Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model(LSM)parameterization schemes,meteorological forcing,and land surface parameters on land surface hydrological simulations,and explore to what extent the quality can be improved,a series of experiments with different LSMs,forcing datasets,and parameter datasets concerning soil texture and land cover were conducted.Six simulations are run for the Chinese mainland on 0.1°×0.1°grids from 1979 to 2008,and the simulated monthly soil moisture(SM),evapotranspiration(ET),and snow depth(SD)are then compared and assessed against observations.The results show that the meteorological forcing is the most important factor governing output.Beyond that,SM seems to be also very sensitive to soil texture information;SD is also very sensitive to snow parameterization scheme in the LSM.The Community Land Model version 4.5(CLM4.5),driven by newly developed observation-based regional meteorological forcing and land surface parameters(referred to as CMFD_CLM4.5_NEW),significantly improved the simulations in most cases over the Chinese mainland and its eight basins.It increased the correlation coefficient values from 0.46 to 0.54 for the SM modeling and from 0.54 to 0.67 for the SD simulations,and it decreased the root-mean-square error(RMSE)from 0.093 to 0.085 for the SM simulation and reduced the normalized RMSE from 1.277 to 0.201 for the SD simulations.This study indicates that the offline LSM simulation using a refined LSM driven by newly developed observation-based regional meteorological forcing and land surface parameters can better model reginal land surface hydrological processes.

Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

In order to reduce the uncertainty of offline land surface model （LSM） simulations of land evapotranspiration （ET）, we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS （Institute of Tibetan Plateau Research, Chinese Academy of Sciences）, Qian] and four LSMs （BATS, VIC, CLM3.0 and CLM3.5）, to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over China's Mainland during 1982-2007. The results showed that various simulations of each member and their arithmetic mean （EnsAVlean） could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates （Obs_MTE）. For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs_MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens_Mean was closer to Obs_MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs_MTE and Ens_Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Nifio event occurred, the Ens_Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs_MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

Evaluation of Surface Relative Humidity in China from the CRA-40 and Current Reanalyses

Recently,the China Meteorological Administration(CMA)released a new Global Atmospheric Reanalysis(CRA-40)dataset for the period 1979−2018.In this study,surface relative humidity(RH)from CRA-40 and other current reanalyses(e.g.,CFSR,ERA5,ERA-Interim,JRA-55,and MERRA-2)is comprehensively evaluated against homogenized observations over China.The results suggest that most reanalyses overestimate the observations by 15%−30%(absolute difference)over the Tibetan Plateau but underestimate the observations by 5%−10%over most of northern China.The CRA-40 performs relatively well in describing the long-term change and variance seen in the observed surface RH over China.Most of the reanalyses reproduce the observed surface RH climatology and interannual variations well,while few reanalyses can capture the observed long-term RH trends over China.Among these reanalyses,the CFSR does poorly in describing the interannual changes in the observed RH,especially in Southwest China.An empirical orthogonal function(EOF)analysis also suggests that the CRA-40 performs better than other reanalyses to capture the first two leading EOF modes revealed by the observations.The results of this study are expected to improve understanding of the strengths and weaknesses of the current reanalysis products and thus facilitate their application.

A New Temperature Channel Selection Method Based on Singular Spectrum Analysis for Retrieving Atmospheric Temperature Profiles from FY-4A/GIIRS

Hyperspectral data have important research and application value in the fields of meteorology and remote sensing.With the goal of improving retrievals of atmospheric temperature profiles,this paper outlines a novel temperature channel selection method based on singular spectrum analysis(SSA)for the Geostationary Interferometric Infrared Sounder(GIIRS),which is the first infrared sounder operating in geostationary orbit.The method possesses not only the simplicity and rapidity of the principal component analysis method,but also the interpretability of the conventional channel selection method.The novel SSA method is used to decompose the GIIRS observed infrared brightness temperature spectrum(700-1130 cm-1),and the reconstructed grouped components can be obtained to reflect the energy variations in the temperature-sensitive waveband of the respective sequence.At 700-780 cm-1,the channels selected using our method perform better than IASI(Infrared Atmospheric Sounding Interferometer)and Cr IS(Cross-track Infrared Sounder)temperature channels when used as inputs to the neural network retrieval model.

