Humidity Sounder Research Articles

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

AbstractTwo years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts.

Fusion Retrieval of Sea Surface Barometric Pressure from the Microwave Humidity and Temperature Sounder and Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.

Evaluation and Assimilation of FY-3C/D MWHS-2 Radiances in the RMAPS-ST

Currently, humidity information can be obtained from the Microwave Humidity Sounder-2 (MWHS-2) mounted on the polar-orbiting satellites FY-3C and FY-3D. However, making full use of the MWHS-2 data remains a challenge, particularly in the application of regional numerical weather models. This study is the first to include MWHS-2 radiance data in the Rapid-refresh Multi-scale Analysis and Prediction System—Short-term (RMAPS-ST) regional model. The results and impact of MWHS-2 radiance data assimilation were investigated and evaluated. It is found that MWHS-2 radiance data can be effectively assimilated in the RMAPS-ST after a series of quality control and variational bias correction. Benefits could be obtained in the reduction of background departures for each humidity sounding channel. Assimilation experiments over a period of one month were carried out, and the impacts of MWHS-2 radiances were quantitatively analyzed on the forecasts of RMAPS-ST system. The results showed that MWHS-2 saw a small but significant improvement for low-level humidity of short-range forecast, by 16.5% and 3.2% in terms of mean bias and root-mean-square error, respectively. The positive impact on short-range forecast also can be found for middle and low level temperature and wind. For quantitative precipitation forecast, the assimilation of MWHS-2 radiances increased the score skills of different rainfall levels in the first 12 h forecast by an average of 1.4%. There was a slight overall improvement in the 24-h precipitation forecast for over-estimation and false alarm of 3-h accumulated rainfall below 1.0 mm, with 0.75% and 0.36%, respectively. The addition of MWHS-2 radiance data gives a small positive impact on low-level humidity, temperature, and wind in the RMAPS-ST regional model, and it also improves short-range forecast of rainfall, particularly in the first 12 h of the forecast.

Angular-Dependent Polarized Characteristics of Surface Emissivity at 150 GHz Over Saharan and Taklimakan Deserts

This study characterizes the marginally-known angular-dependent polarization difference of the land surface emissivity (LSE), using the microwave humidity sounder (MWHS) onboard China’s sun-synchronous satellite FengYun-3B (FY-3B). The instrument has two quasi-polarization channels at 150 GHz in addition to the three water-vapor absorption channels in the vicinity of 183 GHz, allowing observations at quasi vertical and horizontal polarizations with its cross-track scanning geometry. The Saharan and Taklimakan deserts are taken as examples due to the relatively homogeneous surface conditions. In the two 150 GHz channels of MWHS, the polarization bias is corrected according to the measurements at the Earth incidence angle (EIA) of 0°. The monthly-mean quasi-polarization difference (QPD) at 150 GHz is found to be varying with the EIA and exhibits seasonal variations over deserts. The QPD reaches a maximum value at the EIA of ~35°, while the zero value appears near the EIA of ~53° (equal to the satellite viewing angle of 45°) as expected. In contrast to the relatively stable and low QPD over Sahara, the QPD, quasi polarized emissivity difference and pure polarized emissivity difference are generally larger in the Taklimakan Desert, where the maximum monthly QPD is ~4 K and the polarized emissivity difference increases monotonously to ~0.2 at the EIA of 50°. The complex surface parameters and temporal variabilities impinge on the uncertainties of the land polarization. The increasing soil moisture due to precipitation damps the dichroic ability of the land surface, while the frozen surface due to snowfalls in cold seasons enhances the polarization.

Characterization of Brightness Temperature Biases at Channels 13 and 14 for FY-3C MWHS-2

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 paper. 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.

