scholarly journals A Stochastic Precipitation Generator Conditioned by a Climate Index

2018 ◽  
Vol 57 (11) ◽  
pp. 2585-2603 ◽  
Author(s):  
Alejandra De Vera ◽  
Rafael Terra
Keyword(s):  
Daily Precipitation ◽  
Southern Oscillation ◽  
Electric Power System ◽  
Climate Index ◽  
Model Parameters ◽  
Climate Signal ◽  
Seasonal Climate ◽  
Simulated Precipitation ◽  
Planning Processes ◽  
Gaussian Space

AbstractThis work presents a stochastic daily precipitation generator that incorporates a climate index to reflect the associated, seasonally varying, influence on simulated precipitation statistics. The weather generator is based on a first-order, two-state Markov chain to simulate the occurrence of daily precipitation and a gamma distribution to compute the nonzero daily precipitation amounts. Therefore, it has four parameters that are, in turn, allowed to vary daily following an autoregressive linear model in Gaussian space that simulates the parameters’ deviations from their climatological seasonal cycle. This model is forced by the independently predicted evolution of a climate index and captures how the model parameters and, therefore, precipitation are gradually shifted by the associated climate signal. In this case, the Niño-3.4 index is used to account for the influence of the El Niño–Southern Oscillation (ENSO) phenomenon on precipitation in Uruguay. However, the methodology is general and could be readily transferable to indices of other climate modes or downscaling algorithms for seasonal climate prediction. The results show that the proposed methodology successfully captures the ENSO signal on precipitation, including its seasonality. In doing so, it greatly reduces the underestimation of the seasonal and interannual precipitation variability, a well-known limitation of standard weather generators termed the “overdispersion” phenomenon. This work opens interesting opportunities for the application of seasonal climate forecasts in several process-based models (e.g., crop, hydrological, electric power system, water resources), which may be used to inform the decision-making and planning processes to manage climate-related risks.

scholarly journals Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes

2014 ◽  
Vol 27 (18) ◽  
pp. 6940-6959 ◽  
Author(s):  
Geraldine Wong ◽  
Douglas Maraun ◽  
Mathieu Vrac ◽  
Martin Widmann ◽  
Jonathan M. Eden ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Small Scale ◽  
Model Parameters ◽  
Model Output ◽  
Simulated Precipitation ◽  
Random Variability ◽  
Model Output Statistics

Abstract Precipitation is highly variable in space and time; hence, rain gauge time series generally exhibit additional random small-scale variability compared to area averages. Therefore, differences between daily precipitation statistics simulated by climate models and gauge observations are generally not only caused by model biases, but also by the corresponding scale gap. Classical bias correction methods, in general, cannot bridge this gap; they do not account for small-scale random variability and may produce artifacts. Here, stochastic model output statistics is proposed as a bias correction framework to explicitly account for random small-scale variability. Daily precipitation simulated by a regional climate model (RCM) is employed to predict the probability distribution of local precipitation. The pairwise correspondence between predictor and predictand required for calibration is ensured by driving the RCM with perfect boundary conditions. Wet day probabilities are described by a logistic regression, and precipitation intensities are described by a mixture model consisting of a gamma distribution for moderate precipitation and a generalized Pareto distribution for extremes. The dependence of the model parameters on simulated precipitation is modeled by a vector generalized linear model. The proposed model effectively corrects systematic biases and correctly represents local-scale random variability for most gauges. Additionally, a simplified model is considered that disregards the separate tail model. This computationally efficient model proves to be a feasible alternative for precipitation up to moderately extreme intensities. The approach sets a new framework for bias correction that combines the advantages of weather generators and RCMs.

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

Changes in Observed Daily Precipitation over the United States between 1950–79 and 1980–2009

2013 ◽  
Vol 14 (1) ◽  
pp. 105-121 ◽  
Author(s):  
R. W. Higgins ◽  
V. E. Kousky
Keyword(s):  
United States ◽  
Great Plains ◽  
El Niño ◽  
Daily Precipitation ◽  
El Nino ◽  
Southern Oscillation ◽  
Heavy Precipitation ◽  
The United States ◽  
Enso Cycle ◽  
Precipitation Events

