D. Casella

Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall From Satellite Passive Microwave Measurements: Part 1. Algorithm Description and Testing

In this Part 1 paper concerning a new Cloud Dynamics and Radiation Database (CDRD) algorithm, improvements in obtaining satellite retrievals of rainfall from multispectral passive microwave (PMW) radiometer measurements are obtained by transforming a conventional Cloud Radiation Database (CRD) algorithm. The improvements arise by combining parameter constraints derived from model-based dynamical-thermodynamical-hydrological (DTH) meteorological profile variables and additional geographical-seasonal (GS) factors, together with multispectral PMW brightness temperatures (TBs), into a specialized knowledge database underpinning a Bayesian retrieval algorithm. The so-called knowledge variables are produced by a high-resolution nonhydrostatic cloud-resolving model (CRM). The associated knowledge TBs are produced by a calibrated PMW radiative-transfer-equation model system (RMS) that relates CRM environments to expected satellite-view top-of-atmosphere TBs. By first applying the RMS to thousands of meteorological-microphysical situations simulated by the CRM and then by marshaling into the specialized database all the concomitant modeled microphysical profiles, TBs, and linked DTH/GS profiles/factors (from which optimal constraint tags can be derived), it becomes possible to use the database for the Bayesian interpretation of analogous measured TBs and tags. The main purpose of the new algorithm is to reduce ambiguity (nonuniqueness) effects that plague predecessor CRD algorithms. Such schemes restrict the interpretation of observed TBs by ignoring observable DTH/GS parameters that help constrain the influence microphysical profile sets (i.e., the associated hydrometeors, their size distributions, and their concomitant vertical distributions) that feed into the retrieval solutions. A Version 1 CDRD algorithm is tested against its CRD predecessor on two case studies of precipitation over Italys Lazio region which were observed with various satellite PMW radiometers. The measured TBs and corresponding tags obtained from gridded operational global model analyses are used in juxtaposition to produce the final rainfall retrievals. The retrievals are verified against coincident precision polarimetric C-band radar measurements. Skillful improvement is found for a case of intense convective rainfall where even CRD-type algorithm accuracy should be expected, as well as for a case of mixed convective-stratiform rainfall where either algorithms might otherwise be expected to be somewhat inaccurate.

Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall From Satellite Passive Microwave Measurements: Part 2. Overcoming Database Profile Selection Ambiguity by Consideration of Meteorological Control on Microphysics

A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm-mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a proof that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.

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.

Detection of terrestrial gamma-ray flashes with the AGILE satellite

Terrestrial gamma-ray flashes are brief submillisecond gamma-ray emissions, produced during thunderstorms and strictly correlated to lightning and atmospheric electric activity. Serendipitously discovered in 1994 by the Compton Gamma Ray Observatory, these elusive events have been further investigated by several missions and satellites devoted to high-energy astrophysics, such as RHESSI, AGILE and Fermi. Terrestrial gamma-ray flashes are thought to be bremsstrahlung gamma-rays, produced at the top of thunderclouds by avalanches of electrons accelerated within thunderstorm strong electric fields and abruptly braked in the atmosphere. Exhibiting energies ranging from few keV up to several tens of MeV, terrestrial gamma-ray flashes are the most energetic phenomenon naturally occurring on Earth and they can represent a severe risk for airplanes and aircraft transports, both for the crew and the on board electronics, that should be carefully investigated and understood. The AGILE (Astrorivelatore Gamma ad Immagini LEggero) satellite is an entirely Italian mission, launched in 2007 and still operational, aimed at investigating gamma-ray emissions from cosmic sources. The wide energy range and the unique submillisecond trigger logic of its on-board instruments, together with the narrow quasi-equatorial orbit of the spacecraft, make AGILE a very suitable instrument to detect and investigate terrestrial gamma-ray flashes. Recent improvements rose up the terrestrial gamma-ray flashes detection rate and lead to the observation, for the first time, of multiple events occurring within single thunderstorm processes.

A pipeline to link meteorological information and TGFs detected by AGILE

Terrestrial gamma ray flashes (TGFs) are brief (approximately hundreds of microseconds) intense gamma ray emissions coming from Earth’s atmosphere (∼15 km above sea level), correlated with thunderstorms and atmospheric electric activity. Since their unexpected discovery in the early 1990s by the Burst And Transient Source Experiment/Compton Gamma Ray Observatory, TGFs have been further investigated by several satellites devoted to high-energy astrophysics. The Astrorivelatore Gamma ad Immagini LEggero (AGILE) mission turned out to be particularly suitable to detect these events, due to a very wide energy range (up to 100 MeV), an optimized triggering system, and a unique low-inclination near-equatorial orbit (2.5∘). We describe a detection system, developed for the AGILE satellite, whose aim is to provide real-time meteorological information on each detected TGF. We take advantage of data acquired by geostationary satellites to promptly identify the associated storm and follow its evolution in space and time, in order to study its previous onset and development. Data from Low-Earth Orbit meteorological satellites, such as the Global Precipitation Mission, as well as ground measurements from lightning detection networks, can be integrated in the pipeline. This system allows us a prompt characterization of the ground meteorological conditions at TGF time which will provide instrument-independent trigger validation, fill in a database for subsequent statistical analysis, and eventually, on a longer term perspective, serve as a real-time alert service open to the community.