Accurate sea level pressure (SLP) data are critical for the forecasting and monitoring of tropical cyclones (TCs). Previous studies have explored SLP retrieval for TCs using passive microwave observations in a single oxygen band (60 or 118 GHz). Leveraging the Fengyun-3 (FY-3) satellite’s capability to simultaneously observe radiation in both 60 and 118 GHz bands, this study proposes a neural network-based algorithm to retrieve TC SLP fields from combined observations of brightness temperature (TB) and warm TB anomalies in both frequency bands. Application studies were carried out using the joint observations from the Microwave Temperature Sounder-2 (MWTS-2) and Microwave Humidity Sounder-2 (MWHS-2) onboard the FY-3D satellite. Optimal frequency channels for the SLP retrieval were selected based on an information content analysis. SLP retrievals within the TC core regions (within a 2° radius from the centers) were compared with SLP data from the Global Data Assimilation System Final Analysis (GDAS/FNL). The results showed root mean square errors (RMSEs) of 2.78 hPa for tropical depressions and storms, 4.23 hPa for hurricanes or typhoons, and 6.15 hPa for major hurricanes or severe typhoons. Retrievals were also compared within situobservations, and the corresponding RMSEs were 3.82, 4.86, and 5.14 hPa, respectively. Furthermore, comparative experiments between the new algorithm and the previous algorithm that used only observations from a single oxygen band demonstrated that the new algorithm provides more comprehensive SLP information and achieves higher accuracy.

The third generation Microwave Temperature Sounder (MWTS-III) onboard Fengyun-3E (FY-3E) can obtain the atmospheric temperature profile in the early morning time, which is valuable for numerical weather prediction (NWP), climate analysis and other environment studies. However, calibrated brightness temperature (BT) of the surface-insensitive sounding channels (i.e. channels 7-17) have a sea-land contrast bias. It is 0.2 K-0.3 K for channels 9-17 and -1.33 K and -2.86 K for channels 7 and 8, respectively. The bias characteristic of channels 9-17 is similar to that of the corresponding channels of FY-3C, which is caused by the interference from a window channel and can be corrected by empirical method. Moreover, the spectral response functions of channels 7 and 8 were examined over a wider frequency range by measuring the instrument of similar design and construction to the on-orbit MWTS-III. The measured data shows that channels 7 and 8 have out-of-band response located between 50 GHz to 52 GHz, which close to the response range of channel 4. A linear function developed in this letter is effective for the BT bias correction. The sea-land contrast bias of these two channels has been decreased to -0.35K and -0.16K. And the daily mean bias of channels 7 and 8 are improved from -1.8K and -2.3K to -0.5K and -0.4K, respectively. This correction function has been integrated into the FY-3E MWTS-III operational calibration program in December 7, 2022. Meanwhile, the subsequent MWTS-III has been further improved to avoid the sea-land contrast bias.

Multilayer clouds have a significant importance on cloud climate effects and remote sensing retrieval. In this study, a multilayer cloud detection algorithm is developed for the Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite. The algorithm is based on the basic physical assumptions that are also employed for Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) to identify ice overlaying water multilayer clouds. Synchronous observation of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) has been collected and acknowledged as a reliable reference dataset for determination of thresholds. The algorithm used the long-wave infrared bands (8.5 and$10.8~\mu \text{m}$) to determine the phase of the upper layer cloud. Then, the difference between solar reflectance band pairs (1.375 and$1.61~\mu \text{m}$) is used to identify ice overlayer water multilayer clouds when the upper layer is ice cloud. When the upper layer cloud is water, the infrared band ($7.1~\mu \text{m}$) is applied to find misclassified multilayer clouds. The algorithm demonstrates a notable improvement of approximately 0.146 in the probability of detection (POD) compared to MODIS while using CALIOP products as a reference, specifically for cases when the cloud optical depth (COD) surpasses 4. Nevertheless, it does result in a slightly elevated false alarm rate (FAR), around 0.042. In the future, it is necessary to conduct a more comprehensive validation of the algorithm, with particular emphasis on its limits in scenarios where the upper cloud layer is too thin (thick).

Hyperspectral infrared atmospheric sounder II (HIRAS II) onboard in Fengyun-3E (FY-3E) early-morning-orbit meteorological satellite is the second-generation hyperspectral infrared (IR) sounder of Chinese Meteorological Administration (CMA). HIRAS II has three consecutive spectral bands named long-wave (LW), medium-wave (MW), and short-wave (SW) band with 1671 more channels than its predecessor HIRAS I. Channel selection is implemented to get the optimal subset for application in numerical weather prediction (NWP) and to explore the contribution of each spectral band. The iterative channel selection method using information content (IC) is applied in temperature and water vapor channel selection based on temperature and humidity sampled profiles respectively. In total, 150 channels were chosen, consisting of 35 LW temperature channels, 65 MW temperature channels, and 50 MW water vapor channels. The temperature channel selection shows that the selected channels in LW band present most information in upper troposphere, while the selected channels in MW band present most information in the mid and lower troposphere. It is found that the humidity of atmosphere profile has large impact on the water vapor channel selection. As the increase of profile humidity, the maximum contribution layer of channel IC shift upward and the degrees of freedom for signal (DFS) profile become sharper and narrower, which results in the variation of channel selection.

