Community Radiative Transfer Model Research Articles

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026.

Satellite Observation for Evaluating Cloud Properties of the Microphysical Schemes in Weather Research and Forecasting Simulation: A Case Study of the Mei-Yu Front Precipitation System

Radiative transfer model can be used to convert the geophysical variables (e.g., atmospheric thermodynamic state) to the radiation field. In this study, the Community Radiative Transfer Model (CRTM) is used to connect regional Weather Research and Forecasting (WRF) model outputs and satellite observations. A heavy rainfall event caused by the Mei-Yu front on the June 1, 2017, in the vicinity of Taiwan, was chosen as a case study. The simulated cloud performance of WRF with four microphysics schemes (i.e., Goddard (GCE), WRF single-moment 6 class (WSM), WRF double-moment 6 class (WDM), and Morrison (MOR) schemes) was investigated objectively using multichannel observed satellite radiances from a Japanese geostationary satellite Himawari-8. The results over the East Asia domain (9 km) illustrate that all four microphysics schemes overestimate cloudy pixels, in particular, the high cloud of simulation with MOR when comparing with satellite data. Sensitivity tests reveal that the excess condensation of ice at ≥14 km with MOR might be associated with the overestimated high cloud cover. However, GCE displayed an improved performance on water vapor channel in clear skies. When focusing on Taiwan using a higher (3 km) model resolution, each scheme displayed a decent performance on cloudy pixels. In the grid-by-grid skill score analysis, the distribution of high clouds was the most accurate among the three cloud types. The results also suggested that all schemes required a longer simulation time to describe the low cloud horizontal extend.

Improving the Understanding of CrIS Full Spectral Resolution Nonlocal Thermodynamic Equilibrium Radiances Using Spectral Correlation

AbstractIn recent years, significant progress has been made in fast radiative transfer model (RTM) simulation of daytime nonlocal thermodynamic equilibrium (NLTE) emission. However, NLTE remains as one important reason that prevents the hyperspectral shortwave infrared (SWIR) radiance observations from being assimilated into numerical weather prediction (NWP) models. To better understand the limitations of existing RTM‐based NLTE simulation, this study introduces a new statistical method, called Spectral Correlations to Estimate Non‐Local Thermal Equilibrium (SCENTE), to estimate the NLTE radiances in the Cross‐track Infrared Sounder (CrIS) SWIR radiance observations. SCENTE is applied to four typical season days, including spring equinox, summer solstice, fall equinox, and winter solstice. By analyzing calculation/background minus observation (BMO) of CrIS SWIR brightness temperature (BT), results show that SCENTE characterizes the NLTE well with standard deviation of differences (STD) comparable to observation noise for both daytime and nighttime, while the community RTM (CRTM) has substantially larger STD at night, mainly due to the lack of daytime NLTE just beyond the day/night terminator and the lack of aurora‐related NLTE. Detailed investigation of the biases of BMO shows that CRTM underestimates daytime SWIR NLTE effects by 0.76 K, while SCENTE overestimates SWIR NLTE effects by 0.70 K. The overestimation is because SCENTE uses CRTM‐simulated SWIR local thermodynamic equilibrium (LTE) radiances in the training, which is underestimated by 0.70 K in BT. SCENTE, complementary to RTM‐based simulations, can be used for quality control of SWIR radiances for assimilation and retrieval of atmospheric soundings.

