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A method named BTREC, which is in fact an extension of tracking a radar echo by cross-correlation (TREC) method, is proposed based on the Barnes filter in this paper. BTREC is an efficient objective analysis method for smoothing the motion vectors of radar echo patterns. A comparative analysis of the BTREC vectors and the COTREC (another extension of TREC, which is restricted by the 2D continuity equation) vectors is conducted. The results show that the BTREC method corrects the noise and inconsistencies in TREC vectors (often induced by shielding, ground clutter, and rapid morphological changes of the radar patterns) more thoroughly than COTREC. Then the BTREC, COTREC, and TREC methods are applied to extrapolate the real radar echo pattern over Jiangxi Province in China for two cases (1900–2100 UTC 4 June and 0900–1100 UTC 30 June 2018). Results show that the BTREC method performs the best out of the three methods to forecast the radar echo patterns in the following 1 and 2 h, and has the least distortion. To further confirm that, 892 radar composite reflectivity mosaic images with precipitation in summer (June–August) over Jiangxi are collected to test the three methods. The results show that in the 1- and 2-h extrapolations, the mean differences of the threat score (TS), correlation (CORR), and probability of detection (POD) between BTREC and TREC are obviously more than 0, while that of the false alarm ratio (FAR) is remarkably less than 0 (the threshold to identify whether a grid is correctly predicted is set to 10 dBZ). Although the mean difference of TS, CORR, POD, and FAR between COTREC and TREC have similar variation, their magnitudes are obviously decreased (especially for the 2-h extrapolation). This further indicates that the BTREC method obviously improves the forecast of the radar echo pattern within the next 2 h compared to the COTREC and TREC methods. [ABSTRACT FROM AUTHOR]

Seafloor observatories enable long-term, continuous, real-time, weather-independent, and multidisciplinary scientific observation and research that will promise major breakthroughs in ocean sciences. China has started to establish a seafloor observation system in the East China Sea (ECSSOS), and the purpose of this paper is to describe the remote control for ECSSOS. Technological development in system architecture and remote communication was first performed in this paper. Based on this work, an overall framework for the remote control solution for ECSSOS was developed, adopting a client-server architecture containing three components, that is, a control center, a shore station, and observatory facilities, as well as two strands of information flow, that is, flow of observation data and flow of control commands. To implement the present solution, operational data flow and the core module were further designed and the corresponding remote control subsystem for ECSSOS was developed against all design specifications. This backbone system has been put into operation in China's first seafloor observatory in 2009, and in this paper the practical performance is evaluated addressing both the general descriptions and results from case studies. Given the successful trial in the Xiaoqushan seafloor observatory, the remote control solution proposed in this paper could be a useful reference implementation to the following construction phases of ECSSOS. [ABSTRACT FROM AUTHOR]

The harmonic constants extracted from tidal gauge stations and satellite altimeter observations are usually sparsely distributed in the continental marginal seas, but they are precious data for addressing the main characteristics of different constituents. In this paper, a dynamically constrained interpolation methodology (DCIM) is developed and applied to interpolate the observed harmonic constants of the M2 constituent from satellite altimeter observations in the Bohai, Yellow, and East China Seas (BYECS) with those from tidal gauge stations for validation. In the DCIM, the tide model provides dynamical constraints to interpolate the observations, and the adjoint assimilation method provides iterative optimization for the interpolated results by adjusting key model parameters. In particular, a substantial quantity of enhanced "observations" generated from the interpolated results of the domain are further interpolated with the sparse observations in the subdomain. The final interpolated results for each subdomain, naturally blending the dynamical constraints from the dynamical model with statistical information from observations, can describe the main characteristics of the M2 constituent in the BYECS, the continental shelf sea of the East China Sea, and the Zhejiang-Fujian coastal area, respectively. The results indicate that the DCIM is feasible and effective to utilize the observations to obtain high-accuracy cotidal charts for regional ocean. [ABSTRACT FROM AUTHOR]

The effects of the response characteristics of the Fengyun-4A Lightning Mapping Imager (LMI) on its detection capability were studied using the raw event data of LMI in 2020. The simultaneous observation data of the Lightning Imaging Sensor (LIS) on the International Space Station (ISS) were used to evaluate the LMI detection capability. The results reveal that the minimum detectable radiance of lightning events in the 16 subregions of LMI has shown regional differences, with the southern subregions lower than the northern subregions, indicating that the southern ones are more conducive to the identification of events. The diurnal variation of the detectable event radiance in all subregions presents the main peak around noon, which comes from the influence of the bright background and varies largely in different subregions depending on the subregions' response capability. The overall high values and regional differences of flash properties observed by LMI also show strong correlation with the variation of the minimum detectable radiance of events. Moreover, it is found that the southwest subregions have the highest coincidence ratio (CR) with ISS LIS, followed by the southeast subregions and the northeast subregions, and the northwest subregions have the lowest CR, which is closely related to the response of each subregion. The LIS flashes that can be detected by LMI are brighter, larger, and last longer compared to the total LIS flashes. The findings in this study will help explain the inconsistency of the LMI detection capability and promote the LMI data processing associated with pixel energy distribution. Significance Statement: The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first geostationary satellite-borne lightning imager developed in China, which has the ability to continuously observe lightning within a large field of view. However, in the application of the data, it was found that the detection capability of the LMI differed significantly in different regions and exhibited diurnal variations, which may be related to the response characteristics of the LMI detector. This study reveals the effect of the response characteristics of the LMI detector on its detection results. The findings will help improve the usability of LMI data and improve the processing of these data to adjust the observation inconsistency. [ABSTRACT FROM AUTHOR]

