scholarly journals Bias correction of daily precipitation over South Korea from the long-term reanalysis using a composite Gamma-Pareto distribution approach

2019 ◽  
Vol 50 (4) ◽  
pp. 1138-1161 ◽  
Author(s):  
Dong-Ik Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
Bias Correction ◽  
Pareto Distribution ◽  
Daily Precipitation ◽  
Interpolation Method ◽  
Climate Impact ◽  
Systematic Bias ◽  
Ungauged Catchments ◽  
Rainfall Analysis ◽  
Composite Distribution

Abstract Long-term precipitation data plays an important role in climate impact studies, but the observation for a given catchment is very limited. To significantly expand our sample size for the extreme rainfall analysis, we considered ERA-20c, a century-long reanalysis daily precipitation provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary studies have already indicated that ERA-20c can reproduce the mean reasonably well, but rainfall intensity is underestimated while wet-day frequency is overestimated. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal threshold. Moreover, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases over the conventional method for the extremes. We suggested an interpolation method for the set of parameters of bias correction approach in ungauged catchments. A comparison of the corrected precipitation using spatially interpolated parameters shows that the proposed modelling scheme, particularly with the 99th percentile, can reliably reduce the systematic bias.

scholarly journals Exploring the Long-Term Reanalysis of Precipitation and the Contribution of Bias Correction to the Reduction of Uncertainty over South Korea: A Composite Gamma-Pareto Distribution Approach to the Bias Correction

2018 ◽  
Author(s):  
Dong-Ik Kim ◽  
Hyun-Han Kwon ◽  
Dawei Han
Keyword(s):  
South Korea ◽  
Gamma Distribution ◽  
Bias Correction ◽  
Pareto Distribution ◽  
Daily Precipitation ◽  
Regional Scale ◽  
Systematic Bias ◽  
Quantile Mapping ◽  
Contour Map

Abstract. The long-term record of precipitation data plays an important role in climate impact studies. The local observation is often considered to be the truth in regional-scale analyses, but the long-term meteorological record for a given catchment is very limited. Recently, ERA-20c, a century-long reanalysis of the data has been published by the European Centre for Medium-Range Weather Forecasts (ECMWF), which includes daily precipitation over the whole 20th century with high spatial resolution of 0.125° × 0.125°. Preliminary studies have already indicated that the ERA-20c can reproduce the mean reasonably well, but rainfall intensity was underestimated and wet-day frequency was overestimated. The primary focus of this study was to expand our sample size significantly for extreme rainfall analysis. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal lower threshold. We found that the systematic errors are fairly well captured by the conventional quantile mapping method with a gamma distribution, but the extremes in daily precipitation are still somewhat underestimated. In such a context, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. We suggest a new interpolation method based on the parameter contour map for bias correction in ungauged catchments. The strength of this approach is that one can easily produce the bias-corrected daily precipitation in ungauged or poorly gauged catchments. A comparison of the corrected datasets using contour maps shows that the proposed modelling scheme can reliably reduce the systematic bias at a grid point that is not used in the process of parameter estimation. In particular, the contour map with the 99th percentile shows a more accurate representation of the observed daily rainfall than other combinations. The findings in this study suggest that the proposed approach can provide a useful alternative to readers who consider the bias correction of a regional-scale modelled data with a limited network of rain gauges. Although the study has been carried out in South Korea, the methodology has its potential to be applied in other parts of the world.

A method to fill-in discontinued daily precipitation series from nearby stations

2020 ◽  
Author(s):  
Manolis G. Grillakis ◽  
Christos Polykretis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Bias Correction ◽  
Missing Values ◽  
Climate Model ◽  
Daily Precipitation ◽  
Interpolation Method ◽  
Target Point ◽  
Precipitation Series ◽  
Research And Innovation ◽  
Using Data ◽  
Existing Data

<p>Cornerstone of the meteorological and climatological science is the quality measurements of the precipitation. Large instrumentation gaps occur due to network destructions (fires, wars) or even technical limitations that dictate network reorganizations. This is a difficult to tackle issue as there are legacy networks that provide decades of valuable data, but for various reasons have been discontinued. A method to work out such problems is to include only part of the data to the analyses, or to use methods to fill the measuring gaps from nearby stations, such as interpolation techniques, regression techniques. In this work, we present and assess a method to estimate missing values in daily precipitation series based on a quantile mapping approach, originally used for bias correction of climate model output. The overall methodology is based on a three-step procedure. The first is to assess the missing values from nearby stations using inverse distance weighting interpolation method. Then, as a second step, the wet day fraction is adjusted to fit the respective fraction of the target point existing data. The third step is to adjust the biases in the probability density function of the filled values towards the target point existing data, using the Multi-segment Statistical Bias Correction methodology (MSBC- Grillakis et al., 2013). The methodology is applied to each calendar month separately. The presented methodology has the advantage of correcting the number of rainy days that is usually overestimated by conventional interpolation approaches, as well as, better reproduces large daily precipitation values. The methodology is assessed for its performance on completing the timeseries of a dense precipitation stations network, using data of a second, also dense station network for the island of Crete – Greece. Conceptual limitations of the method are discussed.</p><p><em>Acknowledgments: This research has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology Hellas (GSRT), under Agreement No 651.</em></p>

scholarly journals Bias correction demonstration in two of the Indian Himalayan river basins

