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David W. Reynolds

Cloud seeding to increase winter snowpacks over mountainous regions of the western United States have been in existence for almost 40 years. However, our understanding of the physical processes taking place in the clouds in response to this seeding and the expected precipitation increases are still subjects of great scientific interest and investigation. Recent field observations that have emphasized direct physical observations of winter clouds, their structure and liquid water content, as well as their response to the injection of glaciogenic seeding agents have added to our knowledge. These physical observations are helping to provide some insight into the mechanisms of precipitation increases, inferred from statistical analyses, that have been reported in certain winter orographic cloud seeding programs. This paper attempts to compare physical and statistical results, to show consistency, and to help provide limits to what one might expect when winter snowpack augmentation is applied within suitable cloud systems.

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David W. Reynolds and Eric A. Smith

A technique is developed to digitally composite satellite and radar imagery in a common coordinate reference frame. Results obtained from using Geosynchronous Operational Environmental Satellite (GOES) visible and infrared data, 5 cm radar data, and recording raingage data are presented. The composite displays are created on Colorado State University's All Digital Video Imaging System for Atmospheric Research (ADVISAR), an interactive image processing system that uses modern high fidelity digital video display technology. An efficient methodology based on analytic transforms for remapping dissimilar digital image formats into common map projections is discussed. Applications of multi-sensor composite images are demonstrated with the use of two case studies. The technique is shown to enhance our understanding of a) convective development, b) organization of mesoscale features as they relate to the synoptic scale, c) severe storm development, and d) precipitation mechanisms. Our final comments concern the compositing technique's potential for on-line interactive forecast systems, particularly in terms of an embedding approach.

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David W. Reynolds, Thomas H. Vonder Haar, and Lewis O. Grant

During the past several years, many weather modification programs have been incorporating meteorological satellite data into both the operations and the analysis phase of these projects. This has occurred because of the advancement of the satellite as a mesoscale measurement platform, both temporally and spatially, and as the availability of high quality data has increased. This paper surveys the applications of meteorological satellite data to both summer and winter weather modification programs. A description of the types of observations needed by the programs is given, and an assessment of how accurately satellites can determine these necessary parameters is made.

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David W. Reynolds and Arnett S. Dennis

The Sierra Cooperative Pilot Project (SCPP) is an investigation of cloud seeding as a means of increasing winter precipitation on the Sierra Nevada. It is a concerted effort in the development of a physically sound cloud-seeding technology. It involves the use of remote-sensing devices, in situ observations, and the application of a numerical targeting model in randomized seeding experiments. The results have led SCPP scientists to believe that shallow but widespread orographic clouds provide the best opportunity for glaciogenic seeding in the central Sierra Nevada.

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David W. Reynolds, David A. Clark, F. Wesley Wilson, and Lara Cook

During summer, marine stratus encroaches into the approach to San Francisco International Airport (SFO) bringing low ceilings. Low ceilings restrict landings and result in a high number of arrival delays, thus impacting the National Air Space (NAS). These delays are managed by implementation of ground delay programs (GDPs), which hold traffic on the ground at origination airports in anticipation of insufficient arrival capacity at SFO. In an effort to reduce delays and improve both airport and NAS efficiency, the Federal Aviation Administration (FAA) funded a research effort begun in 1995 to develop an objective decision support system to aid forecasters in the prediction of stratus clearing times. By improving forecasts at this major airport, the scope and duration of ground and airborne holds can be reduced. The Marine Stratus Forecast System (MSFS) issues forecasts both deterministically and probabilistically. Following transition to NWS operations in 2004, the system continued to provide reliable forecasts but showed no significant improvement in delay reduction. Changes to the FAA GDP issuance procedures in 2008 allowed them to utilize the improved forecasts, leading to quantifiable reductions in ground and airborne holds for SFO equating to dollars saved. To further reduce delays, a refined statistically based model, the Ground Delay Parameters Selection Model (GPSM) for selecting an optimal ground delay strategy has been developed, utilizing the available archive of objective MSFS probabilistic forecasts and accompanying traffic flow data. This effort represents one of the first systematic attempts to integrate objective probabilistic weather information into the air traffic flow decision process, which is a cornerstone element of the FAA's visionary NextGen program.

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Russell S. Vose, Derek Arndt, Viva F. Banzon, David R. Easterling, Byron Gleason, Boyin Huang, Ed Kearns, Jay H. Lawrimore, Matthew J. Menne, Thomas C. Peterson, Richard W. Reynolds, Thomas M. Smith, Claude N. Williams Jr., and David B. Wuertz

This paper describes the new release of the Merged Land–Ocean Surface Temperature analysis (MLOST version 3.5), which is used in operational monitoring and climate assessment activities by the NOAA National Climatic Data Center. The primary motivation for the latest version is the inclusion of a new land dataset that has several major improvements, including a more elaborate approach for addressing changes in station location, instrumentation, and siting conditions. The new version is broadly consistent with previous global analyses, exhibiting a trend of 0.076°C decade−1 since 1901, 0.162°C decade−1 since 1979, and widespread warming in both time periods. In general, the new release exhibits only modest differences with its predecessor, the most obvious being very slightly more warming at the global scale (0.004°C decade−1 since 1901) and slightly different trend patterns over the terrestrial surface.

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Allen B. White, Brad Colman, Gary M. Carter, F. Martin Ralph, Robert S. Webb, David G. Brandon, Clark W. King, Paul J. Neiman, Daniel J. Gottas, Isidora Jankov, Keith F. Brill, Yuejian Zhu, Kirby Cook, Henry E. Buehner, Harold Opitz, David W. Reynolds, and Lawrence J. Schick

The Howard A. Hanson Dam (HHD) has brought flood protection to Washington's Green River Valley for more than 40 years and opened the way for increased valley development near Seattle. However, following a record high level of water behind the dam in January 2009 and the discovery of elevated seepage through the dam's abutment, the U.S. Army Corps of Engineers declared the dam “unsafe.” NOAA's Office of Oceanic and Atmospheric Research (OAR) and National Weather Service (NWS) worked together to respond rapidly to this crisis for the 2009/10 winter season, drawing from innovations developed in NWS offices and in NOAA's Hydrometeorology Test-bed (HMT).

New data telemetry was added to 14 existing surface rain gauges, allowing the gauge data to be ingested into the NWS rainfall database. The NWS Seattle Weather Forecast Office produced customized daily forecasts, including longer-lead-time hydrologic outlooks and new decision support services tailored for emergency managers and the public, new capabilities enabled by specialized products from NOAA's National Centers for Environmental Prediction (NCEP) and from HMT. The NOAA Physical Sciences Division (PSD) deployed a group of specialized instruments on the Washington coast and near the HHD that constituted two atmospheric river (AR) observatories (AROs) and conducted special HMT numerical model forecast runs. Atmospheric rivers are narrow corridors of enhanced water vapor transport in extratropical oceanic storms that can produce heavy orographic precipitation and anomalously high snow levels, and thus can trigger flooding. The AROs gave forecasters detailed vertical profile observations of AR conditions aloft, including monitoring of real-time water vapor transport and comparison with model runs.

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Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

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