Improving the calibration accuracy of infrared hyperspectral interferometers is a prerequisite for their quantitative application. Updating satellite in-orbit calibration parameters is an important way to keep calibration accuracy. However, how to separate the deviation of one or more calibration parameters from the coupling error of observation data is a key issue that needs to be solved. Therefore, we propose a variational-based calibration parameter optimization algorithm (VarCalPOA), which uses only observation data and reference data to optimize the key calibration parameters based on variational assimilation theory to obtain the optimized values of the calibration parameters, thereby improving the calibration accuracy. Based on the observation and calibration simulation model, we choose$T_{\mathrm {ict}}$,$e_{\mathrm {ict}}$, and$a_{2}$as the key calibration parameters. Using the VarCalPOA method, the simulation results show that the optimized calibration parameters have a certain positive effect on the improvement of calibration accuracy. This study proves that the VarCalPOA method is potentially an effective method for monitoring the in-orbit state of infrared hyperspectral interferometers.

The second-generation Microwave Humidity Sounder (MWHS-2) onboard Fengyun (FY)-3C has a data quality comparable to that of counterpart microwave moisture sounders. However, channels 13 and 14 have large biases that are negatively correlated with the instrument temperature and prevent the operational assimilation of the data of these two channels. To better understand the biases of channels 13 and 14, the correlation of the observation minus simulation data (O–B bias) of different channels with the instrument temperature and other factors is investigated for FY-3C MWHS-2 in this article. A sensitivity analysis using a gradient boosting decision tree indicates that the instrument temperature and scan position are the two dominant factors, with total bias-contribution scores exceeding 0.8 for both channels 13 and 14. This conclusion is verified through further analysis of the bias distributions for the scan position, scene brightness temperature, and ascending/descending orbits. Using 12-week data recorded in 2016, the correlations of the O–B bias with the scan position and instrument temperature are specifically investigated and a correction algorithm is formulated, with which data for 2016 and 2017 are corrected. The corrected data in both channels have smaller, more stable biases, and are less affected by the instrument temperatures and scan position. The bias contributions associated with these two factors should thus be further studied.

Ground-based microwave radiometers (MWR) are used to provide temperature and relative humidity (RH) profiles mainly at boundary layer with temporal resolution of 1–2 minutes. However, accuracy of MWR away from radiosondes locations is not satisfactory. In this study, we proposed temperature and RH retrieval models based on fifth-generation reanalysis (ERA5) data that allow the replacement of the original model based on adjacent radiosondes. The study focuses on how to resolve the unrepresentativeness of ERA5 data. A quality control scheme was proposed after analyzing the characteristics of ERA5 data, which was confirmed to have positive effects for retrieval results. Three retrieval models for MWR at Nanjiao site based on ERA5 data (ERA5-retrieval), co-located radiosondes (RS-retrieval 1), and adjacent radiosondes (RS-retrieval 2) were developed. An extensive validation that was carried out showed ERA5-retrieval improved the accuracy of temperature and RH profiles compared with the original method RS-retrieval 2 and LV2 product from MWR. Furthermore, the proposed retrieval method was applied to six MWR sites without radiosondes co-located in Beijing and illustrated the feasibility and accuracy of the proposed method. In overall, the retrieval approach proposed based on ERA5 data and data quality control can improve both temperature and RH accuracy for MWR sites away from radiosondes.

Data assimilation of satellite microwave sounders are very important for numerical weather prediction. Fengyun-3 (FY-3) polar satellites carry two such sounders: MicroWave Temperature Sounder (MWTS) and MicroWave Humidity Sounder (MWHS). The microwave observations should be quality-controlled before assimilation and rain check is one of the crucial steps. Microwave Radiation Imager (MWRI), on board FY-3 too, can provide useful rain information due to more sensitive to precipitation than sounders. A remapping methodology is described in this paper to make full use of data from sounders and imager simultaneously. Observations and products from MWRI can thus be remapped to field of view of sounders. Comprehensive information obtained in this way can be used to not only assimilation in numerical weather prediction models but also satellite observations quality monitoring.

This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts' assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2-4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels.