CLDASSD:Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology

Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.

Increases in Anthropogenic Heat Release from Energy Consumption Lead to More Frequent Extreme Heat Events in Urban Cities

With economic development and rapid urbanization,increases in Gross Domestic Product and population in fastgrowing cities since the turn of the 21st Century have led to increases in energy consumption.Anthropogenic heat flux released to the near-surface atmosphere has led to changes in urban thermal environments and severe extreme temperature events.To investigate the effects of energy consumption on urban extreme temperature events,including extreme heat and cold events,a dynamic representation scheme of anthropogenic heat release(AHR)was implemented in the Advanced Research version of the Weather Research and Forecasting(WRF)model,and AHR data were developed based on energy consumption and population density in a case study of Beijing,China.Two simulations during 1999−2017 were then conducted using the developed WRF model with 3-km resolution with and without the AHR scheme.It was shown that the mean temperature increased with the increase in AHR,and more frequent extreme heat events were produced,with an annual increase of 0.02−0.19 days,as well as less frequent extreme cold events,with an annual decrease of 0.26−0.56 days,based on seven extreme temperature indices in the city center.AHR increased the sensible heat flux and led to surface energy budget changes,strengthening the dynamic processes in the atmospheric boundary layer that reduce AHR heating efficiency more in summer than in winter.In addition,it was concluded that suitable energy management might help to mitigate the impact of extreme temperature events in different seasons.

CRA-40/Atmosphere—The First-Generation Chinese Atmospheric Reanalysis(1979–2018):System Description and Performance Evaluation

Atmospheric reanalysis reproduces the past atmospheric conditions through assimilation of historical meteorological observations with fixed version of a numerical weather prediction(NWP)model and data assimilation(DA)system.It is widely used in weather,climate,and even business-related research and applications.This paper reports the development of CMA’s first-generation global atmospheric reanalysis(RA)covering 1979–2018(CRA-40;CRA refers to CMA-RA).CRA-40 is produced by using the Global Spectral Model(GSM)/Gridpoint Statistical Interpolation(GSI)at a 6-h time interval and a TL574 spectral(34-km)resolution with the model top at 0.27 hPa.A large number of reprocessed satellite data and widely collected conventional observations were assimilated during the reanalyzing process,including the reprocessed atmospheric motion vector(AMV)products from FY-2C/D/E/G satellites,dense conventional observations(at about 120 radiosonde and 2400 synoptic stations)over China,as well as MWHS-2 and GNSS-RO observations from FY-3C.The reanalysis fitting to observations is improved over time,especially for surface pressure with root-mean-square error reduced from 1.05 hPa in 1979 to 0.8 hPa,and for upper air temperature from 1.65 K in 1979 to 1.35 K,in 2018.The patterns of global analysis increments for temperature,specific humidity,and zonal wind are consistent with the changes in the observing system.Near surface temperature from the model’s 6-h forecast reflects the global warming trend reasonably.The CRA-40 precipitation pattern matches well with those of GPCP and other reanalyses.CRA-40 also successfully captures the QBO and its vertical and temporal development,hemispherical atmospheric circulation change,and moisture transport by the East Asian summer monsoon.CRA is now operationally running in near real time as a climate data assimilation system in CMA.

A Deep Learning Method for Statistical Downscaling of CLDAS Relative Humidity with Different Sources of Data:Sensitivity Analysis

