•Variable-Step Topography (VST) surface coordinate system introduced into Nonhydrostatic Modeling System (NMS), a regional/mesoscale model.•Detailed description of new version of host NMS model described, including its dynamical conservation constraints.•Conservation of mass, momentum, energy, vorticity and enstrophy used for effective application of VST coordinates on obstacle flow problems.•VST formulation combines advantages of both terrain-following and step coordinate systems without suffering either of their disadvantages.

In this second part of a two-part sequence of papers, the performance metrics and quantitative advantages of a new VST surface coordinate system, implemented within a dynamically constrained, nonhydrostatic, cloud mesoscale atmospheric model, are evaluated in conjunction with seven orthodox obstacle flow problems. [The first part presented a full formulation of the VST model, prefaced by a description of the framework of the newly re-tooled nonhydrostatic modeling system (NMS) operating within integral constraints based on the conservation of the foremost quantities of mass, energy and circulation.] The intent behind VST is to create a vertical surface coordinate system boundary underpinning a nonhydrostatic atmosphere capable of reliable simulations of flows over both smooth and steep terrain without sacrificing dynamical integrity over either type of surface. Model simulation results are analyzed for six classical fluid dynamics problems involving flows relative to obstacles with known analytical or laboratory-simulated solutions, as well as for a seventh noteworthy mountain wave breaking problem that has well-studied numerical solutions. For cases when topography becomes excessively severe or poorly resolved numerically, atmospheric models using transform (terrain-following) coordinates produce noteworthy errors rendering a stable integration only if the topography is smoothed. For cases when topography is slowly varying (smooth or subtle), models using discrete-step coordinates also produce noteworthy errors relative to known solutions. Alternatively, the VST model demonstrates that both limitations of the two conventional approaches, for the entire range of slope severities, can be overcome. This means that VST is ideally suited for a scalable, nonhydrostatic atmospheric model, safeguarded with physically realistic dynamical constraints. (C) 2014 Elsevier B.V. All rights reserved.

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-a-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 optimal 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.

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 Italy's 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.

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.

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.

The potential usefulness of spaceborne Doppler radar as a tropical cyclone observing tool is assessed by conducting a high-resolution simulation of an intense hurricane and generating synthetic observations of reflectivity and radial velocity. The ground-based velocity track display (GBVTD) technique is used to process the radial velocity observations and generate retrievals of meteorologically relevant metrics such as the maximum wind (MW), radius of maximum wind (RMW), and radius of 64-kt wind (R64). Results indicate that the performance of the retrieved metrics compares favorably with the current state-of-the-art satellite methods for intensity estimation and somewhat better than current methods for structure (i.e., wind radii).

We present the second part of a study on the development of a framework for precipitation retrieval from space-base passive microwave measurements using a three-dimensional time-dependent cloud model to establish the microphysical setting. We first develop the theory needed to interpret the vertically distributed radiative sources and the emission-absorption-scattering processes responsible for the behavior of frequency-dependent top-of-atmosphere brightness temperatures T(B)'s. This involves two distinct types of vertical weighting functions for the T(B)'s: an emission-source weighting function describing the origin of emitted radiation that eventually reaches a satellite radiometer, and a generalized weighting function describing emitted-scattered radiation undergoing no further interactions prior to interception by the radiometer. The weighting-function framework is used for an analysis of land-based precipitation processes within a hail-storm simulation originally described in Part I. The individual roles of cloud drops, rain drops, graupel particles, ice crystals, and snow aggregates-as well as absorbing gases, the earth's surface, and cosmic background-on generating and modulating the frequency-dependent T(B)'s are examined in detail. The analysis emphasizes how microwave T(B) measurements are highly regulated by mixtures of hydrometeors, with particular emphasis on the importance of the vertical profile structures. We demonstrate how scattering produces sequential, frequency-dependent, vertical "break aways" of the peak amplitudes in the generalized weighting functions, thus explaining how a multichannel radiometer can be used to depth probe a precipitating cloud. We also seek to explain the extent to which 19-, 37-, and 85-GHz T(B)'s are responding to separate and distinct processes in precipitating cells in an unambiguous fashion, helping to elucidate the two key aspects of these standard satellite frequencies. That is, 1 ) they are best suited to decipher certain microphysical profile features above the main rain layers and near cloud top, and 2) they are ill suited for directly sensing precipitation intensity information within the main rain layers, particularly the surface rain rates. Finally, a summary of the various components of a hybrid statistical-physical rainfall algorithm used to produce liquid-ice profile information, as well as surface rain rates, is given. The algorithm employs the cloud model to provide a consistent and objectively generated source of detailed microphysical information as the underpinnings to an inversion-based perturbative retrieval scheme.

