O. Contributions to NCAR Initiatives [Water Cycle Across Scales] Goal of the Water Cycle Initiative The main goal of the NCAR water cycle initiative for the next three years is to improve our understanding and prediction of the summer diurnal cycle of water across the continental United States, with emphasis on the mid-continent region and the diurnal cycle of precipitation. This emphasis is motivated by the relatively poor representation of the diurnal cycle of convective precipitation in weather and climate models in this region. Four main areas of emphasis were identified to address this goal: 1) diagnostic analysis of precipitation on a continental scale from observations and models; 2) cloud-system simulations of warm season convection over the IHOP mid-continent domain and development of improved convective parameterizations; 3) analysis of water vapor data from the IHOP field program and investigations into the triggering mechanisms for warm season convection using IHOP data; and 4) measurement, analysis, and prediction of land-surface processes and their interaction with the atmosphere. A schematic summarizing these phase I activities is given in Figure 1. The first row in this figure gives the areas of emphasis described above. The second row provides the key objectives under each emphasis area. The achievement of these objectives will lead to a better understanding of the water cycle and also improved prediction. Note the progression from understanding to improved prediction of the water cycle through the development of improved convective and land surface parameterizations in weather and climate models. The key role of the IHOP field data is also emphasized.
Figure 1. Schematic diagram of the Phase I activities of the Water Cycle initiative
During FY02, Roy Rasmussen and Vidal Salazar participated in the IMPROVE II field program in the Oregon Cascades. This program was designed to collect a dataset for improving microphysical parameterizations in numerical weather models. Comprehensive measurements in thirteen storms were collected. Jim Wilson and Rita Roberts also participated in the International H2O ield program (IHOP) in June 2002. Their role was S-Pol radar data collection. An excellent dataset describing all components of the water cycle was obtained during IHOP, with special emphasis on the measurement of water vapor, surface fluxes, and convective precipitation. Data from this field program will be used in many of the proposed FY03 – FY05 water cycle activities.
Figure 2. Example of boundary layer forcing mechanisms as observed by S-pol during IHOP. The large storm was initiated as the cold front moved south intersecting the dry line. Subsequent storms were also initiated at their intersection. Atmosphere-land interaction Precipitation estimation Ed Brandes developed an improved precipitation estimation algorithm that was implemented during IHOP. The USGS Precipitation Runoff Modeling System was adapted to a few demonstration runoff watersheds in the Denver area by David Yates and Nancy Rehak. In addition, radar estimated rainfall has been merged with Denver and Front Range rain-gauge data to facilitate comparison and interpretation. Dave Yates, Jeff Cole, Fei Chen (RAP), Peggy LeMone (MMM), Steve Semmer (ATD), and Richard Cuenca (OSU) obtained soil moisture and temperature measurements to enhance the IHOP surface-tower observation array. They enhanced ten surface flux measurement sites with measurements of soil temperature and moisture, weekly vegetation characteristics, and sampled horizontal heterogeneity along 45 m transects. This allowed for comprehensive soil and vegetation measurements at the same location of the flux measurements. An excellent dataset was collected during IHOP, and analysis of this data will be a focus of study during the next three years.
The Wildland Fire R&D Collaboratory, an NCAR strategic initiative that began in 2001, is focused on research that is associated with understanding wildfire in the context of atmospheric processes and the impact of these interactive processes on our society, both in the near term and for the long haul. Not only is the direct interaction of the atmosphere with the fire in real time important, but it is also very important to understand the delicate and complicated dance that occurs between the atmosphere and potential fuels for future fires, the emissions that come from wildfire, the actual combustion process in some detail, and the long-term effects of wildfire on climatic factors such as carbon sequestration. Development focuses on validating software, hardware and system engineering practices that can transfer the science base from the laboratory to an operational setting, new training and education programs, and knowledge-based systems that can rapidly convey these new understandings into useful information for policy makers and planners. The collaboratory is dedicated to bringing together the many pockets of good research and development that are going on around the world. Much of the results of the collaborative R&D will be focused on traditional problems of fire suppression; however, over time there will be an increased focus on goals of the National Fire Plan such as shifting from a short-term response to fire as a catastrophic event towards a long-term, proactive, collaborative, community-based perspective that recognizes fire as a natural part of the ecosystem, and emphasizes fire mitigation through the use of fuels reduction. The collaboratory is also dedicated to bringing stakeholders in both the operations and the research sectors closer together for the purpose of a) assessing specific operational user requirement which will influence outcomes from the R&D conducted by the research members of the collaboratory, b) to facilitate testing and validation of developed products in an operational environment, and c) to ultimately accelerate the transfer of technology to the operational sector. The over-arching goals of the collaboratory are to:
Although the priorities for research and development and specific goals of the collaboratory will naturally evolve, some are clear at the beginning of this endeavor.
Determine
how better scientific understanding of fire risk, fire behavior and
atmospheric processes can be translated into training and educational
systems to teach "best practices" to policy makers, land managers,
insurers and residents living in the urban/wildland interface. The
wildland fire initiative is linked to several other NCAR initiatives,
some already established, some in the process of being With regard to linkage to NCAR's base programs, the collaboratory will have close associations with the development of WRF (fire component), RAP's general technologies (decision support systems), land surface modeling and data assimilation work being done in MMM and RAP, societal impact studies in ESIG, carbon cycle research in CGD, fire emissions work being done in ACD, remote sensing work being done in ATD, and the GIS initiative being led by SCD, RAP, MMM and ESIG. Accomplishments during the past year:
Additional information about the Wildland Fire R&D Collaboratory can be found at the NCAR website: Wildland Fire R&D. Any individual or organization involved in wildland fire research is invited to join. 3. Weather & Climate Applications
of Extreme Value Theory RAP has been working with ESIG on the NCAR Weather and Climate Impact Assessment Science Initiative to apply extreme value analysis techniques to prediction of in-flight icing severity. Extreme value analysis is a means to deal with relatively rare events, that is, those appearing on the "tails" of statistical distributions. Development of a web-based Extreme Value Analysis toolbox began to enable investigators to these techniques to their own datasets. Work was also initiated on the application of extreme value analysis techniques to the problem of predicting severe icing events. RAP and ESIG scientists examined the problem, developed an approach, and identified data sets to be used. During the proposed second phase, in FY03, the data will be processed and analyzed using the toolbox developed elsewhere under this initiative. The third phase will be application of these results to the real time detection or forecast algorithm. The expected outcome is an algorithm that will take the available data sets and determine the most likely severity of icing, given that the icing diagnosis algorithm says icing of any severity is likely.
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