Analyzing Uncertainty in Probable Maximum Precipitation Estimation with a Large Ensemble Climate Simulation Data

2021 ◽  
Author(s):  
Youngkyu Kim ◽  
Sunmin Kim ◽  
Yasuto Tachikawa
Keyword(s):  
Climate Simulation ◽  
Large Ensemble ◽  
Simulation Data ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Precipitation Estimation

scholarly journals Evaluating Lossy Data Compression on Climate Simulation Data within a Large Ensemble

2016 ◽  
Author(s):  
Allison H. Baker ◽  
Dorit M. Hammerling ◽  
Sheri A. Mickleson ◽  
Haiying Xu ◽  
Martin B. Stolpe ◽  
...  
Keyword(s):  
Data Compression ◽  
Climate Science ◽  
Original Data ◽  
Earth System Model ◽  
System Model ◽  
Climate Simulation ◽  
Earth System ◽  
Climate Data ◽  
Large Ensemble ◽  
Simulation Data

Abstract. High-resolution earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives by forcing reductions in data output frequency, simulation length, or ensemble size, for example. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that has undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

scholarly journals Evaluating lossy data compression on climate simulation data within a large ensemble

2016 ◽  
Vol 9 (12) ◽  
pp. 4381-4403 ◽  
Author(s):  
Allison H. Baker ◽  
Dorit M. Hammerling ◽  
Sheri A. Mickelson ◽  
Haiying Xu ◽  
Martin B. Stolpe ◽  
...  
Keyword(s):  
Data Compression ◽  
Climate Science ◽  
Original Data ◽  
Earth System Model ◽  
System Model ◽  
Climate Simulation ◽  
Earth System ◽  
Climate Data ◽  
Large Ensemble ◽  
Simulation Data

Abstract. High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that have undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

scholarly journals Probable Maximum Precipitation Estimation in a Humid Climate

2018 ◽  
Author(s):  
Zahra Afzali Gorouh ◽  
Bahram Bakhtiari ◽  
Kourosh Qaderi
Keyword(s):  
Physical Method ◽  
Hydraulic Structures ◽  
Humid Climate ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Java Programming ◽  
Precipitation Estimation ◽  
Water Resources Projects ◽  
User Friendly ◽  
Convergence Model

Abstract. Due to the importance of probable maximum precipitation (PMP) for designing and planning hydraulic structures, the aim of this study is the estimation of 24-hour PMP (PMP24) by using the statistical and physical methods in a humid climate of Qareh-Su Basin which is located in the northern part of Iran. For statistical estimate of PMP, the equations of empirical curves of Hershfield method were extracted. Then the standard and revised approaches of Hershfield method were written in JAVA programming language, as a user friendly and multi-platform application called the PMP Calculator. Convergence model was considered to calculate PMP by physical method. The depth–area–duration (DAD) curves were extracted to estimate PMP24 using physical method and then PMP24 was estimated for each storm. The results showed that for the standard and revised approaches, Km was found to be varied the range of 17–18.0 and 2.2–5.3, respectively. The maximum values of PMP24 for the first approach was obtained 447.7 mm and for second approach was 200.7 mm. Using the physical method, PMP24 was 143.1 mm. The results of this study will be helpful for planning, designing, and management of hydraulic structures and water resources projects in the study area.

scholarly journals Flood risk curve development with probabilistic rainfall modelling and large ensemble climate simulation data: a case study for the Yodo River basin

2018 ◽  
Vol 12 (4) ◽  
pp. 28-33 ◽  
Author(s):  
Tomohiro Tanaka ◽  
Yasuto Tachikawa ◽  
Yutaka Ichikawa ◽  
Kazuaki Yorozu
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Climate Simulation ◽  
Large Ensemble ◽  
Simulation Data ◽  
Risk Curve
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