Aircraft Meteorological Data Relay (AMDAR) weather reports are a type of high spatiotemporal data currently widely used in weather monitoring and prediction. A recent Chinese AMDAR project began in 2003 has made rapid progress. However, the assessment and accuracy of these Chinese AMDAR reports have yet to be thoroughly discussed. A comparison of temperature and wind observations between Chinese AMDAR reports and rawinsonde data between 2004 and 2010 is conducted in this paper. Results demonstrate that the root-mean-square error (RMSE) between these two sets of data is 1.40°C for temperature, 3.56 m s−1 for wind speed, and 28° for wind direction. Because of the particularity of observation and inversion method, comparison results are not only affected by AMDAR measurement and reporting error but also by spatial and temporal representativeness, flight phases, and the environment. This evaluation helps create a complete estimation of the accuracy of Chinese AMDAR in order to assist with data assimilation. [ABSTRACT FROM AUTHOR]

The use of high-density remote sensing buoys and ship-based observations play an increasingly crucial role in the operational assimilation and forecast of oceans. With the recent release of several high-resolution observation datasets, such as the Global Ocean Data Assimilation Experiment (GODAE) high-resolution SST (GHRSST) datasets, the development of observation-thinning schemes becomes important in the process of data assimilation because the huge quantity and dense spatial–temporal distributions of these datasets might make it expensive to assimilate the full dataset into ocean models or even decay the assimilation result. In this paper, an objective model simulation ensemble-based observation-thinning scheme is proposed and applied to a Chinese shelf–coastal seas eddy-resolving model. A successful thinning scheme should select a subset of observations yielding a small analysis error variance (AEV) while keeping the number of observations to as few as possible. In this study, the background error covariance (BEC) is estimated using the historical ensemble and then the subset of observations to minimize the AEV is selected, which is estimated from the Kalman theory. The authors used this method in the GHRSST product to cover the shelf and coastal seas around China and then verified the result with an estimation function and assimilation–forecast systems. [ABSTRACT FROM AUTHOR]

Among the single-Doppler radar wind analysis methods, the velocity–azimuth display (VAD), velocity–azimuth process (VAP), and uniform-wind (UW) methods are widely used because of their simplicity. This paper shows that the VAD, VAP, and UW methods can all be derived from the same relationship based on the azimuthal uniform-wind assumption. Using this assumption, an integrating VAP (IVAP) method is developed that can provide a smoother wind field than the VAP and UW methods and a higher resolution than the VAD method. Using the IVAP technique, the wind fields associated with a heavy rainfall case in Shanghai, China, are retrieved and compared with those from surface observations and wind-profiler data. [ABSTRACT FROM AUTHOR]

Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms the Saastamoinen model (SAAS-PWV), in which the precision is improved by 44.4%. The BIAS ranged from −1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%. Significance Statement: Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate. [ABSTRACT FROM AUTHOR]

Wavenumber–frequency spectra obtained with coherent microwave radar at upwind-grazing angle consist of energy along the ocean wave dispersion relation and additional features that lie above this relation labeled as "high-order harmonic" and below this relation known as "group line." Due to these nonlinear features, low-frequency components appear in the radar-estimated wave spectrum and the energy and peak frequency of the dominant wave spectrum decrease, which are responsible for the overestimation of radar-measured wave period. According to the component distribution in the wavenumber–frequency spectrum, a mean wave period inversion method based on a dispersion relation filter for coherent S-band radar is proposed. The method filters out the "group line" and preserves the high-order harmonic to compensate for the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. A two-dimensional inverse Fourier transform is applied to the filtered wavenumber–frequency spectrum. Then the radar-measured velocity sequence is selected to obtain the velocity spectrum via a one-dimension Fourier transform. The wave height spectrum is estimated from the one-dimensional velocity spectrum by the direct transform relationship between the one-dimensional velocity spectrum and the wave height spectrum. Later, mean wave periods can be derived by the first moment of the wave height spectrum. A 13-day dataset collected with a shore-based coherent S-band radar deployed at Zhelang, China, is reanalyzed and used to retrieve mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted. The results indicate that the proposed method improves the wave period measurement for coherent S-band radar. Significance Statement: This work provides a mean wave period inversion method for coherent S-band radar. The mean wave period is always overestimated due to the "group line" in the wavenumber–frequency spectrum and the energy loss caused by the decrease of peak frequency of the dominant wave spectrum. Therefore, dealing with these estimation errors is important. [ABSTRACT FROM AUTHOR]

