scholarly journals Development of a Pressure–Precipitation Transmitter

2019 ◽  
Vol 58 (11) ◽  
pp. 2453-2468
Author(s):  
Masaru Inatsu ◽  
Tamaki Suematsu ◽  
Yuta Tamaki ◽  
Naoto Nakano ◽  
Kao Mizushima ◽  
...  
Keyword(s):  
Regression Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Pattern ◽  
Multilevel Regression ◽  
Extreme Statistics ◽  
Spatial Continuity ◽  
Daily Precipitation Data ◽  
Multilevel Regression Model

AbstractA novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

scholarly journals Bias corrections of long-term (1973-2004) daily precipitation data over the northern regions

2005 ◽  
Vol 32 (19) ◽  
pp. n/a-n/a ◽  
Author(s):  
Daqing Yang ◽  
Douglas Kane ◽  
Zhongping Zhang ◽  
David Legates ◽  
Barry Goodison
Keyword(s):  
Daily Precipitation ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Northern Regions ◽  
Bias Corrections

scholarly journals A Multilevel Regression Model for Geographical Studies in Sets of Non-Adjacent Cities

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0133649 ◽  
Author(s):  
Marc Marí-Dell’Olmo ◽  
Miguel Ángel Martínez-Beneito
Keyword(s):  
Regression Model ◽  
Multilevel Regression ◽  
Multilevel Regression Model

Long-term change of warm-season precipitation climatology in South Korea

2020 ◽  
Author(s):  
Hyeon-seok Do ◽  
Joowan Kim
Keyword(s):  
South Korea ◽  
Daily Precipitation ◽  
Warm Season ◽  
Precipitation Intensity ◽  
Precipitation Data ◽  
Precipitation Characteristics ◽  
Daily Precipitation Data ◽  
Long Term Changes ◽  
Precipitation Increase

<div> <div> <div> <p>This study examines long-term changes of precipitation characteristics in South Korea focusing on warm season (June-September). Daily precipitation data are obtained from 15 surface stations that have continuously observed precipitation for 58 years (1961 – 2018). Precipitation characteristics and their long-term changes are examined including trend, amount, and intensity. The warm- season precipitation in South Korea is largely affected by the East Asian Summer Monsoon, which causes rainy season in late July and mid August (these are called “Changma” and “Post-Changma” seasons in Korea). Thus, these characteristics are also analyzed focusing on Changma season.</p> <p>The warm-season precipitation increased roughly by 1.0 mm per day for the last thirty years. The change is particularly pronounced during Changma season, and it shows 1.6 mm of daily precipitation increase. Trend analysis for the 58 years also showed a consistent and significant result. The precipitation change is mostly founded in the intensity of 30 – 110 mm per day implying that the precipitation intensity is increasing in warm season. Multiple regression analysis further suggests that this change is more related to precipitation intensity than precipitation frequency. Global precipitation data reveals the similar change in precipitation over central eastern China presenting a band-like precipitation increase extending to the Korean peninsula. These results are likely caused by near-surface temperature and moisture increase in a warming climate.</p> </div> </div> </div>

Predicting Depression among Patients with Diabetes Using Longitudinal Data

2015 ◽  
Vol 54 (06) ◽  
pp. 553-559 ◽  
Author(s):  
H. Jin ◽  
I. Vidyanti ◽  
P. Di Capua ◽  
B. Wu ◽  
S. Wu
Keyword(s):  
Major Depression ◽  
Regression Model ◽  
Predictive Accuracy ◽  
Patient Records ◽  
Historical Records ◽  
Multilevel Regression ◽  
Population Average ◽  
Subject Specific ◽  
Patients With Diabetes ◽  
Multilevel Regression Model

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.Background: Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression.Objectives: This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes.Methods: Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression.Results: Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions.Conclusions: The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.

scholarly journals Patterns of Extreme Precipitation and Characteristics of Related Systems in the Northern Xinjiang Region

