scholarly journals Evaluation of regional droughts using monthly gridded precipitation for Korea

2012 ◽  
Vol 14 (4) ◽  
pp. 1036-1050 ◽  
Author(s):  
Syed Zakir Hossein ◽  
Han Man Shin ◽  
Choi Gyewoon
Keyword(s):  
Water Resources ◽  
Kriging Interpolation ◽  
Drought Severity ◽  
Drought Risk ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Gridded Data ◽  
Data Set ◽  
Available Water

This paper attempts to characterize regional drought using 0.5 degree reanalyzed GPCC (Global Precipitation Climatology Center) gauge-based gridded monthly precipitation data sets in Korea. Drought is a function of precipitation and long-term observed precipitation was performed to enhance this characterization. There are limited long-term records from each station, therefore, a global gridded data set has been employed. Before using this data, 10 corresponding grids with KMA (Korea Meteorology Administration) stations were validated through cross-correlations (0.93–0.99). The impacts of drought are dependent on its duration, severity and spatial extent. Drought occurs when a below average water availability persists and becomes regionally extensive. In this study, 66 GPCC gridded precipitations were employed to estimate the effective drought index along with the available water resource index. The results of the 10 KMA corresponding stations were as accurate as those of the global data. Consequently, gridded data are suitable for a monthly drought severity investigation. In addition, spatial distribution of drought and available water resources were exposed by kriging interpolation technique over Korea. Through this study, drought risk city Taebaek in Kangwon province was classified by its 2009 intensity of monthly precipitations, droughts and available water resources.

scholarly journals Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

2014 ◽  
Vol 18 (5) ◽  
pp. 1995-2006 ◽  
Author(s):  
S. K. Sigaroodi ◽  
Q. Chen ◽  
S. Ebrahimi ◽  
A. Nazari ◽  
B. Choobin
Keyword(s):  
Time Lag ◽  
Precipitation Forecast ◽  
Climate Indices ◽  
Drought Risk ◽  
Monthly Precipitation ◽  
Ann Model ◽  
Data Set ◽  
Climatic Indices ◽  
Drought Relief

Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the north of Persian Gulf, southern Iran, and is continuously suffering from drought disaster, as a case to investigate the relationships between climatic indices and precipitation. Cross-correlation in combination with stepwise regression technique was used to determine the best variables among 40 indices and identify the proper time lag between dependent and independent variables for each month. The monthly precipitation was predicted using an artificial neural network (ANN) and multi-regression stepwise methods, and results were compared with observed rainfall data. Initial findings indicated that climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and El Niño are the main indices to forecast drought in the study area. According to R2, root mean square error (RMSE) and Nash–Sutcliffe efficiency, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Moreover, the model accuracy to forecast the rare rainfall events in dry months (June to October) was higher than the other months. From the findings it can be concluded that there is a relationship between monthly precipitation anomalies and climatic indices in the previous 10 months in Maharloo Basin. The highest and lowest accuracy of the ANN model were in September and March, respectively. However, these results are subject to some uncertainty due to a coarse data set and high system complexity. Therefore, more research is necessary to further elucidate the relationship between climatic indices and precipitation for drought relief. In this regard, consideration of other climatic and physiographic factors (e.g., wind and physiography) can be helpful.

Trophic Level Relationships in Pelagic Food Webs: Comparisons Derived from Long-Term Data Sets for Oneida Lake, New York (USA), and Lake St. George, Ontario (Canada)

1992 ◽  
Vol 49 (8) ◽  
pp. 1588-1596 ◽  
Author(s):  
Donald J. McQueen ◽  
Edward L. Mills ◽  
John L. Forney ◽  
Mark R. S. Johannes ◽  
John R. Post
Keyword(s):  
New York ◽  
Total Phosphorus ◽  
Trophic Level ◽  
Data Sets ◽  
Phytoplankton Abundance ◽  
Data Set ◽  
Oneida Lake ◽  
Pelagic Food Webs ◽  
Term Data

We used standardized methods to analyze a 14-yr data set from Oneida Lake and a 10-yr data set from Lake St. George. We estimated mean summer concentrations of several trophic level indicators including piscivores, planktivores, zooplankton, phytoplankton, and total phosphorus, and we then investigated the relationships between these variables. Both data sets yielded similar long-term and short-term trends. The long-term mean annual trends were that (1) the relationships between concentrations of planktivores and zooplankton (including daphnids) were always negative, (2) the relationships between concentrations of zooplankton and various measures of phytoplankton abundance were unpredictable and never statistically significant, and (3) the relationships between total phosphorus and various measures of phytoplankton abundance were always positive. Over short periods, the data from both lakes showed periodic, strong top-down relationships between concentrations of zooplankton (especially large Daphnia) and chlorophyll a, but these events were unpredictable and were seldom related to piscivore abundance.