The coarseModerate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product (spatial resolution: 3 km) retrieved by the dark-target algorithm always generates the missing values when being adopted to estimate the ground-level PM2.5 concentrations. In this study, we developed a two-stage random forest using MODIS 3-km AOD to obtain the PM2.5 concentrations with full coverage in a contiguous coastal developed region, i.e., Yangtze River delta--Fujian--Pearl River delta (YRD--FJ--PRD) region of China. A first-stage random forest--integrated six meteorological fields was employed to predict the missing values of AOD product, and the combined AOD (i.e., random forest--derived AOD and MODIS 3-km AOD) incorporated with other ancillary variables were developed for predicting PM2.5 concentrations within a second-stage random forest model. The results showed that the first-stage random forest could explain 94% of the AOD variability over YRD--FJ--PRD region, and we achieved a site-based cross validation (CV) R² of 0.87 and a time-based CV R² of 0.85. The full-coverage PM2.5 concentrations illustrated a spatial pattern with annual-mean PM2.5 of 46, 40, and 35 µg m-3 in YRD, PRD, and FJ, respectively, sharing the same trend with previous studies. Our results indicated that the proposed two-stage random forest model could be effectively used for PM2.5 estimation in different areas. [ABSTRACT FROM AUTHOR]

Tropospheric hydrostatic delay is one of the major sources of errors in Global Navigation Satellite System (GNSS) navigation and positioning, and an important parameter in GNSS meteorology. This work first proposes a new method of computing zenith hydrostatic delay (ZHD) based on precipitable water vapor (PWV), using radiosonde data. Next, using these calculations as a reference, the performance of three empirical ZHD models and three ZHD integral models in China is assessed using benchmark values obtained from 8 years (2010–17) of radiosonde data recorded at 75 stations across China. Finally, we provide a new revised ZHD model that can be applied to China and validate its performance using radiosonde data collected in China in 2018. The statistical results indicate that the ZHD can be estimated by this new model with an accuracy of several millimeters. Due to its performance and simplicity, this new model is shown to be the optimal ZHD model for use in China. [ABSTRACT FROM AUTHOR]

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet's nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts. [ABSTRACT FROM AUTHOR]

A method of detecting atmospheric ducts using a wind profiler radar (WPR) and a radio acoustic sounding system (RASS) is proposed. The method uses the RASS to measure the virtual temperature profile and calculate the Brunt–Väisälä frequency; it also uses the WPR to measure the spectral width of the atmosphere and the atmospheric refractive index structure constant. Then the profile of the atmospheric refractive index gradient and modified refractivity are calculated using virtual temperature, spectral width, and the atmospheric refractive index structure constant. Finally, the height and intensity of the atmospheric duct are calculated to achieve continuous monitoring of the atmospheric duct. To verify the height and intensity of the atmospheric duct, comparison experiments between WPR-RASS and radiosondes were carried out from June 2014 to June 2015 in Dalian, Liaoning Province, China. The results show that the profile of modified refractivity by WPR-RASS has exactly the same trend as the radiosondes, the two methods have a good consistency, and the atmospheric duct value from WPR-RASS is in good agreement with that from radiosondes. [ABSTRACT FROM AUTHOR]

A modified Cressman interpolation method (MCIM) is presented for the routine monitoring data of total nitrogen (TN) in the Bohai Sea to reduce interpolation errors by decreasing the influence radius and introducing background value. In twin experiments, two prescribed distributions are successfully estimated by MCIM with lower interpolation errors than the traditional Cressman interpolation method (TCIM) and the kriging method. In practical experiments, cross validation is applied to evaluate the interpolation results for four quarters in 2009 and 2010. Practical experimental results show that the interpolation results obtained with MCIM are greatly improved and can describe the spatial distribution characteristics of TN in the Bohai Sea with lower mean absolute error than the kriging method. [ABSTRACT FROM AUTHOR]

Performance evaluation for the lightning location system (LLS) of the power grid in Guangdong Province, China, was conducted based on observation data of the triggered lightning flashes obtained in Conghua, Guangzhou, during 2007-11 and natural lightning flashes to tall structures obtained in Guangzhou during 2009-11. The results show that the flash detection efficiency and stroke detection efficiency were about 94% (58/62) and 60% (97/162), respectively. The arithmetic mean and median values for location error were estimated to be about 710 and 489 m, respectively, when more than two reporting sensors were involved in the location retrieval (based on 87 samples). After eliminating one obviously abnormal sample, the absolute percentage errors of peak current estimation were within 0.4%-42%, with arithmetic mean and median values of about 16.3% and 19.1%, respectively (based on 21 samples). [ABSTRACT FROM AUTHOR]