Quantifying and Characterizing Striping of Microwave Humidity Sounder With Observation and Simulation

Quantifying and Characterizing Striping of Microwave Humidity Sounder With Observation and Simulation

Hyperspectral Microwave Sensors—Advantages and Limitations

Temperature and humidity soundings form the bedrock of modern data assimilation due to their ability to directly constrain the atmospheric state variables. Because of their ability to penetrate clouds and work in all weather conditions, microwave sounders have very large impacts on constraining numerical weather prediction models. Recent advancements in integrated microwave assembly, space-grade high speed analog to digital converters, gigabit-per-second data interconnects, and field programmable gate arrays have enabled a transition from traditional analog detector-based demodulators to digitally channelized systems that allow for hyperspectral, or fine spectral resolution microwave sounders to be viable replacements to the current operational instruments. This article demonstrates that retrievals of temperature and moisture soundings can be improved by as much as 50% when 60–80 appropriately chosen pseudochannels are employed. While the current simulations were limited to cloud free oceans, perhaps even greater benefits can be realized over land and cloud conditions where additional channels can help constrain the surface and clouds. The article also demonstrated the advantages of hyperspectral sensors as a way to detect radio frequency interference in the few Kelvin range, as well as its ability to improve intercalibration efforts due to its ability to match frequency response functions of target sensors.

The Use of Artificial Neuron Networks for Retrieval Temperature and Humidity Sounding of the Atmosphere According to the Data of the MTVZA-GY Microwave Radiometer Installed on the Meteor-M No. 2-2 Satellite

The Use of Artificial Neuron Networks for Retrieval Temperature and Humidity Sounding of the Atmosphere According to the Data of the MTVZA-GY Microwave Radiometer Installed on the Meteor-M No. 2-2 Satellite

Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

Observations of the downwelling far-infrared atmospheric emission at the Zugspitze observatory

Abstract. Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm−1 (100–15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100–1000 cm−1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018–2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA). The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm−1; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not well-characterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow. The consolidated dataset is freely available via the ESA campaign dataset website at https://doi.org/10.5270/ESA-38034ee (Palchetti et al., 2020a).

On the addition of microwave sounders and numerical weather prediction skill

AbstractThe utility of microwave sounding for numerical weather prediction (NWP) is well established. However, the volume of sounder data assimilated in NWP systems may change substantially in upcoming years, as there is an ageing constellation of satellites but also the prospect of proliferating small satellite constellations. This study examines the addition of temperature and humidity sounders from a baseline that includes no microwave sounders at all, aiming to elucidate the incremental benefit gained from adding sounders to the assimilation system. Framed as a series of observing‐system experiments (OSEs), large improvements in forecast skill and background fits to independent observations are gained from the first sounders added. Significant further benefit is observed from additional sounders, with the impacts largest at higher latitudes and no saturation evident in the maximal setup. In the second part of this study, the relative importance of updating background errors for OSEs is investigated to give greater context to the sounder addition results. The ensemble of data assimilations (EDA) underlying the background‐error specification is rerun for this purpose to be consistent with the observation usage in the OSE. Comparing experiments with updated and unchanged background errors shows that the effect of updating background errors is secondary to that caused by the observing‐system change itself. Rerunning the EDA can reduce some of the forecast skill apparently lost in a data‐denial experiment or gained when assessing data addition, but this is roughly 10% of the overall change and should not alter conclusions resulting from typical OSEs, in which background errors are not tailor‐made.

All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System

Microwave radiances from passive polar-orbiting radiometers have been, until recently, assimilated in the Met Office global numerical weather prediction system after the scenes significantly affected by atmospheric scattering are discarded. Recent system upgrades have seen the introduction of a scattering-permitting observation operator and the development of a variable observation error using both liquid and ice water paths as proxies of scattering-induced bias. Applied to the Fengyun 3 Microwave Temperature Sounder 2 (MWTS-2) and the Microwave Humidity Sounder 2 (MWHS-2), this methodology increases the data usage by up to 8% at 183 GHz. It also allows for the investigation into the assimilation of MWHS-2 118 GHz channels, sensitive to temperature and lower tropospheric humidity, but whose large sensitivity to ice cloud have prevented their use thus far. While the impact on the forecast is mostly neutral with small but significant short-range improvements, 0.3% in terms of root mean square error, for southern winds and low-level temperature, balanced by 0.2% degradations of short-range northern and tropical low-level temperature, benefits are observed in the background fit of independent instruments used in the system. The lower tropospheric temperature sounding Infrared Atmospheric Sounding Interferometer (IASI) channels see a reduction of the standard deviation in the background departure of up to 1.2%. The Advanced Microwave Sounding Unit A (AMSU-A) stratospheric sounding channels improve by up to 0.5% and the Microwave Humidity Sounder (MHS) humidity sounding channels improve by up to 0.4%.

