scholarly journals MONTHLY PRESSURE VARIATIONS IN THE NORTHERN HEMISPHERE AND SEASONAL WEATHER FORECASTING

1925 ◽  
Vol 53 (12) ◽  
pp. 528-534
Author(s):  
ALFRED J. HENRY
Keyword(s):  
Northern Hemisphere ◽  
Weather Forecasting ◽  
Seasonal Weather

scholarly journals Designing Multimodel Applications with Surrogate Forecast Systems

2020 ◽  
Vol 148 (6) ◽  
pp. 2233-2249
Author(s):  
Leonard A. Smith ◽  
Hailiang Du ◽  
Sarah Higgins
Keyword(s):  
Bayesian Model Averaging ◽  
Weather Forecasting ◽  
Model Averaging ◽  
Probabilistic Forecasting ◽  
Single Model ◽  
Forecast System ◽  
Mathematical System ◽  
Probabilistic Forecasts ◽  
Perfect Model ◽  
Seasonal Weather

Abstract Probabilistic forecasting is common in a wide variety of fields including geoscience, social science, and finance. It is sometimes the case that one has multiple probability forecasts for the same target. How is the information in these multiple nonlinear forecast systems best “combined”? Assuming stationarity, in the limit of a very large forecast–outcome archive, each model-based probability density function can be weighted to form a “multimodel forecast” that will, in expectation, provide at least as much information as the most informative single model forecast system. If one of the forecast systems yields a probability distribution that reflects the distribution from which the outcome will be drawn, Bayesian model averaging will identify this forecast system as the preferred system in the limit as the number of forecast–outcome pairs goes to infinity. In many applications, like those of seasonal weather forecasting, data are precious; the archive is often limited to fewer than 26 entries. In addition, no perfect model is in hand. It is shown that in this case forming a single “multimodel probabilistic forecast” can be expected to prove misleading. These issues are investigated in the surrogate model (here a forecast system) regime, where using probabilistic forecasts of a simple mathematical system allows many limiting behaviors of forecast systems to be quantified and compared with those under more realistic conditions.

scholarly journals Studies on the Seasonal Weather Forecasting (V, c)

1956 ◽  
Vol 6 (3-4) ◽  
pp. 228-233
Author(s):  
M. Hirose ◽  
M. Okuta ◽  
T. Asakura
Keyword(s):  
Weather Forecasting ◽  
Seasonal Weather

NOTE ON PROGRESS OF SEASONAL WEATHER FORECASTING IN CALIFORNIA

1930 ◽  
Vol 11 (8-9) ◽  
pp. 149-151
Author(s):  
Arthur F. Gorton ◽  
Agnes G. Partridge
Keyword(s):  
Weather Forecasting ◽  
Seasonal Weather

scholarly journals Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium

2009 ◽  
Vol 5 (5) ◽  
pp. 2115-2156 ◽  
Author(s):  
M. Widmann ◽  
H. Goosse ◽  
G. van der Schrier ◽  
R. Schnur ◽  
J. Barkmeijer
Keyword(s):  
Data Assimilation ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Last Millennium ◽  
Proxy Data ◽  
Using Data

Abstract. Climate proxy data provide noisy, and spatially incomplete information on some aspects of past climate states, whereas palaeosimulations with climate models provide global, multi-variable states, which may however differ from the true states due to unpredictable internal variability not related to climate forcings, as well as due to model deficiencies. Using data assimilation for combining the empirical information from proxy data with the physical understanding of the climate system represented by the equations in a climate model is in principle a promising way to obtain better estimates for the climate of the past. Data assimilation has been used for a long time in weather forecasting and atmospheric analyses to control the states in atmospheric General Circulation Models such that they are in agreement with observation from surface, upper air, and satellite measurements. Here we discuss the similarities and the differences between the data assimilation problem in palaeoclimatology and in weather forecasting, and present and conceptually compare three data assimilation methods that have been developed in recent years for applications in palaeoclimatology. All three methods (selection of ensemble members, Forcing Singular Vectors, and Pattern Nudging) are illustrated by examples that are related to climate variability over the extratropical Northern Hemisphere during the last millennium. In particular it is shown that all three methods suggest that the cold period over Scandinavia during 1790–1820 is linked to anomalous northerly or easterly atmospheric flow, which in turn is related to a pressure anomaly that resembles a negative state of the Northern Annular Mode.

scholarly journals Regional-seasonal weather forecasting

1980 ◽  
Author(s):  
H. Abarbanel ◽  
H. Foley ◽  
G. MacDonald ◽  
O. Rothaus ◽  
M. Rudermann ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Seasonal Weather
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