Initial Radiance Validation of the Microsized Microwave Atmospheric Satellite-2A

The Microsized Microwave Atmospheric Satellite (MicroMAS-2A) is a 3U CubeSat that was launched in January 2018 as a technology demonstration for future microwave sounding constellation missions, such as the NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission now in development. MicroMAS-2A has a miniaturized 1U ten-channel passive microwave radiometer with channels near 90, 118, 183, and 206 GHz for moisture and temperature profiling and precipitation imaging [4]. MicroMAS-2A provided the first CubeSat atmospheric vertical sounding data from orbit, and to date it is the only CubeSat to provide temperature and moisture sounding and surface imaging. In this article, we analyze six segments of data collected from MicroMAS-2A in April 2018 and compare them to ERA5 reanalysis fields coupled with the Community Radiative Transfer Model (CRTM). This initial assessment of CubeSat radiometric accuracy shows biases relative to ERA5 with magnitudes ranging from 0.4 to 2.2 K (with standard deviations ranging from 0.7 to 1.2 K) for the four mid-tropospheric temperature channels and biases of 2.2 and 2.8 K (standard deviations 1.8 and 2.6 K) for the two lower tropospheric water vapor channels.

Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS

Abstract Motivated by the use of the GFDL microphysics scheme in the Finite-Volume Cubed-Sphere Dynamical Core Global Forecast System (FV3GFS), the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow, and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Subgrid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500-hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the tropics.

Comparison of Assimilating All-Sky and Clear-Sky Satellite Radiance for Typhoon Chan-Hom and Nangka Forecasts

The impacts of assimilating all-sky satellite radiance from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on typhoon Chan-hom and Nangka are evaluated over traditional clear-sky radiance assimilation. Results show that more AMSR2 radiance data around typhoon core area are assimilated in all-sky experiment than clear-sky, which improves the utilization of satellite radiance data. Community Radiative Transfer Model (CRTM) brightness temperature simulation under all-sky conditions is in better agreement with observations than in the case of clear-sky conditions. In a cycle assimilation experiment, all-sky assimilation reduces typhoon track forecast errors by 14.84%, and intensity errors by approximately 16.89%. Wind, temperature and humidity analysis are clearly improved in all-sky assimilation, as evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data. All-sky assimilation better captures the structures of typhoons, with a stronger warm core and tighter circulation around the typhoon eye. This study explores the contributions to the improvements in all-sky assimilation. These improvements are attributed to the enhancements in initial geopotential height, temperature and moisture in the typhoon core areas. Moreover, assimilating cloud- and precipitation-affected radiance data improves hydrometer simulations, which leads to higher hydrometeor concentrations than clear-sky radiance and conventional data assimilation. The results demonstrate that assimilation of all-sky AMSR2 data improves the analysis and forecast of multiple typhoons.

An accurate and efficient radiative transfer model for simulating all-sky images from Fengyun satellite radiometers

Forward radiative transfer models (RTM) are an indispensable tool for quantitative applications of satellite radiometers, e.g., for data calibration, instrument development, retrieval, and so on. In this study, we develop an accurate and efficient RTM for radiometers onboard Fengyun satellites, namely FYRTM (RTM for Fengyun Radiometers). Correlated k-distribution models are developed to improve the computational efficiency for gas absorption, and the effects of cloud and aerosol multiple scattering and emission are accelerated with pre-computed look-up tables. FYRTM is evaluated with a rigorous simulation based on discrete ordinate radiative transfer model (DISORT) as well as a popular fast forward model, i.e., the Community Radiative Transfer Model (CRTM). Results indicate that FYRTM-based simulations are two to three orders of magnitudes faster than the DISORT-based simulations. Compared to the rigorous model, FYRTM relative errors are within 2% at solar channels, and brightness temperatures (BT) differences are within 1 K at infrared channels. Compared with CRTM, FYRTM is computationally similar at solar channels, but three times faster at infrared channels. Furthermore, simulated reflectances/BTs using FYRTM are in a good agreement with the satellite observations. Overall, FYRTM is capable to simulate satellite observations under different atmospheric conditions, and can be extended to other radiometers onboard the Fengyun satellites (both geostationary and polar-orbiting satellites). It is expected to play important roles in future applications with Fengyun observations.

