Search Results (56)

All-sky radiance assimilation can increase the utilization of satellite observations in cloudy regions and improve typhoon forecasts. This study focuses on the newly launched FengYun-3F satellite equipped with the Microwave Humidity Sounder II (MWHS-II) and develops an all-sky assimilation capability for its radiance data. A series of assimilation experiments were conducted to evaluate their impacts on the forecast of Typhoon Yagi (2024), demonstrating that all-sky assimilation leads to reductions in track error (23.14%) and improvements in precipitation forecasts (Equitable Threat Score increase of 16.92%) compared to clear-sky assimilation. Furthermore, a detailed comparison of assimilation experiments shows that using only the 183 GHz humidity channels yields limited improvement in tropospheric humidity, whereas assimilating the 118 GHz temperature channels significantly enhances temperature and wind forecasts. Combined assimilation of both frequency bands synergistically maintains accurate track and intensity predictions while further improving precipitation prediction. These findings demonstrate the value of multichannel all-sky assimilation and inform future satellite data assimilation strategies. Full article

The Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, the Back Propagation Neural Network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data with the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs at spatial resolutions of 0.25° in longitude and latitude between 65°S and 65°N over both sea and land surfaces are retrieved from the pixel-aggregated FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and superior to the D-matrix method, the Ridge method, the Lasso method, the physical method, the random forest (RF) method, the support vector machine (SVM) method, and the eXtreme Gradient Boosting (XGBoost) method. Against the radiosonde TPWs, the mean error (ME), the root mean square error (RMSE), and mean absolute error (MAE) of the TPWs retrieved in this work are −1.17 mm, 3.46 mm, and 2.63 mm over sea surfaces, respectively, and they are −0.80 mm, 4.04 mm, and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SSMIS TPWs. Full article

Fengyun-3F (FY-3F) satellite was launched in 2023 with a MicroWave Temperature Sounder (MWTS) and a MicroWave Humidity Sounder (MWHS) onboard. This study evaluates the in-orbit performances of these two instruments and compares them with similar instruments onboard FY-3E and NOAA-20 satellites. It is found that the polarization of FY-3F MWHS at channel 1 is different from FY-3E from the quasi-horizontal to quasi-vertical, whereas the rest of the channels are revised to quasi-horizontal polarization. FY-3F MWTS performance at the upper air channels is, in general, better than FY-3E MWTS, with 0.3 K smaller in biases (O-B) and 0.13 K lower in standard deviation. The striping noise between FY-3E and 3F MWHS is similar in magnitude for most of the channels. The FY-3F can form a satellite constellation with the FY-3E and NOAA-20, enabling better monitoring of many weather events, such as typhoons and hurricanes, through the use of all three satellites. Using the Global-Scene Dependent Atmospheric Retrieval Testbed (GSDART), Typhoon Yagi warm cores are retrieved from both MWTS/MWHS and ATMS. It is shown the warm core structures of Typhoon Yagi are consistent with the three satellites in terms of their magnitudes and locations. Full article

This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment’s TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL. Full article

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system. Full article

The commonly used reference atmospheric profiles for the validation of retrieved atmospheric profiles for microwave sounders have bias compared with real atmospheric profile values, which is detrimental to the validation of the retrieval. Microwave sounder observations are the direct measurements of microwave radiation in atmospheric conditions and are a true representation of the status of the atmosphere. This paper proposed a validation method for the retrieved atmospheric temperature and atmospheric humidity profiles of the satellite-based microwave sounder using its own in-orbit observations. The validation experiments are performed both for the retrievals of the microwave temperature sounder-II (Xi’an Branch, China Academy of Space Technology, Xi’an, China. MWTS-II) and the microwave humidity and temperature sounder (National Space Science Center, Chinese Academy of Sciences, Beijing, China. MWHTS). The validation results show that the retrieved temperature profiles of MWTS-II have higher accuracy compared to the temperature profiles of ERA5 in the atmospheric pressure range of 3–30 hPa, and the accuracy of the rest of the pressure range is comparable between the profiles of ERA5 and the retrieved profiles. And the retrieved temperature profiles of MWHTS have higher accuracy compared to the temperature profiles of ERA5 in the atmospheric pressure level around 50 hPa and lower accuracy in the rest of the pressure levels. In addition, the retrieved humidity profiles of MWHTS have higher accuracy compared to the humidity profiles of ERA5 in the atmospheric pressure range of 350–925 hPa. The proposed validation method for the retrieved atmospheric temperature and atmospheric humidity profiles of MWHTS using its own observations is promising for improving the feasibility and reliability of the validation, and can be a good reference for the application of the satellite in-orbit observations and the optimization of the microwave sounders. Full article

