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Robert E. Kistler and David F. Parrish

Abstract

The evolution of the NMC global data assimilation system in the period 1978–81 is presented. The improvements include revisions to the analysis programs and the replacement of the initialization and the prediction model. Data are analyzed over a finer analysis grid, are scrutinized by a more thorough “buddy cheek,” and are subject to a multivariate wind relationship. The impact of the changes upon the wind analysis is examined with respect to the case of 1200 GMT, 21 October 1979. The system changes concurrent with the addition of the spectral prediction model are noted. Experimental evidence demonstrates the superiority of the spectral system with respect to the gridpoint system previously in use.

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Louis W. Uccellini, Paul J. Kocin, Joseph Sienkiewicz and, Robert Kistler, and Michael Baker

Abstract

Fred Sanders' career extended over 55 yr, touching upon many of the revolutionary transformations in the field of meteorology during that period. In this paper, his contributions to the transformation of synoptic meteorology, his research into the nature of explosive cyclogenesis, and related advances in the ability to predict these storms are reviewed. In addition to this review, the current status of forecasting oceanic cyclones 4.5 days in advance is presented, illustrating the progress that has been made and the challenges that persist, especially for forecasting those extreme extratropical cyclones that are marked by surface wind speeds exceeding hurricane force. Last, Fred Sanders' participation in a forecast for the historic 1947 snowstorm (that produced snowfall amounts in the New York City area that set records at that time) is reviewed along with an attempt to use today's operational global model to simulate this storm using data that were available at the time. The study reveals the predictive limitations involved with this case based on the scarcity of upper-air data in 1947, while confirming Fred Sanders' forecasting skills when dealing with these types of major storm events, even as a young aviation forecaster at New York's LaGuardia Airport.

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M. Steven Tracton, Kingtse Mo, Wilbur Chen, Eugenia Kalnay, Robert Kistler, and Glenn White

Abstract

Early results are presented of an experimental program in Dynamical Extended Range Forecasting at the National Meteorological Center. The primary objective of this program is to assess the feasibility of extending operational numerical weather prediction beyond the medium range to the monthly outlook problem. Additionally, the extended integrations provide greater insight into systematic errors and climate drift and thereby feedback to model development. In this paper the principal focus is upon assessment of a contiguous set of 108 thirty-day integrations generated with the then operational Medium Range Forecast model from initial conditions 24 hours apart between 14 December 1986 and 31 March 1987.

Results indicate some serious model deficiencies such as the tendency for zonalization, i.e., systematically stronger midlatitude zonal flow than observed, and a stratospheric cold bias, which continues to grow through the 30--day integrations.

In the 1–30 day mean Northern Hemisphere 500 mb height fields the dynamical model is almost always more skillful than persistence. Most of this skill, however, is concentrated in the earlier time ranges so that on average the best estimate of the 30-day mean circulation is not the forecast 30-day mean, but the average of only the first 7–10 days. Beyond 10 days the average skill is low, but the variability in skill is large with many individual cases of skillful predictions.

We consider the problem of enhancing the forecast skill by statistical postprocessing, including time averaging Legged Average Forecasting (LAF), correction of systematic errors and Empirical Orthogonal Function filtering. A main finding is that these procedures separately or in combination, can significantly enhance the skill of already skillful predictions but do not have a significant effect on poor forecasts.

Four potential predictors of skill have been examined. Forecast agreement, the degree of consistency between members of LAF ensembles, explains on average about 10% of the regional skill variance, and forecast persistence an additional 5%. The magnitude of the forecast anomaly has virtually no relationship with skill except for small anomalies for which the skill also becomes very small. lie Pacific North American (PNA) teleconnection index of the initial circulation regime is an extremely good indicator of forecast skill at midranges where the correlation between the PNA index and skill reaches 0.77.

Finally, a major finding is that a large component of the variability in forecast results from the inability to predict the evolution of blocking events beyond a few days into the forecast. We also found a relationship between blocking episodes and the antecedent PNA index, but whether this relationship is more than coincidental has not yet been established.

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Robert Kistler, Eugenia Kalnay, William Collins, Suranjana Saha, Glenn White, John Woollen, Muthuvel Chelliah, Wesley Ebisuzaki, Masao Kanamitsu, Vernon Kousky, Huug van den Dool, Roy Jenne, and Michael Fiorino
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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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