Goal 4, Priority 3: Conducting Research in Computer Science, Applied Mathematics, Statistics, and Numerical Methods
The research activity within CISL enhances the computational infrastructure at NCAR and supports more efficient scientific computation and simulation. This research is necessary to maintain an innovative computational facility and to lead the geophysics community in incorporating new numerical methods and models. Given this broad priority, the research in CISL must span several disciplines and address computational science at many levels. Integrated with the computational science are areas of applied mathematics that include data analysis, models for multiscale processes, and techniques for assimilating data into numerical models. Because these different elements are coordinated through a single Lab, there is an easy transfer of technology and ideas from prototypes and theoretical results in IMAGe to implementation and workflow issues in CISL, and finally into tools and models for the communities served by CISL. There is also a valuable reverse transfer whereby emerging computational capability and data storage concepts spur particular research that takes advantage of these features.
RAL also makes significant contributions to this priority area through the work of its Verification Group, led by Barbara Brown. In an effort to address the limitations of traditional, relatively simple performance metrics, RAL has developed new verification approaches and tools that provide more meaningful and relevant information about forecast performance. The focus of this effort is on diagnostic, statistically valid approaches, including object-based evaluation of precipitation and convective forecasts and other approaches (e.g., distribution-based) that can provide more useful information about forecast performance. Development and dissemination of new forecast verification approaches requires research and application in several areas, including statistical methods, exploratory data analysis, statistical inference, pattern recognition, and evaluation of user needs.
FY2007 Accomplishments
RAL scientists are working to understand weather extremes, particularly with regard to convection. Initial work in this area has focused on identification of useful measures of large-scale environments that are relevant for severe thunderstorm formation based on NCAR global model reanalysis data. These data have been used to investigate trends in the large-scale environmental characteristics, as well as spatial and extreme value distribution attributes. This work has led to the identification of several statistical challenges/new areas for research with regard to modeling extreme values in a spatial context; addressing the issue of multiple comparisons inherent in working with gridded data; and making inferences about changes in distribution parameters.
A more recent area of inquiry within RAL has been fueled by the U.S. Joint Planning and Development Office’s mandate to transform use of the nation’s airspace by 2025. Inherent in this charge is a rethinking of how weather data is acquired, stored, and disseminated to the aviation community. While graphical presentations of weather data are useful to human users, there is also a need for machine-to-machine data dissemination to provide data to decision support tools and systems that manage air traffic. The challenge is to provide four-dimensional weather data using standard formats for the request and delivery. To address this problem, NCAR-RAL has teamed with MIT-Lincoln Labs and NOAA-Global Systems Division to explore standards-based, net-centric data access. The goal of this research is to create a virtual weather database spanning more than one physical location, organization, and data system.
For the past decade, NCAR’s Geophysical Statistics Project (GSP) has led training and research efforts that emphasize the synergy between the geosciences and the statistical sciences. Aside from basic methodological and theoretical statistical research, GSP has had a strong training component, supporting from four to six postdoctoral visiting scientists. In FY2007, a project reconstructing Northern Hemisphere temperatures was completed based on the original proxies of Mann Bradley Hughes. Uncertainty in the reconstruction is quantified using a statistical ensemble that facilitates drawing complex inferences about decadal maxima of the reconstructed series.
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Comparison of MOPITT sensitivity to CO at the surface and at 700 hPa.
In another area, Space-Time Modeling of Atmospheric Carbon Monoxide, a simple dynamical model with stochastic components has successfully assimilated carbon monoxide (CO) satellite retrievals from the MOPITT instrument. This project is an alternative to using more complex atmospheric transport models, and it includes statistical standard errors for the estimated concentrations.
The Method for Object-based Diagnostic Evaluation (MODE), developed by RAL and ESSL/MMM provides a new approach for diagnostic evaluation of spatial forecasts that directly measures the performance of the forecasts in terms of specific attributes – spatial displacement, intensity, storm size, and so on – and attributes may be designed to represent the use of the forecast for specific applications. In 2007 MODE was implemented as a tool in the DTC’s Model Evaluation Tools (MET) and has been disseminated to the NWP community.
The RAL Verification Group has also organized and coordinated an intercomparison project (ICP) for spatial forecast verification methods, involving scientists from around the world who are developing new methods for evaluation of spatial forecasts. The project will include applications of all of the methods to the same real and idealized datasets, and comparisons of the capabilities of the various methods, with a goal of determining which methods should be applied to achieve different goals, and to identify the kinds of information that each method can provide.
The NCAR reanalysis dataset has been analyzed to include two important severe weather indicators, Convective Available Potential Energy (CAPE) and vertical shear. Analyses of trends in CAPE, Shear, and functions of these variables, such as CAPE x Shear, were applied to the global set of gridpoints included in this dataset. The focus of this work has begun to shift to consider the evaluation of convective extremes in projections of a changed climate. Output of the NCAR Community Climate System Model (CCSM3), AB1 scenario, are being used to represent the current climate and will be used to compare to results associated with the reanalysis data. These analyses are being done in collaboration with H. Brooks at the NOAA National Severe Storms Laboratory and P. Marsh at the University of Oklahoma.
RAL engineers have instantiated a gridded weather products server using a Web Coverage Service (WCS). Using the WCS specification, NCAR-RAL now distributes a variety of three-dimensional gridded weather products including analyses and forecasts of icing, turbulence, winds, and temperatures. Additionally, the data available through the WCS services were exposed in a catalog as an Open Geospatial Consortium Catalogue Service which allows the automated discovery of data as it comes in and provides information about the data that may be accessed through the WCS services. This includes information such as the quality of the data, the organization that originally created or gathered it, the data format in which it is available, and its geographic extent.
FY2008 Plans for Strategic Priority 3
FY2008 will see the analysis of computer models expanded to validation of the Thermosphere Ionosphere Electrodynamic General Circulation Model climatology with observations, and the functional data approach developed in combination with the analysis of regional climate fields. The paleoclimate reconstruction problem will be cast as a hierarchical model and estimated using Monte Carlo/ensemble methods in the DART framework. The assimilation of CO with a statistical model and parameter will also be integrated into DART and tested using a long period of MOPITT retrievals and aircraft observations.
MODE will be applied to additional datasets and types of forecasts. An initial effort will be made to examine ensemble forecasts of precipitation from an object perspective. MODE will also be applied to convective and precipitation forecasts as part of NCAR’s program on Short Term Explicit Prediction. The concept of user-focused verification will be further developed and presented to the forecasting and verification communities. And a workshop will be held in the spring for participants in the intercomparison project to begin discussion of project results.
Techniques developed with the reanalysis data will be applied to output of the CCSM3 to determine whether the characteristics of CAPE and Shear based on GCM output for an unchanged climate are consistent with the characteristics of these parameters in the reanalysis data. Subsequently (assuming consistency is found), the parameters will be analyzed using CCSM3 output for changed climate scenarios to study how the frequency and intensity of environments conducive to severe weather activity can be expected to change under a future climate scenario. The aim of this work is to determine the current distributions of environments conducive to severe weather, and study how these environments are changing.
Related Lab Annual Report Sections:
Goal 4, Priority 3