Huan Meng

Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting.

Advanced Microwave Scanning Radiometer-2 (AMSR2) is a successor of AMSR for Earth-Observation System (AMSR-E), while the third generation of AMSR (AMSR3) will be launched in the near future. The AMSR2 soil moisture product is also an important component of the Soil Moisture Operational Products System (SMOPS) datasets that are operationally produced by National Oceanic and Atmospheric Administration (NOAA). Refinement of the NOAA AMSR2 soil moisture data product can not only benefit the past AMSR-E and the upcoming AMSR3, but also improve the SMOPS data quality. In this second paper of the two-part series, the Extreme Gradient Boosting (XGB) model was trained using the AMSR2 6.925 GHz, 10.65 GHz, 18.7 GHz and 36.5 GHz brightness temperature (Tb) measurements in dual polarizations, ancillary maps and the vegetation index datasets, and in turn used to predict the daily global AMSR2 soil moisture retrievals from 2012 to 2021. Validation results show that the refined AMSR2 soil moisture retrievals (AMSRr) show an overwhelming advantage in data accuracy over the currently operational AMSR2 product (AMSRc). Comapred to the AMSRc, the developed AMSRr presents a significant improvement on data availability. Results also indicate that the refined AMSR2 datasets are comparable with the latest version Soil Moisture Active Passive soil moisture product. Based on this study, higher quality AMSRr soil moisture data product will be operationally produced in the NOAA, and will eventually benefit the NOAA SMOPS blended soil moisture product and its users.

Soil Moisture is an important variable for hydrological, meteorological and agricultural studies and applications. The Soil Moisture Operational Products System (SMOPS) was developed by the National Oceanic and Atmospheric Administration (NOAA)-National Environmental Satellite, Data, and Information Service (NESDIS) to operationally provide an integrated satellite soil moisture data product. The Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture retrieval is an important component of the currently operational SMOPS. This study is proposed to refine the AMSR2 data product using an optimal machine learning model, and this first paper of the two-part series is to intercompare the six commonly-used machine learning models including multiple linear regression (MLR), Regression Tree (RRT), Random Forest (RFT), Gradient Boosting (GBR), Extreme Gradient Boosting (XGB) and Artificial Neural Network (ANN). Results indicate that all of the six approaches can preserve the reference data information beyond the training time period, which ensures them to predict past and future satellite retrievals without a new training procedure. Relative to other models, the XGB method is more successful to respect to the reference data Soil Moisture Active Passive (SMAP) and the in-situ observations from the U. S. Department of Agriculture Soil Climate Analysis Network (SCAN). It has a good implication on the implementation of the XGB model to refine the AMSR2 soil moisture retrievals in the second paper.

Previous studies used the temperature-related variables from model outputs (e.g., 2-m temperature) for precipitation phase determination (i.e., rain–snow separation). This study presents a new idea for precipitation phase determination using brightness temperatures (TBs) from Advanced Technology Microwave Sounder (ATMS). It is found that TB-based phase discrimination shows comparable determination performance to that from model outputs over land. In contrast, TB-based phase discrimination over the ocean performs noticeably worse than that from model outputs. Further analyses reveal that the phase determination performance over land from TBs slightly depends on the satellite’s local zenith angle. However, the determination performance over the ocean strongly depends on the satellite’s local zenith angle, with the skill score decreasing sharply from 0.74 near the nadir to 0.55 near the edge. These results imply that over land, TBs may be used directly for phase determination, which can be extended to other operational and future microwave sounders with similar channels available.

The purpose is to respond to the call of the government on the artificial intelligence (AI) policy of music online education. First, wireless network technology and AI technology are described in detail; the application principle of AI technology in traditional music teaching mode and online teaching mode is analyzed; then, the combination model of AI technology and music online education system in 5th-generation (5G) wireless network environment is proposed. These models include music online composition teaching model, music online teaching scoring model, and music online lyric writing teaching model. Finally, the models are verified in the application of AI technology. The results show that AI technology has good application performance in the models above. Moreover, the direction of music online teaching reform and innovation along the combination of 5G network environment and AI is correct under the background of the new era. This exploration expands the application scenario of AI technology and provides ideas for the reform and innovation of music online education in the context of the new era.

This paper explores the capability of high frequency microwave measurements at vertical and horizontal polarizations in detecting snowfall over land. Surface in-situ meteorological data were collected over Conterminous US during two winter seasons in 2014–2015 and 2015–2016. Statistical analysis of the in-situ data, matched with Global Precipitation Measurement (GPM) Microwave Imager (GMI) measurements on board NASA/JAXA Core Observatory, showed that the polarization difference at 166 GHz had the highest correlation to measured snowfall rate compared to the single channel high frequency measurements and the polarization difference at 89 GHz. A logistic regression model applied to the match-up data, using the polarization difference at 166 and 89 GHz as predictors, yielded an overall snowfall classification rate of 69.0%, with the largest contribution coming from the polarization difference at 166 GHz. Logistic regression using the four single channels as predictors (at 89 and 166 GHz, horizontal and vertical polarizations) further indicated that the horizontal polarization at 166 GHz was the most important contributor. An overall classification rate of 73% was achieved by including the 183.31 ± 3 GHz and 183.31 ± 7 GHz vertical polarization channels in the final logistic regression model. Evaluation of the final algorithm demonstrated skill in snowfall detection of two significant events.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A.

This study investigates the correlation between the upwelling microwave brightness temperature measured by satellite radiometer and surface precipitation rate from ground radar observations at different time lags. Results show that brightness temperatures correlate more strongly with the lagged surface precipitation rate than the simultaneous surface precipitation rate. The lag time for snowfall ranges from 30 to 60 min. This time lag effect has an important influence when evaluating the satellite retrieval results relative to ground observations. For example, the falsely identified pixels can decrease by as much as 23.88% when considering a 30‐min lag time. Furthermore, the satellite‐retrieved snowfall rate shows much stronger correlation with the time‐lagged surface snowfall rate than the simultaneous snowfall rate in cold environments and for tall storms. This work implies that the time of the level‐2 swath‐retrieved snowfall rate needs to shift forward when incorporated into the level‐3 gridded products.

A new data product calculates snowfall rates from weather data beamed directly from several satellites, helping meteorologists provide fast, accurate weather reports and forecasts.