Showing papers on "Meteorological reanalysis published in 2015"

Towards a high‐resolution regional reanalysis for the European CORDEX domain

Abstract: Atmospheric reanalyses covering the European region are mainly available as part of relatively coarse global reanalyses The aim of this article is to present the development and evaluation of a next generation regional reanalysis for the European CORDEX EUR-11 domain with a horizontal grid spacing of approximately 6 km In this context, a reanalysis is understood to be an assimilation of heterogeneous observations with a physical model such as a numerical weather prediction (NWP) model The reanalysis system presented here is based on the NWP model COSMO by the German Meteorological Service (Deutscher Wetterdienst) using a continuous nudging scheme In order to assess the added value of data assimilation, a dynamical downscaling experiment has been conducted, ie an identical model set-up but without data assimilation Both systems have been evaluated for a 1 year test period, employing standard measures such as analysis increments, biases, or log-odds ratios, as well as tests for distributional characteristics An important aspect is the evaluation from different perspectives and with independent measurements such as satellite infrared brightness temperatures using forward operators, integrated water vapour from GPS stations, and ceilometer cloud cover It can be shown that the reanalysis better resolves local extreme events; this is basically an effect of the higher spatio-temporal resolution, as known from dynamical downscaling approaches However, an important criterion for regional reanalyses is the coherence with independent observations of high temporal and spatial resolution, resulting in significant improvement over dynamical downscaling The system is intended to become operational within a year, continuously reprocessing and evaluating longer time periods The reanalysis data are planned to become available to the research community within a year

An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results

Abstract: An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments.It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reductio...

A Hybrid Global Ocean Data Assimilation System at NCEP

Abstract: Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR).T...

The Low-Level Jet over the Southern Great Plains Determined from Observations and Reanalyses and Its Impact on Moisture Transport

Abstract: This study utilizes six commonly used reanalysis products, including the NCEP–Department of Energy Reanalysis 2 (NCEP2), NCEP Climate Forecast System Reanalysis (CFSR), ECMWF interim reanalysis (ERA-Interim), Japanese 25-year Reanalysis Project (JRA-25), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and North American Regional Reanalysis (NARR), to evaluate features of the southern Great Plains low-level jet (LLJ) above the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) Southern Great Plains site. Two sets of radiosonde data are utilized: the six-week Midlatitude Continental Convective Clouds Experiment (MC3E) and a 10-yr period spanning 2001 through 2010. All six reanalyses are compared to MC3E data, while only the NARR, MERRA, and CFSR are compared to the 10-yr data. The reanalyses are able to represent most aspects of the composite LLJ profile, although there is a tendency for each reanalysis to overestimat...

Comparison of surface air temperature derived from NCEP/DOE R2, ERA-Interim, and observations in the arid northwestern China: a consideration of altitude errors

Abstract: The surface air temperatures measured at 68 meteorological stations in the arid northwestern China during 1979–2012 are compared with temperatures interpolated from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis 2 (NCEP R2) and the European Center for Medium-Range Weather Forecasts ERA-Interim. The altitude effects on reanalysis temperature errors are discussed, and the interpolated reanalysis data are calibrated by altitude errors between reanalysis and observation. Using a simple correction method with a constant lapse rate, the elevation-related errors can be greatly removed and an improvement is achieved for the interpolated temperature from both NCEP R2 and ERA-Interim. The cold bias of reanalysis data becomes weak after calibration. On an annual basis, root mean square error of temperature derived from NCEP R2 for each stations has decreased from 6.0 (raw data) to 2.6 °C (calibrated data) and that from ERA-Interim has decreased from 3.2 to 1.4 °C. Similarly, correlation coefficients between raw reanalysis-based and observed temperature are 0.191 and 0.709 for NCEP R2 and ERA-Interim, respectively, whereas the correlation coefficients using the calibrated annual data are 0.819 and 0.932 for NCEP R2 and ERA-Interim, respectively. Generally, ERA-Interim is closer to the ground-based observations than NCEP R2. The topographic correction is more effective in summer than in winter, which may be related to the temperature inversion in winter. Evaluation and correction of reanalysis datasets is a crucial work before the gridded data are applied in climate research, and the altitude-related errors should be calibrated especially in the regions with complex topography.

A Detailed Cloud Fraction Climatology of the Upper Indus Basin and Its Implications for Near-Surface Air Temperature*

Abstract: Clouds play a key role in hydroclimatological variability by modulating the surface energy balance and air temperature. This study utilizes MODIS cloud cover data, with corroboration from global meteorological reanalysis (ERA-Interim) cloud estimates, to describe a cloud climatology for the upper Indus River basin. It has specific focus on tributary catchments in the northwest of the region, which contribute a large fraction of basin annual runoff, including 65% of flow originating above Besham, Pakistan or 50 km3 yr−1 in absolute terms. In this region there is substantial cloud cover throughout the year, with spatial means of 50%–80% depending on the season. The annual cycles of catchment spatial mean daytime and nighttime cloud cover fraction are very similar. This regional diurnal homogeneity belies substantial spatial variability, particularly along seasonally varying vertical profiles (based on surface elevation).Correlations between local near-surface air temperature observations and MODIS c...

