Simulation of daily precipitation in the Pacific Northwest using a weather classification scheme

175

102

Abstract. We compare regional climate change scenarios (temperature and precipitation) over eastern Nebraska produced by a semiempirical statistical downscaling (SDS) technique and regional climate model (RegCM2) experiments, both using large scale information from the same coarse resolution general circulation model (GCM) control and 2 x COe simulations. The SDS method is based on the circulation pattern classification technique in combination with stochastic generation of daily time series of temperature and precipitation. It uses daily values of 700 mbar geopotential heights as the large-scale circulation variable. The regional climate model is driven by initial and lateral boundary conditions from the GCM. The RegCM2 exhibited greater spatial variability than the SDS method for change in both temperature and precipitation. The SDS method produced a seasonal cycle of temperature change with a much larger amplitude than that of the RegCM2 or the GCM. Daily variability of temperature mainly decreased for both downscaling methods and the GCM. Changes in mean daily precipitation varied between SDS and RegCM2. The RegCM2 simulated both increases and decreases in the probability of precipitation, while the SDS method produced only increases. We explore possible dynamical and physical reasons for the differences in the scenarios produced by the two methods and the GCM.

Section: Introductionmentioning

confidence: 99%

Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling

Mearns¹,

Bogárdi²,

Giorgi³

et al. 1999

175

102

“…If such a relationship can indeed be established, it will improve rainfall prediction on a local scale. With regard to these aspects, research was recently carried out [Wilson et al, 1991;Bardossy and Plate, 1992;Hay et al, 1991;Hughes and Guttorp, 1994;Wilby, 1994Wilby, , 1995, especially in the modeling and simulation of daily rainfall in specific areas utilizing the local meteorological experience.…”

Section: 268mentioning

confidence: 99%

Influence of atmospheric circulation types on space‐time distribution of intense rainfall

Mamassis¹,

Koutsoyiannis²

1996

The influence of the prevailing weather situation on the temporal evolution and geographical distribution of intense rainfall is studied, as a potential tool to improve rainfall prediction. A classification scheme of the atmospheric circulation over the east Mediterranean territory is used for the analysis. The study area is the Sterea Hellas region (central Greece) with an area of about 25,000 km2. Daily data from 71 rain gages and hourly data from three rain recorders over a 20 year period are used. From these data sets, the intense rainfall events were extracted and analyzed. Several empirical and statistical methods (also including the available tools of a Geographical Information System) are used for the analysis and comparison of rainfall distribution both in time and in space. The analysis shows that the contribution of the concept of weather types to the quantitative point rainfall prediction in short timescale is small, and only the estimation of the probability of occurrence of an intense event is feasible. On the contrary, the relation between the spatial distribution of rainfall and the atmospheric circulation patterns is significant and may be used for improving the forecasting of the geographical distribution of rainfall.

“…These synthetic weather types were then used to simulate precipitation amounts based on observed relationships between the weather types and rainfall. Wilson This paper is an extension of the preliminary investigation conducted by Wilson et al [1991]. Herein we undertake a more extensive analysis of the climate component of the model prior to developing a multisite precipitation model.…”

Section: Much Of This Work Which Is Reviewed In Georgakakosmentioning

confidence: 99%

“…Once the inputs for the weather classes have been determined, different methods of defining the weather classes are explored and compared using an information measure. The weather class process is then modeled using a semi-Markov model as in Wilson et al [1991], except that the precipitation states are not directly included within the weather model. Once the weather process has been adequately described, a multiple-station point process model is developed for the precipitation process at three stations.…”

Section: Much Of This Work Which Is Reviewed In Georgakakosmentioning

confidence: 99%

See 1 more Smart Citation

A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation

Wilson¹,

Lettenmaier²,

Skyllingstad³

1992

Self Cite

A stochastic model of weather states and concurrent daily precipitation at multiple precipitation stations is described. Four algorithms are investigated for classification of daily weather states: k‐means clustering, fuzzy clustering, principal components, and principal components coupled with k‐means clustering. A semi‐Markov model with a geometric distribution for within‐class lengths of stay is used to describe the evolution of weather classes. A hierarchical modified Pólya urn model is used to simulate precipitation conditioned on the regional weather type. An information measure that considers both the probability of weather class occurrence and conditional precipitation probabilities is developed to quantify the extent to which each of the weather classification schemes discriminates the precipitation states (rain‐no rain) at the precipitation stations. Evaluation of the four algorithms using the information measure shows that all methods performed equally well. The principal components method is chosen due to its ability to incorporate information from larger spatial fields. Precipitation amount distributions are assumed to be drawn from spatially correlated mixed exponential distributions, whose parameters varied by season and weather class. The model is implemented using National Meteorological Center historical atmospheric observations for the period 1964–1988 mapped to 5° × 5° grid cells over the eastern North Pacific, and three precipitation stations west of the Cascade mountain range in the state of Washington. Comparison of simulated weather class‐station precipitation time series with observational data shows that the model preserved weather class statistics and mean daily precipitation quite well, especially for stations highest in the hierarchy. Precipitation amounts for the lowest precipitation station in the hierarchy, and for precipitation extremes, are not as well preserved.