DPR and GMI-based criteria were adopted to identify overshooting top events and potential hailstorms, identify extreme deep convection signatures, like those observed for tropical and subtropical systems, and the most intense occur mostly over the sea. Although the analysis is limited to four years, the results show that the GPM-CO offers unprecedented measurements to identify and characterize extreme weather events in the Mediterranean region, with unique potentials for future long-term climatology and interannual variability analysis

RAINBOW: An Operational Oriented Combined IR-Algorithm

In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring