Uncertainties of ENSO-related Regional Hadley Circulation Anomalies within Eight Reanalysis Datasets

El Nino-Southern Oscillation(ENSO),the leading mode of global interannual variability,usually intensifies the Hadley Circulation(HC),and meanwhile constrains its meridional extension,leading to an equatorward movement of the jet system.Previous studies have investigated the response of HC to ENSO events using different reanalysis datasets and evaluated their capability in capturing the main features of ENSO-associated HC anomalies.However,these studies mainly focused on the global HC,represented by a zonal-mean mass stream function(MSF).Comparatively fewer studies have evaluated HC responses from a regional perspective,partly due to the prerequisite of the Stokes MSF,which prevents us from integrating a regional HC.In this study,we adopt a recently developed technique to construct the three-dimensional structure of HC and evaluate the capability of eight state-of-the-art reanalyses in reproducing the regional HC response to ENSO events.Results show that all eight reanalyses reproduce the spatial structure of HC responses well,with an intensified HC around the central-eastern Pacific but weakened circulations around the Indo-Pacific warm pool and tropical Atlantic.The spatial correlation coefficient of the three-dimensional HC anomalies among the different datasets is always larger than 0.93.However,these datasets may not capture the amplitudes of the HC responses well.This uncertainty is especially large for ENSO-associated equatorially asymmetric HC anomalies,with the maximum amplitude in Climate Forecast System Reanalysis(CFSR)being about 2.7 times the minimum value in the Twentieth Century Reanalysis(20CR).One should be careful when using reanalysis data to evaluate the intensity of ENSO-associated HC anomalies.

Decadal trends in precipitable water vapor over the Indus River Basin using ERA5 reanalysis data

Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.

Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea

Wave energy resources assessment is a very important process before the exploitation and utilization of the wave energy. At present, the existing wave energy assessment is focused on theoretical wave energy conditions for interesting areas. While the evaluation for exploitable wave energy conditions is scarcely ever performed. Generally speaking, the wave energy are non-exploitable under a high sea state and a lower sea state which must be ignored when assessing wave energy. Aiming at this situation, a case study of the East China Sea and the South China Sea is performed. First, a division basis between the theoretical wave energy and the exploitable wave energy is studied. Next, based on recent 20 a ERA-Interim wave field data, some indexes including the spatial and temporal distribution of wave power density, a wave energy exploitable ratio, a wave energy level, a wave energy stability, a total wave energy density, the seasonal variation of the total wave energy and a high sea condition frequency are calculated. And then the theoretical wave energy and the exploitable wave energy are compared each other; the distributions of the exploitable wave energy are assessed and a regional division for exploitable wave energy resources is carried out; the influence of the high sea state is evaluated. The results show that considering collapsing force of the high sea state and the utilization efficiency for wave energy, it is determined that the energy by wave with a significant wave height being not less 1 m or not greater than 4 m is the exploitable wave energy. Compared with the theoretical wave energy, the average wave power density, energy level, total wave energy density and total wave energy of the exploitable wave energy decrease obviously and the stability enhances somewhat. Pronounced differences between the theoretical wave energy and the exploitable wave energy are present. In the East China Sea and the South China Sea, the areas of an abundant and stable exploitable wave energy are primarily located in the north-central part of the South China Sea, the Luzon Strait, east of Taiwan, China and north of Ryukyu Islands; annual average exploitable wave power density values in these areas are approximately 10-15 kW/m; the exploitable coefficient of variation （COV） and seasonal variation （SV） values in these areas are less than 1.2 and 1, respectively. Some coastal areas of the Beibu Gulf, the Changjiang Estuary, the Hangzhou Bay and the Zhujiang Estuary are the poor areas of the wave energy. The areas of the high wave energy exploitable ratio is primarily in nearshore waters. The influence of the high sea state for the wave energy in nearshore waters is less than that in offshore waters. In the areas of the abundant wave energy, the influence of the high sea state for the wave energy is prominent and the utilization of wave energy is relatively difficult. The developed evaluation method may give some references for an exploitable wave energy assessment and is valuable for practical applications.

Accumulation over the Greenland Ice Sheet as Represented in Reanalysis Data

Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet （GrIS）,based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim （363 mm yr 1） and mean annual accumulation （319 mm yr 1） are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.

Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988

This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.

Validation of ECMWF and NCEP–NCAR Reanalysis Data in Antarctica

The European Center for Medium-Range Weather Forecast （ECMWF） Re-Analysis （ERA-40） and the National Centers for Environmental Prediction-National Center for Atmospheric Research （NCEP-NCAR） ECMWF （ERA-40） and NCEP–NCAR reanalysis data were compared with Antarctic station observations, including surface-layer and upper-layer atmospheric observations, on intraseasonal and interannual timescales. At the interannual timescale, atmospheric pressure at different height levels in the ERA-40 data are in better agreement with observed pressure than that in the NCEP–NCAR reanalysis data. ERA-40 reanalysis also outperforms NCEP–NCAR reanalysis in atmospheric temperature, except in the surface layer where the biases are somewhat larger. The wind velocity fields in both datasets do not agree well with surface-and upper-layer atmospheric observations. At intraseasonal timescales, both datasets capture the observed intraseasonal variability in pressure and temperature during austral winter.

An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone(TC)genesis is of great societal importance but scientifically challenging.It requires fineresolution coupled models that properly represent air−sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks,with aid from coherent coupled initialization.This study uses three sets of highresolution regional coupled models(RCMs)covering the Asia−Pacific(AP)region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific.The APRCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting−Regional Ocean Model System(WRF-ROMS):27-km WRF with 9-km ROMS,and 9-km WRF with 3-km ROMS.In this study,a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis.Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved,the enhanced-resolution coupled model tends to improve the predictability of TC genesis,which could be further improved by improving planetary boundary layer physics,thus resolving better air−sea and air−land interactions.

Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data: A Case Study of the White Volta Basin

Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.

Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals

The quality of regional ocean reanalysis data for "the joining area of Asia and the Indian-Pacific Ocean(AIPO)" has been assessed from the perspective of ENSO-related ocean signals.The results derived from the AIPO reanalysis,including SST,sea surface height(SSH),and subsurface ocean temperature and currents,are compared with those of Hadley Center Sea Ice and Sea Surface Temperature(HadISST) data set and Simple Ocean Data Assimilation(SODA) reanalysis data.Both the spatial pattern and the characteristics of evolution of the ENSO-related ocean temperature anomalies are well reproduced by the AIPO reanalysis data.The physical processes proposed to explain the life cycle of ENSO,including the delayed oscillator mechanism,recharge-discharge mechanism,and the zonal advection feedback,are reasonably represented in this dataset.However,the westward Rossby wave signal in 1992 is not obvious in the AIPO data,and the magnitude of the heat content anomalies is different from that of the SODA data.The reason for the discrepancies may lie in the different models and methods for data assimilation and differences in wind stress forcing.The results demonstrate the high reliability of the AIPO reanalysis data in describing ENSO signals,implying its potential application value in ENSO-related studies.

An evaluation of new satellite-derived latent and sensible heat fluxes with moored buoy data, OAFlux and NCEP2 reanalysis products

New satellite-derived latent and sensible heat fluxes are performed by using Wind Sat wind speed, Wind Sat sea surface temperature, the European Centre for Medium-range Weather Forecasting（ECMWF） air humidity, and ECMWF air temperature from 2004 to 2014. The 55 moored buoys are used to validate them by using the 30 min and 25 km collocation window. Furthermore, the objectively analyzed air-sea heat fluxes（OAFlux） products and the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 2（NCEP2） products are also used for global comparisons. The mean biases of sensible and latent heat fluxes between Wind Sat flux results and buoy flux data are –0.39 and –8.09 W/m^2, respectively. In addition, the rootmean-square（RMS） errors of the sensible and latent heat fluxes between them are 5.53 and 24.69 W/m^2,respectively. The RMS errors of sensible and latent heat fluxes are observed to gradually increase with an increasing buoy wind speed. The difference shows different characteristics with an increasing sea surface temperature, air humidity, and air temperature. The zonal average latent fluxes have some high regions which are mainly located in the trade wind zones where strong winds carry dry air in January, and the maximum value centers are found in the eastern waters of Japan and on the US east coast. Overall, the seasonal variability is pronounced in the Indian Ocean, the Pacific Ocean, and the Atlantic Ocean. The three sensible and latent heat fluxes have similar latitudinal dependencies; however, some differences are found in some local regions.

An Integrated Framework for Road Detection in Dense Urban Area from High-Resolution Satellite Imagery and Lidar Data

Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.

Study on the Applicability of ERA5 Reanalysis Data at Lake Taihu

Lakes are an important component of the earth climate system. They play an important role in the study of basin weather forecasting, air quality forecasting, and regional climate research. The accuracy of driving variables is the basic premise to ensure the rationality of lake mode simulation. Based on the in-situ observations at Bifenggang site of the Lake Taihu Eddy flux Network from 2012 to 2017, this paper investigated temporal variations in temperature, relative humidity, wind speed, radiation components at different time scales (hourly, seasonal and interannual). ERA5 reanalysis data were compared with in-situ observation to quantify the error and evaluate the performance of reanalysis data. The results show that: 1) On the hourly scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. 2) On the seasonal variation scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. However, the descriptions of wind speed, relative humidity and downward short-wave have large deviations. 3) On the interannual scale, the ERA5 reanalysis data show a good performance for temperature, followed by downward longwave radiation, downward shortwave radiation and relative humidity.