2020 ◽  
Author(s):  
A. P. Dimri
Keyword(s):  
Temperature Field ◽  
River Basin ◽  
Bias Correction ◽  
Climate Models ◽  
River Basins ◽  
Climate Impact ◽  
Systematic Bias ◽  
Western Himalayas ◽  
Precipitation Temperature ◽  
Basin Scale

Abstract There is imperative need of robust basin-scale data for climate impact studies over the topographically varying and landuse heterogenous river basins in the Indian Himalayan Region (IHR). Even finer resolution regional climate models’ (RCMs) information is elusive for these purposes. Based on available model fields and corresponding in-situ observed fields, bias correction for precipitation over Upper Ganga River Basin (UGRB) and temperature over Satluj River Basin (SRB) is demonstrated. These chosen river basins are in central and western Himalayas, respectively. Model precipitation (temperature) field from RegCM4.7 (REMO) and corresponding observed precipitation (temperature) field from nine (eight) stations of UGRB (SRB) are considered. Empirical quantile mapping (inverse function method) method is used. It is seen that each model has a distinct systematic bias relating to both precipitation and temperature means with respect to their corresponding observed means. Applying bias correction methods to the model fields resulted in reducing these mean biases and other errors. These findings illustrate handling and improving the model fields for hydrology, glaciology studies, etc.

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

2013 ◽  
Vol 17 (11) ◽  
pp. 4481-4502 ◽  
Author(s):  
S. Hwang ◽  
W. D. Graham
Keyword(s):  
Bias Correction ◽  
Daily Precipitation ◽  
Transition Probabilities ◽  
Small Scale ◽  
Analog Method ◽  
Dry Spell ◽  
Spatial Disaggregation ◽  
Dry Seasons ◽  
Stochastic Analog ◽  
Wet And Dry Seasons

Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

scholarly journals A novel bias correction methodology for climate impact simulations

2015 ◽  
Vol 6 (2) ◽  
pp. 1999-2042 ◽  
Author(s):  
S. Sippel ◽  
F. E. L. Otto ◽  
M. Forkel ◽  
M. R. Allen ◽  
B. P. Guillod ◽  
...  
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Terrestrial Ecosystem ◽  
Climate Impact ◽  
Correlation Structure ◽  
Terrestrial Biosphere ◽  
Terrestrial Ecosystem Model ◽  
Multivariate Correlation ◽  
Impact Simulations

Abstract. Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes.

scholarly journals Atmospheric Deposition Effects on Agricultural Soil Acidification State — Key Study: Krupanj Municipality

2014 ◽  
Vol 40 (2) ◽  
pp. 137-148 ◽  
Author(s):  
Dragan Čakmak ◽  
Jelena Beloica ◽  
Veljko Perović ◽  
Ratko Kadović ◽  
Vesna Mrvić ◽  
...  
Keyword(s):  
Soil Acidification ◽  
Interpolation Method ◽  
Natural Processes ◽  
Target Loading ◽  
Distance Weighting ◽  
The Status ◽  
Inverse Distance ◽  
Sustainable Productivity

Abstract Acidification, as a form of soil degradation is a process that leads to permanent reduction in the quality of soil as the most important natural resource. The process of soil acidification, which in the first place implies a reduction in soil pH, can be caused by natural processes, but also considerably accelerated by the anthropogenic influence of excessive S and N emissions, uncontrolled deforestation, and intensive agricultural processes. Critical loads, i.e. the upper limit of harmful depositions (primarily of S and N) which will not cause damages to the ecosystem, were determined in Europe under the auspices of the Executive Committee of the CLRTAP in 1980. These values represent the basic indicators of ecosystem stability to the process of acidification. This paper defines the status of acidification for the period up to 2100 in relation to the long term critical and target loading of soil with S and N on the territory of Krupanj municipality by applying the VSD model. The Inverse Distance Weighting (IDW) geostatistic module was used as the interpolation method. Land management, particularly in areas susceptible to acidification, needs to be focused on well-balanced agriculture and use of crops/seedlings to achieve the optimum land use and sustainable productivity for the projected 100-year period.

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