scholarly journals Global mean frequency increases of daily and sub-daily heavy precipitation in ERA5

Author(s):  
Maria Joao Chinita ◽  
Mark Richardson ◽  
Joao Teixeira ◽  
Pedro M. A. Miranda
2021 ◽  
pp. 1-49
Author(s):  
Ming Zhao

AbstractAtmospheric rivers (AR), tropical storms (TS) and mesoscale convective systems (MCS) are important weather phenomena which often threaten society through heavy precipitation and strong winds. Despite their potentially vital role in global and regional hydrological cycles, their contributions to long-term mean and extreme precipitation have not been systematically explored at the global scale. Using observational and reanalysis data, and NOAA’s Geophysical Fluid Dynamics Laboratory’s new high-resolution global climate model, we quantify that despite their occasional (13%) occurrence globally, AR, TS, and MCS days together account for ~55% of global mean precipitation and ~75% of extreme precipitation with daily rates exceeding its local 99th percentile. The model reproduces well the observed percentage of mean and extreme precipitation associated with AR, TS and MCS days. In an idealized global warming simulation with a homogeneous 4K SST increase, the modeled changes in global mean and regional distribution of precipitation correspond well with changes in AR/TS/MCS precipitation. Globally, the frequency of AR days increases and migrates towards higher latitudes while the frequency of TS days increases over the central Pacific and part of the South Indian Ocean with a decrease elsewhere. The frequency of MCS days tends to increase over parts of the equatorial western and eastern Pacific warm pools and high latitudes and decreases over most part of the tropics and subtropics. The AR/TS/MCS mean precipitation intensity increases by ~5%/K due primarily to precipitation increases in the top 25% of AR/TS/MCS days with the heaviest precipitation, which are dominated by the thermodynamic component with the dynamic and microphysical components playing a secondary role.


2019 ◽  
Vol 13 (2) ◽  
pp. 52-58
Author(s):  
V. B. Korobov ◽  
I. V. Miskevich ◽  
A. S. Lokhov ◽  
K. A. Seredkin

Abstract: pH is one of the most important parameters characterizing the state of water systems. The arithmetic mean values of samples are often used when averaging serial pH measurements in water bodies, as is usually done for other characteristics of the state of the natural environment (temperature, salinity, oxygen concentrations, suspended solids, etc.). However, in this case such an operation is illegal, since the addition of logarithms, which by definition are pH, is non-additive. The authors conducted a study to determine the extent to which pH variability in natural objects such an operation would not distort the results. For this, several samples of the pH index were generated in various ranges of its theoretically possible and natural variability. It was established that with pH variability of less than a unit characteristic of marine pH values, the statistical characteristics of the indicator and [H+ ] concentrations differ slightly, and the medians of the samples coincide. It is concluded that with such ranges characteristic of the waters of the oceans, there is no need to recalculate previously obtained results. However, for the estuaries of rivers flowing into tidal seas, as shown by field measurements, the pH variability in the mixing zone of sea and river waters is several times higher. Similar situations may occur when heavy precipitation falls on the water surface, as well as during floods. In these cases, a simple averaging of the pH values will no longer be correct. In such cases, the use of other averaging algorithms and the choice of stable statistical characteristics are required.


2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.


Icarus ◽  
2021 ◽  
Vol 364 ◽  
pp. 114477
Author(s):  
M. Dobrijevic ◽  
J.C. Loison ◽  
V. Hue ◽  
T. Cavalié

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jinping Wang ◽  
John A. Church ◽  
Xuebin Zhang ◽  
Xianyao Chen

AbstractThe ability of climate models to simulate 20th century global mean sea level (GMSL) and regional sea-level change has been demonstrated. However, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) sea-level projections have not been rigorously evaluated with observed GMSL and coastal sea level from a global network of tide gauges as the short overlapping period (2007–2018) and natural variability make the detection of trends and accelerations challenging. Here, we critically evaluate these projections with satellite and tide-gauge observations. The observed trends from GMSL and the regional weighted mean at tide-gauge stations confirm the projections under three Representative Concentration Pathway (RCP) scenarios within 90% confidence level during 2007–2018. The central values of the observed GMSL (1993–2018) and regional weighted mean (1970–2018) accelerations are larger than projections for RCP2.6 and lie between (or even above) those for RCP4.5 and RCP8.5 over 2007–2032, but are not yet statistically different from any scenario. While the confirmation of the projection trends gives us confidence in current understanding of near future sea-level change, it leaves open questions concerning late 21st century non-linear accelerations from ice-sheet contributions.


2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y Takahashi ◽  
T Kitai ◽  
T Watanabe ◽  
T Fujita

Abstract Background Low-voltage zone (LVZ) in the left atrium (LA) seems to represent fibrosis. LA longitudinal strain assessed by speckle tracking method is known to correlate with the extent of fibrosis in patients with mitral valve disease. Purpose We sought to identify the relationship between LA longitudinal strain and LA bipolar voltage in patients with atrial fibrillation (AF). We tested the hypothesis that LA strain can predict LA bipolar voltage. Methods A total of 96 consecutive patients undergoing initial AF ablation were analyzed. All patients underwent transthoracic echocardiography including 2D speckle tracking measurement on the day before ablation during sinus rhythm (SR group, N=54) or during AF (AF group, N=42). LA longitudinal strain was measured at basal, mid, and roof level of septal, lateral, anterior, and inferior wall in apical 4- and 2-chamber view. Global longitudinal strain (GLS) was defined as an average value of the 12 segments. LA voltage map was created using EnSite system, and global mean voltage was defined as a mean of bipolar voltage of the whole LA excluding pulmonary veins and left atrial appendage. LVZ was defined as less than 1.0 mV. Results There was a significantly positive correlation between GLS and global mean voltage (r=0.708, p<0.001). Multivariate regression analysis showed that GLS and age were independent predictors of global mean voltage. There was a significant negative correlation between global mean voltage and LVZ areas. Conclusions There was a strong correlation between LA longitudinal strain and LA mean voltage. GLS can independently predict LA mean voltage, subsequently LVZ areas in patients with AF. Funding Acknowledgement Type of funding source: None


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.


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