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Does KNN weather generator consider Rcp Scenarios?

The k-NN technique is based on selecting a specified number of days similar in characteristics to the day of interest from the historical record. In another word, k-NN has a resampling strategy for generating data on the basis of "similarity" from the historical period of weather data. The vector of input data consists of p variables across each station for each day of the historical record. The K-nearest neighbors (K-NN) is an analogous approach. This method has its origin as a non-parametric statistical pattern recognition procedure to distinguish between different patterns according to a selection criterion. Through this method, researchers can generate future data. In other words, the KNN is a technique that conditionally resamples the values from the observed record based on the conditional relationship specified. The KNN is a most simple approach.K-NN a promising nonparametric technique for generating weather data is the k-NN resampling approach. Forecasting weather data through analogue approaches has been applied in several studies, including Lorenz (1969), Barnett and Preisendorfer (1978), and Shabbar and Knox (1986).

So, this method doesn't consider RCP scenarios. A Representative Concentration Pathway (RCP) is a greenhouse gas concentration (not emissions) trajectory adopted by the IPCC for its fifth Assessment Report (AR5) in 2014. It supersedes Special Report on Emissions Scenarios (SRES) projections published in 2000. Four pathways have been selected for climate modeling and research, which describe different climate futures, all of which are considered possible depending on how much greenhouse gases are emitted in the years to come. The four RCPs, namely RCP2.6, RCP4.5, RCP6, and RCP8.5, are labeled after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+2.6, +4.5, +6.0, and +8.5 W/m^2, respectively).

Each RCP could result from different combinations of economic, technological, demographic, policy, and institutional futures. For example, the second-to-lowest RCP could be considered as a moderate mitigation scenario. However, it is also consistent with a baseline scenario that assumes a global development that focuses on technological improvements and a shift to service industries but does not aim to reduce greenhouse gas emissions as a goal in itself (similar to the B1 scenario of the SRES scenarios).

KNN-WG just generate data based on similarity with historical data and KNN-WG doesn't consider RCPs.

Name: Javad