The surface energetics of the southwest Gobi Desert, including the temporal variations and diurnally averaged properties of the surface energy budget components, was investigated. The field program was conducted during the spring and summer of 1984, with the measurement system designed to monitor radiative exchange, heat/moisture storage in the soil, and sensible and latent heat exhange between the ground and the atmosphere. Results of the analysis reveal a seasonal transition feature not expected of a midlatitude desert. Namely, the differences in both surface radiation exchange and the distibution of sensible and latent heat transfer arise within a radiatively forced environment that barely deviates from spring to summer in terms of available solar energy at the surface. Both similarities and differences in the spring and summer surface energy budgets arise from differences imparted to the system by an increase in the summertime atmospheric moisture content. Changes in the near-surface mixing ratio are shown to alter the effectiveness of the desert surface in absorbing radiative energy and redistibuting it to the lower atmosphere through sensible and latent heat exchange.

Electron beam damage is the fundamental limit to resolution in electron cryomicroscopy (cryo-EM) of frozen, hydrated specimens. Radiation damage increases with the number of electrons used to obtain an image and affects information at higher spatial frequencies before low-resolution information. For the experimentalist, a balance exists between electron exposures sufficient to obtain a useful signal-to-noise ratio (SNR) in images and exposures that limit the damage to structural features. In single particle cryo-EM this balance is particularly delicate: low-resolution features must be imaged with a sufficient SNR to allow image alignment so that high-resolution features recorded below the noise level can be recovered by averaging independent images. By measuring the fading of Fourier components from images obtained at 200 kV of thin crystals of catalase embedded in ice, we have determined the electron exposures that will maximize the SNR at resolutions between 86 and 2.9 Å. These data allow for a rational choice of exposure for single particle cryo-EM. For example, for 20 Å resolution, the SNR is maximized at ∼20 e−/Å2, whereas for 3 Å resolution, it is maximized at ∼10 e−/Å2. We illustrate the effects of exposure in single particle cryo-EM with data collected at ∼12–15 and ∼24–30 e−/Å2.

In order to properly utilize remotely sensed precipitation estimates in hydrometeorological applications, knowledge of the accuracy of the estimates are needed. However, relatively few ground validation networks operate with the necessary spatial density and time-resolution required for validation of high-resolution precipitation products (HRPP) generated at fine space and time scales (e.g., hourly accumulations produced on a 0.25° spatial scale). In this article, we examine over-land validation statistics for an operationally designed, meteorological satellite-based global rainfall analysis that blends intermittent passive microwave-derived rainfall estimates aboard a variety of low Earth-orbiting satellite platforms with sub-hourly time sampling capabilities of visible and infrared imagers aboard operational geostationary platforms. The validation dataset is comprised of raingauge data collected from the dense, nearly homogeneous, 1-min reporting Automated Weather Station (network of the Korean Meteorological Administration during the June to August 2000 summer monsoon season. The space-time RMS error, mean bias, and correlation matrices were computed using various time windows for the gauge averaging, centered about the satellite observation time. For ±10 min time window, a correlation of 0.6 was achieved at 0.1° spatial scale by averaging more than 3 days; coarsening the spatial scale to 1.8° produced the same correlation by averaging over 1 h. Finer than approximately 24-h and 1° time and space scales, respectively, a rapid decay of the error statistics was obtained by trading-off either spatial or time resolution. Beyond a daily time scale, the blended estimates were nearly unbiased and with an RMS error of no worse than 1 mm day−1.