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- Author or Editor: Milton Halem x
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Abstract
Simulation studies on the performance of IR sounders under varying conditions of cloud cover and cloud heights are carried out for Nimbus 6. An analytic function is derived for calculating the relative response to cloud height errors for arbitrary cloud-sensing channels. Based on the values of the response function, the best choice of channels for determining cloud amounts are obtained. An algorithm is described for determining cloud heights and the sensitivity of cloud-height sensing channels are tested. It is found that for the HIRS instrument, the most transparent channel in the 4.3 μm band is optimal for adjusting cloud heights while the channel in the 15 μm band peaking closest to the surface is best for determining cloud amounts.
Abstract
Simulation studies on the performance of IR sounders under varying conditions of cloud cover and cloud heights are carried out for Nimbus 6. An analytic function is derived for calculating the relative response to cloud height errors for arbitrary cloud-sensing channels. Based on the values of the response function, the best choice of channels for determining cloud amounts are obtained. An algorithm is described for determining cloud heights and the sensitivity of cloud-height sensing channels are tested. It is found that for the HIRS instrument, the most transparent channel in the 4.3 μm band is optimal for adjusting cloud heights while the channel in the 15 μm band peaking closest to the surface is best for determining cloud amounts.
A series of simulation studies has been conducted in an effort to obtain information relevant to the planning of the First GARP Global Experiment. Thus far, the studies have used only the Mintz-Arakawa 1969 model, and have been concerned mainly with the utilization of IR vertical sounding measurements. The initial results indicate that temperature profiles derived from these measurements can play a valuable role, provided they are used on a continuing, day-to-day basis over an extended period. Temperature data used in this way appear to have a controlling influence on all other meteorological variables in the model, including winds and pressure in particular. Assuming a mean error of 1C in the temperature data, and assuming the coverage provided by the planned GARP satellite configuration of two polar orbiting satellites containing IR sounders with full side-scan capability, the experiments indicate that winds are determined within an error of 2 m sec−1 and pressures within an error of 2 mb. The wind and pressure determinations are significantly improved if (i) IR sounders are added to the geostationary satellites, (ii) the number of polar orbiting satellites is increased, or (iii) the errors in the temperature data are decreased. If the side-scan capability is reduced, the wind and pressure determinations are substantially worsened, and may fail entirely. The validity of these results is limited by (i) the defects of the model, especially in the tropics, (ii) the use of simulated data in place of real observations, and (iii) the fact that the experiments use a comparison with the solutions to the model as a test of the accuracy of the results, in place of a comparison with actual observations of winds and pressures.
A second series of experiments has been concerned with the relationship between the error limits specified in the global observations and the accuracy of the forecasts that will be based on these observations. The results indicate that with the present GARP data specifications, i.e., ± 3 m sec−1 in winds, ± 1C in temperature, and ± 3 mb in pressure, the forecasts begin to deteriorate on the 5th day and are misleading in major respects after the 8th day. The limit of deterministic predictability is reached in three weeks in agreement with the results of other studies. Further experiments indicate that the accuracy of the forecasts is limited primarily by the errors in the wind components, and secondarily by pressure errors. If the error limits are tightened to 1.5 m sec−1 in winds and to 2 mb in pressure, the forecasts are accurate in all major respects for 7 or 8 days. In order to secure accurate forecasts for 12 to 14 days, the error limits must be tightened to 0.5 m sec−1, 0.5 mb and 0.5C. In all cases, the limit of deterministic predictability is two or three times greater than the range of accurate forecasts.
A series of simulation studies has been conducted in an effort to obtain information relevant to the planning of the First GARP Global Experiment. Thus far, the studies have used only the Mintz-Arakawa 1969 model, and have been concerned mainly with the utilization of IR vertical sounding measurements. The initial results indicate that temperature profiles derived from these measurements can play a valuable role, provided they are used on a continuing, day-to-day basis over an extended period. Temperature data used in this way appear to have a controlling influence on all other meteorological variables in the model, including winds and pressure in particular. Assuming a mean error of 1C in the temperature data, and assuming the coverage provided by the planned GARP satellite configuration of two polar orbiting satellites containing IR sounders with full side-scan capability, the experiments indicate that winds are determined within an error of 2 m sec−1 and pressures within an error of 2 mb. The wind and pressure determinations are significantly improved if (i) IR sounders are added to the geostationary satellites, (ii) the number of polar orbiting satellites is increased, or (iii) the errors in the temperature data are decreased. If the side-scan capability is reduced, the wind and pressure determinations are substantially worsened, and may fail entirely. The validity of these results is limited by (i) the defects of the model, especially in the tropics, (ii) the use of simulated data in place of real observations, and (iii) the fact that the experiments use a comparison with the solutions to the model as a test of the accuracy of the results, in place of a comparison with actual observations of winds and pressures.
A second series of experiments has been concerned with the relationship between the error limits specified in the global observations and the accuracy of the forecasts that will be based on these observations. The results indicate that with the present GARP data specifications, i.e., ± 3 m sec−1 in winds, ± 1C in temperature, and ± 3 mb in pressure, the forecasts begin to deteriorate on the 5th day and are misleading in major respects after the 8th day. The limit of deterministic predictability is reached in three weeks in agreement with the results of other studies. Further experiments indicate that the accuracy of the forecasts is limited primarily by the errors in the wind components, and secondarily by pressure errors. If the error limits are tightened to 1.5 m sec−1 in winds and to 2 mb in pressure, the forecasts are accurate in all major respects for 7 or 8 days. In order to secure accurate forecasts for 12 to 14 days, the error limits must be tightened to 0.5 m sec−1, 0.5 mb and 0.5C. In all cases, the limit of deterministic predictability is two or three times greater than the range of accurate forecasts.
Abstract
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.
Abstract
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.