scholarly journals Intention, Principle, Outputs and Aims of the Experimental Pavilion Research of Building Envelopes Including Windows for Wooden Buildings

2017 ◽  
Vol 13 (1) ◽  
pp. 42-51 ◽  
Author(s):  
Daniela Štaffenová ◽  
Ján Rybárik ◽  
Miroslav Jakubčík
Keyword(s):  
Experimental Research ◽  
Climatic Conditions ◽  
Point Of View ◽  
Physical Parameters ◽  
Data Sets ◽  
Data Set ◽  
Building Envelopes ◽  
Climate Conditions ◽  
Time Period

AbstractThe aim of experimental research in the area of exterior walls and windows suitable for wooden buildings was to build special pavilion laboratories. These laboratories are ideally isolated from the surrounding environment, airtight and controlled by the constant internal climate. The principle of experimental research is measuring and recording of required physical parameters (e.g. temperature or relative humidity). This is done in layers of experimental fragment sections in the direction from exterior to interior, as well as in critical places by stable interior and real exterior climatic conditions. The outputs are evaluations of experimental structures behaviour during the specified time period, possibly during the whole year by stable interior and real exterior boundary conditions. The main aim of this experimental research is processing of long-term measurements of experimental structures and the subsequent analysis. The next part of the research consists of collecting measurements obtained with assistance of the experimental detached weather station, analysis, evaluation for later setting up of reference data set for the research locality, from the point of view of its comparison to the data sets from Slovak Hydrometeorological Institute (SHMU) and to localities with similar climate conditions. Later on, the data sets could lead to recommendations for design of wooden buildings.

scholarly journals Prediction of Progestin Affinity for the Human Progesterone Receptor Based on Corrected RBA Data

2018 ◽  
Vol 1 (4) ◽  
pp. e00080
Author(s):  
A.V. Mikurova ◽  
V.S. Skvortsov
Keyword(s):  
Progesterone Receptor ◽  
Prediction Equation ◽  
Binding Activity ◽  
Data Sets ◽  
Data Set ◽  
The Third ◽  
Relative Binding ◽  
Human Progesterone Receptor ◽  
Nuclear Progesterone Receptor

The modeling of complexes of 3 sets of steroid and nonsteroidal progestins with the ligand-binding domain of the nuclear progesterone receptor was performed. Molecular docking procedure, long-term simulation of molecular dynamics and subsequent analysis by MM-PBSA (MM-GBSA) were used to model the complexes. Using the characteristics obtained by the MM-PBSA method two data sets of steroid compounds obtained in different scientific groups a prediction equation for the value of relative binding activity (RBA) was constructed. The RBA value was adjusted so that in all samples the actual activity was compared with the progesterone activity. The third data set of nonsteroidal compounds was used as a test. The resulted equation showed that the prediction results could be applied to both steroid molecules and nonsteroidal progestins.

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

2020 ◽  
Author(s):  
Tianyu Xu ◽  
Yongchuan Yu ◽  
Jianzhuo Yan ◽  
Hongxia Xu
Keyword(s):  
Prediction Model ◽  
Feature Fusion ◽  
Forecast Model ◽  
Data Sets ◽  
Good Prediction ◽  
Data Set ◽  
Rainfall Prediction ◽  
Improve Model ◽  
Probability Prediction

Abstract Due to the problems of unbalanced data sets and distribution differences in long-term rainfall prediction, the current rainfall prediction model had poor generalization performance and could not achieve good prediction results in real scenarios. This study uses multiple atmospheric parameters (such as temperature, humidity, atmospheric pressure, etc.) to establish a TabNet-LightGbm rainfall probability prediction model. This research uses feature engineering (such as generating descriptive statistical features, feature fusion) to improve model accuracy, Borderline Smote algorithm to improve data set imbalance, and confrontation verification to improve distribution differences. The experiment uses 5 years of precipitation data from 26 stations in the Beijing-Tianjin-Hebei region of China to verify the proposed rainfall prediction model. The test set is to predict the rainfall of each station in one month. The experimental results shows that the model has good performance with AUC larger than 92%. The method proposed in this study further improves the accuracy of rainfall prediction, and provides a reference for data mining tasks.

scholarly journals Calibration methods for rotating shadowband irradiometers and optimizing the calibration duration

2016 ◽  
Vol 9 (4) ◽  
pp. 1601-1612 ◽  
Author(s):  
Wilko Jessen ◽  
Stefan Wilbert ◽  
Bijan Nouri ◽  
Norbert Geuder ◽  
Holger Fritz
Keyword(s):  
Spectral Response ◽  
Atmospheric Condition ◽  
Resource Assessment ◽  
Data Sets ◽  
Data Set ◽  
Direct Normal Irradiance ◽  
Calibration Methods ◽  
Long Term Result ◽  
Time Of Year

