New and Improved 30 day Global Forecasts
MWA updated its proprietary 30-day global weather forecast ensemble to the MPAS model (Model Prediction Across Scales), which was collaboratively developed by the National Center for Atmospheric Research (NCAR) and the climate modeling group at Los Alamos National Laboratory (COSIM). MWA modeling staff selected the latest update (Version 5) of this unique research model for conversion to an operational forecast platform based on several key features distinguishing MPAS from all other weather forecast models. Initial verification analyses show the model is highly stable and more accurate than the GFS model at forecast time scales beyond day-5.
The model is comprised of individual simulation components specific to atmosphere, ocean, land ice, and sea ice employing a unique hexagonal grid system especially suitable for higher resolution applications over any geographic area. Model output available to standard subscriptions include 30 day global forecasts of upper atmosphere and surface meteorological parameters; displayed graphically (including 5 day composites of temperature anomaly and precipitation), as well as in numerical form for over 200 cities. Forecasts specific to any city or global region can be customized to greatly enhanced resolution to satisfy individual user requirements
MWA Forecast Model and Lead Scientists
One of the National Center for Atmospheric Research’s (NCAR’s) primary areas of research is the development of computer models designed to improve our understanding of complex interaction between the atmosphere, Earth, and sun. These models, developed over the span of decades were originally designed to run on the largest (Cray) computer platforms. Among these are the Community Climate System model (CCSM) which consists of several stand alone components defining affects of atmosphere, land, ocean, and sea ice to the total climate system.
The atmospheric component, Community Atmosphere Model (CAM) is a global spectral model that has never been run in an operational environment, except by our group. Recent technological advances enable the operational use of this model at a forecast time horizon of 30 days. In order to serve as an operational weather forecast model for daily use, software to ingest current atmospheric observations, and perform the initial ensemble member perturbations have been developed by Pete Stamus and Dr. John Snook.
Pete Stamus has considerable experience with numerical analysis and the creation of products for forecasters and other end-users. During his 14 years with NOAA’s Forecast Systems Laboratory (FSL), he co-developed the Local Analysis and Forecast System, and with John Snook led its installation and use at the 1996 Olympics in Atlanta. Leaving FSL in 2000 Pete joined Foresight Weather, eventually becoming VP – Operations where he supervised both the development and daily operations of the Foresight modeling system. He has also worked on projects for the US Air Force, the National Weather Service (NWS), and NCAR Comet program.
Dr. John Snook worked as an applied research meteorologist with the NOAA/Forecast Systems Laboratory from 1984 through 1999. He participated in the development of the Local Analysis and Prediction System (LAPS), which is now a part of the NWS operational meteorological workstation, since its inception in 1987. John completed a doctoral program in 1993 at the Colorado State University while remaining full-time at FSL. He studied high-resolution numerical weather prediction (NWP) with an emphasis on local area applications, which provided the opportunity to incorporate a meteorological computer model into the FSL LAPS package. John received a NOAA bronze medal for efforts contributing to the successful implementation of a local-area NWP system to provide operational support for the 1996 Centennial Olympic Games. John moved to the private sector in 1999 to install numerical weather prediction NWP systems designed to meet client requirements in the utility industry and various other private and public sectors who require NWP services including the US Forest Service.
With acquisition of a set of new dual quad core servers, development and testing of the 30 day MWA ensemble forecast model are complete as of early spring 2010. Global upper level and surface graphical output is currently available and has been utilized in an operational mode in support of MWA 30 day forecasts in March 2010. Consistent model forecasts have verified excellently and effectively cut through the high level of error and noise exhibited in standard forecast model output. Soon numerical point source parameters for any city in the world will be available out to 30 days.
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November 29 - Long Range Summary
Above average warmth which predominated the majority of the West and northern Plains throughout November is quickly expanding eastward, with the Northeast last to shift warm. A cold start to the coming week across the Northeast featuring temperatures 5° below average is certain to briefly reverse 5° above average midweek, before cold air returns at the end of the week. All models prolong this modest volatility across the Northeast characterized by near seasonal mean temperatures when averaged over several day time scales through the end of the 1st full week of December. Extended range models eventually flood the Northeast with milder air during the 11-15 day period, but this accompanies large scale pattern change directing notably colder air through west central Canada into the Rockies and Plains. Central U.S. cooling forecast to start late in the 6-10 day period may not mark onset of sustained below average temperatures. However, that is not the case across Canada where progressive cooling sets the stage for significantly colder mid December conditions across the northern Plains and Upper Midwest, which are likely to expand into the East by the final week of the month to persist through early January.
If your business or career depends on correctly predicting the weather, you can follow the pack or you can get ahead with MWA’s proprietary models and expert forecasts.