scholarly journals Snow Cover Characteristics over the Main Russian River Basins as Represented by Reanalyses and Measured Data

2008 ◽  
Vol 47 (6) ◽  
pp. 1819-1833 ◽  
Author(s):  
V. Khan ◽  
L. Holko ◽  
K. Rubinstein ◽  
M. Breiling
Keyword(s):  
Interannual Variability ◽  
River Basins ◽  
Reanalysis Data ◽  
Measured Data ◽  
Department Of Energy ◽  
Weather Forecasts ◽  
Northern European ◽  
Snow Water ◽  
Russian River ◽  
Environmental Prediction

Abstract Snow water equivalents (SWE) produced by the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) and 40-yr European Centre for Medium-Range Weather Forecasts (ERA-40) reanalyses and snow depths (SD) produced by the 25-yr Japanese “JRA-25” reanalysis over the main Russian river basins for 1979–2000 were examined against measured data. The analysis included comparisons of mean basin values and correlation of anomalies, as well as seasonal and interannual variabilities and trends. ERA-40 generally provided better estimates of mean SWE values for river basins than did the NCEP–DOE reanalysis. Mean SD values from the JRA-25 reanalysis were systematically underestimated. The best correlations among the anomalies were given by ERA-40, followed by JRA-25. All reanalyses reproduced seasonal variability well, although the differences in absolute values varied substantially. The highest differences were typically connected with the snowmelt period (April and May). Interannual variability confirmed the errors of ERA-40 and JRA-25 in 1992–94 and 1979–83, respectively. Otherwise, the reproduction of the interannual variability of SWE and SD was reasonable. Strong biases in SD data from JRA-25 that decrease with time induce artificial positive trends. Significant underestimations of SWE data by ERA-40 for 1991–94 influenced the values of the trends. NCEP–DOE reasonably represented the trend found in measured data. In general, the highest discrepancies between measured and reanalysis data were found for the northern European and eastern Asian rivers (Pechora, Lena, and Amur). The assessment of the quality of SWE and SD reanalysis data can help potential users in the selection of a particular reanalysis as being appropriate to the purpose of their studies.

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 A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

2012 ◽  
Vol 25 (7) ◽  
pp. 2527-2534 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Quality Analysis ◽  
Resolution Model ◽  
Decadal Trend ◽  
Environmental Prediction

Abstract A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efficient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

scholarly journals Snow water scarcity induced by record-breaking warm winter in 2020 in Japan

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Satoshi Watanabe ◽  
Shunji Kotsuki ◽  
Shinjiro Kanae ◽  
Kenji Tanaka ◽  
Atsushi Higuchi
Keyword(s):  
Water Resources ◽  
High Temperatures ◽  
Water Scarcity ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
River Basins ◽  
Reanalysis Data ◽  
Temperature And Precipitation ◽  
Snow Water ◽  
The Impact

Abstract This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

scholarly journals Worldwide Survey of Awareness and Needs Concerning Reanalyses and Respondents Views on Climate Services

2016 ◽  
Vol 97 (8) ◽  
pp. 1461-1473 ◽  
Author(s):  
H. Gregow ◽  
K. Jylhä ◽  
H. M. Mäkelä ◽  
J. Aalto ◽  
T. Manninen ◽  
...  
Keyword(s):  
Online Survey ◽  
Reanalysis Data ◽  
Japan Meteorological Agency ◽  
Observation Data ◽  
Climate Services ◽  
Weather Forecasts ◽  
Data Policy ◽  
Earth Observation Data ◽  
Environmental Prediction ◽  
Agriculture And Food

Abstract A worldwide online survey about user awareness of reanalyses and climate services was conducted in the period from November 2013 to February 2014 by the Coordinating Earth Observation Data Validation for Re-Analysis for Climate Services (CORE-CLIMAX) project. The 2,578 respondents were mostly users of global reanalyses [particularly the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), National Aeronautics and Space Administration (NASA), and Japan Meteorological Agency (JMA) reanalyses]. They answered queries arranged in 11 sections by choosing from preprepared check-box responses and left several hundred free comments. Here, we analyze responses related to characteristics of reanalysis data and the perceived obstacles for using reanalysis in climate services. After examining responses from all survey participants, we focus on the answers from subgroups working in specific disciplines related to natural resource management: freshwater, agriculture and food production, forestry, and energy. Although the survey attracted mostly self-selected respondents from the education and public research and development (R&D) sectors, one-third of the energy-related subgroup were from the private sector. A large majority (91%) of the respondents use ECMWF reanalyses, but other reanalysis products are also widely used by them. Respondents expressed desire for reanalysis development in the areas of 1) training and online plotting tools, 2) more frequent updates, 3) explanations about uncertainties (the energy subgroup emphasizes this), 4) smaller biases, 5) less restrictive data policy, and 6) higher temporal and spatial resolution (the energy and water subgroups highlight this). Additionally, the subgroups (excluding energy) expressed interest in including in future climate services activities for applied weather and climate research for impact assessment and/or statistical impact analyses for improving weather warnings and their criteria.

scholarly journals Spatial and Temporal Soil Moisture Variations over China from Simulations and Observations

