Assessment of ECMWF reanalysis data in complex terrain: Can the CERA‐20C and ERA‐Interim data sets replicate the variation in surface air temperatures over Sichuan, China?

2019 ◽  
Vol 39 (15) ◽  
pp. 5619-5634 ◽  
Author(s):  
Haolin Luo ◽  
Fei Ge ◽  
Kangquan Yang ◽  
Shoupeng Zhu ◽  
Ting Peng ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Reanalysis Data ◽  
Data Sets ◽  
Air Temperatures ◽  
Ecmwf Reanalysis

scholarly journals Southern Hemisphere atmospheric blocking diagnostic by ECMWF and NCEP/NCAR data

2012 ◽  
Vol 27 (3) ◽  
pp. 263-271 ◽  
Author(s):  
Monica Cristina Damião Mendes ◽  
Iracema F. A. Cavalcanti ◽  
Dirceu Luis Herdies
Keyword(s):  
Atlantic Ocean ◽  
Southern Hemisphere ◽  
Geopotential Height ◽  
Seasonal Cycle ◽  
South Pacific ◽  
Reanalysis Data ◽  
Data Sets ◽  
Atmospheric Blocking ◽  
Assimilation Scheme

An assessment of blocking episodes over the Southern Hemisphere, selected from the Era-40 and NCEP/NCAR reanalysis are presented in this study. Blocking can be defined by an objective index based on two 500 hPa geopotential height meridional gradients. The seasonal cycle and preferential areas of occurrence are well reproduced by the two data sets. In both reanalysis used in this study, South Pacific and Oceania were the preferred regions for blocking occurrence, followed by the Atlantic Ocean. However the results revealed differences in frequencies of occurrences, which may be related to the choice of assimilation scheme employed to produce the reanalysis data sets. It is important to note that the ERA 40 and NCEP/NCAR reanalysis were produced using consistent models and assimilation schemes throughout the whole reanalyzed period, which are different for each set.

scholarly journals Multivariate Extreme Value Theory - A Tutorial with Applications to Hydrology and Meteorology

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Anne Dutfoy ◽  
Sylvie Parey ◽  
Nicolas Roche
Keyword(s):  
Extreme Value Theory ◽  
High Speed ◽  
Value Theory ◽  
Extreme Value ◽  
Asymptotic Independence ◽  
Data Sets ◽  
Overhead Lines ◽  
Multivariate Extreme Value Theory ◽  
Air Temperatures ◽  
Multivariate Extreme Value

AbstractIn this paper, we provide a tutorial on multivariate extreme value methods which allows to estimate the risk associated with rare events occurring jointly. We draw particular attention to issues related to extremal dependence and we insist on the asymptotic independence feature. We apply the multivariate extreme value theory on two data sets related to hydrology and meteorology: first, the joint flooding of two rivers, which puts at risk the facilities lying downstream the confluence; then the joint occurrence of high speed wind and low air temperatures, which might affect overhead lines.

scholarly journals Comparison of Reanalysis Data Sets to Comprehend the Evolution of Tropical Cyclones Over North Indian Ocean

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
P. Malakar ◽  
A.P. Kesarkar ◽  
J.N. Bhate ◽  
V. Singh ◽  
A. Deshamukhya
Keyword(s):  
Indian Ocean ◽  
Tropical Cyclones ◽  
Reanalysis Data ◽  
North Indian Ocean ◽  
Data Sets

scholarly journals Asian water tower evinced in total column water vapor: a comparison among multiple satellite and reanalysis data sets

2019 ◽  
Vol 54 (1-2) ◽  
pp. 231-245 ◽  
Author(s):  
Yin Zhao ◽  
Tianjun Zhou
Keyword(s):  
Water Vapor ◽  
Satellite Data ◽  
Annual Cycle ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Data Sets ◽  
Important Indicator ◽  
Ensemble Mean ◽  
Asian Water ◽  
Total Column

Abstract The total column water vapor (TCWV) over the Tibetan Plateau (TP) is one important indicator of the Asian water tower, and the changes in the TCWV are vital to the climate and ecosystem in downstream regions. However, the observational data is insufficient to understand the changes in the TCWV due to the high elevation of the TP. Satellite and reanalysis data can be used as substitutes, but their quality needs to be evaluated. In this study, based on a homogenized radiosonde data set, a comprehensive evaluation of the TCWV over the TP derived from two satellite data sets (AIRS-only and AIRS/AMSU) and seven existing reanalysis data sets (MERRA, MERRA2, NCEP1, NCEP2, CFSR, ERA-I, JRA55) is performed in the context of the climatology, annual cycle and interannual variability. Both satellite data sets reasonably reproduce the characteristics of the TCWV over the TP. All reanalysis data sets perform well in reproducing the annual mean climatology of the TCWV over the TP (R = 0.99), except for NCEP1 (R = 0.96) and NCEP2 (R = 0.92). ERA-I is more reliable in capturing the spatial pattern of the annual cycle (R = 0.94), while NCEP1 shows the lowest skill (R = 0.72). JRA55 performs best in capturing the features of the interannual coherent variation (EOF1, R = 0.97). The skill-weighted ensemble mean of the reanalysis data performs better than the unweighted ensemble mean and most of the single reanalysis data sets. The evaluation provides essential information on both the strengths and weaknesses of the major satellite and reanalysis data sets in measuring the total column water vapor over the TP.