Methodologies to characterize uncertainties in regional reanalyses

Abstract: . When using climate data for various applications, users are confronted with the difficulty to assess the uncertainties of the data. For both in-situ and remote sensing data the issues of representativeness, homogeneity, and coverage have to be considered for the past, and their respective change over time has to be considered for any interpretation of trends. A synthesis of observations can be obtained by employing data assimilation with numerical weather prediction (NWP) models resulting in a meteorological reanalysis. Global reanalyses can be used as boundary conditions for regional reanalyses (RRAs), which run in a limited area (Europe in our case) with higher spatial and temporal resolution, and allow for assimilation of more regionally representative observations. With the spatially highly resolved RRAs, which exhibit smaller scale information, a more realistic representation of extreme events (e.g. of precipitation) compared to global reanalyses is aimed for. In this study, we discuss different methods for quantifying the uncertainty of the RRAs to answer the question to which extent the smaller scale information (or resulting statistics) provided by the RRAs can be relied on. Within the European Union's seventh Framework Programme (EU FP7) project Uncertainties in Ensembles of Regional Re-Analyses (UERRA) ensembles of RRAs (both multi-model and single model ensembles) are produced and their uncertainties are quantified. Here we explore the following methods for characterizing the uncertainties of the RRAs: (A) analyzing the feedback statistics of the assimilation systems, (B) validation against station measurements and (C) grids derived thereof, and (D) against gridded satellite data products. The RRA ensembles (E) provide the opportunity to derive ensemble scores like ensemble spread and other special probabilistic skill scores. Finally, user applications (F) are considered. The various methods are related to user questions they can help to answer.

Impact of data assimilation on high‐resolution rainfall forecasts: A spatial, seasonal, and category analysis

Abstract: In a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012–2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

Abstract: The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean (hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving, hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles (mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.

Tropical cyclones in the North American Regional Reanalysis: The impact of satellite‐derived precipitation over ocean

Abstract: Continued advancement in the realm of tropical cyclone (TC) forecasting requires a more accurate depiction of these storms at model initialization. This study examines the impact of precipitation assimilation on the representation of TCs in the North American Regional Reanalysis before and after the 2004 introduction of precipitation assimilation over ocean in the vicinity of TCs. The probability distribution function of rainfall rates indicates that light (heavy) precipitation was overforecast (underforecast) in the early time period. Since the precipitation assimilation is applied through an adjustment to the latent heating distribution, the data assimilation system in the later time period initializes a low-level moisture and heating profile that is more conducive to the initiation of deep convection and the generation of precipitation. Consequently, the deep convection and enhanced latent heat release lead to a more robust warm-core temperature perturbation and a better developed secondary circulation, which supplies the TC with larger quantities of moisture from the large-scale environment. Furthermore, the evolution of TC size, which was objectively estimated though the radius of outermost closed isobar, is significantly more skillful (p < 0.05) in post-2003 storms. Based on this study, precipitation assimilation leads to a better analysis of temperature, winds, and moisture in the vicinity of TCs, resulting in improved representations of the water budget and storm life cycle. Therefore, we conclude that efforts toward the development of precipitation assimilation techniques from radar and satellite data sets will be valuable toward the construction of improved TC forecasting tools with more authentic TC representation.