Improving Cross-track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing

AbstractPrevious studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) onboard the NPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective vs. stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multi-satellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers onboard.

Effects of Assimilating Clear-Sky FY-3D MWHS2 Radiance on the Numerical Simulation of Tropical Storm Ampil

A new advanced microwave humidity sounder FY-3D MWHS2 radiance has been assimilated under the clear-sky conditions by implementing its data assimilation interface. The case of the tropical storm Ampil in 2018 is selected to address the effectiveness of the new-built module in the initialization and forecast of typhoons. Apart from the experiment assimilating both the Global Telecommunications System (GTS) data and the FY-3D MWHS2 radiance data, an experiment with only GTS data is also conducted for comparison. The results show that the bias correction of this humidity sounder is effective, and the analysis field after assimilating its radiance data matches well with the observation. The increment of specific humidity below the middle layers is evident after the assimilation of the radiance data. Besides, the geopotential height increment and the specific humidity increment at 500 hPa and 850 hPa, respectively, are favorable, resulting in more accurate rain belt distribution and a higher fraction skill score (FSS). In the deterministic forecast, the track error of the FY-3D MWHS2 experiment is consistently less than 90 km.

The assimilation of microwave humidity sounder observations in all‐sky conditions

AbstractNumerical weather prediction (NWP) schemes use a wide variety of satellite observations to help constrain atmospheric analyses. Passive microwave humidity data from channels operating in the water vapour band at 183 GHz, such as those on the Microwave Humidity Sounder (MHS), are an important component of the satellite observing network. At this frequency the observations are very sensitive to scattering from ice crystals and, until recently, quality‐control tests were used at the Met Office to exclude assimilation of these data in regions of strong scattering such as thick cirrus clouds. In the work described here we report on improvements to the pre‐processing and assimilation of MHS data within the global NWP scheme at the Met Office, with the goal of utilising the data in scenes where liquid and ice cloud strongly affect the radiances. These improvements include the introduction of an observation operator that models the effects of ice scattering, and a scene‐dependent observation error model. The error model is based on the estimated cloud ice and cloud liquid in the field of view. The new scheme has increased observation usage of MHS data over ocean by up to 40%. Trials have shown benefits to forecasts in both boreal summer and winter seasons. In the boreal summer period, for example, low‐level wind forecast errors have improved in the Northern Hemisphere extratropics by up to 0.6%. Also, our experiments show evidence of significant improvements to the fit between observations and short‐range forecasts for humidity‐sensitive channels operating in both the microwave and infrared regions of the electromagnetic spectrum.

Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

Observation error diagnostics and applicability evaluation of MHS brightness temperatures in RMAPS-CA

ABSTRACT Due to the insufficiencies of conventional observations and the effectiveness of bias corrections, it is still necessary to assimilate data from polar orbit satellites into a limited-area rapidly updated multiscale analysis and prediction system for Central Asia (RMAPS-CA). This study diagnoses the observation errors of the radiation brightness temperature (BT) of satellite Microwave Humidity Sounder (MHS) from multiple platforms through comparisons to the simulated BTs by the Community Radiative Transfer Model (CRTM) with soundings and a GFS input separately. The results show that the majority BIAS of the MHS BT compared with the simulated BT falls within −1.5–2.0 K and increases with an increase in the peak energy contribution height (PECH). No significant bias differences for the same channel of MHS and the same satellite are found across the different sounding stations, but obvious differences are found for the same channel from different satellites. Additionally, most of the GFS BTs are higher than the MHS BTs, which increase with the PECH. Moreover, almost all PDF distributions of the observed MHS BT bias relative to the GFS BT form a nearly Gaussian distribution, with the centre value being positive for channels 3 and 4. An assessment of the initial applications of MHS to RMAPS-CA is then conducted through two one-month retrospective runs, and forecasts initialized from analyses with and without MHS are verified against the observations. The results show that compared to the natural run for which no observations are assimilated, an improvement in the geopotential height of approximately 55.3% can be obtained by the assimilation of MHS, along with improvements of 12.8%, 4.7% and 2.3% for temperature, specific humidity (SPFH), and wind speeds below 200 hPa, respectively, for an average RMSE with initial time, which is valued at 2.9% and 3.3% for the SPFH at 2 m and wind speed at 10 m, respectively In addition, significant improvements in 24-h precipitation of 11.3%, 28.7% and 20.0% are obtained for light, medium and heavy rain, respectively, while no positive effects are obtained for rainstorm forecasts.

