Severe short‐term (monthly to seasonal) droughts frequently occurred over China in recent years, with devastating impacts on crop production. This study assesses the capability of microwave remote ...sensing in detecting soil moisture (agricultural) droughts over China and in providing early warnings. The 22 year (1992–2013) European Space Agency satellite soil moisture retrievals are compared against the in situ observations at 312 stations in China, the global soil moisture reanalysis, and the observed rainfall deficit. Both the reanalysis and remote sensing products can only detect less than 60% of drought months at in situ station scale, but they capture the interannual variations of short‐term drought area at river basin scales quite well. As compared with reanalysis, the passive and merged microwave products have better drought detection over sparsely vegetated regions in northwestern China and the active microwave product with better vegetation penetration works the best in eastern China.
Key Points
ESA CCI soil moisture products are assessed for short‐term drought analysis
It adds value to reanalysis for drought monitor at local and river basin scales
The ESA CCI merged product could be better reconciled with new reanalysis data
•CLDASv2/CSSPv2 soil moisture simulation is better than ERA5 and GLDASv2.1 over China.•CLDASv2 meteorological forcings are better than that of ERA5 and GLDASv2.1 reanalysis.•CSSPv2 is superior to ...LSMs of ERA5 and GLDASv2.1 for soil moisture simulation.•The advantage of CLDASv2/CSSPv2 against reanalysis originates mainly from the CSSPv2.
Land surface model (LSM) simulations forced by observed meteorological data provide spatially continuous and temporally complete soil moisture estimates, but the influences of models and meteorological forcings are yet to be validated over a large area due to the lack of in situ measurements. In this study, the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model was driven by three meteorological forcings, namely, CLDASv2.0, ERA5 and GLDASv2.1, to provide soil moisture simulations over China. The validations over 2090 in situ stations during 2012–2017 showed that CLDASv2.0/CSSPv2 soil moisture simulation performed better than ERA5 and GLDASv2.1 reanalysis products, with an increased correlation of 26%–68% and reduced errors of 14%–24% at the daily time scale. The improvements mostly originate from the use of an advanced LSM because CLDASv2.0/CSSPv2 only increased the correlation by 5%–35% and decreased the errors by up to 9% when compared with ERA5/CSSPv2 and GLDASv2.1/CSSPv2. In contrast, ERA5/CSSPv2 and GLDASv2.1/CSSPv2 soil moisture simulations increased the correlations from their alternative reanalysis LSMs by 17%–63%, and decreased the errors by up to 18%. The results are similar when using the SMAP satellite product as the validation data. The influence of the LSM was more obvious over semiarid regions, such as northern China. The influence of meteorological forcing was more significant for soil moisture simulations at the surface layer, while the LSMs played a more critical role for the middle and deep layers, especially during the cold season due to freeze-thaw processes. This study demonstrates the possibility to further improve soil moisture estimates at a large scale with advanced LSMs, even with the emergence of modern reanalyses.
Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations ...of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage.
Short-term exposure to ambient air pollution has been linked to occurrence of myocardial infarction (MI); however, only a limited number of studies investigated its association with death from MI, ...and the results remain inconsistent.
This study sought to investigate the association of short-term exposure to air pollution across a wide range of concentrations with MI mortality.
A time-stratified case-crossover study was conducted to investigate 151,608 MI death cases in Hubei province (China) from 2013 to 2018. Based on each case’s home address, exposure to particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5), particulate matter with an aerodynamic diameter ≤10 μm (PM10), sulfur dioxide, nitrogen dioxide (NO2), carbon monoxide, and ozone on each of the case and control days was assessed as the inverse distance–weighted average concentration at neighboring air quality monitoring stations. Conditional logistic regression models were implemented to quantify exposure-response associations.
Exposure to PM2.5, PM10, and NO2 (mean exposure on the same day of death and 1 day prior) was significantly associated with increased odds of MI mortality. The odds associated with PM2.5 and PM10 exposures increased steeply before a breakpoint (PM2.5, 33.3 μg/m3; PM10, 57.3 μg/m3) and flattened out at higher exposure levels, while the association for NO2 exposure was almost linear. Each 10-μg/m3 increase in exposure to PM2.5 (<33.3 μg/m3), PM10 (<57.3 μg/m3), and NO2 was significantly associated with a 4.14% (95% confidence interval CI: 1.25% to 7.12%), 2.67% (95% CI: 0.80% to 4.57%), and 1.46% (95% CI: 0.76% to 2.17%) increase in odds of MI mortality, respectively. The association between NO2 exposure and MI mortality was significantly stronger in older adults.
