The Geostationary Interferometric Infrared Sounder (GIIRS) on board FengYun-4B (FY-4B), a Chinese second-generation hyperspectral infrared, enables the provision of critical data for forecasting high-impact weather events such as typhoons. To evaluate the reliability of FY-4B/GIIRS data, this study conducted three comparative assimilation trials for both Typhoon Gaemi and Typhoon Doksuri, assimilating observations from the Infrared Atmospheric Sounding Interferometer (IASI), Advanced Microwave Sounding Unit-A (AMSU-A), and FY-4B/GIIRS, respectively. Results demonstrate that the assimilation of GIIRS observations yields more stable forecasts of the wind field at 300 hPa and 500 hPa compared to AMSU-A and IASI, with biases within ±6 m/s relative to NCEP FNL data. However, GIIRS assimilation produces systematic underprediction of vertical velocity, whereas AMSU-A forecasts align more closely with reanalysis. For track forecasts, the GIIRS-assimilated trajectory exhibits closer alignment with observations than AMSU-A and IASI experiments, maintaining biases below 50 km throughout 48 h forecast period of Gaemi. This study provides valuable experience for the application of FY-4B/GIIRS data assimilation.

Beamforming with post-processing methods are widely used for underwater target detection through the uniform linear array. By employing the minimum variance distortionless response (MVDR) beamforming with diagonal loading, the bearing time recorder (BTR) can be obtained, but usually contains sidelobe and noise, which is not beneficial to target detection. Although the sub-band peak energy detection (SPED) brings a raise of the resolution and a reduction to most of the noise and sidelobe, it may also increase the strong sidelobe and cause significant false alarm. In order to improve the detection performance and further utilize spatial continuity of beam scanning azimuth spectrums, an underwater passive target detection algorithm based on sub-band peak modified energy and number filtering is proposed. After selecting the peaks by SPED and applying the Eckart filtering to raise the signal-to-noise ratio, a joint selection of azimuths according to the peak accumulated energy and number is put forward, and only the peaks at the azimuths where the targets are probably located are preserved. Compared to the SPED, the sidelobe could be removed through the proposed work, which results in lower false alarm rate and improved detection performance of weak targets.

Abstract Maliciously or not, jamming is one of the most serious threats to GNSS users, since it worsens the signals’ quality and causes the receivers out of function. There are many ways for the GNSS anti-jam; among them, the space-time array processing (STAP) technology is the most effective. However, the traditional STAP introduces carrier phase bias to signals, which may decrease the precision of phase tracking or even cause unlock of the receiver tracking loop. Although plenty of algorithms are studied to solve this problem, many prerequisites are needed for them to be applied, such as the calibration of antennas or the signals’ direction. Hence, these methods cannot be widely used in the general array receivers. In this paper, the carrier phase bias correction (CPBC) algorithm is proposed for robust GNSS carrier phase tracking in the static STAP anti-jamming receivers, which does not require the intricate pre-information compared to the traditional methods. The CPBC estimates the STAP-introduced bias in the phase tracking loop and corrects it by phase shift. The simulation and two types of open-sky tests show that the CPBC effectively mitigates the phase bias with a residual error less than 8°, and it maintains the robust phase tracking in the GNSS receiver.

Cloud detection is an essential step in the application of hyperspectral infrared (HIR) data. In this paper, a new cloud detection method using a LightGBM algorithm based on principal component (PC) space is proposed for HIR data from the High Spectral Infrared Atmospheric Sounder (HIRAS). Considering the difference of infrared radiation between ocean and land, day and night, this paper respectively builds the LightGBM day-land, day-sea, night-land and night-sea models. The truth cloud fraction of the HIRAS field of view (FOV) is determined by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI) onboard the FY-3D satellite. The HIRAS infrared channels were transformed into the leading K PCs as predictors through principal component analysis (PCA), with the advantages of reducing the correlation of infrared channels, accelerating model convergence and prediction. The cloudy FOVs in global datasets are randomly down-sampling to alleviate the impact of dataset imbalance. The validation experiments have shown that the LightGBM model has high accuracy for the completely cloudy and completely clear-sky FOVs. However, the partially cloudy FOVs are sometimes detected as the clear-sky. It may be because these partially cloudy FOVs have prominent clear-sky radiation properties. The cloud detection performance of the LightGBM model (accuracy=0.93, HSS=0.85) for land HIR data is better than that of sea HIR data (accuracy=0.89, HSS=0.65). It may be because there are more partially cloudy FOVs in the ocean. In addition, the imbalance of the ocean dataset significantly reduced its Heidke skill score (HSS=0.65). Compared with the HIRAS Lie cloud cover product, the accuracy of the LightGBM model for land (sea) HIR data is increased by about 0.22 (0.07). The time complexities of the algorithms have shown that the cloud detection speed of the LightGBM model is approximately 670 times that of the HIRAS/MERSI collocation cloud detection method. The higher cloud detection accuracy and faster efficiency are helpful to the operational application of the LightGBM cloud detection method.