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Lai ◽  
Jun Wen ◽  
Sixian Cen ◽  
Xi Huang ◽  
Hui Tian ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Variations ◽  
Reanalysis Data ◽  
Weather Forecasts ◽  
Community Land Model ◽  
Princeton University ◽  
Humid Region ◽  
Precipitation Variation ◽  
Environmental Prediction

The Community Land Model version 4.0 (CLM4.0) driven by the forcing data of Princeton University was used to simulate soil moisture (SM) from 1961 to 2010 over China. The simulated SM was compared to the in situ SM measurements from International Soil Moisture Network over China, National Centers for Environmental Prediction (NCEP) Reanalysis data, a new microwave based multiple-satellite surface SM dataset (SM-MW), and European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA Interim/Land) SM data. The results showed that CLM4.0 simulation is capable of capturing characteristics of the spatial and temporal variations of SM. The simulated, NCEP, SM-MW, and ERA Interim/Land SM products are reasonably consistent with each other; based on the simulated SM of summer, it can be concluded that the spatial distribution in every layer was characterized by a gradually increasing pattern from the northwest to southeast. The SM increased from surface layer to deeper layer in general. The variation trends basically showed consistencies at all depths. The simulated SM of summer demonstrated different responses to the precipitation variation. The variation distribution of SM and measured precipitation had consistencies. The humid region significantly responded to precipitation, while the semiarid and arid regions were ranked second.

scholarly journals Reanalysis intercomparisons of stratospheric polar processing diagnostics

2018 ◽  
Author(s):  
Zachary D. Lawrence ◽  
Gloria L. Manney ◽  
Krzysztof Wargan
Keyword(s):  
Interannual Variability ◽  
Winter Season ◽  
Polar Vortex ◽  
High Sensitivity ◽  
Stratospheric Polar Vortex ◽  
Weather Forecasts ◽  
Scientific Results ◽  
Environmental Prediction ◽  
Low Sensitivity ◽  
Stratospheric Clouds

Abstract. We compare herein polar processing diagnostics derived from the five most recent full-input reanalysis datasets: the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR), the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim), the Japanese Meteorological Agency's Japanese 55-year Reanalysis (JRA-55), and the National Aeronautics and Space Administration's Modern Era Retrospective-analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2). We focus on diagnostics based on temperatures and potential vorticity (PV) in the lower to middle stratosphere that are related to formation of polar stratospheric clouds (PSCs), chlorine activation, and the strength, size, and longevity of the stratospheric polar vortex. Polar minimum temperatures (Tmin) and the area of regions having temperatures below PSC formation thresholds (APSC) show large persistent differences between the reanalyses, especially in the southern hemisphere (SH), for years prior to 1999. Average absolute differences between the reanalyses in Tmin are as large as 6 K at some levels in the SH (2 K in the NH), and absolute differences in APSC larger than 2 % of a hemisphere (1 % of a hemisphere in the NH). After 1999, there is a dramatic convergence toward better agreement between the reanalyses in both hemispheres throughout the lower stratosphere, with average Tmin differences generally less than 1 K in both hemispheres, and average APSC differences less than 0.5 % of a hemisphere. The comparisons of diagnostics based on isentropic PV for assessing polar vortex characteristics, including maximum PV gradients (MPVG) and the area of the vortex in sunlight (or sunlit vortex area, SVA), show more complex behavior: Some reanalyses show convergence toward better agreement in vortex diagnostics after 1999, while others show some persistent differences across all years. While the average differences are generally small for these vortex diagnostics, understanding such differences among the reanalyses is complicated by the need to use different methods to obtain vertically-resolved PV for the different reanalyses. We also evaluated other winter season summary diagnostics, including the number of days below PSC thresholds integrated over vertical levels, the winter mean volume of air below PSC thresholds, and vortex decay dates. For these summary diagnostics, the reanalyses generally agree best in the SH, where relatively small interannual variability has led to many winter seasons with similar polar processing potential and duration, and thus low sensitivity to differences in meteorological conditions among the reanalyses. In contrast, the large interannual variability of NH winters has given rise to many seasons with marginal conditions and high sensitivity to reanalysis differences. Our results indicate that the transition from the reanalyses assimilating Tiros Operational Vertical Sounder (TOVS) data to Advanced TOVS and other data around 1998–2000 data had a profound effect on the agreement of the temperature diagnostics presented, and to a lesser extent the agreement of the vortex diagnostics. Our results lead to several recommendations for usage of reanalyses in polar processing studies, particularly related to the sensitivity to changes in data inputs and assimilation. Because of these sensitivities, we urge great caution for studies aiming to assess trends derived from reanalysis temperatures. We also argue that one of the best ways to present the sensitivity of scientific results on polar processing is to use multiple reanalysis datasets.

scholarly journals Intraseasonal Ensemble Forecasting for the Brazilian Northeastern