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 358
Author(s):  
Rui Xu ◽  
Jie Ming
Keyword(s):  
Water Vapor ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Principal Component ◽  
Reanalysis Data ◽  
Water Vapor Transport ◽  
Northern Xinjiang ◽  
The North ◽  
Western Tianshan ◽  
Daily Precipitation Data

Based on 40 years of daily precipitation, 272 extreme precipitation days in the Northern Xinjiang region are defined. Using the daily precipitation data on these days, four precipitation spatial patterns were obtained through principal component analysis. Then, daily-averaged reanalysis data were used to analyze the variations of synoptic systems on extreme precipitation days and the two days before and after. The rainfall centers shifted with the influential systems at 500 hPa. Water vapor of the western Tianshan type (Type WT) and the north of Northern Xinjiang type (Type NN) comes from the west, while vapor of the Central Tianshan type (Type CT) mainly comes from the east. In the east of Northern Xinjiang type (Type EN), water vapor converges from both sides. The centers of the upper-level jets are located west of 80° E in Type WT and CT. However, they are to the east of 80° E in the other types. This article summarizes the variations of the systems at 500 hPa, the South Asia High, the westerly jet, and the water vapor transport between the surface and 500 hPa in four types of patterns, and builds the conceptual model for each type. The models built can be applied to the heavy rainfall forecast of Northern Xinjiang.

scholarly journals Observations and climate models confirm precipitation pattern is changing over Nepal

Jalawaayu ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 25-46
Author(s):  
Rocky Talchabhadel
Keyword(s):  
Climate Models ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Precipitation Pattern ◽  
Daily Precipitation Data ◽  
Comprehensive Picture ◽  
Intercomparison Project ◽  
Precipitation Indices

This paper presents a comprehensive picture of precipitation variability across Nepal over the present (1985-2014) and future (2021-2050) based on gauge-based observations from 28 precipitation stations distributed throughout the country and thirteen climate models of the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP 245 and SSP 585). Seventeen different precipitation indices are computed using daily precipitation data based on gauge-based observations and climate models. Along with absolute extreme precipitation indices, such as maximum 1-day, maximum consecutive 3-day, 5-day, and 7-day precipitation amounts, this study also computes the contribution of such instances to the annual precipitation. The selected precipitation indices not only allow for the analyses of heavy precipitation-related extremes but also guide the evaluation of agricultural productivity and drought indications, such as consecutive dry and wet days (CDD and CWD). The number of wet days and average precipitation during those wet days, along with the information of the number of days with daily precipitation ≥ 10, 20, 50, and 100 mm, summarize the distribution of total precipitation. This study emphasizes changing precipitation patterns by looking at these indices over the present and future periods. Observations and climate models show a changing nature of precipitation over Nepal. However, different climate models exhibit a different severity of changes. Though the yearly precipitation amount is not altered noticeably, this study finds that the extremes are expected to alter significantly than the averages. It is also to be noted that climate models are unable to capture localized extremes in Nepal Himalayas.

scholarly journals Gaussian Copula Method for Bias Correction of Daily Precipitation Generated by a Dynamical Model

2019 ◽  
Vol 58 (2) ◽  
pp. 269-289 ◽  
Author(s):  
Moosup Kim ◽  
Yoo-Bin Yhang ◽  
Chang-Mook Lim
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Gaussian Copula ◽  
Precipitation Pattern ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Aggregation Step

AbstractThe daily precipitation data generated by dynamical models, including regional climate models, generally suffer from biases in distribution and spatial dependence. These are serious flaws if the data are intended to be applied to hydrometeorological studies. This paper proposes a scheme for correcting the biases in both aspects simultaneously. The proposed scheme consists of two steps: an aggregation step and a disaggregation step. The first one aims to obtain a smoothed precipitation pattern that must be retained in correcting the bias, and the second aims to make up for the deficient spatial variation of the smoothed pattern. In both steps, the Gaussian copula plays important roles since it not only provides a feasible way to correct the spatial correlation of model simulations but also can be extended for large-dimension cases by imposing a covariance function on its correlation structure. The proposed scheme is applied to the daily precipitation data generated by a regional climate model. We can verify that the biases are satisfactorily corrected by examining several statistics of the corrected data.

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