Abstract. Resource assessment for concentrated solar power (CSP) needs accurate direct normal irradiance (DNI) measurements. An option for such measurement campaigns is the use of thoroughly calibrated rotating shadowband irradiometers (RSIs). Calibration of RSIs and Si-sensors is complex because of the inhomogeneous spectral response of these sensors and incorporates the use of several correction functions. One calibration for a given atmospheric condition and air mass might not be suitable under different conditions. This paper covers procedures and requirements of two calibration methods for the calibration of rotating shadowband irradiometers. The necessary duration of acquisition of test measurements is examined with regard to the site-specific conditions at Plataforma Solar de Almería (PSA) in Spain. Seven data sets of long-term test measurements were collected. For each data set, calibration results of varying durations were compared to its respective long-term result. Our findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Calibration results within certain periods (i.e. November to January and April to May) show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods. Consequently, the duration of calibrations at PSA can now be selected depending on the time of year in which measurements commence.

scholarly journals High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2013 ◽  
Vol 6 (2) ◽  
pp. 779-809 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
Horizontal Grid ◽  
Global Reanalysis ◽  
Marine Climate

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

scholarly journals Revising the retrieval technique of a long-term stratospheric HNO<sub>3</sub> data set: from a constrained matrix inversion to the optimal estimation algorithm

2011 ◽  
Vol 29 (7) ◽  
pp. 1317-1330 ◽  
Author(s):  
I. Fiorucci ◽  
G. Muscari ◽  
R. L. de Zafra
Keyword(s):  
Optimal Estimation ◽  
Matrix Inversion ◽  
The Arctic ◽  
Inversion Method ◽  
Data Sets ◽  
Vertical Profiles ◽  
Mixing Ratio ◽  
Data Set ◽  
The Antarctic

Abstract. The Ground-Based Millimeter-wave Spectrometer (GBMS) was designed and built at the State University of New York at Stony Brook in the early 1990s and since then has carried out many measurement campaigns of stratospheric O3, HNO3, CO and N2O at polar and mid-latitudes. Its HNO3 data set shed light on HNO3 annual cycles over the Antarctic continent and contributed to the validation of both generations of the satellite-based JPL Microwave Limb Sounder (MLS). Following the increasing need for long-term data sets of stratospheric constituents, we resolved to establish a long-term GMBS observation site at the Arctic station of Thule (76.5° N, 68.8° W), Greenland, beginning in January 2009, in order to track the long- and short-term interactions between the changing climate and the seasonal processes tied to the ozone depletion phenomenon. Furthermore, we updated the retrieval algorithm adapting the Optimal Estimation (OE) method to GBMS spectral data in order to conform to the standard of the Network for the Detection of Atmospheric Composition Change (NDACC) microwave group, and to provide our retrievals with a set of averaging kernels that allow more straightforward comparisons with other data sets. The new OE algorithm was applied to GBMS HNO3 data sets from 1993 South Pole observations to date, in order to produce HNO3 version 2 (v2) profiles. A sample of results obtained at Antarctic latitudes in fall and winter and at mid-latitudes is shown here. In most conditions, v2 inversions show a sensitivity (i.e., sum of column elements of the averaging kernel matrix) of 100 ± 20 % from 20 to 45 km altitude, with somewhat worse (better) sensitivity in the Antarctic winter lower (upper) stratosphere. The 1σ uncertainty on HNO3 v2 mixing ratio vertical profiles depends on altitude and is estimated at ~15 % or 0.3 ppbv, whichever is larger. Comparisons of v2 with former (v1) GBMS HNO3 vertical profiles, obtained employing the constrained matrix inversion method, show that v1 and v2 profiles are overall consistent. The main difference is at the HNO3 mixing ratio maximum in the 20–25 km altitude range, which is smaller in v2 than v1 profiles by up to 2 ppbv at mid-latitudes and during the Antarctic fall. This difference suggests a better agreement of GBMS HNO3 v2 profiles with both UARS/ and EOS Aura/MLS HNO3 data than previous v1 profiles.

scholarly journals ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

2021 ◽  
Vol 13 (12) ◽  
pp. 5711-5729
Author(s):  
Sandip S. Dhomse ◽  
Carlo Arosio ◽  
Wuhu Feng ◽  
Alexei Rozanov ◽  
Mark Weber ◽  
...  
Keyword(s):  
Transport Model ◽  
Stratospheric Ozone ◽  
Chemical Transport ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Profile Data ◽  
Data Set ◽  
Ozone Profile ◽  
Satellite Instruments

Abstract. High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).

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