Short-term exposure to PM2.5, PM10, and NO2 was associated with increased risk of MI mortality.
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In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a ...deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm −2 for instantaneous SSR, 105.4 Wm −2 for hourly SSR, 31.9 Wm −2 for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm −2 . The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.
Optical properties of clouds and heavy aerosol retrieved from satellite measurements are the most important elements for calculating surface solar radiation (SSR). The Himawari-8/Advanced Himawari ...Imager (AHI) satellite measurements receive high spatial, temporal and spectral signals, which provides an opportunity to estimate cloud, aerosol and SSR accurately.
In this study, we developed the AHI official cloud property product (version 1.0) for JAXA P-Tree system. A look-up table (LUT) method was used to calculate high-temporal (10 min) and high-spatial (5 km) SSR from AHI cloud properties. First, the LUT of the SSR estimation was optimized through a radiative transfer model to account for solar zenith angle, cloud optical thickness (COT), effective particle radius (CER), aerosol optical thickness and surface albedo. Following this, COT and CER were retrieved from the AHI data, with ice cloud parameters being retrieved from an extended Voronoi ice crystal scattering database and water cloud parameters being retrieved from the Mie–Lorenz scattering model. The retrieved COT and CER for water clouds were compared well with MODIS collection 6 cloud property products, with correlation coefficients of 0.77 and 0.82, respectively. The COT of ice cloud also shows good consistency, with a correlation coefficient of 0.85. Finally, the SSR was calculated based on the SSR LUT and the retrieved cloud optical parameters. The estimated SSR was validated at 122 radiation stations from several observing networks covering the disk region of Himawari-8. The root-mean-square error (RMSE) at CMA (China Meteorological Administration) stations was 101.86 Wm−2 for hourly SSR and 31.42 Wm−2 for daily SSR; RMSE at non-CMA stations was 119.07 Wm−2 for instantaneous SSR, 81.10 Wm−2 for hourly SSR and 26.58 Wm−2 for daily SSR. Compared with the SSR estimated from conventional geostationary satellites, the accuracy of the SSR obtained in this study was significantly improved.
•The AHI official cloud algorithm (version 1.0) is developed for the JAXA P-Tree system.•The Voronoi ice crystal scattering model is used to develop the ice cloud product.•High-accuracy SSR is estimated using the AHI cloud parameters.
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.
Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) ...have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP.
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°C, 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.
As China’s first operational second-generation geostationary satellite, Fengyun-4B carries the newly developed Advanced Geostationary Radiation Imager (AGRI), which adds a low-level water vapor ...detection channel and an adjusted spectrum range of four channels to improve the quality of observation. To characterize biases of the infrared (IR) channels of Fengyun-4B/AGRI, RTTOV was applied to simulate the brightness temperature of the IR channels during the period of Fengyun-4B trial operation (from June to November 2022) under clear-sky conditions based on ERA5 reanalysis, which may provide beneficial information for the operational applications of Fengyun-4B/AGRI, such as data assimilation and severe weather monitoring. The results are as follows: (1) due to the sun’s influence on the satellite instrument, the brightness temperature observations of the Fengyun-4B/AGRI 3.75 μm channel were abnormally high around 1500 UTC in October, although the data producer made efforts to eliminate abnormal data; (2) the RTTOV simulations were in good agreement with the observations, and the absolute mean biases of the RTTOV simulations were less than 1.39 K over the ocean, and less than 1.77 K over land, for all IR channels under clear-sky conditions, respectively; (3) for the variation of spatial distribution bias over land, channels 12–15 were more obvious than channels 9–11, which indicates that the skin temperature of ERA-5 reanalysis and surface emissivity may have greater spatial uncertainty than the water vapor profile; (4) the biases and standard deviations of Fengyun-4B/AGRI channels 9–15 had negligible dependence on the satellite zenith angles over the ocean, while the standard deviation of channels 8 and 12 had a positive correlation with satellite zenith angles when the satellite zenith angles were larger than 30°; and (5) the biases and standard deviations of Fengyun-4B/AGRI IR channels showed scene brightness temperature dependence over the ocean.
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