Sensitivity Analysis Of Probabilistic Multi-Model Ensemble Forecasts Of Wintertime Fronts Over Northwestern Nevada

Probabilistic ensemble forecasting has become an essential tool to numerical weather prediction. With the chaotic nature of the atmosphere, decisions made by operational meteorologists are made with imperfect weather models. These deterministic numerical weather forecasts can be complemented with the use of regional ensemble predictions incorporating enhanced probabilistic, statistical analysis tools. The challenge is providing better statistical information using ensemble probabilistic information forecasts of mesoscale frontal features to better characterize frontal precipitation fields, intensity, and direction of movement. The purpose of this study was aimed at drawing attention to certain probabilistic distribution patterns for specific mesoscale circulations when physical parameterizations and/or initial conditions are varied for specific ensemble forecast members. A statistical sensitivity error-trend analysis of multi-model (MM5, COAMPS, and WRF) ensemble prediction system (EPS) was conducted to provide insight into how inherent changes to model parameterizations, i.e. PBL, convection, radiation, and microphysics can manifest intrinsic variability to ensemble predictability. Most studies in ensemble prediction used a single model in an ensemble mode, using variations in model initial conditions as the basis to produce simulation ensemble members and in most cases the total ensemble members were limited to 6-10. A total of 153 ensemble members with a horizontal resolution of 36 km were evaluated for this study using three state of the art regional-mesoscale models. Its focus was directed towards the use of a multi-model EPS to measure the statistical sensitivity of a sequence of three winter-time fronts observed over western Nevada during the period of 12-27 December 2008. The corresponding analysis and evaluation underscored a process through which 500 hPa thermal field dataset temperature differences, as it applied to rank data calculated for the three cold frontal systems observed over the period of the 15 day simulation, can also be applied to ensemble model spread and error trend analysis. This study enabled the extension of the forecast simulation period to two weeks, which is the assumed predictability limit for atmospheric simulations. Therefore, it became apparent that the use of statistical rank data error trends and ensemble model spread can improve predictability of certain aspects of frontal activity based on COAMPS smaller (high a priori forecast accuracy) ensemble simulation spread as compared to MM5 and WRF larger (low a priori forecast accuracy) ensemble spread

Cloud-radiation interactions and their parameterization in climate models

This report contains papers from the International Workshop on Cloud-Radiation Interactions and Their Parameterization in Climate Models met on 18-20 October 1993 in Camp Springs, Maryland, USA. It was organized by the Joint Working Group on Clouds and Radiation of the International Association of Meteorology and Atmospheric Sciences. Recommendations were grouped into three broad areas: (1) general circulation models (GCMs), (2) satellite studies, and (3) process studies. Each of the panels developed recommendations on the themes of the workshop. Explicitly or implicitly, each panel independently recommended observations of basic cloud microphysical properties (water content, phase, size) on the scales resolved by GCMs. Such observations are necessary to validate cloud parameterizations in GCMs, to use satellite data to infer radiative forcing in the atmosphere and at the earth's surface, and to refine the process models which are used to develop advanced cloud parameterizations

Assessment of a General Circulation Model with Modified Convection and Clouds

General circulation models (GCMs) allow atmospheric scientists to tinker with atmospheric processes and study the resulting climate trends. Atmospheric trends, such as temperature fluctuations, wind shifts, and precipitation patterns are extensively studied in an attempt to realize their impacts on people, places, and other natural processes. Although useful, GCMs have shortcomings with respect to the representation of subgrid-scale meteorological processes, and thus, parameterization is required. One of the toughest components to simulate in climate models is that of clouds, as they are variable over time and spatial scales. Cumulus parameterizations, used to represent convection, have major implications for the precipitation. Cloud-resolving model (CRM) experiments have aided in the improvement of convection parameterizations. Depending on convection closure and trigger mechanisms, precipitation may be suppressed or occur more often. The cumulus scheme also alters the radiation budget as radiation processes are coupled with hydrological ones. The National Center for Atmospheric Research (NCAR) General Circulation Model (CTL) and the Iowa State University General Circulation Model (EXP) are two such models used to study differences in parameterizations, specifically those to convection. Convection scheme modifications in EXP (based on CRM studies) are found to produce closer to observed mean climate simulations in precipitation, convection, and cloud-related variables. A diurnal cycle of precipitation more resembles observations in EXP than CTL. EXP\u27s precipitation occurs less frequently but with more vigor than CTL. Through decomposition of the water vapor flux into rotational and divergent wind components, we find EXP to have a more distinguishable Southeast Asian Monsoon trough and generally stronger convergent centers in monsoon regions. This agrees with precipitation in EXP being less frequent but more vigorous than CTL. Eddy components of the water vapor flux for each model simulation appropriately indicate poleward water vapor transport

Parallelization and visual analysis of multidimensional fields: Application to ozone production, destruction, and transport in three dimensions

Atmospheric modeling is a grand challenge problem for several reasons, including its inordinate computational requirements and its generation of large amounts of data concurrent with its use of very large data sets derived from measurement instruments like satellites. In addition, atmospheric models are typically run several times, on new data sets or to reprocess existing data sets, to investigate or reinvestigate specific chemical or physical processes occurring in the earth's atmosphere, to understand model fidelity with respect to observational data, or simply to experiment with specific model parameters or components

On the use of climate models to assess the impacts of regional climate change on water resources

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1994.Includes bibliographical references (p. 207-213).by James Sydney Risbey.Ph.D

ATMOL: A Domain-Specific Language for Atmospheric Modeling

This paper describes the design and implementation of ATMOL: a domain-specific language for the formulation and implementation of atmospheric models. ATMOL was developed in close collaboration with meteorologists at the Royal Netherlands Meteorological Institute (KNMI) to ensure ease of use, concise notation, and the adoptation of common notational conventions. ATMOL’s expressiveness allows the formulation of high-level and low-level model details as language constructs for problem refinement and code synthesis. The atmospheric models specified in ATMOL are translated into efficient numerical codes with CTADEL, a tool for symbolic manipulation and code synthesis

Global Change Research: Summaries of research in FY 1993

This document describes the activities and products of the Global Research Program in FY 1993. This publication describes all of the projects funded by the Environmental Sciences Division of DOE under annual contracts, grants, and interagency agreements in FY 1993. Each description contains the project`s title; its 3-year funding history (in thousands of dollars); the period over which the funding applies; the name(s) of the principal investigator(s); the institution(s) conducting the projects; and the project`s objectives, products, approach, and results to date (for most projects older than 1 year). Project descriptions are categorized within the report according to program areas: climate modeling, quantitative links, global carbon cycle, vegetation research, ocean research, economics of global climate change, education, information and integration, and NIGEC. Within these categories, the descriptions are grouped alphabetically by principal investigator. Each program area is preceded by a brief text that defines the program area, states its goals and objectives, lists principal research questions, and identifies program managers

ATMOL: A Domain-Specific Language for Atmospheric Modeling

This paper describes the design and implementation of ATMOL: a domain-specific language for the formulation and implementation of atmospheric models. ATMOL was developed in close collaboration with meteorologists at the Royal Netherlands Meteorological Institute (KNMI) to ensure ease of use, concise notation, and the adoptation of common notational conventions. ATMOL’s expressiveness allows the formulation of high-level and low-level model details as language constructs for problem refinement and code synthesis. The atmospheric models specified in ATMOL are translated into efficient numerical codes with CTADEL, a tool for symbolic manipulation and code synthesis