A Comparison Study of Tropical Pacific Ocean State Estimation:Low-Resolution Assimilation vs.High-Resolution Simulation

A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution. A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features. From 1990 to 2000, the average of HR's RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97℃ compared to a RMSE of 0.56℃ for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.

Evaluation of the China Ocean Reanalysis (CORA) in the South China Sea

The daily regional reanalysis product of the China Ocean Reanalysis(CORA)product was released in website in 2018.Using in situ observational data including Argo profiling floats,drifters,and cruise data,the performance of CORA in the South China Sea in terms of temperature,salinity,current and mixed layer depths is evaluated based on timescale(seasonal and interannual)and spatial distribution characteristics.The CORA temperature,salinity,and mixed layer depth show certain seasonal and interannual variations.In 50-400 m depth in the SCS,the CORA temperature is colder in winter and warmer in summer and autumn.In 0-150 m in the SCS,the CORA salinity is higher in most time of the year.However,in the second half of the year,the salinity is slightly weaker in 100-150 m depth.In most years,the CORA mixed layer depths tend to be shallower,and in season,shallower in winter and deeper in summer.In spatial distribution,the closer the area is to the coast,the greater the CORA errors would be.The CORA temperature is colder in the western side and warmer in the eastern side,resulting in a weaker SCS western boundary current(SCSwbc).In most areas,the CORA mixed layer depths are shallower.In the area close to the coast,the CORA mixed layer depths change rapidly,and the deviations in the mixed layer depths are larger.In the central SCS,the CORA mixed layer depths change slowly,and the deviations in the mixed layer depths are also small.

An Ocean Reanalysis System for the Joining Area of Asia and Indian-Pacific Ocean

An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.

Recent Advances in Japanese Fisheries Science in the Kuroshio-Oyashio Region through Development of the FRA-ROMS Ocean Forecast System: Overview of the Reproducibility of Reanalysis Products

To address various fisheries science problems around Japan, the Japan Fisheries Research and Education Agency (FRA) has developed an ocean forecast system by combining an ocean circulation model based on the Regional Ocean Modeling System (ROMS) with three-dimensional variational analysis schemes. This system, which is called FRA-ROMS, is a basic and essential tool for the systematic conduct of fisheries science. The main aim of FRA-ROMS is to realistically simulate mesoscale variations over the Kuroshio-Oyashio region. Here, in situ oceanographic and satellite data were assimilated into FRA-ROMS using a weekly time window. We first examined the reproducibility through comparison with several oceanographic datasets with an Eulerian reference frame. FRA-ROMS was able to reproduce representative features of mesoscale variations such as the position of the Kuroshio path, variability of the Kuroshio Extension, and southward intrusions of the Oyashio. Second, using a Lagrangian reference frame, we estimated position errors between ocean drifters and particles passively transported by simulated currents, because particle tracking is an essential technique used in applications of reanalysis products to fisheries science. Finally, we summarize recent and ongoing fisheries studies that use FRA-ROMS and mention several new developments and enhancements that will be implemented in the near future.

Reanalysis on Hail Suppression Weather Prewarning of the "8.21" Rare Hail in Shenyang

[Objective]The research aimed to analyze hail weather during 18：30-19：30 on August 21,2011.[Method]Based on NCEP 6 h reanalysis data,Micaps data and FY-2 satellite cloud image,weather situation field,meteorological element field and satellite cloud image of the hail during 18：30-19：30 on August 21,2011 were reanalyzed.Weather causes of the hail and characteristics of the satellite cloud image were discussed.We summed up how to conduct prewarning analysis of the hail suppression weather from live weather data and numerical forecast products.[Result]In occurrence and development process of the hail weather,cold vortex at 500 hPa was background weather system.850 hPa warm tongue and ground wind shear were essential conditions for forming strong convective hail cloud.Early weather chart was analyzed.It could analyze occurrence reason of the weather from mechanism aspect of the weather system,and issue weather prewarning as early as possible.Numerical forecast products were used to analyze meteorological element field.It could further analyze and accurately predict probability,time and range of the hail occurrence.Infrared satellite cloud image was more accurate for monitoring strong convective hail cloud,which could intuitively predict occurrence of the hail weather.[Conclusion]The research offered some hail suppression prewarning experiences for the future similar hail weather.