Frank S. Marzano

HyMeX-SOP1: The Field Campaign Dedicated to Heavy Precipitation and Flash Flooding in the Northwestern Mediterranean

The Mediterranean region is frequently affected by heavy precipitation events associated with flash floods, landslides, and mudslides that cause hundreds of millions of euros in damages per year and often, casualties. A major field campaign was devoted to heavy precipitation and flash floods from 5 September to 6 November 2012 within the framework of the 10-year international HyMeX (Hydrological cycle in the Mediterranean Experiment) dedicated to the hydrological cycle and related high-impact events. The 2- month field campaign took place over the Northwestern Mediterranean Sea and its surrounding coastal regions in France, Italy, and Spain. The observation strategy of the field experiment was devised to improve our knowledge on the following key components leading to heavy precipitation and flash flooding in the region: i) the marine atmospheric flows that transport moist and conditionally unstable air towards the coasts; ii) the Mediterranean Sea acting as a moisture and energy source; iii) the dynamics and microphysics of the convective systems producing heavy precipitation; iv) the hydrological processes during flash floods. This article provides the rationale for developing this first HyMeX field experiment and an overview of its design and execution. Highlights of some Intense Observation Periods illustrate the potential of the unique datasets collected for process understanding, model improvement and data assimilation.

Results of WetNet PIP-2 Project

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution‐instantaneous space‐timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 608N‐ 178S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution‐instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal ‘‘front-end’’ combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates. It is found that the bias uncertainty of many current PMW algorithms is on the order of 630%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature‐rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called ‘‘fan map’’ analysis. The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the ‘‘ground truth’’ validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

Use of Cloud Model Microphysics for Passive Microwave-Based Precipitation Retrieval: Significance of Consistency between Model and Measurement Manifolds

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud‐radiation databases. In this study cloud‐radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space‐time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space‐time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud‐radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

Bayesian estimation of precipitating cloud parameters from combined measurements of spaceborne microwave radiometer and radar

The objective of this paper is to evaluate the potential of a Bayesian inversion algorithm using microwave multisensor data for the retrieval of surface rainfall rate and cloud parameters. The retrieval scheme is based on the maximum a posteriori probability (MAP) method, extended for the use of both spaceborne passive and active microwave data. The MAP technique for precipitation profiling is also proposed to approach the problem of the radar-swath synthetic broadening; that is, the capability to exploit the combined information also where only radiometric data are available. In order to show an application to airborne data, two case studies are selected within the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE). They refer to a stratiform storm region and an intense squall line of two mesoscale convective systems, which occurred over the ocean on February 20 and 22, 1993, respectively. The estimated rainfall rates and columnar hydrometeor contents derived from the proposed algorithms are compared to each other and to radar estimates based on reflectivity-rainrate (Z-R) relationships. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are also discussed. A database of combined measurements acquired at nadir during various TOGA-COARE flights, is used for applying the radar-swath synthetic broadening technique in the case of an along-track radar-failure countermeasure. A simulated test of the latter technique is performed using the case studies of February 20 and 22, 1993.

Model-based prediction of amplitude scintillation variance due to clear-air tropospheric turbulence on Earth-satellite microwave links

A statistical method to predict tropospheric amplitude scintillation parameters along Earth-space microwave links from meteorological data is proposed. The evaluation of the mean value and the variance of the refractive-index structure constant and of the scintillation power (i.e. the variance of the log-amplitude fluctuations of the received electromagnetic field) is carried out from conventional radio-sounding measurements. A large radio-sounding data set, collected in Northern Italy over ten years is utilized to simulate clear-air amplitude scintillation variance at microwaves and millimeter-waves on slant paths. Scintillation statistics of interest for link-budget design are also derived from the radio-sounding data set for short and long-term applications. Scintillation prediction formulas, based on measurements of surface temperature and relative humidity, are also derived and regression coefficient tables are given on an hourly and a monthly basis. Comparisons of short-term and long-term prediction results with Olympus down-link measurements at 19.8 GHz are shown and discussed. A model investigation about the statistical correlation between scintillation power and brightness temperature is performed, deriving an extension of the estimation methods to include integrated water vapor measurements from ground-based microwave radiometers.

Temperature and humidity profile retrievals from ground-based microwave radiometers during TUC