Abstract Changes in observed daily precipitation over the conterminous United States between two 30-yr periods (1950–79 and 1980–2009) are examined using a 60-yr daily precipitation analysis obtained from the Climate Prediction Center (CPC) Unified Raingauge Database. Several simple measures are used to characterize the changes, including mean, frequency, intensity, and return period. Seasonality is accounted for by examining each measure for four nonoverlapping seasons. The possible role of the El Niño–Southern Oscillation (ENSO) cycle as an explanation for differences between the two periods is also examined. There have been more light (1 mm ≤ P < 10 mm), moderate (10 mm ≤ P < 25 mm), and heavy (P ≥ 25 mm) daily precipitation events (P) in many regions of the country during the more recent 30-yr period with some of the largest and most spatially coherent increases over the Great Plains and lower Mississippi Valley during autumn and winter. Some regions, such as portions of the Southeast and the Pacific Northwest, have seen decreases, especially during the winter. Increases in multiday heavy precipitation events have been observed in the more recent period, especially over portions of the Great Plains, Great Lakes, and Northeast. These changes are associated with changes in the mean and frequency of daily precipitation during the more recent 30-yr period. Difference patterns are strongly related to the ENSO cycle and are consistent with the stronger El Niño events during the more recent 30-yr period. Return periods for both heavy and light daily precipitation events during 1950–79 are shorter during 1980–2009 at most locations, with some notable regional exceptions.

scholarly journals Precipitation Extremes Estimated by GPCP and TRMM: ENSO Relationships

2007 ◽  
Vol 8 (4) ◽  
pp. 678-689 ◽  
Author(s):  
Scott Curtis ◽  
Ahmed Salahuddin ◽  
Robert F. Adler ◽  
George J. Huffman ◽  
Guojun Gu ◽  
...  
Keyword(s):  
El Niño ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
El Nino ◽  
Southern Oscillation ◽  
Global Precipitation Climatology Project ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Precipitation Frequency ◽  
Rain Rates

Abstract Global monthly and daily precipitation extremes are examined in relation to the El Niño–Southern Oscillation phenomenon. For each month around the annual cycle and in each 2.5° grid block, extremes in the Global Precipitation Climatology Project dataset are defined as the top five (wet) and bottom five (dry) mean rain rates from 1979 to 2004. Over the tropical oceans El Niño–Southern Oscillation events result in a spatial redistribution and overall increase in extremes. Restricting the analysis to land shows that El Niño is associated with an increase in frequency of dry extremes and a decrease in wet extremes resulting in no change in net extreme months. During La Niña an increase in frequency of dry extremes and no change in wet extremes are observed. Thus, because of the juxtaposition of tropical land areas with the ascending branches of the global Walker Circulation, El Niño (La Niña) contributes to generally dry (wet) conditions in these land areas. In addition, daily rain rates computed from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis are used to define extreme precipitation frequency locally as the number of days within a given season that exceeded the 95th percentile of daily rainfall for all seasons (1998–2005). During this period, the significant relationships between extreme daily precipitation frequency and Niño-3.4 in the Tropics are spatially similar to the significant relationships between seasonal mean rainfall and Niño-3.4. However, to address the shortness of the record extreme daily precipitation frequency is also related to seasonal rainfall for the purpose of identifying regions where positive seasonal rainfall anomalies can be used as proxies for extreme events. Finally, the longer (1979–2005) but coarser Global Precipitation Climatology Project analysis is reexamined to pinpoint regions likely to experience an increase in extreme precipitation during El Niño–Southern Oscillation events. Given the significance of El Niño–Southern Oscillation predictions, this information will enable the efficient use of resources in preparing for and mitigating the adverse effects of extreme precipitation.

scholarly journals Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

2008 ◽  
Vol 15 (1) ◽  
pp. 221-232 ◽  
Author(s):  
A. J. Cannon ◽  
W. W. Hsieh
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Mean Squared Error ◽  
Synthetic Dataset ◽  
Model Parameters ◽  
Inverse Mapping ◽  
Seasonal Climate ◽  
The Mean ◽  
Improved Performance

Abstract. Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

scholarly journals Decadal Variations of Wood Decay Hazard and El Niño Southern Oscillation Phases in Iran

2021 ◽  
Vol 4 ◽  
Author(s):  
Jalil Helali ◽  
Hossein Momenzadeh ◽  
Vahideh Saeidi ◽  
Christian Brischke ◽  
Ghanbar Ebrahimi ◽  
...  
Keyword(s):  
El Niño ◽  
Extreme Values ◽  
El Nino ◽  
Southern Oscillation ◽  
Wood Decay ◽  
El Niño Southern Oscillation ◽  
Climate Index ◽  
El Nino Southern Oscillation ◽  
Climatic Parameters ◽  
The Caspian Sea