High-resolution relative humidity(RH)data are essential in studies of climate change and in numerical meteorological forecasting.However,because high-resolution meteorological grid data require a large number of stations,the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration’s Land Assimilation System(CLDAS)dataset.To retrieve high-quality and high-resolution RH data before 2008,we propose a statistical downscaling model(SDM)based on a generative adversarial network(GAN)to transform the original RH data from a resolution of0.05°to 0.01°.The GAN-based SDM(GSDM)is trained with the RH of the CLDAS(0.05°)dataset after 2008 as its input,and the RH of the high-resolution CLDAS(HRCLDAS,0.01°)dataset after 2008 as its target for training.The2-m air temperature data from the HRCLDAS dataset are also included in the input,and the station observations of RH are incorporated in the target for training.To select the optimum data combination for the model,we compared three methods:(1)incorporating without auxiliary data(GSDM),(2)incorporating air temperature as an additional input(GSDM_T),and(3)incorporating air temperature as an additional input and the RH data at stations as an additional target for training(GSDM_TO).Taking the Beijing–Tianjin–Hebei region as an example,we trained the GSDM by using data from 2018 and tested the model performance in 2019.The experimental results showed that the GSDM_TO algorithm achieved the lowest root-mean-square error(3.85%),followed by the GSDM_T(4.01%)and GSDM(4.95%)algorithms.The proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear interpolation.In general,the GSDM_TO algorithm using a combination of different sources of data(air temperature and observed RH)achieved the best results among the various deep learning approaches,indicating that more auxiliary data and more accurate observations are beneficial in downscaling.This may be helpful for the statistical downscaling of other meteorological data.

Comparison of the Global Energy Cycle between Chinese Reanalysis Interim and ECMWF Reanalysis

The global energy cycle is a diagnostic metric widely used to gauge the quality of datasets. In this paper, the "Mixed Space-Time Domain" method for diagnosis of energy cycle is evaluated by using newly developed datasets-the Chinese Reanalysis Interim (CRAI) and ECMWF Reanalysis version 5 (ERA5), over a 7-yr period (2010-16) on seasonal and monthly timescales. The results show that the energy components calculated from the two reanalysis datasets are highly consistent;however, some components in the global energy integral from CRAI are slightly larger than those from ERA5. The main discrepancy in the energy components stems from the conversion of baroclinic process, whereas the dominant difference originates from the conversion from stationary eddy available potential energy to stationary eddy kinetic energy (CES), which is caused by systematic differences in the temperature and vertical velocity in low-mid latitudes of the Northern Hemisphere and near the Antarctic, where there exist complex terrains. Furthermore, the monthly analysis reveals that the general discrepancy in the temporal variation between the two datasets also lie mainly in the CES as well as corresponding generation and dissipation rates.

A 10-Yr Global Land Surface Reanalysis Interim Dataset(CRA-Interim/Land):Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.

Evaluation of Daily Precipitation Product in China from the CMA Global Atmospheric Interim Reanalysis

The China Meteorological Administration(CMA)recently produced a CMA Global Atmospheric Interim Reanalysis(CRAI)dataset for the years 2007–2016.A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation,in terms of both mean states and extreme indicators over China,is performed.Comparisons are made with other current reanalysis datasets,namely,the ECMWF interim reanalysis(ERAI),Japanese 55-yr reanalysis(JRA55),NCEP Climate Forecast System Reanalysis(CFSR),and NASA Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2),as well as NCEP Climate Prediction Center(CPC)observations.The results show that,for daily variations of rainfall during warm seasons in eastern China,CRAI and CFSR overestimate the precipitation of the main rain belt,while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2;whereas ERAI tends to underestimate the precipitation in most regions of eastern China.Two extreme metrics,the total amount of precipitation on days where daily precipitation exceeds the 95 th percentile(R95 pTOT)and the number of consecutive dry days(CDDs)in one month,are examined to assess the performance of reanalysis datasets.In terms of extreme events,CRAI,ERAI,and JRA55 tend to underestimate the R95 pTOT in most of eastern China,whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2;and all of the reanalyses underestimate the CDDs.Among the reanalysis products,CRAI and JRA55 show better agreement with the observed R95 pTOT than the other datasets,with fewer biases,higher correlation coefficients,and much more similar linear trend patterns,while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.

A soil moisture assimilation scheme based on the ensemble Kalman filter using microwave brightness temperature

This study presents a soil moisture assimilation scheme, which could assimilate microwave brightness temperature directly, based on the ensemble Kalman filter and the shuffled complex evolution method (SCE-UA). It uses the soil water model of the land surface model CLM3.0 as the forecast operator, and a radiative transfer model (RTM) as the observation operator in the assimilation system. The assimilation scheme is implemented in two phases: the parameter calibration phase and the pure soil moisture assimilation phase. The vegetation optical thickness and surface roughness parameters in the RTM are calibrated by SCE-UA method and the optimal parameters are used as the final model parameters of the observation operator in the assimilation phase. The ideal experiments with synthetic data indicate that this scheme could significantly improve the simulation of soil moisture at the surface layer. Further- more, the estimation of soil moisture in the deeper layers could also be improved to a certain extent. The real assimilation experiments with AMSR-E brightness temperature at 10.65 GHz (vertical polariza- tion) show that the root mean square error (RMSE) of soil moisture in the top layer (0―10 cm) by as- similation is 0.03355 m3·m-3, which is reduced by 33.6% compared with that by simulation (0.05052 m3·m-3). The mean RMSE by assimilation for the deeper layers (10―50 cm) is also reduced by 20.9%. All these experiments demonstrate the reasonability of the assimilation scheme developed in this study.

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.

Development and Evaluation of Hourly and Kilometer Resolution Retrospective and Real-Time Surface Meteorological Blended Forcing Dataset(SMBFD) in China

A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.

The Tibetan Plateau Surface–Atmosphere Coupling System and Its Weather and Climate Effects: The Third Tibetan Plateau Atmospheric Science Experiment

The Tibetan Plateau(TP) is a key area affecting forecasts of weather and climate in China and occurrences of extreme weather and climate events over the world. The China Meteorological Administration, the National Natural Science Foundation of China, and the Chinese Academy of Sciences jointly initiated the Third Tibetan Plateau Atmospheric Science Experiment(TIPEX-Ⅲ) in 2013, with an 8–10-yr implementation plan. Since its preliminary field measurements conducted in 2013, routine automatic sounding systems have been deployed at Shiquanhe, Gaize, and Shenzha stations in western TP, where no routine sounding observations were available previously. The observational networks for soil temperature and soil moisture in the central and western TP have also been established. Meanwhile, the plateau-scale and regional-scale boundary layer observations, cloud–precipitation microphysical observations with multiple radars and aircraft campaigns, and tropospheric–stratospheric air composition observations at multiple sites, were performed. The results so far show that the turbulent heat exchange coefficient and sensible heat flux are remarkably lower than the earlier estimations at grassland, meadow, and bare soil surfaces of the central and western TP. Climatologically, cumulus clouds over the main body of the TP might develop locally instead of originating from the cumulus clouds that propagate northward from South Asia. The TIPEX-Ⅲ observations up to now also reveal diurnal variations, macro-and microphysical characteristics, and water-phase transition mechanisms, of cumulus clouds at Naqu station. Moreover, TIPEX-Ⅲ related studies have proposed a maintenance mechanism responsible for the Asian "atmospheric water tower" and demonstrated the effects of the TP heating anomalies on African, Asian, and North American climates. Additionally, numerical modeling studies show that the Γ distribution of raindrop size is more suitable for depicting the TP raindrop characteristics compared to the M–P distribution, the overestimation of sensible heat flux can be reduced via modifying the heat transfer parameterization over the TP, and considering climatic signals in some key areas of the TP can improve the skill for rainfall forecast in the central and eastern parts of China. Furthermore, the TIPEX-Ⅲ has been promoting the technology in processing surface observations, soundings, and radar observations, improving the quality of satellite retrieved soil moisture and atmospheric water vapor content products as well as high-resolution gauge–radar–satellite merged rainfall products, and facilitating the meteorological monitoring, forecasting, and data sharing operations.