Thermodynamic atmospheric profiles have been retrieved from ground-based microwave radiometers during the Temperature, hUmidity, and Cloud (TUC) profiling campaign. A variety of inversion methods is presented, in terms of requirements, advantages, and limitations. Results confirm the theoretical expectation that retrievals’ accuracy and resolution degrade steadily with height up to 3 km, then more rapidly. At higher levels the retrievals’ accuracy does not improve on that of a Numerical Weather Prediction model, which provides a background for the variational technique. Most retrieval methods produce a bias in the temperature profile above 1 km, which may be due to a bias in the absorption model used and/or observations at 51–54 GHz. Elevation scanning is shown to improve the accuracy and resolution of the retrievals in the boundary layer, but is limited by technical shortcomings. Zusammenfassung Thermodynamische atmospharische Profile wurden mit bodengestutzten Mikrowellenradiometern wahrend der Temperature, hUmidity, and Cloud (TUC) profiling Kampagne gemessen. Verschiedene Inversionsmethoden werden in Bezug auf Anforderungen, Vorteile und Einschrankungen vorgestellt. Die Resultate bestatigen die theoretische Erwartung, dass die Genauigkeit und die Auflosung der gemessenen Profile kontinuierlich bis 3 km Hohe schwach und daruber starker abnehmen. In den hoheren Schichten ist die Genauigkeit der Profile nicht besser als die des numerischen Wettervorhersagemodells, das die Hintergrundfelder fur das erorterte Variationsverfahren bereitstellt. Die meisten Inversionsmethoden fuhren zu systematischen Fehlern in den gemessenen Profilen oberhalb von 1 km, was auf systematische Fehler im verwendeten Absorptionsmodell und/oder bei der Messung der Helligkeitstemperatur zwischen 51 und 54 GHz hindeutet. Die zusatzliche Einbeziehung von Messungen unterschiedlicher Elevationswinkel verbessern die Genauigkeit und die Auflosung der abgeleiteten Profile in der planetaren Grenzschicht, wobei die Vorteile durch technische Unzulanglichkeiten eingeschrankt sind.

Multivariate statistical integration of Satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale

The objective of this paper is to investigate how the complementarity between low earth orbit (LEO) microwave (MW) and geostationary earth orbit (GEO) infrared (IR) radiometric measurements can be exploited for satellite rainfall detection and estimation. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The basic idea behind the investigated statistical integration methods follows an established approach consisting in using the satellite MW-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne IR measurements on sufficiently limited subregions and time windows. The proposed methodologies are focused on new statistical approaches, namely the multivariate probability matching (MPM) and variance-constrained multiple regression (VMR). The MPM and VMR methods are rigorously formulated and systematically analyzed in terms of relative detection and estimation accuracy and computing efficiency. In order to demonstrate the potentiality of the proposed MW-IR combined rainfall algorithm (MICRA), three case studies are discussed, two on a global scale on November 1999 and 2000 and one over the Mediterranean area. A comprehensive set of statistical parameters for detection and estimation assessment is introduced to evaluate the error budget. For a comparative evaluation, the analysis of these case studies has been extended to similar techniques available in literature.

A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data

Abstract The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° spatial resolution. Rainfall is estimated using a neural networks (NN)–based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half- hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

Volcanic Ash Cloud Retrieval by Ground-Based Microwave Weather Radar

The potential of ground-based microwave weather radar systems for volcanic ash cloud detection and quantitative retrieval is evaluated. The relationship between radar reflectivity factor, ash concentration, and fall rate is statistically derived for various eruption regimes and ash sizes by applying a radar-reflectivity microphysical model. To quantitatively evaluate the ash detectability by weather radars, a sensitivity analysis is carried out by simulating synthetic ash clouds and varying ash concentration and size as a function of the range. Radar specifications are taken from typical radar systems at S-, C-, and X-band. A prototype algorithm for volcanic ash radar retrieval (VARR) is discussed. Starting from measured single-polarization reflectivity, the statistical inversion technique to retrieve ash concentration and fall rate is based on two cascade steps, namely: 1) classification of eruption regime and volcanic ash category and 2) estimation of ash concentration and fall rate. Expected accuracy of the VARR algorithm estimates is evaluated using a synthetic data set. An application of the VARR technique is finally shown, taking into consideration the eruption of the Grinodotacutemsvoumltn volcano in Iceland on November 2004. Volume scan data from a Doppler C-band radar, which is located at 260 km from the volcano vent, are processed by means of the VARR algorithm. Examples of the achievable VARR products are presented and discussed

Intercomparison of microwave radiative transfer models for precipitating clouds

An intercomparison of microwave multiple scattering radiative transfer codes used in generating databases for satellite rainfall retrieval algorithms has been carried out to ensure that differences obtained from retrieval techniques do not originate from the underlying radiative transfer code employed for the forward modeling. A set of profiles containing liquid water and ice contents of cloud and rain water as well as snow, graupel and pristine ice were distributed to the participants together with a black box routine providing Mie single scattering, atmospheric background absorption and surface emissivity. Simulations were to be carried out for nadir and off-nadir (53.1/spl deg/) observation angles at frequencies between 10 and 85 GHz. Among the radiative transfer models were two-stream, multiple stream and Monte Carlo models. The results showed that there were two major sources of differences between the codes. 1) If surface reflection/emission was considered isotropic, simulated brightness temperatures were significantly higher than for specular reflection and this effect was most pronounced at nadir observation and over ocean-type surfaces. 2) Flux-type models including delta-scaling could partially compensate for the errors introduced by the two-stream approximation. Largest discrepancies occurred at high frequencies where atmospheric scattering is most pronounced and at nadir observation. If the same surface boundary conditions, the same multiple-stream resolution and the same scaling procedures are used, the models were very close to each other with discrepancies below 1 K.