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels. Full article

A unique End-of-Life (EOL) Deep Space View Test (DSVT) was performed on 27 November 2021 for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard the first EUMETSAT MetOp-A satellite in the deorbiting process. The purpose of this test is to recalibrate the antenna sidelobe, to derive antenna emission, and to quantify the in-orbit asymmetric scan biases of AMSU-A and MHS to, ultimately, improve Near Real-Time (NRT) products for MetOp-B and -C and the entire Fundamental Climate Data Records (FCDR). In this study, MetOp-A AMSU-A and MHS EOL DSVT data on 27 November 2021 have been analyzed. The deep space scene antenna temperatures were first applied for the antenna pattern correction; then, the antenna reflector channel emissivity values were derived from the corrected temperatures. For the MHS, the observed scan-angle-dependent brightness temperatures (BTs) for all channels were well behaved after the antenna pattern correction, except for channel 1. The derived antenna reflector emissivity values from this test are 0.0016, 0.0036, 0.0036, and 0.0019 for channels 1, 3, 4, and 5, respectively. For AMSU-A, the deep space view counts were not homogeneous during the test period, exhibiting large variations in the along-track and cross-track directions, mainly due to the instrument temperature’s rapid change during the test period. The large relative noise in the deep space view observations negatively impacted the data quality and limits the value of this test. The large relative noise may contribute to the different emissivity values derived from the same frequency for channels 9 to 14. We also found unexpected scan-angle-dependent BT after antenna pattern correction for quasi-vertical (QV) channels 1 and 2 when compared to the emission model. Further investigation using a simulation confirmed that channels 1 and 2 are QV channels, as designed. Full article

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced. Full article

Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here, using data from Microwave Humidity Sounder II (MWHS-II) onboard the Chinese Fengyun 3D (FY-3D) satellite in the Global/Regional Assimilation and Prediction System (GRAPES) Four-Dimensional Variational (4D-Var) system of the China Meteorological Administration (CMA), we explore the assimilation application of the water-vapor absorption line at 183.31 ± 1 GHz, 183.31 ± 3 GHz and 183.31 ± 7 GHz, as well as 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz, two added channels, to assess the impact of adding the 183.31 ± 1.8 GHz and 183.31 ± 4.5 GHz sampling channels on data assimilation and numerical weather prediction. Our findings reveal a significant increase in the specific-humidity increment, which in the middle–upper troposphere is numerically much larger than in the lower troposphere. Specifically, the assimilation of 183.31 ± 1.8 GHz observations, positioned near the center of the water-vapor absorption line, results in a pronounced adjustment compared with the 183.31 ± 4.5 GHz observations. And under the strong constraint of the numerical model, the Root Mean Square Error (RMSE) of the wind field diminishes more significantly (by an average of 2–4%) after assimilating the water-vapor observations at greater heights. Full article

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable. Full article

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data. Full article

This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development. Full article

With the Microwave Humidity Sounder-2 (MWHS-2)/Fengyun (FY)-3D in operation, this is the first study to evaluate the impact of a joint assimilation of MWHS-2 radiances under all-sky conditions from both the FY-3C and FY-3D satellites on typhoon forecasting within regional areas. In this study, Typhoon Hagupit in 2020 was chosen to investigate the impacts of assimilating MWHS-2 radiances; the forecasting performances of the joint assimilation method were slightly better than the experiments assimilating MWHS-2 observations from FY-3C or FY-3D only, and the results of the latter two experiments were comparable, especially in terms of the landfall location of Hagupit. With additional assimilated cloud- and precipitation-affected MWHS-2 observations, improved typhoon track and intensity forecasts as well as forecasts of the precipitation caused by Hagupit were achieved due to the improved analyses of relative humidity, temperature and wind fields around Hagupit compared to the clear-sky assimilation experiments. In addition, the channel-selection scheme evidently affected the forecasting performance; that is, the radiances from the MWHS-2 118 GHz and 183 GHz channels provided opposite results in terms of the Hagupit track, and this finding needs further investigation in the future. Full article

This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument—scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1—has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A/MHS model evaluation. The results clearly show the NN algorithm’s high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A/MHS)-simulated dataset, respectively. Full article