To use infrared observation data from the High Spectral Infrared Atmospheric Sounder (HIRAS) which is onboard the FengYun 3D (FY-3D) satellite, we have proposed a new cloud detection method based on random forest (RF). The true cloud distribution of field of views (FOVs) is generated by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI). The long-wave infrared radiations of 781 channels in the HIRAS FOVs are used as the input features of the model. The matched observation data of HIRAS and MERSI in East Asia (May 2019 to April 2020) are used as training and testing datasets. Given the significant differences in the radiation characteristics between land and sea, we respectively build the sea and land cloud detection models based on random forest. Both of them have achieved good cloud detection performance. The sea model produced slightly higher performance (ACC of 0.96, a FAR of 0.03, an F1-score of 0.96, and AUC of 0.99) than the land model (ACC of 0.95, FAR of 0.04, F1-score of 0.96, and AUC of 0.99). The RF cloud detection models have adequate generalization performances for the observations of HIRAS at different times and regions. Besides, the RF cloud detection models have faster computing efficiency and lower data dependency than HIRAS-MERSI matching method. The validation experiments have shown that the RF models can detect the dense cloud scenes and the large clear-sky areas with higher accuracy. However, the RF model has relatively low detection accuracy for broken clouds and thin clouds. This may be because the infrared radiation properties of these cloud FOVs and clear-sky FOVs are relatively similar.

Under the motivation of the great success of four-dimensional variational (4D-Var) data assimilation methods and the advantages of ensemble methods (e.g., Ensemble Kalman Filters and Particle Filters) in numerical weather prediction systems, we introduce the implicit equal-weights particle filter scheme in the weak constraint 4D-Var framework which avoids the filter degeneracy through implicit sampling in high-dimensional situations. The new variational particle smoother (varPS) method has been tested and explored using the Lorenz96 model with dimensions N x = 40 , N x = 100 , N x = 250 , and N x = 400 . The results show that the new varPS method does not suffer from the curse of dimensionality by construction and the root mean square error (RMSE) in the new varPS is comparable with the ensemble 4D-Var method. As a combination of the implicit equal-weights particle filter and weak constraint 4D-Var, the new method improves the RMSE compared with the implicit equal-weights particle filter and LETKF (local ensemble transformed Kalman filter) methods and enlarges the ensemble spread compared with ensemble 4D-Var scheme. To overcome the difficulty of the implicit equal-weights particle filter in real geophysical application, the posterior error covariance matrix is estimated using a limited ensemble and can be calculated in parallel. In general, this new varPS performs slightly better in ensemble quality (the balance between the RMSE and ensemble spread) than the ensemble 4D-Var and has the potential to be applied into real geophysical systems.

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables’ (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS’s spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast.

Self-calibration of UV cameras was demonstrated for the first time. This novel method has the capability of real-time continuous calibration by using the raw images at 310 nm and 330 nm without changing the viewing direction or adding any additional equipment. The methodology was verified through simulations and experiments and demonstrated to be of greatly improved effectiveness and accuracy. The errors of self-calibration mothed are estimated by comparison with the differential optical absorption spectroscopy (DOAS) approach, and it can be reduced to 1.8% after filter transmittance corrections. The results show that the self-calibration method appears to have great potential as a future technique for quantitative and visual real-time monitoring of SO2 emissions from ships and other point sources (such as oil refineries, power plants, or more broadly, any industrial stack) when the field of view (FOV) of the system is not completely covered by the SO2 plumes.

Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low. Synthetic Aperture Radar (SAR) can provide a more detailed wind structure of the tropical cyclone. In addition, the cross-polarization observation of SAR can provide more detailed information of high speed wind (>25 m·s − 1 ) than the scatterometer. Nevertheless, due to the narrow swath of SAR, the number of retrieved sea surface wind data used in the data assimilation is limited, and another limitation of SAR wind observation is that it does not provide true wind direction information. In this paper, the joint assimilation of the Advanced Scatterometer (ASCAT) wind and Sentinel-1 SAR wind was investigated. Another limitation in the current operational typhoon prediction is the inefficient quality control (QC) method used in the data assimilation since a large number of high speed wind observations was rejected by the traditional Gaussian distribution QC. We introduce the Huber norm distribution quality control (QC) into the data assimilation successfully. A numerical simulation experiment of typhoon by Lionrock (2016) is conducted to test the proposed method. The experimental results showed that the new quality control scheme not only greatly increases the availability of wind data in the area of the typhoon center, but also improves the typhoon track prediction, as well as the intensity prediction. The joint assimilation of scatterometer and SAR winds does have a positive impact on the typhoon prediction.

High-resolution synthetic aperture radar (SAR) wind observations provide fine structural information for tropical cycles and could be assimilated into numerical weather prediction (NWP) models. However, in the conventional method assimilating the u and v components for SAR wind observations (SAR_uv), the wind direction is not a state vector and its observational error is not considered during the assimilation calculation. In this paper, an improved method for wind observation directly assimilates the SAR wind observations in the form of speed and direction (SAR_sd). This method was implemented to assimilate the sea surface wind retrieved from Sentinel-1 synthetic aperture radar (SAR) in the basic three-dimensional variational system for the Weather Research and Forecasting Model (WRF 3DVAR). Furthermore, a new quality control scheme for wind observations is also presented. Typhoon Lionrock in August 2016 is chosen as a case study to investigate and compare both assimilation methods. The experimental results show that the SAR wind observations can increase the number of the effective observations in the area of a typhoon and have a positive impact on the assimilation analysis. The numerical forecast results for this case show better results for the SAR_sd method than for the SAR_uv method. The SAR_sd method looks very promising for winds assimilation under typhoon conditions, but more cases need to be considered to draw final conclusions.

This paper focuses on the data assimilation methods for sea surface winds, based on the level-2B HY-2A satellite microwave scatterometer wind products. We propose a new feature thinning method, which is herein used to screen scatterometer winds while maintaining the key structure of the wind field in the process of data thinning for highresolution satellite observations. We also accomplish feeding the ambiguous wind solutions directly into the data assimilation system, thus making better use of the retrieved information while simplifying the assimilation process of the scatterometer products. A numerical simulation experiment involving Typhoon Danas shows that our method gives better results than the traditional approach. This method may be a valuable alternative for operational satellite data assimilation.

The scatterometer (SCAT) on-board China’s HY-2A satellite has the capability to provide high resolution wind vector information over the global ocean surface. These wind vector data produced by the HY-2A scatterometer (HY-2A SCAT) are available to the data assimilation system with real-time information of high accuracy. In this paper, two experiments are designed to investigate the impact of HY-2A SCAT data in the three-dimensional variational assimilation system for the Weather Research and Forecast model (WRF 3DVAR). The powerful Typhoon Bolaven, which struck South Korea in August 2012, is selected for this case study. The results clearly demonstrate that HY-2A SCAT data can effectively complement the scarce observations over the ocean surface and improve the prediction of the wind and pressure fields of a typhoon. The case study of Typhoon Bolaven exhibits the significant and positive impact of HY-2A SCAT data on the numerical prediction of the tropical cyclone track.

Methodology is explored for the reconstructed radiances from the Infrared Atmosphere Sounding (IASI) for the purpose of assimilation into numerical weather prediction models. A principal component based fast radiative transfer model has been implemented into the WRF data assimilation system, thus allow the investigation of the reconstructed radiances from the IASI observations. Testing of a prototype system with 165 reconstructed channel radiances in band 1 shows marginal improvement as there are a few channel-correlated errors existed in the reconstructed radiances even the noise reduced much. The method implemented in this regard is examined and the potential for channel selection for reconstructed radiances is explored.

