Advanced Microwave Sounding Unit-A Research Articles

Comparison of tropical cyclone thermal structures derived from ATMS and synthetic AMSU-A/MHS

The warm core of a tropical cyclone (TC) reflects its intensity and evolution. In the past, microwave sounding data have been widely used for detecting the TC warm core. However, the observed magnitude of the TC warm core can vary with instruments. This study utilizes the oversampling data from the Advanced Technology Microwave Sounder (ATMS) to reconstruct the brightness temperature data consistent with the data from Advanced Microwave Sounding Unit-A (AMSU-A), which is referred to as AMSU-like data. Through comparisons, it is found that brightness temperature from the original ATMS has more detailed structures, depicting well the TC eye region and cloud bands. Both ATMS and AMSU-like data are used as inputs to the Global Scene-Dependent Atmospheric Retrieval Testbed (GSDART) to retrieve the warm cores of multiple TCs. At 250 hPa, the warm-core intensity retrieved using ATMS data is about 1–2 K higher than that from AMSU-like data and the warm-core structure from ATMS is more detailed. It is recommended that the warm-core intensity retrieved from AMSU-A be corrected to the ATMS values for multi-sensor monitoring of TCs using our newly defined relationship.摘要热带气旋(TC)的暖心反映了其强度和变化. 微波探测数据被广泛用于探测TC暖心, 但观测到的 TC 暖心强度可能因仪器而异. 本研究利用先进技术微波探测仪 (ATMS) 的过采样数据重采样至与先进微波探测仪 (AMSU-A) 一致的亮温, 称为类-AMSU数据. 通过对比发现, ATMS的观测更加细致, 较好地刻画了TC眼区和云带. 使用ATMS和类-AMSU数据反演多个TC的暖心发现, 在250 hPa, 使用ATMS数据反演的暖心强度比类-AMSU高约1–2K, 并且其暖心结构更详细.

Primary Impact Evaluation of Surface Temperature Observations for Microwave Temperature Sounding Data Assimilation over Land

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land.

Estimation of AMSU-A and MHS Antenna Emission from MetOp-A End-of-Life Deep Space View Test

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.

Evaluation of observation impact on the meteorological forecasts associated with heat wave in 2018 over East Asia using observing system experiments

Heat waves are meteorological disasters that inflict damage on public health and societal systems over extensive areas. The frequency and intensity of heat waves are increasing in many regions worldwide. However, insufficient research has been conducted to reduce forecast errors for heat waves in terms of short-range predictions. In this study, observing system experiments were performed using the Weather Research and Forecasting model and a three-dimensional variational data assimilation (DA), and the effects of observations used for DA on forecast errors for meteorological variables (i.e., upper atmospheric geopotential height, upper temperature, upper wind, and near-surface 2-m temperature) associated with heat wave were analyzed. As the forecast time increased, the 200 and 500 hPa geopotential heights in East Asia and the 2-m temperatures around Korea, Japan, and eastern China tended to be underestimated. All observations used for DA reduced the forecast errors for the meteorological variables associated with heat wave. Upper atmospheric observations (i.e., Advanced Microwave Sounding Unit-A (AMSU-A), aircraft, atmospheric motion vector, and radiosonde) played a more significant role in reducing forecast errors than near-surface observations. Among the upper atmospheric observations, AMSU-A had the greatest impact on reducing underestimation in forecasts for 200 and 500 hPa geopotential heights and the second greatest impact on 2-m temperature. The contraction of Tibetan high area at 200 hPa and Northern Pacific high area at 500 hPa in the experiments without using specific observations, compared to the analysis from the experiment using all observations, caused the deflection of the upper winds clockwise through the geostrophic relationship. Radiosonde observations had the greatest impact on reducing forecast errors of 2-m temperatures. Therefore, upper atmospheric observations are important for reducing errors in heat wave simulations in East Asia. The results of this study could help in designing an optimal observing system that reduces heat wave forecast errors in East Asia.

Real-time GNSS tropospheric parameter prediction of extreme rainfall events in China based on WRF multi-source data assimilation

In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m.

Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)

Abstract. To improve the initial condition (“analysis”) for numerical weather prediction, we attempt to assimilate observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the low-Earth-orbiting satellites. The data assimilation system, used in this study, consists of the Data Assimilation Research Testbed (DART) and the Community Earth System Model as the global forecast model. Based on the ensemble Kalman filter scheme, DART supports the radiative transfer model that is used to simulate the satellite radiances from the model state. To make the AMSU-A data available to be assimilated in DART, preprocessing modules are developed, which consist of quality control, spatial thinning, and bias correction processes. In the quality control, two sub-processes are included, outlier test and channel selection, depending on the cloud condition and surface type. The bias correction process is divided into scan-bias correction and air-mass-bias correction. Like input data used in DART, the observation errors are also estimated for the AMSU-A channels. In the trial experiments, a positive analysis impact is obtained by assimilating the AMSU-A observations on top of the DART data assimilation system that already makes use of the conventional measurements. In particular, the analysis errors are significantly reduced in the whole troposphere and lower stratosphere over the Northern Hemisphere. Overall, this study demonstrates a positive impact on the analysis when the AMSU-A observations are assimilated in the DART assimilation system.

Estimation of AMSU-A Radiance Observation Impacts in an LETKF-Based Atmospheric Global Data Assimilation System: Comparison with EFSO and Observing System Experiments

Abstract This work assesses the contribution of assimilating AMSU-A satellite-based radiance measurements to a global data assimilation system based on an atmospheric general circulation model and the local ensemble transform Kalman filter (LETKF). The radiance measurements were from three channels that are sensitive to the upper troposphere and lower stratosphere. The contribution of these measurements, or AMSU-A observation impact, was estimated both through ensemble-based forecast sensitivity to observations (EFSO) and observing system experiments (OSEs). Two streams of data-denial experiments for the AMSU-A observations were performed for about one month during winter in each hemisphere. The OSEs quantified the accumulated observation impact by cycling (repeating) data denials: including AMSU-A observations reduced the total observation impact for all observations of each data assimilation cycle. In contrast, EFSO estimated AMSU-A to increase the total observation impact. The opposing effects were attributed to the accumulated observation impact in the OSEs; the accumulation could stabilize the data assimilation cycles. In both experiments, the accumulated observation impact of AMSU-A was strongest in the upper troposphere, particularly in the austral midlatitudes where westerly jets exist and observations of other types are sparse. EFSO also assessed AMSU-A to have the most beneficial observation impact in similar locations. The AMSU-A observation impact tended to accumulate just downstream of where EFSO estimated the beneficial observation impact signals. The accumulated AMSU-A observation impact was tied to dynamic processes in the upper-tropospheric and general stratospheric circulation. Therefore, EFSO helps estimate the beneficial distributions of AMSU-A accumulated observation impact by considering their dynamical propagation. Significance Statement The Advanced Microwave Sounding Unit-A (AMSU-A) satellite radiance assimilation technique was successfully integrated into the Atmospheric General Circulation Model for the Earth Simulator (AFES)–LETKF data assimilation system. We conducted OSEs and used EFSO to assess the AMSU-A observation impact. The two estimation methods identified opposite observation impacts due to the cycling (repeating) OSEs of the AMSU-A observations. We interpreted the causes of the opposite estimations. However, even for the cycling OSEs, EFSO appeared to help estimate distributions of the accumulated observation impact. It is important to consider the dynamical propagation of accumulated observation impact in general circulation.

A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1

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.

Mid‐Tropospheric Layer Temperature Record Derived From Satellite Microwave Sounder Observations With Backward Merging Approach

