scholarly journals Multisite downscaling of daily precipitation with a stochastic weather generator

1999 ◽  
Vol 11 ◽  
pp. 125-136 ◽  
Author(s):  
DS Wilks
Keyword(s):  
Daily Precipitation ◽  
Weather Generator ◽  
Stochastic Weather Generator

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.

scholarly journals PENENTUAN NILAI PREMI ASURANSI PERTANIAN BERBASIS INDEKS CURAH HUJAN PADA KOMODITAS KEDELAI YANG DISIMULASI MENGGUNAKAN DISTRIBUSI WEIBULL

2020 ◽  
Vol 9 (4) ◽  
pp. 257
Author(s):  
NI PUTU AYUNDA SURYA DEWI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Insurance Premium ◽  
Weather Generator ◽  
Rainfall Data ◽  
Simulated Rainfall ◽  
Crop Failure ◽  
Agricultural Insurance ◽  
Stochastic Weather Generator ◽  
Insurance Premiums ◽  
Rainfall Index ◽  
Insurance Costs

Agricultural insurance protects farmers who experience crop failure. This study aims to calculate the value of agricultural insurance premium by applying simulated rainfall index-based using stochastic weather generator on soybean commodities in Negara sub-district. This study are used rainfall data to determine the probability of the transition, then perform rainfall simulations using the Stochastic Weather Generator method to obtain trigger values and continued with the calculation of agricultural insurance premiums. Results of this study provide the value that higher trigger is taken, the greater the insurance premium that must be paid. The value of insurance premiums to be paid is 4,18% - 5,66% of insurance costs Rp2.605.000,00.

scholarly journals Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1896 ◽  
Author(s):  
Gabriel-Martin ◽  
Sordo-Ward ◽  
Garrote ◽  
García
Keyword(s):  
Peak Flow ◽  
Total Duration ◽  
Weather Generator ◽  
Return Periods ◽  
Storm Events ◽  
Stochastic Weather Generator ◽  
Rainfall Events ◽  
Annual Peak ◽  
Extreme Flood ◽  
Physically Based

This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.

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