On the capabilities of VIS/IR satellite data to resolve orographic precipitation

Satellite-based rainfall estimation techniques over complex orography usually show unsatisfactory results at any frequency, also averaging over large basin and/or integrating over many hours intervals. At shorter wavelengths (visible-infrared) cloud top radiances are not sensitive to lower cloud layers forcing, resulting in a rainfall underestimation. On the other side, passive microwave algorithms are sensitive to the variability of the ground emissivity over complex terrain so that they cannot work using the emission signal of lower precipitating layers; in such a way it becomes difficult to take properly into account the orographic forcing as well. To overcome these difficulties, ancillary parameters are derived from satellite data (e.g. cloud motion winds) or independent datasets (e.g. digital elevation model) and the sensitivity of measured and estimated rainfall to orographic forcing is evaluated. The use of cloud resolving models outputs are used to test independently the sensitivity of the rainfall fields to the considered parameters and to drive their use inside the rainfall estimation techniques. The application to heavy-rainfall flood-causing rainfall event in the Mediterranean area is discussed and performances evaluated

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

[No abstract available

The 9-10 November, 2001 Algerian flood: a numerical study

On 9–10 November 2001, Algeria was struck with its most devastating flood since records were first kept in 1908 (Fig. 1). The f lood was caused by an intense meso-?1-scale cyclone that struck the west side of the city of Algiers, producing accumulated rainfall up to ~285 mm and onshore winds of ~33 m s?1, resulting in some 740 deaths. Fortunately, the overall cyclogenesis event was detected by the European Centre for Medium-Range Weather Forecast (ECMWF) model, enabling the Office National de la Meteorologie de Algier (ONMA) to issue a flood forecast as early as 5 November. Whereas the apparent predictability of the event suggests a controlling role by large-scale forcing, closer examination reveals that significant mesoscale development led to the actual weather pattern within the Algiers locale

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed

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 quantitie

Transitioning from CRD to CDRD in Bayesian retrieval of rainfall from satellite passive microwave measurements: Part 3 – Identification of optimal meteorological tags

In the first two parts of this study we have presented a performance analysis of our new Cloud Dynamics and Radiation Database (CDRD) satellite precipitation retrieval algorithm on various convective and stratiform rainfall case studies verified with precision radar ground truth data, and an exposition of the algorithm's detailed design in conjunction with a proof-of-concept analysis vis-à-vis its theoretical underpinnings. In this third part of the study, we present the underlying analysis used to identify what we refer to as the <i>optimal</i> metrological and geophysical tags, which are the optimally effective atmospheric and geographic parameters that are used to refine the selection of candidate microphysical profiles used for the Bayesian retrieval. These tags enable extending beyond the conventional Cloud Radiation Database (CRD) algorithm by invoking meteorological-geophysical guidance, drawn from a simulated database, which affect and are in congruence with the observed precipitation states. This is guidance beyond the restrictive control provided by only simulated radiative transfer equation (RTE) model-derived database brightness temperature (TB) vector proximity information in seeking to relate physically consistent precipitation profile solutions to individual satellite-observed TB vectors. The first two parts of the study have rigorously demonstrated that the optimal tags effectively mitigate against solution ambiguity, where use of only a CRD framework (TB guidance only) leads to pervasive non-uniqueness problems in finding rainfall solutions. Alternatively, a CDRD framework (TB + tag guidance) mitigates against non-uniqueness problems through improved constraints. It remains to show how these optimal tags are identified. By use of three statistical analysis procedures applied to a database from 120 North American atmospheric simulations of precipitating storms (independent of the 60 simulations for the European-Mediterranean basin region used in the Parts 1 and 2 studies), we examine 25 separate dynamical-thermodynamical-hydrological (DST) and geophysical parameters for their relationships to rainfall variables – specifically, surface rain rate and columnar liquid/ice/total water paths of precipitating hydrometeors. The analysis identifies seven optimal parameter tags which exceed all others in the strengths of their correlations to the precipitation variables but also have observational counterparts in the operational global forecast model outputs. The seven optimal tags are (1 and 2) vertical velocities at 700 and 500 hPa; (3) equivalent potential temperature at surface; (4) convective available potential energy; (5) moisture flux 50 hPa above surface; (6) freezing level height; and (7) terrain height, i.e., surface height

Precipitation Estimation: From the RAO to EURAINSAT and Beyond

The key objective of the project “Use of the MSG SEVIRI channels in a combined SSM/I, TRMM and geostationary IR method for rapid updates of rainfall” is the development of algorithms for rapid-update of satellite rainfall estimations at the geostationary (GEO) scale. The new channels available with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer in the visible (VIS), near infrared (NIR) and infrared (IR) portions of the spectrum provide new insights into the microphysical and dynamic structure of precipitating clouds thus allowing for a more precise identification of precipitation intensities. Passive microwave (PMW) radiometers on board low Earth orbiting (LEO) satellites are used to determine information on the vertical cloud structure. Key features of the new method(s) are: 1. Microphysical characterization of precipitating clouds with VIS/IR sensors; 2. Creation of cloud microphysical and radiative databases from cloud model outputs and aircraft penetrations; 3. Tuning of PMW algorithms for different cloud systems (maritime, continental, convective, stratiform,...); 4. Combination of data from different algorithms and application to a rapid update cycle at the GEO scale. The project provided the background for EURAINSAT “European Satellite Rainfall Estimation and Monitoring at the Geostationary Scale”, a research project co-funded by the Energy, Environment and Sustainable Development Programme of the European Commission within the topic “Development of generic Earth observation technologies”. The project web site is accessible at http://www.isac.cnr.it/~eurainsat/. Moreover, it has represented the European framework for the launch of the International Precipitation Working Group (IPWG)

HEAVY PRECIPITATION SYSTEMS IN THE MEDITERRANEAN AREA: THE ROLE OF THE GPM

Heavy precipitation systems typical of the Mediterranean area that often devastate the coastal regions, are described and analyzed here by exploiting active and passive microwave measurements and state of the art precipitation products available in the Global Precipitation Measurement (GPM) mission era. The GPM is boosting its key role in integrating the established observational ground-based and satellite-borne tools not only for precipitation monitoring, but also for understanding and characterizing severe weather in the Mediterranean. In this Chapter, we present three events that have recently challenged observational and forecasting capabilities, and caused damages at the ground. Making use of ground based and satellite-borne instruments, we address the problem of estimating precipitation of a small-scale and short-living intense thunderstorm, the capability to render the 3D structure of a mesoscale organized convective system, and the key role of satellite view in the classification and monitoring of a tropical-like cyclonic system. To this end, we exploited satellite measurements probably beyond the role they have been designed for, showing few strategies to blend satellite data and products with conventional meteorological data, with the aim to increase the knowledge of severe systems in the Mediterranean area and to support operational forecasting activities in a climate change perspective