Evaluation of the Long-term Performance of Microwave Radiation Imager Onboard Chinese Fengyun Satellites

Accurate brightness temperature(BT)is a top priority for retrievals of atmospheric and surface parameters.Microwave Radiation Imagers(MWRIs)on Chinese Fengyun-3(FY-3)serial polar-orbiting satellites have been providing abundant BT data since 2008.Much work has been done to evaluate short-term MWRI observations,but the long-term performance of MWRIs remains unclear.In this study,operational MWRI BTs from 2012–19 were carefully examined by using simultaneous Advanced Microwave Scanning Radiometer 2(AMSR2)BTs as the reference.The BT difference between MWRI/FY3B and AMSR2 during 2012–19 increased gradually over time.As compared with MWRI/FY3B BTs over land,those of MWRI/FY3D were much closer to those of AMSR2.The ascending and descending orbit difference for MWRI/FY3D is also much smaller than that for MWRI/FY3B.These results suggested the improvement of MWRI/FY3D over MWRI/FY3B.A substantial BT difference between AMSR2 and MWRI was found over water,especially at the vertical polarization channels.A similar BT difference was found over polar water based on the simultaneous conical overpassing(SCO)method.Radiative transfer model simulations suggested that the substantial BT differences at the vertical polarization channels of MWRI and AMSR2 over water were partly contributed by their difference in the incident angle;however,the underestimation of the operational MWRI BT over water remained a very important issue.Preliminary assessment of the operational and recalibrated MWRI BT demonstrated that MWRI BTs were substantially improved after the recalibration,including the obvious underestimation of the operational MWRI BT at the vertical polarization channels over water was corrected,and the time-dependent biases were reduced.

IAP's Solar-Powered Unmanned Surface Vehicle Actively Passes through the Center of typhoon Sinlaku(2020)

The solar-powered marine unmanned surface vehicle(USV) developed by the USV team of the Institute of Atmospheric Physics is a rugged, long-duration, and autonomous navigation vessel designed for the collection of longrange, continuous, real-time, meteorological and oceanographic measurements, especially under extreme sea conditions(sea state 6–7). These solar-powered USVs completed a long-term continuous navigation observation test over 26 days.During this time, they coordinated double-USV observations and actively navigated into the path of Typhoon Sinlaku(2020) before collecting data very close to its center during the 2020 USV South China Sea Typhoon Observation Experiment. Detailed high temporal resolution(1 min) real-time observations collected by the USV on the typhoon were used for operational typhoon forecasting and warning for the first time. As a mobile meteorological and oceanographic observation station capable of reliable, automated deployment, data collection, and transmission, such solar-powered USVs can replace traditional observation platforms to provide valuable real-time data for research, forecasting, and early warnings for potential marine meteorological disasters.

Analysis of Low-level Temperature Inversions and Their Effects on Aerosols in the Lower Atmosphere

High-quality and continuous radiosonde, aerosol and surface meteorology datasets are used to investigate the statistical characteristics of meteorological parameters and their effects on aerosols. The data were collected at the Atmospheric Radiation Measurement Southern Great Plains climate research facility during 2000–15. The parameters and vertical distribution of temperature inversion layers were found to have strong diurnal and seasonal changes. For surface-based temperature inversion (SBI), the mean frequency and depth of temperature inversion layers were 39.4% and 198 m, respectively. The temperature difference between the top and bottom of SBI was 4.8℃, and so the temperature gradient was 2.4℃(100 m)^-1. The detailed vertical distributions of temperature inversion had been determined, and only the temperature inversion layers below 1000 m showed diurnal and seasonal variations. Mean surface aerosol number concentrations increased by 43.0%, 21.9% and 49.2% when SBIs were present at 0530, 1730 and 2330 LST, respectively. The effect of SBI on surface aerosol concentration was weakest in summer (18.1%) and strongest in winter (58.4%). During elevated temperature inversion events, there was no noticeable difference in surface aerosol number concentrations. Temperature differences and temperature gradients across SBIs correlated fairly well with aerosol number concentrations, especially for temperature gradients. The vertical distribution of aerosol optical properties with and without temperature inversions was different. Surface aerosol measurements were representative of the air within (below), but not above, SBIs and EIs. These results provide a basis for developing a boundary layer aerosol accumulation model and for improving radiative transfer models in the lower atmosphere.

