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Masao Kanamitsu

Abstract

The National Meteorological Center's (NMC) Global Data Assimilation and Forecast System is described in some detail. The system consists of 1) preprocessing of the initial guess, 2) optimum interpolation objective analysis, 3) update of the initial guess, 4) initialization, 5) forecast, and 6) postprocessing of the forecast.

The assimilation and forecast system are continually evolving; the version described here was implemented on 30 November 1988.

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Hideki Kanamaru and Masao Kanamitsu

Abstract

Systematic large-scale errors are often found within the regional domain in the regional dynamical downscaling procedure. This paper proposes a method to suppress such errors using a combination of spectral tendency damping and area average correction of temperature, humidity, and surface pressure in the Regional Spectral Model. The proposed scale-selective bias-correction method reduces the time tendency of the zonal and meridional wind components for the physical scale greater than a predetermined length. In addition, the area mean perturbations of temperature and humidity are forced to zero. The surface pressure difference between the model field and the global field is adjusted from the hydrostatic equation using the mean elevation difference between the two fields and the area mean temperature. Each of these three components of the technique is necessary for the model to effectively reduce large-scale errors in the regional domain. With this method, the downscaled field becomes less dependent on the domain size. Furthermore, the downscaled precipitation compares better with observations, as do the near-surface temperature and wind fields. The scheme allows much weaker lateral boundary relaxation, although it is still an essential part of the regional model. The use of a similar scheme is recommended for any regional model in the application of dynamical downscaling of analysis for climate studies.

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Masao Kanamitsu and Suranjana Saha

Abstract

Atmospheric budget calculations suffer from various observational and numerical errors. This paper demonstrates that all budget calculations applied to a large number of samples suffer from additional errors originating from systematic tendency errors of the budget equation used. Quantitative evaluation of this systematic tendency error for various types of budget computations showed that the systematic tendency errors are generally comparable in magnitude to the leading terms in the budget equations. Because of this error, the calculated budget does not satisfy conservation properties under steady conditions.

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Masao Kanamitsu and Suranjana Saha

Abstract

The budget of the systematic component of the short-range forecast error in the National Meteorological Center's Medium-Range Forecast Model (NMC MRF) is examined. The budget is computed for the spectral coefficients and the variances of vorticity, divergence, virtual temperature, and specific humility at every time step during the 24-h model integration. Two months in winter and three months in summer, totaling 150 cases, were integrated with the budget diagnostics. The results of the budget of the spectral coefficients—that is, the budget of mean error—showed compensation among large terms except near the model boundary; therefore, it is difficult to point to a significant source of the systematic error in the free atmosphere. Near the model lower boundaries, dynamics cannot fully compensate physical forcing, and estimation of some physical processes responsible for the mean errors is possible. In contrast, the budget of the variance of the coefficients—that is, the energy budget—is more interesting and informative. The most apparent problem found in the model is a loss of rotational kinetic energy in the medium (total wavenumber n = 11–40) and small (n = 41–80) scales in the free atmosphere. About 50% of the loss is explained by the excessive horizontal and vertical diffusion. There is a strong indication that the rest of the loss of kinetic energy is related to the insufficient generation of available potential energy in the medium scale.

To isolate further the cause of the error in the energetics, several forecasts with budget diagnostics were performed. The experiments showed complex interactions between the physics and dynamics and among the different physical processes. Particularly noteworthy are (a) the compensation between horizontal and vertical diffusion and (b) the balance among horizontal/vertical diffusion, the barotropic scale interaction, and the baroclinic conversion terms in the rotational kinetic energy equation. The results of this study guided the design and implementation of changes in the NMC model in the horizontal diffusion and the cumulus parameterization.

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Hideki Kanamaru and Masao Kanamitsu

Abstract

As an extreme demonstration of regional climate model capability, a dynamical downscaling of the NCEP–NCAR reanalysis was successfully performed over the Northern Hemisphere. Its success is due to the use of the scale-selective bias-correction scheme, which maintains the large-scale analysis of the driving global reanalysis in the interior of the domain where lateral boundary forcing has very little control. The downscaled analysis was found to produce reasonable regional details by comparison against 0.5° gridded analysis from the Climatic Research Unit of the University of East Anglia. Comparisons with smaller-area regional downscaling runs in India, Europe, and Japan using the same downscaling system showed that there is no degradation of quality in downscaled climate analysis by expanding the domain from a regional scale to a hemispherical scale.