Near-infrared (NIR) channels, such as the 1.6- and 2.13-μm channels of Moderate Resolution Imaging Spectroradiometer (MODIS), play an important role in inferring cloud properties because of their sensitivity to cloud amount and particle size. Instead of the 2.13-μm channel, which has shown great success on MODIS, the central wavelength of the Visible Infrared Imaging Radiometer Suite (VIIRS) is shifted to 2.25 μm. This paper investigates the influences of NIR channels (i.e., 2.13 and $2.25 μm) on cloud optical and microphysical property retrievals and reveals the potential applications of the 2.25-μm channel to cloud thermodynamic phase and multilayer cloud detections by combining with the 1.6-μm channel. Rigorous radiative transfer simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. Our results indicate a minor influence of the 2.25-μm channel on cloud optical depth and effective particle size retrievals. In combination with the 1.6-μm channel, the 2.25-μm channel provides additional information indicating cloud phases. However, the 1.6- and 2.13-μm channels do not show any sensitivity to cloud phase. Furthermore, by considering the infrared-based cloud phase results, the 1.6- and 2.25-μm channel combination becomes possible to infer multilayer clouds. Case studies based on simultaneous MODIS and VIIRS observations demonstrate the capability of the 1.6-2.25-μm channel combination for determining cloud phase and multilayer clouds. Collocated satellite-based active lidar observations further validate these advantages of the 2.25-μmu channel over the original 2.13-μm channel.

On 27 May 2008, China has successfully launched the FY-3A meteorological satellite as a research and development (R&D) satellite, with 11 payloads mounted, and the three instruments among the 11 payloads are vertical atmospheric sounding instruments similar to ATOVS: MicroWave Temperature Sounder (MWTS), MicroWave Humidity Sounder and InfraRed Atmospheric Sounder, generally called Vertical Atmospheric Sounder System, i.e., VASS, which will help to improve NWP forecast skill. Chinese Meteorological Administration has developed a three dimension variational data assimilation system (Global/Regional Assimilation and Prediction System, shorten as Grapes 3Dvar), and with the ATOVS radiance data directly assimilated by RTTOV as observation operator. In this study some challenging research works related to assimilating the FY-3A VASS data into Chinese 3Dvar assimilation system Grapes 3Dvar are performed, such as generation of the RTTOV coefficients for FY-3A VASS three instruments, data quality control, channel selection and bias correction, etc. To investigate the effects of the FY-3A VASS data in NWP model, 2 sets of assimilation experiments conducted: one is single instrument data assimilation experiment and the other is combinatorial data assimilation experiments. The preliminary results indicate that: the forecast skills are improved greater after FY-3A VASS data assimilation than before, and the different instrument has different effects on NWP forecast; the microwave sounders have more contribution than infrared sounder; the combinatorial data assimilation experiments get more positive effect than single instrument data assimilation experiment; the positive impact on the South Hemisphere is more clear than on North Hemisphere.

For the land products retrieved from the remotely sensed datasets better using in the land surface model and weather/climate model, the details on how to develop the Land Data Assimilation Systems (LDAS) based on EnKF, 3DVAR technology and Community Land Model, has been discussed in this study. The constructed LDAS can provide optimal estimates of land surface state initial conditions by integrating with an ensemble of land surface models, the available atmospheric forcing data, remotely sensed observations of precipitation, radiation and some land surface parameters such as land cover and leaf area index. The verification results from soil temperature and moisture assimilation at Yucheng comprehensive experiment site and from 19-year LAI assimilation indicate that the constructed LDAS takes its effect and shows agreement with some past researches. There are still many detailed work to do for the routine operation of LDAS, such as how to get dynamic P in 3dvar, how to select the spacing interpolation algorithm, etc.

A trail network data model provides an infrastructure that can be used for non-motorized transportation analysis and decision making. In this paper, a trail network model, TNM, is discussed, and a web-based bikeway routing service system, BikerRouting, is presented to provide routing services to bikers on the Virginia trail network implemented based on TNM. In this paper, 1) the trail network model design is presented and its differences from a standard street network model are analyzed, and 2) the online bikeway routing service system is discussed.

This paper argues that a camera sensor network containing heterogeneous elements provides numerous benefits over traditional homogeneous sensor networks. We present the design and implementation of senseye---a multi-tier network of heterogeneous wireless nodes and cameras. To demonstrate its benefits, we implement a surveillance application using senseye comprising three tasks: object detection, recognition and tracking. We propose novel mechanisms for low-power low-latency detection, low-latency wakeups, efficient recognition and tracking. Our techniques show that a multi-tier sensor network can reconcile the traditionally conflicting systems goals of latency and energy-efficiency. An experimental evaluation of our prototype shows that, when compared to a single-tier prototype, our multi-tier senseye can achieve an order of magnitude reduction in energy usage while providing comparable surveillance accuracy.