Statistics of cloud heights over the Tibetan Plateau and its surrounding region derived from CloudSat data

Cloud-radiation interaction has a large impact on the Earth＇s weather and climate change, and clouds with different heights cause different radiative forcing. Thus, the information on the statistics of cloud height and its variation in space and time is very important to global climate change studies. In this paper, cloud top height （CTH）, cloud base height （CBH） and cloud thickness over regions of the Tibetan Plateau, south slope of the plateau and South Asian Monsoon are analyzed based on CloudSat data during the period from June 2006 to December 2007. The results show that frequency of CTH and CBH in unit area over the studied regions have certain temporal-spatial continuity. The CTH and CBH of different cloud types have different variation scopes, and their seasonal variations are distinct. Cloud thickness is large （small） in summer （winter）, and the percentages of different cloud types also have certain regularity.

A rapid assessment of MWRI-RM/FY3G brightness temperature

2023年4月发射的FY-3G上搭载17个频率的测雨微波成像仪(MWRI-RM),其业务定标的微波亮温(Tb)数据于2023年10月23日公开发布.本文比对了发布10天的MWRI-RM与全球降水测量(GPM)卫星上的微波成像仪(GMI)相匹配的Tb数据,并借助辐射传输模式进行双差分(DD)交叉定标分析,旨在快速评估新一代MWRI-RM观测数据质量.观测比较表明,MWR1-RM与GMI测量的Tb一致性很好,大多通道的平均偏差(MBE)和均方根误差(RMSE)均小于1K.两种传感器由于观测角度略有不同在水面产生约0.5 K差异.DD分析也展示出MWRI-RM的大多数通道性能与GMI相当接近,尤其是首次启用的高频166 GHz和183 GHz.

Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.

Validation of AMSU-A measurements from two different calibrations in the lower stratosphere using COSMIC radio occultation data

GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate(COSMIC) mission were used to validate the measurements of the advanced microwave sounding unit-A(AMSU-A) in the lower stratosphere from different satellites. AMSU-A observations from two different calibrations—the pre-launch operational and post-launch simultaneous nadir overpass(SNO) calibrations—were compared to microwave brightness temperatures(Tb)simulated from COSMIC data. Observations from three satellites(NOAA-15,-16, and-18) were used in the comparison. The results showed that AMSU-A Tb measurements from both calibrations and from all three NOAA satellites were underestimated in the lower stratosphere,and that the biases were larger in polar winters, especially over the southern high latitudes. In comparison to operational calibration, the SNO-calibrated AMSU-A data produced much smaller biases relative to the COSMIC data.The improvement due to SNO calibration was quantified by a Ratio index, which measured the bias changes from operational to SNO calibrations relative to the biases between the operational-calibrated AMSU-A data and the COSMIC data. The Ratio values were 70 % for NOAA-15and [80 % for NOAA-18 and-16, indicating that the SNO calibration method significantly reduced AMSU-A biases and effectively improved AMSU-A data quality.

A Thorough Evaluation of the Passive Microwave Radiometer Measurements onboard Three Fengyun-3 Satellites

Microwave Radiometer Imager(MWRI) is a key payload of China’s second generation polar meteorological satellite, i.e., Fengyun-3 series(FY-3). Up to now, 5 satellites including FY-3A(2008), FY-3B(2010), FY-3C(2013), FY-3D(2018), and FY-3E(2021) have been launched successfully to provide multiwavelength, all-weather, and global data for decades. Much progress has been made on the calibration of MWRI and a recalibrated MWRI brightness temperature(BT) product(V2) was recently released. This study thoroughly evaluates the accuracy of this new product from FY-3B, 3C, and 3D by using the simultaneous collocated Global Precipitation Measurement(GPM)Microwave Imager(GMI) measurements as a reference. The results show that the mean biases(MBEs) of the BT between MWRI and GMI are generally less than 0.5 K and the root mean squares(RMSs) between them are less than1.5 K. The previous notable ascending and descending difference of the MWRI has disappeared. This indicates that the new MWRI recalibration procedure is very effective in removing potential errors associated with the emission of the hot-load reflector. Analysis of the dependence of MBE on the latitude and earth scene temperature shows that MBE decreases with decreasing latitude over ocean. Furthermore, MBE over ocean decreases linearly with increasing scene temperature for almost all channels, whereas this does not occur over land. A linear regression fitting is then used to modify MWRI, which can reduce the MBE over ocean to be within 0.2 K. The standard deviation of error of GMI, FY-3B, and FY-3D MWRI BT data derived by using the three-cornered hat method(TCH) shows that GMI has the best overall performance over ocean except at 10.65 GHz where its standard deviation of error is slightly larger than that of FY-3D. Over land, the standard deviation of error of FY-3D is the lowest at almost all channels except at 89V. MWRI onboard FY-3 series satellites would serve as an important passive microwave radiometer member of the constellation to monitor key surface and atmospheric properties.