AbstractWe present a new version (v5.0) of the NOAA Center for Satellite Applications and Research (STAR) mid‐tropospheric temperature (TMT) time series. This data set uses a backward‐merging approach to intercalibrate 16 satellite‐based microwave sounding records. The instrument observations included those from the Microwave Sounding Unit (MSU) during 1979–2004, Advanced Microwave Sounding Unit‐A (AMSU‐A) during 1998–2017, and Advanced Technology Microwave Sounder (ATMS) from 2011 to present. A TMT time series during 2002–present based on satellite microwave observations in stable sun‐synchronous orbits was used as a reference in the backward merging process in which earlier satellites were adjusted and merged to the reference. Observations from earlier satellites were recalibrated to remove their calibration drifting errors relative to the reference using sequential overlapping observations. This included removal of spurious warming drifts in the MSU observations onboard NOAA‐11, NOAA‐12, and NOAA‐14 and a spurious cooling drift in the NOAA‐15 AMSU‐A observations. Temperature changes resulting from diurnal sampling drifts were corrected using an observation‐based semi‐physical model developed in this study. Other adjustments included channel frequency differences between MSU and AMSU‐A companion channels and instrument blackbody warm target effect on observed radiances. These adjustments resulted in inter‐consistent TMT records spanning MSU, AMSU‐A, and ATMS. The merged time series produced a global mean TMT trend of 0.092 ± 0.043 K/decade during 1979–2021 and a total tropospheric trend of 0.142 ± 0.045 K/decade after removal of a stratospheric cooling effect in TMT. Remarkably, the total tropospheric trends during the latest half period were nearly doubled the earlier half period over the global ocean.

Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting

Abstract To better understand the assimilation contribution and influence mechanism of different satellite platforms and different microwave instruments, the radiance data of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) onboard NOAA-15 and NOAA-18 are assimilated to investigate various assimilation effects on the prediction of path and intensity of Typhoon Megi (2016) based on Weather Research and Forecasting (WRF) three-dimensional variation and the WRF model. The community radiative transfer model is employed as the forward operator. The quality control and bias correction procedures before the radiance data assimilation (DA) are performed to improve the simulations. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016)’s path and intensity is investigated by six experiments (without and with AMSU-A and MHS DA) with initial conditions at 1200 UTC on 25 September 2016 and 60 h forecast integration. The results are compared to the observational data from the China Meteorological Administration tropical cyclone database. The impact mechanisms of DA adjustments to the initial fields and assimilation increment analysis of each physical quantity field are investigated in detail. The findings show that NOAA-15 AMSU-A assimilation produces the best output over the course of the 60 h simulation, demonstrating that assimilation satellite data from multiple platforms is not always better than assimilation satellite data from a single platform. In comparison, MHS assimilation has a favorable effect on short-term path and strength forecasts, but has a negative impact on long-term forecasts. The effect of MHS DA needs to be further investigated.

Direct Assimilation of Chinese FY-3E Microwave Temperature Sounder-3 Radiances in the CMA-GFS: An Initial Study

FengYun-3E (FY-3E), the fifth satellite in China’s second-generation polar-orbiting satellite FY-3 series, was launched on 5 July 2021. FY-3E carries a third-generation microwave temperature sounder (MWTS-3). For the first time, this study demonstrates that MWTS-3 radiances data assimilation can improve the China Meteorological Administration Global Forecast System (CMA-GFS). By establishing a cloud detection module based on the retrieval results of the new channels of MWTS-3, a quality control module according to the error characteristics of MWTS-3 data, and a bias correction module considering the scanning position of satellite and weather systems, the effective assimilation of MWTS-3 data in the CMA-GFS has been realized. Through one-month cycling experiments of assimilation and forecasts, the error characteristics and assimilation effects of MWTS-3 data are carefully evaluated. The results show that the observation errors in MWTS-3 data are similar to those in advanced technology microwave sounder (ATMS) data within the same frequency channel, are slightly larger than those in the advanced microwave-sounding unit-A (AMSU-A) data, and are much better than those in the MWTS-2 data. The validation of the assimilation and prediction results demonstrate the positive contribution of MWTS-3 data assimilation, which can remarkably reduce the analysis errors in the Northern and Southern Hemispheres. Specifically, the error growth on the upper layer of the model is obviously suppressed. When all other operational satellite observations are included, the assimilation of MWTS-3 data has a neutral or slightly positive contribution to the analysis and forecast results, and the improvement is mainly found in the Southern Hemisphere. The relevant evaluation results indicate that the MWTS-3 data assimilation has good application prospects for operation.