A reliability assessment of upper-level reanalysis datasets over China

Abstract: East Asia is one of the most sensitive and vulnerable areas in the world to the global climate changes.The study of the climate changes in this region is much more difficult than other places.With high quality,long time series and high resolution,atmosphere reanalysis has become the most widely used datasets in atmospheric science.However,the systematic biases can influence the quantity of reanalysis datasets.Therefore,an evaluation of the reliability and accuracy of reanalysis datasets is significant for global and regional climate research.In recent years,lots of work have been made to investigate the reliability of reanalysis.However,most of them focused on surface variables,few reanalysis and the time period were limited.This work evaluated the upper-level variables extracted from following sources:the National Centers for Environment Prediction(NCEP)-National Centers for Atmospheric Research(NCAR)reanalysis-1,the NCEP-Department of Energy(DOE)renalysis-2,the NCEP-Climate Forecast System Reanalysis(CFSR),the 25-year Japanese Meteorological Agency(JRA-25)reanalysis,the European Centre for Medium-Range Weather Forecast(ECMWF)Interim reanalysis(ERAInterim)and the Modern-Era Retrospective analysis for Research and Applications(MERRA)reanalysis products,and Integrated Global Radiosonde Archive(IGRA)global sounding observations over China from 1989 to 2008.Three sets of reliability and accuracy studies were carried out:the first evaluated the spatial distributions of summer mean values of the several high-variables represented by all reanalysis mentioned above.The second aimed to assess the performance of the inter-annual variation of high-variables described by reanalysis.The third compared the similarities of empirical orthogonal function(EOF)modes between observations and reanalysis.It was found that the mean values of geopotential height and temperature in each reanalysis dataset are consistent with the observations,but the wind fields,especially the meridional wind,are not.Besides,the reanalysis products do a bad job in revealing the interannual variation of meridional wind.The results of EOF analysis imply that all reanalysis datasets exhibit better performance in depicting the temporal and spatial distributions of geopotential height and temperature than other variables,especially the wind fields;MERRA performs specific humidity better than other reanalysis products.Generally,NCEP/NCAR,NCEP/DOE and NCEP/CFSR products are not as good as JRA-25,ERA-Interim and MERRA.Based on the study,we recognized the differences of each reanalysis in describing the characteristics of upper-level variables.In this way,more desirable alternative will be made when considering which reanalysis will be used in climate change research.However,most of these reanalysis do a bad work in revealing the wind field,which may be caused by the wind rotation.Hence,more studies are needed on the ability of reanalysis in representing wind field in the future.Besides,only upper-level variables have been compared,the research on surface variables,such as surface temperature and precipitation,can be added in the next step.Nowadays,the field of regional climate modeling is booming due to enormous demands for prediction of future regional climate change,downscaling seasonal prediction,and for use in a variety of regional climate applications.It is,therefore,imperative to better understand the strengths,deficiencies,and limitations as well as the sources of uncertainties that can occur with regional climate model(RCM)simulations.As we know,running a RCM needs lateral boundary(LB)forcing fields and initial conditions.Hence,different LB forcing fields and initial conditions may cause different simulation results.In the future,the uncertainties of RCMs caused by different driving reanalysis datasets will be researched.

On the performance of the new NWP nowcasting system at the Danish Meteorological Institute during a heavy rain period

Abstract: At the Danish Meteorological Institute, the NWP nowcasting system has been enhanced to include assimilation of 2D precipitation rates derived from weather radar observations The assimilation is performed using a nudging-based technique Here the rain rates are used to estimate the changes in the vertical profile of horizontal divergence needed to induce the observed rain rate Verification of precipitation forecasts for a 17-day period in August 2010 based on the NWP nowcasting system is presented and compared to a reference without assimilation of precipitation data In Denmark, this period was particularly rainy, with several heavy precipitation events Three of these events are studied in detail The verification is mainly based on scatter plots and fractions skill scores, which give scale-dependant indicators of the spatial skill of the forecasts The study shows that the inclusion of precipitation observations has a positive impact on the spatial skill of the forecasts This positive impact is the largest in the first hour, and then gradually decreases On the average, the forecasts with assimilation of precipitation are skilful after 4 h on scales down to a few tens of kilometers For the events studied, the assimilation improves the forecasted frequencies of heavy and light precipitation relative to the control, while there is some tendency to overpredict intermediate precipitation levels

Using meteorological reanalysis data for multi-decadal hindcasts of larval connectivity in the coastal ocean

Abstract: Ocean circulation models are useful for determining population connectivity, but are only available for a limited number of years. In contrast, meteorological reanalyses are available over decades. Since planktonic larvae are typically found in surface waters which are highly influ- enced by winds, the relationship between connectivity as estimated using an ocean circulation model and wind was used to develop a long-term hindcast of larval dispersal. The University of California Santa Cruz (UCSC) 31 yr Regional Ocean Modeling System (ROMS) hindcast of the California Current System was used to model inter-estuarine transport of larvae with a 6 d larval duration from 1981 to 2010, and between 3 and 8 connectivity patterns were identified using the self-organizing map (SOM) clustering algorithm. Regression models were developed for those connectivity patterns using meteorological reanalyses of winds. Training periods of 5, 10, and 30 yr were used for model development; in all cases there were strong associations between SOM connectivity estimates and winds. Regression models were validated using connectivity estimates from the ocean model. Validated regression models were used with winds from 1950 to 1980 to hindcast connectivity beyond the time range of the original ocean model. Connectivity as esti- mated from winds was correlated with the Pacific Decadal Oscillation and with upwelling from 1950 to 2010. Multi-decadal hindcasts of population connectivity can be carried out using meteoro - logical reanalysis winds and statistical clustering of connectivity patterns derived from ocean hind- casts of 5 to 10 yr duration.

An Evaluation of Surface Atmospheric Changes over the Arctic Ocean for 2000–09 Using Recent Reanalyses

Abstract: The authors examine five recent reanalysis products [NCEP Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Japanese 25-year Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Arctic System Reanalysis (ASR)] for 1) trends in near-surface radiation fluxes, air temperature, and humidity, which are important indicators of changes within the Arctic Ocean and also influence sea ice and ocean conditions, and 2) fidelity of these atmospheric fields and effects for an extreme event: namely, the 2007 ice retreat. An analysis of trends over the Arctic for the past decade (2000–09) shows that reanalysis solutions have large spreads, particularly for downwelling shortwave radiation. In many cases, the differences in significant trends between the five reanalysis products are comparable to the estimated trend within a particular product. These discrepancies make it difficult to establish a consensus on likely changes occur...