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Vasubandhu Misra and Masao Kanamitsu

Abstract

In this paper a methodology is proposed to downscale coarse-resolution atmospheric general circulation model (AGCM) seasonal simulations. Anomaly nesting involves replacing the climatology of the driving AGCM with observed (in this case the National Centers for Environmental Prediction reanalysis) climatology at the lateral boundaries of the nested regional climate model (the regional spectral model). In this study the methodology is tested over South America and the neighboring ocean basins. A comparison of the austral summer seasonal simulation with the conventional way of nesting, namely driving the regional model with full AGCM forcing, reveals that substantial gains in the deterministic skill are realized through anomaly nesting. It is also shown that the high-frequency variance (at 3–30- and 30–40-day time scales) is more realistic from the anomaly nesting procedure.

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Hideki Kanamaru and Masao Kanamitsu

Abstract

This study examines the mechanisms of nighttime minimum temperature warming in the California Central Valley during summer due to irrigation. The Scripps Experimental Climate Prediction Center (ECPC) Regional Spectral Model (RSM) was used to simulate climate under two land surface characteristics: potential natural vegetation and modern land use that includes irrigation and urbanization. In irrigated cropland, soil moisture was prescribed in three different ways: 1) field capacity, 2) half of field capacity, and 3) no addition of water. In the most realistic case of half-field capacity, the July daily minimum temperature in the California Central Valley increased by 3.5°C, in agreement with station observation trends over the past century in the same area. It was found that ground heat flux efficiently keeps the surface warm during nighttime due to increased thermal conductivity of wet soil.

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Tosiyuki Nakaegawa and Masao Kanamitsu

Abstract

Cluster analysis was used to study seasonal forecast skills of the winter season NCEP seasonal forecast model (SFM) hindcasts over the Pacific–North America (PNA) sector. Two skill scores based on cluster mean and ensemble mean are compared. It was shown that the anomaly correlation coefficients (ACCs) of cluster mean are generally higher than those of the simple ensemble mean. The results indicated that the skill was affected by the existence of multiple atmospheric regimes. Multiple regimes tend to appear more often in near-normal tropical Pacific sea surface temperature (SST) episodes, while a single regime tends to appear during warm/cold episodes. The dissimilarity among the cluster members is small and the number of the dominant cluster members is also small when the tropical SST anomaly is large, suggesting that the external forcing reduces the frequency of occurrence of the multiple regimes. The ACC improvements from the ensemble mean ACCs to the cluster mean ACCs are statistically significant. Thus, the cluster mean can be used as a supplementary tool for seasonal forecasting.

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Kei Yoshimura and Masao Kanamitsu

Abstract

This research was motivated by the need for an improved method compared to the conventional brute-force approach to ensemble downscaling. That method simply applies dynamical downscaling to each ensemble member. It obtains a reliable forecast by taking the ensemble average of all the downscaled ensemble members. This approach, although straightforward, has a problem in that the computational cost is too large for an operational environment. Herein a method for downscaling ensemble mean forecasts is proposed. Although this method does not provide probabilistic forecasts, it will provide the regional-scale detail at minimum cost. In this product, all of the predicted parameters are dynamically and physically consistent (i.e., most likely to occur on a seasonal time scale). It is believed that such a product has great utility for regional climate forecast and application products. The method applies a correction to one of the global forecast members in such a way that the seasonal mean is equal to that of the ensemble mean, and it then downscales the corrected global forecast. This method was tested for a 140-yr period by using the Twentieth-Century Reanalysis dataset, which is a product of ensemble Kalman filtering data assimilation. Use of the method clearly improves the downscaling skill compared to the case of using only a single member; the skill becomes equivalent to that achieved when between two and six members are used directly.

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Kei Yoshimura and Masao Kanamitsu

Abstract

With the aim of producing higher-resolution global reanalysis datasets from coarse-resolution reanalysis, a global version of the dynamical downscaling using a global spectral model is developed. A variant of spectral nudging, the modified form of scale-selective bias correction developed for regional models is adopted. The method includes 1) nudging of temperature in addition to the zonal and meridional components of winds, 2) nudging to the perturbation field rather than to the perturbation tendency, and 3) no nudging and correction of the humidity. The downscaling experiment was performed using a T248L28 (about 50-km resolution) global model, driven by the so-called R-2 reanalysis (T62L28 resolution, or about 200-km resolution) during 2001. Evaluation with high-resolution observations showed that the monthly averaged global surface temperature and daily variation of precipitation were much improved. Over North America, surface wind speed and temperature are much better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. Three well-known synoptic/subsynoptic-scale weather patterns over the United States, Europe, and Antarctica were shown to become more realistic. This study suggests that the global downscaling is a viable and economical method for obtaining high-resolution reanalysis without rerunning a very expensive high-resolution full data assimilation.

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