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation

Abstract. On 24 September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI) was publicly released for community use. Operating directly on the native MPAS unstructured mesh, JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble–variational (EnVar) schemes as well as the ensemble of DA (EDA) technique. On the observation side, one advanced feature in JEDI-MPAS is the full all-sky approach for satellite radiance DA with the introduction of hydrometeor analysis variables. This paper describes the formulation and implementation of EnVar for JEDI-MPAS. JEDI-MPAS 1.0.0 is evaluated with month-long cycling 3DEnVar experiments with a global 30–60 km dual-resolution configuration. The robustness and credible performance of JEDI-MPAS are demonstrated by establishing a benchmark non-radiance DA experiment, then incrementally adding microwave radiances from three sources: Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and Microwave Humidity Sounder (MHS) water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with a substantial and significant positive impact obtained for almost all aspects of forecast verification when progressively adding more microwave radiance data. In particular, the day 5 forecast of the best-performing JEDI-MPAS experiment yields an anomaly correlation coefficient (ACC) of 0.8 for 500 hPa geopotential height, a gap of roughly a half day when compared to cold-start forecasts initialized from operational analyses of the National Centers for Environmental Prediction, whose ACC does not drop to 0.8 until a lead time of 5.5 d. This indicates JEDI-MPAS's great potential for both research and operations.

Impacts of Multi-Source Microwave Satellite Radiance Data Assimilation on the Forecast of Typhoon Ampil

This study investigates the impacts of the joint assimilation of microware temperature sensor, Advanced Microwave Sounding Unit-A (AMSUA), and microware humidity sensors, Microwave Humidity Sounder (MHS) and Microwave Humidity Sounder-2 (MWHS2), on the analyses and forecasts for the tropical cyclone (TC) system. Experiments are conducted using a three-dimensional variation (3DVAR) algorithm in the framework of the weather research and forecasting data assimilation (WRFDA) system for the forecasting of Typhoon Ampil (2018). The results show that the assimilation of MWHS2 radiance data improves the analyses better in terms of TC’s structure and moisture conditions than those of the MHS experiment. To some extent, the experiment with only AMSUA radiance delivers some positive impacts of the precipitation, track, and intensity forecast than the other two experiments do. In addition, the skill of the precipitation forecast is notably enhanced with higher equitable threat score (ETS) by the simultaneous assimilation of the MHS, MWHS2, and AMSUA radiance. Generally, assimilation of radiance from all sources of MHS, MWHS2, and AMSUA could combine the advantages of assimilating each type of sensors rather than individually. The consistent improvement is also confirmed for the TC’s track forecast with reduced error on average, whereas the improvement of intensity forecast is not obvious.

Effects of Direct Assimilation of FY-4A AGRI Water Vapor Channels on the Meiyu Heavy-Rainfall Quantitative Precipitation Forecasts

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

Accounting for Lambertian reflection in the assimilation of microwave sounding radiances over snow and sea‐ice

AbstractThis article investigates two aspects aimed at an increased use of surface‐sensitive microwave sounding radiances for numerical weather prediction (NWP), namely the assimilation of humidity‐sounding radiances from the Advanced Technology Microwave Sounder (ATMS) over snow, as well as the use of Lambertian reflection in radiative‐transfer modelling over snow and sea‐ice surfaces for temperature‐ and humidity‐sounding radiances. The Lambertian characteristics are modelled through a parameterisation of the effective zenith angle used to calculate the downwelling radiation in the radiative‐transfer model. Using Lambertian rather than specular reflection improves the forward modelling significantly for 183‐GHz humidity‐sounding channels over snow and sea‐ice in comparisons with observations, including a reduction of significant zenith‐angle‐dependent biases. For temperature‐sounding channels in the 50‐GHz band, comparisons between observations and background equivalents also suggest improvements from treating snow and sea‐ice surfaces as Lambertian or semi‐Lambertian, but the characteristics appear more variable and there are indications of other sources of error, such as biases in the skin temperature. For both spectral regions, the improvement from using nonspecular reflection is clearest for snow‐covered land surfaces. Assimilation experiments show that the addition of 183‐GHz humidity‐sounding channels from ATMS over snow‐covered land leads to a slight positive impact over higher latitudes in winter. On top of this, replacing the specular surface assumption over snow and sea‐ice with a fully Lambertian one for 183‐GHz channels on ATMS and semi‐Lambertian one for 50‐GHz channels on ATMS and the Advanced Microwave Sounding Unit–A (AMSU‐A) means that more observations pass quality control, giving a neutral to slightly positive impact at higher latitudes in winter. Combining the two changes leads to small forecast benefits that are statistically significant up to day 3 at higher latitudes.