The intensive use of wood resources is a challenging subject around the world due to urbanization, population growth, and the biodegradability of wooden materials. The study of the climatic conditions and their effects on biotic wood degradation can provide a track of trends of wood decay and decomposition at regional and global scales to predict the upcoming responses. Thus, it yields an overview for decision-makers and managers to create a precise guideline for the protection of wooden structures and prolonged service life of wooden products. This study aimed at investigating the decay hazard in Iran, its decadal changes, and how it is affected by different phases of the El Niño Southern Oscillation (ENSO). Therefore, the risk for fungal decay of wood was estimated based on the Scheffer Climate Index (SCI) at 100 meteorological stations located in Iran, for the period 1987–2019 (separately for first, second, and third decade as decadal analysis). Subsequently, SCI value trends were analyzed using the Mann–Kendall and Sen’s slope method. Finally, the relationship between SCI and climatic parameters (temperature and precipitation) was explored. Generally, the SCI fluctuated between 2 and 75 across the region. The decay risk was ranked as low in most parts, but moderate in the northern part of the country along the Caspian Sea coastlines. Decadal analysis demonstrated that the highest mean SCI values took more place in the third decade (58% of stations) and the lowest mean SCI values in the second decade (71% of stations). Furthermore, the highest and the lowest SCI values occurred at 70 and 66% of stations in El Niño and Neutral phase, respectively. Trend analysis of SCI values showed that large parts of several provinces (i.e., Markazi, Tehran, Alborz, Qazvin, Zanjan, Ardebil, East Azarbayjan, West Azarbayjan, Kurdestan, Kermanshah, and Ilam) exhibited a significantly increasing decay hazard with a mean SCI of 2.9 during the period of 33 years. An analysis of causative factors (climatic parameters) for these changes revealed that all the meteorological stations experienced a significant increase in temperature while the number of days with more than 0.25 mm precipitation increased at some stations but decreased at others. However, in summary, the SCI increased over time. Hence, in this study, the effect of precipitation on SCI was confirmed to be greater than the temperature. Analysis of the results shows that the correlation between the SCI and ENSO was positive in most of the stations. Moreover, the results of spectral coherent analysis of SCI and ENSO in different climates of Iran showed that the maximum values of SCI do not correspond to the maximum values of ENSO and are associated with lag time. Therefore, the extreme values of the SCI values cannot be interpreted solely on the basis of the ENSO.

scholarly journals Stochastic generation of annual, monthly and daily climate data: A review

2001 ◽  
Vol 5 (4) ◽  
pp. 653-670 ◽  
Author(s):  
R. Srikanthan ◽  
T. A. McMahon
Keyword(s):  
Climate Change ◽  
Historical Data ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Model Parameters ◽  
Annual Data ◽  
Climate Data ◽  
Time Step ◽  
Method Of Fragments

Abstract. The generation of rainfall and other climate data needs a range of models depending on the time and spatial scales involved. Most of the models used previously do not take into account year to year variations in the model parameters. Long periods of wet and dry years were observed in the past but were not taken into account. Recently, Thyer and Kuczera (1999) developed a hidden state Markov model to account for the wet and dry spells explicitly in annual rainfall. This review looks firstly at traditional time series models and then at the more complex models which take account of the pseudo-cycles in the data. Monthly rainfall data have been generated successfully by using the method of fragments. The main criticism of this approach is the repetitions of the same yearly pattern when only a limited number of years of historical data are available. This deficiency has been overcome by using synthetic fragments but this brings an additional problem of generating the right number of months with zero rainfall. Disaggregation schemes are effective in obtaining monthly data but the main problem is the large number of parameters to be estimated when dealing with many sites. Several simplifications have been proposed to overcome this problem. Models for generating daily rainfall are well developed. The transition probability matrix method preserves most of the characteristics of daily, monthly and annual characteristics and is shown to be the best performing model. The two-part model has been shown by many researchers to perform well across a range of climates at the daily level but has not been tested adequately at monthly or annual levels. A shortcoming of the existing models is the consistent underestimation of the variances of the simulated monthly and annual totals. As an alternative, conditioning model parameters on monthly amounts or perturbing the model parameters with the Southern Oscillation Index (SOI) result in better agreement between the variance of the simulated and observed annual rainfall and these approaches should be investigated further. As climate data are less variable than rainfall, but are correlated among themselves and with rainfall, multisite-type models have been used successfully to generate annual data. The monthly climate data can be obtained by disaggregating these annual data. On a daily time step at a site, climate data have been generated using a multisite type model conditional on the state of the present and previous days. The generation of daily climate data at a number of sites remains a challenging problem. If daily rainfall can be modelled successfully by a censored power of normal distribution then the model can be extended easily to generate daily climate data at several sites simultaneously. Most of the early work on the impacts of climate change used historical data adjusted for the climate change. In recent studies, stochastic daily weather generation models are used to compute climate data by adjusting the parameters appropriately for the future climates assumed.

scholarly journals Climatology and Interannual Variability of Floods during the TRMM Era (1998–2013)

