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David M. Straus and Milton Halem


We investigate here the statistical effects of changing boundary conditions on the daily fluctuations of the atmosphere. Weather fluctuations obtained from simulations with the GLAS general circulation model (GCM) and from observed station data are stochastically modeled over the continental United States. Eleven model January integrations with randomly perturbed initial conditions for three different years were carried out with identical climatological boundary conditions. These integrations are considered as independent realizations of an unchanging climate, whose statistical properties represent the natural variability (i.e., the unpredictable component) of the atmosphere.

Finite autoregressive processes (zeroth order or white noise, first order and second order) are used to model the behavior of the surface temperature and sea level pressure at 54 surface stations distributed over the continental United States. It is found that white noise and first-order processes are inconsistent with both the model and the observational data but that for many regions (in particular the midwest United States) a second-order process is consistent with the data. A comparison of the autocovariance functions (acfs) with those of the “best-fit” first- and second-order autoregressive processes indicates that the calculated acfs become negative after a few days, a feature that a first-order process cannot reproduce. Limitations to the statistical confidence of all these results were the fact that the integrations were only 31 days long and the initial conditions were not as independent as randomly chosen states.

Comparisons of model results with those from observations indicate that the changing boundary conditions do not affect the sea level pressure fluctuations on time scales less than one month, but that this assumption may not be true for surface temperature. This suggests an intrinsic limitation for inferring monthly or seasonal estimates of natural fluctuations of surface temperature from the observed daily statistics. Two further results suggested by the study are (i) that modeling climate noise at a station may require higher order autoregressive processes or must take into account spatial correlations, and (ii) that the applicability of stochastic processes may be geographically dependent.

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R. Atlas, M. Ghil, and M. Halem


An experiment was performed to study the effect of increased model resolution on satellite sounding data impact. Assimilation cycles were carried out with data from 0000 GMT 29 January to 0300 GMT 21 February 1976, using coarse- and fine-resolution versions of the GLAS second-order general circulation model (GCM). For each model resolution, an assimilation cycle was performed using both conventional and experimental data, which included temperature soundings from the NOAA-4 and Nimbus-6 satellites. A second cycle was run using the same data but excluding the satellite-derived temperature soundings.

The objective analyses produced by the assimilation cycles were used as initial states for a series of evenly spaced 72 h numerical weather forecasts. Eleven forecasts with the same resolution in the forecast model as in the data assimilation were performed at 48 h intervals for each assimilation. Additional forecasts were made with the higher resolution forecast model from the lower resolution assimilation cycle and vice versa. Initial state differences were evaluated in terms of the magnitude, location and structure of large-scale differences between meteorological fields. Numerical prediction differences were evaluated by means of objective scores and subjective comparisons.

Objective scores show a substantially larger beneficial impact of the sounding data at 48 and 60 h with the higher resolution version of the model. Subjective evaluation also revealed a larger positive impact of satellite sounding data with the higher resolution model.

This study has two important limitations: it was carried out with two versions of one model, the GLAS GCM, and the number of forecast cases analyzed is small. Within these limitations, our results indicate that model improvement enhances the impact of satellite data.

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M. Ghil, M. Halem and R. Atlas


Methods are derived for the time-continuous four-dimensional assimilation of satellite sounding temperatures. The methods presented include time-continuous versions of direct insertion, successive correction and statistical linear regression. They are applied to temperature sounding data obtained from radiance measurements taken by instruments aboard the polar-orbiting satellites NOAA 4 and Nimbus 6. The data were collected during the U.S. Data System Test in January-March 1976.

A comprehensive series of experiments was performed to study the effects of using various amounts of satellite data and differing methods of assimilation. The experiments included the assimilation of data from the NOAA 4 satellite only, from Nimbus 6 only, and of data from both satellites combined. Other experiments involved variations in the application of our time-continuous statistical assimilation methods and of asynoptic successive correction methods. Intermittent assimilation of the sounding data was also tested, and its results compared with those of time-continuous assimilation.

Atmospheric states determined in the assimilation experiments served as initial states for a sequence of evenly spaced 3-day numerical weather forecasts corresponding to each experiment. The effects of the satellite data were evaluated according to the following criteria: 1) differences between the initial states produced with and without utilization of satellite data, 2) differences between numerical predictions made from these initial states, and. 3) differences in local weather forecasts resulting from the large-scale numerical predictions.

Initial-state differences were evaluated in terms of magnitude and location of large-scale differences between meteorological fields. Numerical prediction differences were evaluated in terms of SI skill scores and rms errors, as well as by synoptic case studies. An automated forecasting model (AFM) based on quasi-geostrophic theory and on subjective forecasting principles was developed to facilitate the objective evaluation of differences produced in local weather forecasts, especially precipitation forecasts.

These studies suggest the following conclusions: 1) satellite-derived temperature data can have a modest, but statistically significant positive impact on numerical weather prediction in the 2-3 day range; 2) the impact is highly sensitive to the quantity of data available, and increases with data quantity; and 3) the method used to assimilate the satellite data can influence appreciably the magnitude of the impact obtained for the same data.

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W. E. Baker, R. Atlas, M. Halem, and J. Susskind


In this study we examine the sensitivity of forecast to individual components of the First GARP (Global Atmospheric Research Programme) Global Experiment database as well as to some modifications in the data analysis techniques. Several short assimilation experiments (0000 GMT 18 January 1979 through 0000 21 January) are performed in order to test the effects of each database or analysis change. Forecasts are then generated from the initial conditions provided by these experiments. The 0000 21 January case is chosen for a detailed investigation because or the poor forecast skill obtained earlier over North America for that particular case. Specifically, we conduct experiments to test the sensitivity of forecast skill to: 1) the addition of individual satellite observing system components; 2) temperature data obtained with different satellite retrieval methods; and 3) the method of vertical interpolation between the mandatory pressure analysis levels and the model sigma levels.

For the single case examined, TIROS-N infrared land retrievals produced operationally are found to degrade the forecast, while the use of TIROS-N retrievals produced with a physical inversion method as part of an analysis/forecast cycle results in an improved forecast. The use of oceanic VTPR (Vertical Temperature Profile Radiometer) satellite retrievals also results in an improved forecast over North America. The forecast is also found to be sensitive to the method of vertical interpolation between the mandatory pressure analysis levels and the model sigma levels.

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R.C.J. Somerville, P.H. Stone, M. Halem, J.E. Hansen, J.S. Hogan, L.M. Druyan, G. Russell, A.A. Lacis, W.J. Quirk, and J. Tenenbaum


A model description and numerical results are presented for a global atmospheric circulation model developed at the Goddard Institute for Space Studies (GISS). The model version described is a 9-level primitive-equation model in sigma coordinates. It includes a realistic distribution of continents, oceans and topography. Detailed calculations of energy transfer by solar and terrestrial radiation make use of cloud and water vapor fields calculated by the model. The model hydrologic cycle includes two precipitation mechanisms: large-scale supersaturation and a parameterization of subgrid-scale cumulus convection.

Results are presented both from a comparison of the 13th to the 43rd days (January) of one integration with climatological statistics, and from five short-range forecasting experiments. In the extended integration, the near-equilibrium January-mean model atmosphere exhibits an energy cycle in good agreement with observational estimates, together with generally realistic zonal mean fields of winds, temperature, humidity, transports, diabatic heating, evaporation, precipitation, and cloud cover. In the five forecasting experiments, after 48 hr, the average rms error in temperature is 3.9K, and the average rms error in 500-mb height is 62 m. The model is successful in simulating the 2-day evolution of the major features of the observed sea level pressure and 500-mb height fields in a region surrounding North America.

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