Development and Evaluation of AMSU-A Cloud Detection over the Tibetan Plateau

Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) data have been widely assimilated in operational forecasting systems. However, effective distinction between cloudy and clear-sky data is still an essential prerequisite for the assimilation of microwave observations. Cloud detection over the Tibetan Plateau has long been a challenge owing to the influence of low temperatures, terrain height, surface vegetation, and inaccurate background fields. Based on the variations in the response characteristics of different channels of AMSU-A to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) are chosen to establish a cloud detection index. Combined with the existing MHS cloud detection index, a cloud detection scheme over the Tibetan Plateau is proposed. Referring to VISSR-II (Stretched Visible and Infrared Spin Scan Radiometer-II) and CALIPSO (The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) cloud classification products, the detection rate of cloudy data and the rejection rate of clear-sky data under different cloud index thresholds are evaluated. Results show that the new cloud detection scheme can identify more than 80% of cloudy data on average, but this decreases to 72% for area with terrain higher than 5 km, and the false deletion rate remains stable at 45%. The detection rates of mixed clouds and cumulonimbus are higher than 90%, but it is lower than 50% for altostratus with an altitude of about 7–8 km. Comparative analysis shows that the new method is more suitable for areas with terrain higher than 700 m. Based on the cloud detection results, the effects of terrain height on the characteristics of observation error and bias are also discussed for AMSU-A channels 5 and 6.

Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard

Although the Polar Weather Research and Forecasting (PWRF) model has been developed for polar environments, simulations of atmospheric states in the polar region still have large uncertainties. Therefore, the effects of data assimilation (DA) to improve forecasts in the polar region were investigated for September 2017 using PWRF and the three-dimensional variational (3DVAR) DA method. The experiments without DA and those with DA assimilating only conventional observations and both conventional observations and satellite radiance data were performed. The forecasts from all experiments both without and with DA underestimated (overestimated) the downward longwave (shortwave) radiation flux due to the underestimation of the amount of Arctic clouds. When satellite radiance data (i.e., the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS)) were assimilated in addition to conventional observations in the PWRF, the distribution and amount of water vapor became closer to observations, which improves cloud liquid water forecasts. Therefore, when both conventional observations and radiance data were assimilated, the 25–30 h forecast errors of the downward longwave and shortwave radiation fluxes and sensible and latent heat fluxes decreased by 12.7%, 8.1%, 3.2%, and 7.8% with the WSM5 scheme and by 17.1%, 4.7%, 2.5%, and 3.1% with the 2-moment Morrison scheme, respectively, compared to those in the experiments without DA. The forecast errors of the 10 m wind and 2 m temperature with DA were smaller than those without DA at most observation stations. Therefore, the uncertainties of the Arctic forecasts in the PWRF decreased when using DA. To further improve Arctic forecasts, the assimilation of various additional satellite data is necessary.

