scholarly journals Future change of daily precipitation indices in Japan: A stochastic weather generator-based bootstrap approach to provide probabilistic climate information

2012 ◽  
Vol 117 (D11) ◽  
pp. n/a-n/a ◽  
Author(s):  
Toshichika Iizumi ◽  
Izuru Takayabu ◽  
Koji Dairaku ◽  
Hiroyuki Kusaka ◽  
Motoki Nishimori ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Weather Generator ◽  
Climate Information ◽  
Future Change ◽  
Stochastic Weather Generator ◽  
Bootstrap Approach ◽  
Precipitation Indices

scholarly journals Climate benchmarks and input parameters representing locations in 68 countries for a stochastic weather generator, CLIGEN

2020 ◽  
Author(s):  
Andrew T. Fullhart ◽  
Mark A. Nearing ◽  
Gerardo Armendariz ◽  
Mark A. Weltz
Keyword(s):  
Land Surface ◽  
Daily Precipitation ◽  
Weather Generator ◽  
Stochastic Weather Generator ◽  
Spatial Coverage ◽  
Surface Models ◽  
Input Parameters ◽  
International Climate ◽  
Data Commons ◽  
Selection Of

Abstract. This dataset contains input parameters for 12,703 locations around the world to parameterize a stochastic weather generator called CLIGEN. The parameters are essentially monthly statistics relating to daily precipitation, temperature and solar radiation. The dataset is separated into three sub-datasets differentiated by having monthly statistics determined from 30-year, 20-year, and 10-year minimum record lengths. Input parameters related to precipitation were calculated primarily from the NOAA GHCN-Daily network. The remaining input parameters were calculated from various sources including global meteorological and land-surface models that are informed by remote sensing and other methods. The new CLIGEN dataset includes inputs for locations in the U.S., which were compared to a selection of stations from an existing U.S. CLIGEN dataset representing 2648 locations. This validation showed reasonable agreement between the two datasets, with the majority of parameters showing less than 20 % discrepancy relative to the existing dataset. For the three new datasets, differentiated by the minimum record lengths used for calculations, the validation showed only a small increase in discrepancy going towards shorter record lengths, such that the average discrepancy for all parameters was greater by 5 % for the 10-year dataset. The new CLIGEN dataset has the potential to improve the spatial coverage of analysis for a variety of CLIGEN applications, and reduce the effort needed in preparing climate inputs. The dataset is available at the National Agriculture Library Data Commons website at https://data.nal.usda.gov/dataset/international-climate-benchmarks-and-input-parameters-stochastic-weather-generator-cligen and https://doi.org/10.15482/USDA.ADC/1518706 (Fullhart et al., 2020c).

scholarly journals A Constrained Stochastic Weather Generator for Daily Mean Air Temperature and Precipitation

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Feifei Pan ◽  
Lisa Nagaoka ◽  
Steve Wolverton ◽  
Samuel F. Atkinson ◽  
Timothy A. Kohler ◽  
...  
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Weather Conditions ◽  
Weather Generator ◽  
Total Precipitation ◽  
Sinusoidal Wave ◽  
Stochastic Weather Generator ◽  
Temperature And Precipitation ◽  
Unique Method

A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

Spatial patterns and temporal trends of daily precipitation indices in Iran

2014 ◽  
Vol 124 (1-2) ◽  
pp. 239-253 ◽  
Author(s):  
Tayeb Raziei ◽  
Jamal Daryabari ◽  
Isabella Bordi ◽  
Reza Modarres ◽  
Luis S. Pereira
Keyword(s):  
Spatial Patterns ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
Precipitation Indices
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