scholarly journals A multi-site stochastic weather generator for high-frequency precipitation using censored skew-symmetric distribution

2021 ◽  
Vol 41 ◽  
pp. 100474
Author(s):  
Yuxiao Li ◽  
Ying Sun
Keyword(s):  
High Frequency ◽  
Weather Generator ◽  
Symmetric Distribution ◽  
Stochastic Weather Generator

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%.

scholarly journals Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths?

2016 ◽  
Vol 29 (5) ◽  
pp. 1605-1615 ◽  
Author(s):  
Jan Rajczak ◽  
Sven Kotlarski ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Transition Probabilities ◽  
Weather Generator ◽  
Mapping Technique ◽  
Model Output ◽  
Quantile Mapping ◽  
Stochastic Weather Generator ◽  
Simulated Precipitation ◽  
Climate Model Output

Abstract Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, for instance with respect to transition probabilities (TPs) between wet and dry days and spell durations. However, both climate models and commonly applied statistical tools have considerable uncertainties and drawbacks. This paper compares the ability of 1) raw regional climate model (RCM) output, 2) bias-corrected RCM output, and 3) a conventional weather generator (WG) that has been calibrated to match observed TPs to simulate the sequence of dry, wet, and very wet days at a set of long-term weather stations across Switzerland. The study finds systematic biases in TPs and spell lengths for raw RCM output, but a substantial improvement after bias correction using the deterministic quantile mapping technique. For the region considered, bias-corrected climate model output agrees well with observations in terms of TPs as well as dry and wet spell durations. For the majority of cases (models and stations) bias-corrected climate model output is similar in skill to a simple Markov chain stochastic weather generator. There is strong evidence that bias-corrected climate model simulations capture the atmospheric event sequence more realistically than a simple WG.

Simulation of Spatial Temperature-Precipitation Compound Events with Circulation-Conditioned Weather Generator

2020 ◽  
Author(s):  
Martin Dubrovsky ◽  
Ondrej Lhotka ◽  
Jiri Miksovsky
Keyword(s):  
Weather Generator ◽  
European Regions ◽  
Time Step ◽  
Temporal Scales ◽  
Stochastic Weather Generator ◽  
Fire Weather Index ◽  
Spatial And Temporal Scales ◽  
Temperature And Precipitation ◽  
Surface Weather ◽  
Spatial Temperature

<p>GRIMASA project aims to develop a spatial (not only, but especially a gridded version) stochastic weather generator (WG) applicable at various spatial and temporal scales, for both present and future climates. The multi-purpose SPAGETTA generator (Dubrovsky et al, 2019, Theoretical and Applied Climatology) being developed within this project is based on a parametric approach suggested by Wilks (1998, 2009). It was presented already at EGU-2017 and EGU-2018 conferences. It is run mainly at daily time step and allows to produce multivariate weather series for up to 100 (approximately) grid-points. In developing and validating the generator, we employ also various compound weather indices defined by multiple weather variables, which allows to account for the inter-variable correlations in the validation process. In our first experiments, the WG was run at 100 km resolution (50 km EOBS data were used for calibrating the WG) for eight European regions, and its performance was compared with RCMs (CORDEX simulations for EUR-44 domain). In our EGU-2019 contribution, our WG was validated in terms of characteristics of spatial temperature-precipitation compound spells (including dry-hot spells). Most recently, after implementing wind speed and humidity into the generator, the WG was run at much finer resolution (using data from irregularly distributed weather stations in Czechia and Sardinia) and validated in terms of spatial spells of wildfire-prone weather (using Fire Weather Index) (results were presented at AGU-2019).</p><p> </p><p>Present project activities aim mainly at (A) going into finer spatial and temporal scales, and (B) conditioning the surface weather generator on larger scale circulation simulated by circulation weather generator run at much coarser resolution. The development of the circulation generator (CIRCULATOR) has started in 2019. It is based on the first-order multivariate autoregressive model (similar to the one used in SPAGETTA), and the set of generator’s variables consists of larger scale characteristics of atmospheric circulation (derived from the NCEP/NCAR reanalysis), temperature and precipitation defined for a 2.5 degree grid. In our contribution, we will show results related to these two activities, focusing on (i) WG’s ability to reproduce spatial temperature-precipitation spells at various spatial scales (down to EUR-11 resolution) for eight European regions, (ii) validation of the circulation generator in terms of its ability to reproduce frequencies of circulation patterns and larger-scale temperature and precipitation characteristics for the 8 regions, and (iii) assessing an effect of using the circulation generator to drive the surface weather generator on its ability to reproduce the compound spells.</p><p> </p><p>Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).</p>

Ensemble weather forecast of precipitation with a stochastic weather generator based on analogues circulation

2020 ◽  
Author(s):  
Meriem Krouma ◽  
Pascal Yiou ◽  
Céline Déandréis ◽  
Soulivanh Thao
Keyword(s):  
Model Development ◽  
Weather Generator ◽  
Lead Times ◽  
The European Union ◽  
Ensemble Forecasts ◽  
Analogue Model ◽  
Data Set ◽  
Stochastic Weather Generator ◽  
Horizon 2020 ◽  
Skill Scores

<p><strong>Abstract</strong></p><p>The aim of this study is to assess the skills of a stochastic weather generator (SWG) to forecast precipitation in Europe. The SWG is based on the random sampling of circulation analogues, which is a simple form of machine learning simulation. The SWG was developed and tested by Yiou and Déandréis (2019) to forecast daily average temperature and the NAO index. Ensemble forecasts with lead times from 5 to 80 days were evaluated with CRPSS scores against climatology and persistence forecasts. Reasonable scores were obtained up to 20 days.  In this study, we adapt the parameters of the analogue SWG to optimize the simulation of European precipitations. We then analyze the performance of this SWG for lead times of 2 to 20 days, with the forecast skill scores used by Yiou and Déandréis (2019). To achieve this objective, the SWG will use ECA&D precipitation data (Haylock. 2002), and the analogues of circulation will be computed from sea-level pressure (SLP) or geopotential heights (Z500) from the NCEP reanalysis. This provides 100-member ensemble forecasts on a daily time increment. We will evaluate the seasonal dependence of the forecast skills of precipitation and the conditional dependence to weather regimes. Comparisons with “real” medium range forecasts from the ECMWF will be performed.</p><p><strong>References</strong></p><p>Yiou, P., and Céline D.. Stochastic ensemble climate forecast with an analogue model. Geoscientific Model Development 12, 2 (2019): 723‑34.</p><p>Haylock, M. R. et al.. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. J. Geophys. Res. - Atmospheres 113, D20 (2008): doi:10.1029/2008JD010201.</p><p> </p><p><strong>A</strong><strong>cknowledge</strong></p><p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

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