Northeast China Cold Vortex Observed by FY‐3 MWTS‐2 and MetOp AMSU‐A

AbstractThe northeast China cold vortex (NCCV) refers to a closed low‐pressure system over northeast China extending from China‐Siberia to the northwest Pacific coast region. It is cut off from the westerly jet belt in the middle and upper troposphere, has a cold core, lasts several days, and could bring heavy rain events in late spring and summer that are of great challenge to weather forecasters. This study investigates the possibility of observing temperature structures of NCCVs by two microwave temperature sounders, that is, the Microwave Temperature Sounder‐2 (MWTS‐2) and the Advanced Microwave Sounding Unit‐A (AMSU‐A) onboard the two polar‐orbiting operational environmental satellites (POES) Fenyun‐3 satellite FY‐3D and Meteorological Operational satellite MetOp‐B, respectively. The MWTS‐2 and AMSU‐A together provide 3D atmospheric temperatures in the middle and upper troposphere, as well as the lower stratosphere four times daily. Both limb‐corrected direct observations of brightness temperature and retrievals of atmospheric temperature within NCCVs are characterized by cold and warm cores below and above the tropopause, respectively, comparing favorably with the European Centre for Medium‐Range Weather Forecasts ERA5 reanalysis. Compared with the ERA5 reanalysis in June 2019, the MWTS‐2 retrieved atmospheric temperatures have no scan‐dependent biases, the biases are smaller than ±0.2K, and root‐mean‐square errors (RMSEs) are smaller than and 2.0K at all altitudes. This study suggests the possibility of using multiple POES microwave‐temperature‐sounding observations for near‐real‐time monitoring of NCCVs' 3D temperature structures.

Toward All-Sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System

Abstract The all-sky assimilation of radiances from microwave instruments is developed in the 4D-EnVar analysis system at Environment and Climate Change Canada (ECCC). Assimilation of cloud-affected radiances from Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels 4 and 5 for non-precipitating scenes over the ocean surface is the focus of this study. Cloud-affected radiances are discarded in the ECCC operational data assimilation system due to the limitations of forecast model physics, radiative transfer models, and the strong nonlinearity of the observation operator. In addition to using symmetric estimate of innovation standard deviation for quality control, a state-dependent observation error inflation is employed at the analysis stage. The background-state clouds are scaled by a factor of 0.5 to compensate for a systematic overestimation by the forecast model before being used in the observation operator. The changes in the fit of the background state to observations show mixed results. The number of AMSU-A channels 4 and 5 assimilated observations in the all-sky experiment is 5%–12% higher than in the operational system. The all-sky approach improves temperature analysis when verified against ECMWF operational analysis in the areas where the extra cloud-affected observations were assimilated. Statistically significant reductions in error standard deviation by 1%–4% for the analysis and forecasts of temperature, specific humidity, and horizontal wind speed up to maximum 4 days were achieved in the all-sky experiment in the lower troposphere. These improvements result mainly from the use of cloud information for computing the observation-minus-background departures. The operational implementation of all-sky assimilation is planned for the fall of 2021.

A New Methodology on Noise Equivalent Differential Temperature Calculation for On-Orbit Advanced Microwave Sounding Unit-A Instrument

A few deficiencies remain in the current National Oceanic and Atmospheric Administration (NOAA) Integrated Calibration/Validation System (ICVS) noise calculation method for characterizing noise equivalent differential temperature <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(NE\Delta T)$ </tex-math></inline-formula> performance of on-orbit Advanced Microwave Sounding Unit-A (AMSU-A) instruments. This ICVS method only accounts for the noise contribution resulting from one calibration parameter (warm count). The calculation is also dependent upon an assumption that the calibration gain equals to the sensitivity of warm count to temperature. In addition, the dependence of scene temperature is not considered. This study establishes a new methodology by accounting for the noise components resulting from all calibration parameters such as warm counts, warm target temperatures, space view (cold) counts, and their covariance. Each noise component is computed using the product of the overlapping Allan deviation of corresponding parameter and the sensitivity of Earth scene temperature to the parameter. The new method also comprises the variation of scene temperature via scene count in noise estimation. For the AMSU-A instruments aboard the NOAA-18 to NOAA-19 and Metop-A to Metop-C satellites, the magnitudes of the orbital average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$NE\Delta T$ </tex-math></inline-formula> calculated using the ICVS method exceed those from the new method by approximately 8%–38%, corresponding to a range from 0.02 to 0.07 K. Particularly, the deviations at the upper temperature sounding channels from 10 to 14 are around 0.05 K. The magnitude of AMSU-A instrument noise can vary by around 18% for sounding channels and 40% for window channels due to scene temperature change. Therefore, the new method demonstrates its significant improvements in characterizing on-orbit AMSU-A instrument noise more accurately and comprehensively.