Giulia Panegrossi

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.

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.

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.

InSAR Water Vapor Data Assimilation into Mesoscale Model MM5: Technique and Pilot Study

In this study, a technique developed to retrieve integrated water vapor from interferometric synthetic aperture radar (InSAR) data is described, and a three-dimensional variational assimilation experiment of the retrieved precipitable water vapor into the mesoscale weather prediction model MM5 is carried out. The InSAR measurements were available in the framework of the European Space Agency (ESA) project for the “Mitigation of electromagnetic transmission errors induced by atmospheric water vapor effects” (METAWAVE), whose goal was to analyze and possibly predict the phase delay induced by atmospheric water vapor on the spaceborne radar signal. The impact of the assimilation on the model forecast is investigated in terms of temperature, water vapor, wind, and precipitation forecast. Changes in the modeled dynamics and an impact on the precipitation forecast are found. A positive effect on the forecast of the precipitation is found for structures at the model grid scale or larger (1 km), whereas a negative effect is found on convective cells at the subgrid scale that develops within 1 h time intervals. The computation of statistical indices shows that the InSAR assimilation improves the forecast of weak to moderate precipitation (<;15 mm/3 h).

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.

Multisensor observations during the flood event of 4–6 November, 1994 over Northern Italy

In the perspective of flood hazard multisensor monitoring, the event of 4–6 November, 1994 over Northern Italy is described and analyzed in this paper using traditional ground‐based rainfall observations and remote sensing techniques. Satellite imagery is used with different objectives: the interpretation of SAR measurements allows the identification of flooded areas, Meteosat infrared images show high intensity rain areas and SSM/I passive microwave data provide estimates of the rain‐rates. The IFA‐SAP algorithm, a profile based retrieval technique for estimating rainfall rates and precipitating cloud parameters from spacebome multifrequency microwave radiometers, has been used in the latter case. The use of multisensor observations considerably supplements conventional monitoring systems which prove inadequate for many hydrological purposes. The present paper is written in the spirit of encouraging a posteriori analyses of observed heavy rainfall events so as to explore the potential of a real‐time oper...

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.

Microwave multisensor rainfall retrieval applied to TOGA-COARE observations

Rainfall retrieval estimation algorithms, based on passive and active microwave sensor data, are applied to along-track nadir-looking observations of a cyclone over ocean that occurred on February 8, 1993 during TOGA-COARE. The estimated rainfall rates derived from the radiometer data are compared with those obtained from ARMAR radar. Results in terms of reflectivity profiles and upwelling brightness temperatures, reconstructed from the estimated cloud structures, are discussed.

Modeling Microphysical Signatures of Extreme Events in the Western Mediterranean to Provide a Basis for Diagnosing Precipitation from Space

Precipitation occurring over the Mediterranean basin is typically unusually difficult to measure due to variability resulting from the irregular terrain. Radar based precipitation measurements also are compromised by the terrain, which contaminates reflectivity with excessive ground cover and blocks the radar beam at low elevation angles. That same terrain also is instrumental in providing for the localization of rain producing storms that results in long term and flash flooding situations, especially in the fall