Improved Assimilation of Fengyun-3 Satellite-Based Snow Cover Fraction in Northeastern China

Assimilation of snow cover is an important method to improve the accuracy of snow simulation. However, the effects of snow assimilation are poor because satellite observed snow cover data contain erroneous information, such as cloud contamination. In this paper, an improved approach is proposed to reduce the effects of observational errors during assimilation of snow cover fraction acquired by the Fengyun-3(FY-3) satellite in northeastern China. A snow depth constraint was imposed on quality control of a snow depth product from a microwave radiation imager. The assimilation experiments were carried out before and after quality control(denoted as SCFDA and SCFDA_WSD, respectively). The snow cover fraction results were evaluated against the Moderate Resolution Imaging Spectroradiometer(MODIS) snow cover products. When assimilating the snow cover fraction with the snow depth constraint(i.e., SCFDA_WSD), substantially larger improvement was obtained than that without such a constraint/quality control(SCFDA), and the deviation and root mean square error of the snow cover fraction were significantly reduced.The assimilation performance was also evaluated against in-situ snow depth observations. The SCFDA_WSD also showed greater improvements during the snow accumulation and snowmelt periods than the SCFDA. The SCFDA_WSD improvements in woodland and shrubland were the most obvious. At different altitudes, the effects of the SCFDA_WSD were basically equivalent, and the deeper the snow depth was, the better the effect. In addition, the SCFDA_WSD method was found in close agreement with the observations during a sudden snowfall event.

Pre-Processing,Quality Assurance,and Use of Global Atmospheric Motion Vector Observations in CRA

Assimilation of atmospheric motion vectors(AMVs)is important in the initialization of the atmospheric state in numerical weather prediction models,especially over oceans and at high latitudes where conventional data are sparse.This paper presents a detailed description of the pre-processing,quality assurance,and use of global AMVs in China’s first generation of the 40-yr(1979-2018)CRA global atmospheric reanalysis product.A new AMV archive is integrated from near real-time operational Global Telecommunication System data and reprocessed AMV datasets released or produced mainly during 2014-2016 according to a priority principle.To avoid the misuse of data with systematic quality problems,the observations of all 18 types of AMVs from 54 satellites are pre-evaluated over the whole time series.The pre-evaluation system developed by the CRA team is based on the NCEP Gridpoint Statistical Interpolation(GSI)three-dimensional variational assimilation system and the ERA-Interim reanalysis product.The AMVs in the new AMV archive are denser than the AMVs prepared for the Climate Forecast System Reanalysis product,the bias and root-mean-square values are smaller,and the time series are steadier.The new AMV archive is assimilated in the CRA product based on the NCEP GSI assimilation procedure and quality control configuration with reference to the pre-evaluation results.This is the first time that the reprocessed AMVs from Fengyun-2 satellites from June 2005 to July 2017 are assimilated in a reanalysis product.The assimilation features inspire confidence in the accuracy and stability of these data.The mean root-mean-square values of the observation minus analysis infrared,water vapor,and visible AMV were 1.5-3.4,2.7-3.6,and 1.3-2.1 m s-1,respectively.This experience of integrating,pre-evaluating,and assimilating AMV observations is valuable for the next generation of reanalysis products.

Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China

Land data assimilation(DA)is an effective method to provide high-quality spatially and temporally continuous soil moisture datasets that are crucial in weather,climate,hydrological,and agricultural research.However,most existing land DA applications have used remote sensing observations,and are based on one-dimensional(1 D)analysis,which cannot be directly employed to reasonably assimilate the recently expanded in-situ soil moisture observations in China.In this paper,a two-dimensional(2 D)localized ensemble-based optimum interpolation(En OI)scheme for assimilating in-situ soil moisture observations from over 2200 stations into land surface models(LSMs)is introduced.This scheme uses historical LSM simulations as ensemble samples to provide soil moisture background error covariance,allowing the in-situ observation information to be propagated to surrounding pixels.It is also computationally efficient because no additional ensemble simulations are needed.A set of ensemble sampling and localization length scale sensitivity experiments are performed.The En OI performs best for in-situ soil moisture fusion over China with an ensemble sampling of hourly soil moisture from the previous 7 days and a localization length scale of 100 km.Following the evaluation,simulations for in-situ soil moisture fusion are also performed from May 2016 to September 2016.The En OI analysis is notably better than that without in-situ observation fusion,as the wet bias of 0.02 m3 m-3 is removed,the root-mean-square error(RMSE)is reduced by about 37%,and the correlation coefficient is increased by about 25%.Independent evaluation shows that the En OI analysis performs considerably better than that without fusion in terms of bias,and marginally better in terms of RMSE and correlation.