After the successful launches of the first two polar-orbiting satellites in a new Fengyun-3 ( FY-3) series, FY-3A/B, into a morning- and afternoon-configured orbit in May 2008 and November 2010, respectively, China will launch its next three polar-orbiting satellites before 2020. The Microwave Temperature Sounder (MWTS) on the FY-3A/B satellites has four channels that have the same channel frequency as channels 3, 5, 7, and 9 of Advanced Microwave Sounding Unit-A (AMSU-A). Thus, the quality of the brightness temperature measurements from the FY-3A MWTS can be assessed using the AMSU-A brightness temperature observations from the NOAA-18 satellite. Overall, MWTS data compare favorably with AMSU-A data in terms of its global bias to NWP simulations. The standard deviations of global MWTS brightness temperatures are slightly larger than those of AMSU-A data. The scan-angle dependence of the brightness temperature bias is found to be symmetric for MWTS channel 3 as well as AMSU-A channel 7, and asymmetric for MWTS channels 2 and 4 and AMSU-A channels 5 and 9; there is a warm (cold) bias located at the beginning (end) of a scan line for all asymmetric channels except for MWTS channel 4. A major difference between the two instruments is that the MWTS biases in channels 3 and 4 are negative in low latitudes and positive in high latitudes, while the AMSU-A biases are negative in all latitudes. A detailed analysis of the data reveals that such a difference is closely related to the difference in the temperature dependence of biases between the two instruments. The AMSU-A biases are independent of the scene temperature, but MWTS biases vary with the earth scene brightness temperature. The root cause of the bias could be a combination of several factors, including solar contamination on its calibration target, detector nonlinearity, and the center frequency drift. This study further demonstrates the utility of a well-calibrated radiometer like AMSU-A for the assessment of a new instrument with NWP fields that are used as inputs to forward radiative transfer simulations. [ABSTRACT FROM AUTHOR]

Some cloud structure features that can be extracted from infrared images of the sky are suggested for cloud classification. Both the features and the classifier are developed over zenithal images taken by the whole-sky infrared cloud-measuring system (WSIRCMS), which is placed in Nanjing, China. Before feature extraction, the original infrared image was smoothed to suppress noise. Then, the image was enhanced using top-hat transformation and a high-pass filtering. Edges are detected from the enhanced image after adaptive optimization threshold segmentation and morphological edge detection. Several structural features are extracted from the segment image and edge image, such as cloud gray mean value (ME), cloud fraction (ECF), edge sharpness (ES), and cloud mass and gap distribution parameters, including very small-sized cloud mass and gaps (SMG), middle-sized cloud gaps (MG), medium--small-sized cloud gaps (MSG), and main cloud mass (MM). It is found that these features are useful for distinguishing cirriform, cumuliform, and waveform clouds. A simple but efficient supervised classifier called the rectangle method is used to do cloud classification. The performance of the classifier is assessed with an a priori classification carried out by visual inspection of 277 images. The index of agreement is 90.97%%. [ABSTRACT FROM AUTHOR]

Forty-one cloud-free images of Qinghai Lake (QHL) in China and the corresponding digital numbers (DNs) of FengYun-2C (FY-2C) at 0000, 0600, 1200, and 1800 UTC from 1 July to 30 September 2005 are analyzed. The corresponding surface water temperatures of QHL measured by the automated hydrometeorological buoy (AHMB) system and the atmospheric profiles over QHL from the National Centers for Environmental Prediction (NCEP) reanalysis data are inputted into the atmospheric transfer model MODTRAN3.7 to calculate the entrance pupil radiance and brightness temperatures for thermal infrared (TIR) channels of FY-2C. Then, the absolute radiometric calibration coefficients of FY-2C, which are used to calculate the equivalent blackbody (EBB) temperatures TEBB, are calculated by comparing the entrance pupil radiance and brightness temperatures with the corresponding DNs. In addition, the temperatures of onboard blackbody (OBB) TOBB, primary, secondary, refraction, and calibration mirrors on the multichannel scanning radiometer (MSR) of FY-2C are detected remotely. Based on the linear correlation between TEBB – TOBB and temperatures of various mirrors, the transform equations from TOBB to TEBB are developed. Finally, the onboard real-time absolute radiometric calibration for TIR channels of geostationary meteorological satellite FY-2C is implemented with an uncertainty of about 1.5 and 2.1 K for TIR 1 and TIR 2 of FY-2C, respectively. [ABSTRACT FROM AUTHOR]