Impact of assimilating FY-3C MWHS2 data in the RMAPS-ST forecast system on its rainfall forecasts

In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder (MWHS2) data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System–Short Term (RMAPS-ST) operational system, which is developed by the Institute of Urban Meteorology of the China Meteorological Administration, four experiments were carried out in this study: (i) Coldstart (no observations assimilated); (ii) CON (assimilation of conventional observations); (iii) FY3 (assimilation of FY-3C MWHS2 only); and (iv) FY3+CON (simultaneous assimilation of FY-3C MWHS2 and conventional observations). A precipitation process that took place in central-eastern China during 4–6 June 2019 was selected as a case study. When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system, data quality control and bias correction were performed so that the O-B (observation minus background) values of the five humidity channels of MWHS2 became closer to a normal distribution, and the data basically satisfied the unbiased assumption. The results showed that, in this case, the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments. Meanwhile, the prediction of atmospheric parameters for the mesoscale field was also improved, and the RMSE of the specific humidity forecast at the 850–400 hPa height was reduced. This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.摘要 中层水汽初值的误差是数值预报, 尤其是短期预报不确定性的重要来源. 星载微波湿度计资料能很好地弥补常规资料的不足, 同化微波湿度计资料对改善对流层中高层的水汽初始场精度有着重要意义. 本研究基于新一代快速更新多尺度资料分析和预报系统短期数值预报系统 (RMAPS-ST), 选取 2019 年6月 4–6 日中国华中地区降水过程开展了 FY-3C MWHS2 资料同化对于此次降水预报的影响评估. 研究开展了4组试验: 冷启动试验, 控制试验, 同化 FY-3C MWHS 试验, 同时同化常规观测和 FY-3C MWHS 试验. 结果表明:经质量控制及偏差订正之后 FY-3C MWHS2 各通道 O-B 明显趋于正态分布, 同化 FY-3C MWHS 资料后, 对此次降水过程降水落区和强度的预报均优于控制试验和冷启动试验, 同时对于环境场的预报也有改善.

Can machine learning correct microwave humidity radiances for the influence of clouds?

Abstract. A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.

Retrieval of Oceanic Total Precipitable Water Vapor and Cloud Liquid Water from Fengyun-3D Microwave Sounding Instruments

Fengyun-3D (FY-3D) satellite is the latest polar-orbiting meteorological satellite launched by China and carries 10 instruments onboard. Its microwave temperature sounder (MWTS) and microwave humidity sounder (MWHS) can acquire a total of 28 channels of brightness temperatures, providing rich information for profiling atmospheric temperature and moisture. However, due to a lack of two important frequencies at 23.8 and 31.4 GHz, it is difficult to retrieve the total precipitable water vapor (TPW) and cloud liquid water path (CLW) from FY-3D microwave sounder data as commonly done for other microwave sounding instruments. Using the channel similarity between Suomi National Polar-orbiting Partnership (NPP) advanced technology microwave sounder (ATMS) and FY-3D microwave sounding instruments, a machine learning (ML) technique is used to generate the two missing low-frequency channels of MWTS and MWHS. Then, a new dataset named as combined microwave sounder (CMWS) is obtained, which has the same channel setting as ATMS but the spatial resolution is consistent with MWTS. A statistical inversion method is adopted to retrieve TPW and CLW over oceans from the FY-3D CMWS. The intercomparison between different satellites shows that the inversion products of FY-3D CMWS and Suomi NPP ATMS have good consistency in magnitude and distribution. The correlation coefficients of retrieved TPW and CLW between CMWS and ATMS can reach 0.95 and 0.85, respectively.