scholarly journals Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qin Zhang ◽  
Junhua Ye ◽  
Shuangcheng Zhang ◽  
Fei Han
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Reanalysis Dataset ◽  
High Positive Correlation ◽  
Advantages And Disadvantages ◽  
Ecmwf Reanalysis

Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region

2019 ◽  
Vol 39 (15) ◽  
pp. 5791-5800 ◽  
Author(s):  
G. Purnadurga ◽  
T.V. Lakshmi Kumar ◽  
K. Koteswara Rao ◽  
Humberto Barbosa ◽  
R.K. Mall
Keyword(s):  
Reanalysis Data ◽  
Indian Region ◽  
Data Sets

scholarly journals Surface temperature response to the major volcanic eruptions in multiple reanalysis data sets

2019 ◽  
Author(s):  
Masatomo Fujiwara ◽  
Patrick Martineau ◽  
Jonathon S. Wright
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Volcanic Eruptions ◽  
Reanalysis Data ◽  
Data Sets ◽  
Hemispheric Differences ◽  
Quasi Biennial Oscillation ◽  
Cooling Period ◽  
El Chichón ◽  
El Chichon

Abstract. The global response of air temperature at 2 metre above the surface to the eruptions of Mount Agung in March 1963, El Chichón in April 1982, and Mount Pinatubo in June 1991 is investigated using 11 global atmospheric reanalysis data sets (JRA-55, JRA-25, MERRA-2, MERRA, ERA-Interim, ERA-40, CFSR, NCEP-NCAR R-1, 20CR version 2c, ERA-20C, and CERA-20C). Multiple linear regression (MLR) is applied to the monthly mean time series of temperature for two periods, 1980–2010 (for 10 reanalyses) and 1958–2001 (for six reanalyses), by considering explanatory factors of seasonal harmonics, linear trends, Quasi-Biennial Oscillation (QBO), solar cycle, tropical sea surface temperature (SST) variations in the Pacific, Indian, and Atlantic Oceans, and Arctic SST variations. Empirical orthogonal function (EOF) analysis is applied to these climatic indices to obtain a set of orthogonal indices to be used for the MLR. The residuals of the MLR are used to define the volcanic signals for the three eruptions separately. First, latitudinally averaged time series of the residuals are investigated and compared with the results from previous studies. Then, the geographical distribution of the response during the peak cooling period after each eruption is investigated. In general, different reanalyses show similar geographical patterns of the response, but with the largest differences in the polar regions. The Pinatubo response shows largest average cooling in the 60° N–60° S region among the three eruptions, with a peak cooling of 0.10–0.15 K. The El Chichón response shows slightly larger cooling in the NH than in the Southern Hemisphere (SH), while the Agung response shows larger cooling in the SH. These hemispheric differences are consistent with the distribution of stratospheric aerosol optical depth after these eruptions; however, the peak cooling after these two eruptions is comparable in magnitude to unexplained cooling events in other periods without volcanic influence. Other methods in which the MLR model is used with different sets of indices are also tested, and it is found that careful treatment of tropical SST variability is necessary to evaluate the surface response to volcanic eruptions in observations and reanalyses.

scholarly journals A comparison of Loon balloon observations and stratospheric reanalysis products

2017 ◽  
Vol 17 (2) ◽  
pp. 855-866 ◽  
Author(s):  
Leon S. Friedrich ◽  
Adrian J. McDonald ◽  
Gregory E. Bodeker ◽  
Kathy E. Cooper ◽  
Jared Lewis ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Location Information ◽  
Data Sets ◽  
Wind Fields ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
Zonal Winds ◽  
Long Duration ◽  
Environmental Prediction ◽  
Modern Era

Abstract. Location information from long-duration super-pressure balloons flying in the Southern Hemisphere lower stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses including National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP-CFSv2), European Centre for Medium-Range Weather Forecasts (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and the recently released MERRA version 2. Balloon GPS location information is used to derive wind speeds which are then compared with values from the reanalyses interpolated to the balloon times and locations. All reanalysis data sets accurately describe the winds, with biases in zonal winds of less than 0.37 m s−1 and meridional biases of less than 0.08 m s−1. The standard deviation on the differences between Loon and reanalyses zonal winds is latitude-dependent, ranging between 2.5 and 3.5 m s−1, increasing equatorward. Comparisons between Loon trajectories and those calculated by applying a trajectory model to reanalysis wind fields show that MERRA-2 wind fields result in the most accurate simulated trajectories with a mean 5-day balloon–reanalysis trajectory separation of 621 km and median separation of 324 km showing significant improvements over MERRA version 1 and slightly outperforming ERA-Interim. The latitudinal structure of the trajectory statistics for all reanalyses displays marginally lower mean separations between 15 and 35° S than between 35 and 55° S, despite standard deviations in the wind differences increasing toward the equator. This is shown to be related to the distance travelled by the balloon playing a role in the separation statistics.

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.

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