2020 ◽  
Vol 33 (8) ◽  
pp. 3289-3305 ◽  
Author(s):  
Yan Yan ◽  
Huan Wu ◽  
Guojun Gu ◽  
Zhijun Huang ◽  
Lorenzo Alfieri ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Southern Oscillation ◽  
Temporal Variations ◽  
Precipitation Frequency ◽  
Environment Model ◽  
Precipitation Characteristics ◽  
Precipitation Analysis ◽  
Precipitation Indices ◽  
Rain Rates ◽  
Precipitation Events

AbstractSpatial and temporal variations of global floods during the TRMM period (1998–2013) are explored by means of the outputs of the Dominant River Routing Integrated with VIC Environment model (DRIVE) driven by the precipitation rates from the TRMM Multisatellite Precipitation Analysis (TMPA). Climatological and seasonal mean features of floods including frequency (FF), duration (FD), and mean and total intensity (FI and FTI) are examined and further compared to those for a variety of precipitation indices derived from the daily TMPA rain rates. In general, floods and precipitation manifest similar spatial distributions, confirming that more precipitation (both amount and frequency) often indicates higher probability of floods. However, different flood indices can be associated with different precipitation characteristics with a highly region-dependent distribution. FF and FD tend to be more related to daily precipitation frequency globally, especially the mid- to high-end precipitation frequencies (F10, F25, F50). However, FI and FTI tend to be more associated with the mean volume/magnitude of those (extreme) daily precipitation events (Pr10 and Pr25). Nonetheless, daily precipitation intensity except the very high end one (R50) generally has a relatively weak effect on floods. The precipitation–flood relations at the 10 large regions are further examined, providing an improved understanding of precipitation-related flood-generating mechanisms in different locations. On the interannual time scale, El Niño–Southern Oscillation (ENSO) can significantly affect floods in many flood-prone zones. However, it is noted that even though the ENSO effect on floods is mostly through modulating various aspects of precipitation events, significant ENSO signals in precipitation cannot always translate to an effective, simultaneous impact on floods.

scholarly journals Development of Precise Indices for Assessing the Potential Impacts of Climate Change

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1231
Author(s):  
Vinay Kumar
Keyword(s):  
Climate Change ◽  
Southern Oscillation ◽  
Diversity Index ◽  
Region Of Interest ◽  
Heavy Precipitation ◽  
Climate Index ◽  
Climate Indices ◽  
Special Issue ◽  
Indonesian Archipelago ◽  
Weather And Climate

The Special Issue on climate indices and climate change deals with various kinds of indices exits to assess weather and climate over a region. These indices might be based on local, regional, remote variables, which may affect and define the weather and climate of a region. Climate indices are the time series used to monitor the state of the climate and its relationship with other possible causes. With indices being myriad, it is challenging to choose which one is appropriate for a region of interest. However, the relationship between the indices and the climate of a region varies. El-Nino Southern Oscillation (Southern Oscillation Index, SOI/ENSO) is one of the most robust climate signals that stimulate rainfall, temperature, and hurricanes via teleconnections. SOI has a correlation of 0.5 over the Indonesian archipelago. Here, some of the well-known indices Holiday Climate Index (HCI), Tourism Climate Index (TCI), and Simple Diversity Index (SDI) are being reconnoitered to understand the holiday-tourism, end-of-the-day (EOD) judgment. The intrusion of dry air in the middle troposphere can create unstable weather, leading to heavy precipitation. The Special Issue seeks to encourage researchers to discover new indices in multidisciplinary department of atmospheric and physical sciences.

Florida wildfire activity and atmospheric teleconnections

2009 ◽  
Vol 18 (4) ◽  
pp. 476 ◽  
Author(s):  
Scott L. Goodrick ◽  
Deborah E. Hanley
Keyword(s):  
North American ◽  
Southern Oscillation ◽  
Fire Severity ◽  
Fire Season ◽  
Climate Index ◽  
Atmospheric Teleconnection ◽  
Pacific North American ◽  
Teleconnection Patterns ◽  
Area Burned ◽  
The North

Since 1991, the Florida Division of Forestry has been making seasonal fire severity forecasts based on a relationship between area burned in Florida and El Niño–Southern Oscillation (ENSO). The present study extends the original analysis on which these forecasts are based and attempts to augment it with the addition of other patterns of climate variability. Two atmospheric teleconnection patterns, the North Atlantic Oscillation and Pacific–North American pattern, are examined as potential indicators of seasonal and monthly area burned in Florida. Although ENSO was the only climate index to show a significant correlation to area burned in Florida, the Pacific–North American pattern (PNA) is shown to be a factor influencing fire season severity although the relationship is not monotonic and therefore not revealed by correlation analysis.

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