In gradient computations of the variational data assimilation (VDA) by the adjoint method, in order to overcome a lot of shortcomings such as low accuracy, difficult implementation, and great complexity, etc., a novel data assimilation method is proposed based on the dual-number theory. The important advantages are that the coding of adjoint models and reverse integrations are not necessary any more, and the values of cost functional and its corresponding gradient vectors can be attained simultaneously only by one forward computation in dual-number space. Furthermore, the accuracy of gradient can be close to the computer machine precision without other error sources. The paper is organised as follows. Firstly, the dual-number theory and algorithm rules are introduced. Then, the issues of gradient analysis and computation in VDA are transformed into the processes of calculating the cost functional numerically in dual-number space, and the gradient vectors can be obtained at the same time in an easy, efficient and accurate way. Secondly, the new algorithm for data assimilation in nonlinear physical systems is developed by combining accurate gradient information from the dual-number method with classical optimization algorithm. Thirdly, numerical experiments on sensitivity analysis for an ENSO nonlinear air-sea coupled oscillator are implemented, and the results are presented to demonstrate the important advantages of the dual-number method in the calculation of derivative information. Finally, numerical simulations for data assimilation are carried out respectively for the typical Lorenz 63 chaotic systems, the specific humidity evolving equation with physical “on-off” process at a single grid point, and a parabolic partial differential equation. Some conclusions can be drawn from the numerical experiments. The newly proposed method may be suited to many kinds of optimization problems with ordinary or partial differential equations as constraints, such as data assimilation, parameter estimation, inverse problems, sensitivity analysis etc. Results show that the new method can reconstruct the initial conditions or parameters of a nonlinear dynamical system very conveniently and accurately. Its another advantage is being very easy to implement with a high accuracy in gradient computation, so it is robust in the process of numerical optimization. The estimated initial states or parameters are convergent to real value in the cost of no more computations, when there are noises in the observations. But many tests are still needed to demonstrate the validity and advantages of the new data assimilation method, especially in more complex and realistic numerical prediction models of atmosphere and ocean.

The use of Principal Component (PC) algorithm is explored for the efficient representation observations from high-resolution infrared sounders for the purposes of data assimilation into numerical weather prediction (NWP) models. A new version of the fast radiative transfer model has been developed that exploits principal component analysis and then implemented into the WRF 4D-Var data assimilation system, thus allow the investigation of the direct assimilation of PC scores from Atmospheric Infrared Sounder (AIRS). Testing of a prototype system where 119 AIRS spectra replaced by only 20 PC scores show significant computational saving with no detectable loss of skill in the resulting analyses or forecasts. The methodologies implemented in this regard are examined and the potential for future increased use of the data are explored.

Many schemes of interpolation can be used for mapping from various coordinates of the numerical weather predictions (NWP) to the vertical coordinates of data assimilation systems.While most of the schemes do not imply the availability of a corresponding vertical mapping between the coordinates, even they are not implemented expediently.This leads to very noisy gradient with large unrealistic spikes which are simulated by the fast radiance transfer(RT) model for the first guest values from the numerical weather system and, consequently to incorrect data assimilation.The memorandum analyses a new algorithm of piecewise weighted integral interpolation applied in the Radiative Transfer for TIROS Operation Vertical Sounders (RTTOV).Experimental results show that the internal interpolation incorporated into RTTOV can map the coordinate of NWP model to the coordinate of data assimilation system flexibility, and can use the atmospheric information of the profiles on coordinate of NWP model sufficiently.Also the internal RTTOV interpolation can avoid "blind" levels derived from the nearest-neighbor log-linear interpolator and successfully retain the smooth structure of the radiance gradients.

According to the past experience of retrieving cloud parameters, the assimilation of cloud-affected infrared radiances is a difficult problem in the numerical weather prediction.Based on our own four-dimensional variational assimilation (4D-Var) system's characteristics, we have designed our own cloud-affected infrared satellite radiances variational assimilation system which includes a onedimensional variational assimilation (1D-Var) retrieval algorithm of cloud parameters.In this system, cloud parameters are considered as the intermediary to constrain the radiative transfer calculation after they are passed to our 4D-Var system.Consequently, acquiring the accurate cloud parameters is the key step of the assimilation of cloud-affected infrared radiances.This article analyzes the 1D-Var algorithm which is used to retrieve cloud parameters in our variational data assimilation system and validates the good performance of this method.