Anna Cinzia Marra

Use of the GPM Constellation for Monitoring Heavy Precipitation Events Over the Mediterranean Region

Precipitation retrievals exploiting the available passive microwave (PMW) observations by cross-track and conically scanning satellite-borne radiometers in the Global Precipitation Measurement (GPM) mission era are used to monitor and characterize heavy precipitation events that occurred during the Fall 2014 in Italy. Different physically based PMW precipitation retrieval algorithms are used: the Cloud Dynamics and Radiation Database (CDRD) and Passive microwave Neural network Precipitation Retrieval (PNPR), used operationally in the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF), and the National Aeronautics and Space Administration (NASA) Goddard PROFiling algorithm (GPROF). Results show that PMW precipitation retrievals from the GPM constellation of radiometers provide a reliable and quantitative description of the precipitation (instantaneous and on the daily scale) throughout the evolution of the precipitation systems in the Mediterranean region. The comparable relative errors among gauges, radar, and combination of radiometer overpasses legitimize the use of PMW estimates as a valuable and independent tool for monitoring precipitation. The pixel-based comparison with dual-polarization radars and raingauges indicates the ability of the different sensors to identify different precipitation areas and regimes (0.60 <; POD <; 0.76; 0.28 <; FAR <; 0.45; 0.42 <; ETS <; 0.59;-1.6 mm/h <; ME <; 1.1 mm/h}, with values depending on the radiometer and on the precipitation product). This is particularly relevant in the presence of complex orography in proximity of coastal areas, as for the analyzed cases. The different characteristics of the radiometers (i.e., viewing geometry, spatial resolution, channel assortment) and of retrieval techniques, as well as the limitations of the ground-based reference datasets, are taken into consideration in the evaluation of the accuracy and consistency of the retrievals.

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 > 3.6 kg·m−2, IWP > 0.24 kg·m−2 over land, and SIC > 57%, TPW > 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.

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME <−0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications

A new precipitation retrieval algorithm for the AMSR2 is described. The algorithm is based on the cloud dynamics and radiation database (CDRD) Bayesian approach and represents an evolution of the previous version applied to SSMIS observations, and used operationally within the EUMETSAT H-SAF program. This new product presents as main innovation the use of a very large database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA GPM-CO launched on February 28, 2014. The other new aspects are: 1) a new rain-/no-rain screening approach; 2) use of EOF and CCA for dimensionality reduction; 3) use of new ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; and 4) development and implementations of modules for computation time minimization. A verification study for case studies over Italy and for coincident AMSR2/GPM-CO observations over the MSG full disk area has been carried out. Results show remarkable AMSR2 capabilities for RR retrieval over ocean (for RR > 0.1 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that the new CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. The algorithm is also able to handle an extremely large observational database available from GPM-CO and to provide rainfall estimate with minimum latency, making it suitable for NRT hydrological and operational applications.

Use of the constellation of PMW radiometers in the GPM ERA for heavy precipitation event monitoring and analysis during fall 2014 in Italy

In this study, precipitation retrievals exploiting the available overpasses of passive microwave (PMW) cross-track and conically scanning radiometers in the GPM era are used to monitor the evolution of heavy precipitation systems occurred during the fall 2014 in Italy. Two different physically-based retrieval algorithms (CDRD for SSMIS and PNPR for AMSU/MHS and ATMS) are used in conjunction with official NASA/JAXA GPM instantaneous precipitation products (for AMSR-2 and GMI). The comparison with dual-polarization radar observations at ground evidences the ability of the different sensors to identify different precipitation areas and regimes. This is particularly relevant in presence of complex orography, often found in proximity of coastal areas for the analyzed cases. Analysis of the accuracy and consistency of the retrievals is carried out taking into account the different spatial resolution and viewing geometry of the different radiometers and the different approaches used for the precipitation retrieval.

Numerical scattering simulations for interpreting simultaneous observations of clouds by a W-band spaceborne and a C-band ground radar

Abstract The spaceborne W-band (94 GHz) Cloud Profiling Radar (CPR) onboard the CloudSat (CS) satellite, which was launched in 2006, is providing valuable information about global cloud properties. This work aims at interpreting collocated time/space observations from CPR on CS and a ground C-band (5.6 GHz) Radar (GR), with the help of numerical simulations of electromagnetic scattering returns from populations of monodisperse spheres of ice and liquid water. Two cloud systems over Apulia region are investigated. CPR and GR images have been geo-referenced, then combined and displayed for analysis. The numerical simulations of the two radar reflectivities are used as a tool in the inversion procedure, aiming at identifying the hydrometeors, in their phase and size distribution, in the cloud volume simultaneously observed by the two radars. The possible vertical profiles of hydrometeors are presented.

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.