Structural and sampling uncertainty in observed UK daily precipitation extremes derived from an intercomparison of gridded data sets

2018 ◽  
Vol 39 (1) ◽  
pp. 128-142 ◽  
Author(s):  
Ian R. Simpson ◽  
Mark P. McCarthy
Keyword(s):  
Daily Precipitation ◽  
Precipitation Extremes ◽  
Data Sets ◽  
Gridded Data ◽  
Sampling Uncertainty

scholarly journals Evaluation of Daily Precipitation Extremes in Reanalysis and Gridded Observation‐Based Data Sets Over Germany

2020 ◽  
Vol 47 (18) ◽  
Author(s):  
Guannan Hu ◽  
Christian L. E. Franzke
Keyword(s):  
Daily Precipitation ◽  
Precipitation Extremes ◽  
Data Sets

THE HOURS WORKED–PRODUCTIVITY PUZZLE: IDENTIFICATION IN A FRACTIONAL INTEGRATION SETTING

2014 ◽  
Vol 19 (7) ◽  
pp. 1593-1621 ◽  
Author(s):  
Yuliya Lovcha ◽  
Alejandro Perez-Laborda
Keyword(s):  
Business Cycle ◽  
Fractional Integration ◽  
Real Business Cycle ◽  
Data Sets ◽  
Sampling Uncertainty ◽  
Hours Worked ◽  
Long Run ◽  
The Hours ◽  
Short Run ◽  
Technology Shock

A recent finding of the SVAR literature is that the response of hours worked to a (positive) technology shock depends on the assumed order of integration of the hours. In this work we relax this assumption, allowing fractional integration in hours and productivity. We find that the sign and magnitude of the estimated responses depend crucially on the identification assumptions employed. Although the responses of hours recovered with short-run (SR) restrictions are positive in all data sets, long-run (LR) identification results in negative, although sometimes not significant responses. We check the validity of these assumptions with the Sims procedure, concluding that both LR and SR are appropriate to recover responses in a fractionally integrated VAR. However, the application of the LR scheme always results in an increase in sampling uncertainty. Results also show that even the negative responses found in the data could still be compatible with real business cycle models.

scholarly journals Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

2014 ◽  
Vol 45 (5-6) ◽  
pp. 1325-1354 ◽  
Author(s):  
Emilia Paula Diaconescu ◽  
Philippe Gachon ◽  
John Scinocca ◽  
René Laprise
Keyword(s):  
Daily Precipitation ◽  
Monsoon Onset ◽  
Data Sets ◽  
Multiple Data ◽  
Western Sahel ◽  
Multiple Data Sets

scholarly journals Contributions of different bias‐correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes

2017 ◽  
Vol 122 (15) ◽  
pp. 7800-7819 ◽  
Author(s):  
Toshichika Iizumi ◽  
Hiroki Takikawa ◽  
Yukiko Hirabayashi ◽  
Naota Hanasaki ◽  
Motoki Nishimori
Keyword(s):  
Bias Correction ◽  
Precipitation Extremes ◽  
Data Sets ◽  
Meteorological Forcing ◽  
Temperature And Precipitation

scholarly journals Regionalization and susceptibility assessment to daily precipitation extremes in mainland Portugal

2017 ◽  
Vol 86 ◽  
pp. 128-138 ◽  
Author(s):  
Mónica Santos ◽  
Marcelo Fragoso ◽  
João A. Santos
Keyword(s):  
Daily Precipitation ◽  
Precipitation Extremes ◽  
Susceptibility Assessment ◽  
Mainland Portugal

Changing intensity and seasonality of wet extremes over Europe 

2021 ◽  
Author(s):  
Amal John ◽  
Hervé Douville ◽  
Pascal Yiou
Keyword(s):  
Global Warming ◽  
Daily Precipitation ◽  
Surface Air Temperature ◽  
Value Theory ◽  
Precipitation Extremes ◽  
Annual Maximum ◽  
Physical Mechanisms ◽  
Model Distribution ◽  
Future Global Warming ◽  
Global Mean

<p>Daily precipitation extremes are projected to intensify with global warming. Here the focus is on how extreme precipitation scales with the changing global mean surface air temperature (GSAT) and how much their inherent seasonality will change, using historical and SSP5-8.5 scenario simulations from 18 CMIP6 models for different sub-domains over Europe. With strong future global warming, the annual maximum precipitation (RX1DAY) is found to occur later in the year, although this shift is model-dependent and hardly significant in the multi-model distribution. Using generalized extreme value theory also provides evidence for the intensification of wet extremes in the future. In addition, we use monthly model outputs to decompose changes in RX1DAY occurring at the peak of the extreme season into several contributions, which gives insights into the underlying physical mechanisms that control the response of precipitation extremes and their inter-model spread.</p>

Observed Changes in Daily Precipitation Extremes at Annual Timescale Over the Eastern Mediterranean During 1961–2012

2019 ◽  
pp. 155-170
Author(s):  
S. Mathbout ◽  
J. A. Lopez-Bustins ◽  
D. Royé ◽  
J. Martin-Vide ◽  
J. Bech ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Eastern Mediterranean ◽  
Precipitation Extremes

scholarly journals A probabilistic gridded product for daily precipitation extremes over the United States

2019 ◽  
Vol 53 (5-6) ◽  
pp. 2517-2538 ◽  
Author(s):  
Mark D. Risser ◽  
Christopher J. Paciorek ◽  
Michael F. Wehner ◽  
Travis A. O’Brien ◽  
William D. Collins
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
The United States ◽  
Precipitation Extremes

Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets

2020 ◽  
Vol 56 (3) ◽  
Author(s):  
Lei Xu ◽  
Nengcheng Chen ◽  
Hamid Moradkhani ◽  
Xiang Zhang ◽  
Chuli Hu
Keyword(s):  
Remote Sensing ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Data Sets ◽  
Precipitation Estimation
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