2019 ◽  
Vol 41 ◽  
pp. 10
Author(s):  
Cleber Souza Corrêa ◽  
Fabricio Pereira Harter ◽  
Gerson Luiz Camillo
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Reanalysis Data ◽  
Ensemble Prediction ◽  
Weather Forecasts ◽  
Ensemble Techniques ◽  
Ensemble Estimation ◽  
Environmental Prediction ◽  
Wind Intensity

This preliminary analysis, uses simulations performed by the National Centers for Environmental Prediction (NCEP) coupled forecast system model version 2 (CFSv2) /regional climate model RegCM-4.6, allowed to be observed in this work, the data analyzed were the information of the surface wind intensity, by the analysis and comparison of the simulations carried out for the Alcântara region on the coast of the state of Maranhão. These simulations were stored in the period from February to June 2018. The analysis sought to validate with ERA5 reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). The observed result shows great potential for use of prediction ensemble techniques, since in the observed results the smallest anomalies were observed in the intraseasonal ensemble prediction to the Alcântara region in the intensity wind, in comparison to the simulation without being ensemble, presenting greater deviations and when closer to the forecast, in itself, greater deviations presented. The intraseasonal Ensemble estimation ends up filtering the terms of high frequency, being the best estimate and presenting intraseasonal predictions more balanced.

scholarly journals Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

2014 ◽  
Vol 27 (7) ◽  
pp. 2588-2606 ◽  
Author(s):  
R. Lindsay ◽  
M. Wensnahan ◽  
A. Schweiger ◽  
J. Zhang
Keyword(s):  
Arctic Region ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Surface Fluxes ◽  
Department Of Energy ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Volume ◽  
Independent Observations ◽  
Environmental Prediction

Abstract Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 103 km3 decade−1) and least with CFSR (−2.7 × 103 km3 decade−1).

Long-term variation of water vapor content and precipitation in the Haihe river basin

2014 ◽  
Vol 6 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Chun Chang ◽  
Ping Feng ◽  
Fawen Li ◽  
Yunming Gao
Keyword(s):  
Water Vapor ◽  
River Basin ◽  
Reanalysis Data ◽  
Water Vapor Content ◽  
Haihe River Basin ◽  
Haihe River ◽  
Annual Water ◽  
Environmental Prediction ◽  
High Consistency ◽  
The Haihe River Basin

Based on the Haihe river basin National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data from 1948 to 2010 and the precipitation data of 53 hydrological stations during 1957–2010, this study analyzed the variation of water vapor content and precipitation, and investigated the correlation between them using several statistical methods. The results showed that the annual water vapor content decreased drastically from 1948 to 2010. It was comparatively high from the late 1940s to the late 1960s and depreciated from the early 1970s. From the southeast to the northwest of the Haihe river basin, there was a decrease in water vapor content. For vertical distribution, water vapor content from the ground to 700 hPa pressure level accounted for 72.9% of the whole atmospheric layer, which indicated that the water vapor of the Haihe river basin was mainly in the air close to the ground. The precipitation in the Haihe river basin during 1957–2010 decreased very slightly. According to the correlation analysis, the precipitation and water vapor content changes showed statistically positive correlation, in addition, their break points were both in the 1970s. Furthermore, the high consistency between the precipitation efficiency and precipitation demonstrates that water vapor content is one of the important factors in the formation of precipitation.

scholarly journals Regional and local variations in atmospheric aerosols using ground-based sun photometry during Distributed Regional Aerosol Gridded Observation Networks (DRAGON) in 2012

2016 ◽  
Vol 16 (22) ◽  
pp. 14795-14803 ◽  
Author(s):  
Itaru Sano ◽  
Sonoyo Mukai ◽  
Makiko Nakata ◽  
Brent N. Holben
Keyword(s):  
Atmospheric Aerosols ◽  
Urban Areas ◽  
Regional Scale ◽  
Reanalysis Data ◽  
Aerosol Dynamics ◽  
Aerosol Mass ◽  
Combined Use ◽  
Environmental Prediction ◽  
Almost All ◽  
Observation Networks

Abstract. Aerosol mass concentrations are affected by local emissions as well as long-range transboundary (LRT) aerosols. This work investigates regional and local variations of aerosols based on Distributed Regional Aerosol Gridded Observation Networks (DRAGON). We constructed DRAGON-Japan and DRAGON-Osaka in spring of 2012. The former network covers almost all of Japan in order to obtain aerosol information in regional scale over Japanese islands. It was determined from the DRAGON-Japan campaign that the values of aerosol optical thickness (AOT) decrease from west to east during an aerosol episode. In fact, the highest AOT was recorded at Fukue Island at the western end of the network, and the value was much higher than that of urban areas. The latter network (DRAGON-Osaka) was set as a dense instrument network in the megalopolis of Osaka, with a population of 12 million, to better understand local aerosol dynamics in urban areas. AOT was further measured with a mobile sun photometer attached to a car. This transect information showed that aerosol concentrations rapidly changed in time and space together when most of the Osaka area was covered with moderate LRT aerosols. The combined use of the dense instrument network (DRAGON-Osaka) and high-frequency measurements provides the motion of aerosol advection, which coincides with the wind vector around the layer between 700 and 850 hPa as provided by the reanalysis data of the National Centers for Environmental Prediction (NCEP).

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