The NMC Nested Regional Spectral Model

Juang, Hann‐Ming Henry; Kanamitsu, Masao

doi:10.1175/1520-0493(1994)122<0003:tnnrsm>2.0.co;2

Cited by 314 publications

(174 citation statements)

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“…4 is 0.83. Kanamaru (2007a hereafter KK2007a) compare dynamically downscaled (to 10 km) data (labeled CaRD10) from the Regional Spectral Model of Juang and Kanamitsu (1994) along with NCEP/NCAR Reanalysis (hereafter NNR; Kalnay et al 1996) data to maximum temperature observations at 12 California stations, including 5 in the CV. KK2007a show the August 2000 maximum temperature correlations between observations and for those 12 stations to be on average: 0.75 and 0.77 for CaRD10 and NNR respectively.…”

Section: Resultsmentioning

confidence: 99%

Identifying extreme hottest days from large scale upper air data: a pilot scheme to find California Central Valley summertime maximum surface temperatures

Grotjahn

2011

Clim Dyn

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A pilot scheme uses upper air data from a few extreme hottest days to identify those and other extreme hottest days measured by 3 stations sampling the California Central Valley (CV). Prior work showed that CV extreme heat wave onsets have characteristic large scale patterns in many upper-air variables; those patterns also occur for the hottest days. A pilot scheme uses areas of two upper-air variables with high significance and consistency to forecast extreme surface temperatures. The scheme projects key parts of composite patterns for one or more variables onto daily weather maps of the corresponding variables resulting in a 'circulation index' for each day. The circulation index measures how similar the pattern on that day is to the composite patterns in areas dynamically relevant to a CV extremely hot day, with a larger value for a stronger match and larger amplitude. The scheme is tested on the development period (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988) and on the subsequent 18 year 'independent' period (1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006). The pilot scheme captures about half of the rare events in the development period, with similar skill for the independent period. Based only on 16 days of extreme heat in the first 10 years, the scheme is not intended to represent the general distribution; however the circulation index has similar kurtosis, variance, and skewness as the observed maximum temperatures. Properties of the high end tail of the distribution are notably improved by adding the second predictor. The scheme outperforms simply using 850 hPa temperature above the CV.

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Section: Resultsmentioning

confidence: 99%

Identifying extreme hottest days from large scale upper air data: a pilot scheme to find California Central Valley summertime maximum surface temperatures

Grotjahn

2011

Clim Dyn

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“…Moreover, these differences not only occurred in the mean, but also in higher-order moments. Since the reanalysis data normally consist of bias, anomaly nesting has been introduced for LBC coupling (Juang and Kanamitsu, 1994). However, a North American monsoon study revealed that the bias corrections associated with anomaly nesting should be used with caution.…”

Section: Lbc Qualitymentioning

confidence: 99%

“…Although regional climate models (RCMs), which evolved from mesoscale atmospheric models, have been applied for dynamic downscaling since the late 1980s (e.g., Dickinson et al, 1989;Giorgi and Bates, 1989;Kida et al, 1991;Juang and Kanamitsu, 1994;Bosilovich and Sun, 1999;Leung and Ghan, 1999;Laprise et al, 2000;Liang et al, 2001;Xue et al, 2001;Castro et al, 2005), the extensive applications of this approach have taken place only during the last decade. Today, this approach is widely applied not only for downscaling past climate, but also for future climate projection and many other applications such as producing high resolution data for hydrological assessments (e.g., Shukla and Lettenmaier, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Most RCMs originated from limited area mesocale models (e.g., Dickinson et al, 1989;Juang and Kanamitsu, 1994;, and in most cases they actually did not conduct any "regional climate predictions," which is the definition of RCM in the Glossary of Meteorology of the American Meteorological Society. This is in contrast to the GCM (i.e., general circulation model), which is labeled as a "global climate model" only when used for climate prediction.…”

Section: Introductionmentioning

confidence: 99%

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A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

Xue

Janjić

Dudhia

et al. 2014

Atmospheric Research

214

135

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Regional climate models (RCMs) have been developed and extensively applied for dynamically downscaling coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic downscaling are still not well understood. The most important issue is whether, and if so, under what conditions dynamic downscaling is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the downscaling ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic downscaling ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have downscaling ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability. This paper also briefly presents challenges faced in current RCM dynamic downscaling and future prospective, which cover the application of coupled ocean-atmosphere RCMs, ensemble applications, and future projections.

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“…The original higher resolution global analysis was first linearly transformed to a triangular truncation of T62 (192 × 94 global Gaussian grid, roughly 150 km grid space resolution at 40°N) and 18 vertical levels so that the subsequent seasonal scale regional forecasts could be done with the available computer resources. The regional spectral model (RSM) used in this study was originally developed at NCEP (Juang and Kanamitsu 1994; see also Juang et al 1997). The RSM is a regional extension of the global spectral model (GSM; Kalnay et al 1996).…”

Section: Weather Modelmentioning

confidence: 99%

Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices

Preisler

Chen

Fujioka

et al. 2008

Int. J. Wildland Fire

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The National Fire Danger Rating System indices deduced from a regional simulation weather model were used to estimate probabilities and numbers of large fire events on monthly and 1-degree grid scales. The weather model simulations and forecasts are ongoing experimental products from the Experimental Climate Prediction Center at the Scripps Institution of Oceanography. The monthly average Fosberg Fire Weather Index, deduced from the weather simulation, along with the monthly average Keetch–Byram Drought Index and Energy Release Component, were found to be more strongly associated with large fire events on a monthly scale than any of the other stand-alone fire weather or danger indices. These selected indices were used in the spatially explicit probability model to estimate the number of large fire events. Historic probabilities were also estimated using spatially smoothed historic frequencies of large fire events. It was shown that the probability model using four fire danger indices outperformed the historic model, an indication that these indices have some skill. Geographical maps of the estimated monthly wildland fire probabilities, developed using a combination of four indices, were produced for each year and were found to give reasonable matches to actual fire events. This method paves a feasible way to assess the skill of climate forecast outputs, from a dynamical meteorological model, in forecasting the probability of wildland fire severity with known precision.

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The NMC Nested Regional Spectral Model

Cited by 314 publications

References 0 publications

Identifying extreme hottest days from large scale upper air data: a pilot scheme to find California Central Valley summertime maximum surface temperatures

Identifying extreme hottest days from large scale upper air data: a pilot scheme to find California Central Valley summertime maximum surface temperatures

A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices

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