Evaluation of the in-orbit performance of the microwave temperature sounder onboard the FY-3D satellite using different radiative transfer models

The in-orbit performance of the Microwave Temperature Sounder (MWTS) onboard the Fengyun-3D (FY-3D) satellite is evaluated using the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for the Television Infrared Observation Satellite Operational Vertical Sounder (RTTOV) model. Both radio-occultation data from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Data Analysis and Archive Center and ERA-Interim analysis data from the European centre for Medium-Range Weather Forecasts are taken as inputs to the two fast radiative transfer models. The radio-occultation data are quality controlled and the collocation criterion between the radio-occultation data and the MWTS measurements is defined such that the spatial and temporal difference is <50 km and <3 h, respectively. The biases of MWTS channels 5–10 produced by both CRTM and RTTOV agree well over the oceans under clear sky conditions between 60° S and 60° N from July to December 2018. The mean biases simulated by the radio-occultation and ERA-Interim data are negative and the absolute values of the biases are <0.6 and <1.5 K for channels 5–10 of the MWTS, respectively. The biases at channels 4 and 11 between the CRTM and RTTOV simulations are inconsistent and require further investigation of the transmittance difference between the fast models. The standard deviation of the biases from the radio-occultation and ERA-Interim data are <0.5 and <1.8 K for the limited amount of collocated radio-occultation data. Asymmetrical patterns are detected for the MWTS through the scan angle-dependent bias. The long-term mean MWTS bias shows only a weak dependence on latitude, which suggests that biases do not vary systematically with brightness temperature.

Multisource Assessments of the FengYun-3D Microwave Humidity Sounder (MWHS) On-Orbit Performance

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere.

STUDY ON THE ESTIMATION OF MICROWAVE LAND SURFACE EMISSIVITY OVER EAST ASIA

Estimation of the land surface emissivity is crucial to evaluate the boundary conditions of atmospheric modeling data assimilation. Brightness temperatures observed by satellite passive microwave radiometer give information on emission forma raindrops and scattering by frozen particles. By applying radiative transfer model of the atmosphere, we can estimate the atmospheric hydrological process (i. e., rainfall, snowfall, water vapor, cloud water, etc.). However it is difficult to estimate microwave land surface emissivity at higher frequencies over land. Because land use, land cover and surface wetness over land are various and heterogeneous by comparing with over ocean. Therefore, we aim to estimate the microwave land surface emissivity over East Asia by using observed data and numerical models. Firstly, we investigate the characteristics of land surface emissivity derived from SSM/I, AMSR-E and AMSR2 over Asia and the relationship between land surface emissivity and land hydrological variables based on satellite data sets and numerical models. Secondary, we estimate the land surface emissivity by applying the Community Microwave Emission Model (CMEM) and Community Radiative Transfer Model (CRTM).Simulation results shows the estimated LSE is overestimated and its variability is large by comparing the observed data.

Performance of Radiative Transfer Models in the Microwave Region

AbstractWe compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.

NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values.

Impacts of cloud scattering properties on FY-3D HIRAS simulations

Cloudy sky spectral radiance at the top of the atmosphere has always been an important while difficult variable to simulate for fast radiative transfer models. In this paper, we focus on examining the impacts of cloud scattering properties on the spectral radiance signature of the High-spectral Resolution Infrared Atmospheric Sounder (HIRAS) onboard the Fengyun-3D (FY-3D) satellite by using the Advanced Radiative transfer Modeling System (ARMS) and the Community Radiative Transfer Model (CRTM). Cloud scattering properties used in the radiative transfer models are critical for modeling the spectral radiance under cloudy sky, which involves choices of appropriate cloud particle models and particle size distributions, etc. Multiple FY-3D HIRAS observations over Southern China and Southeast Asia with ice or liquid water cloud cover on 6 May 2018 are examined, respectively. Vertical atmospheric profiles are derived from the Modern-Era Retrospective analysis for Research and Applications, Version 2 reanalysis product. Cloud property retrievals from the Moderate Resolution Imaging Spectroradiometer are used. Cloud scattering property parameterization schemes based on spherical and nonspherical cloud particle shapes are implemented for liquid water and ice clouds in ARMS and CRTM, respectively. Results show that both ARMS and CRTM can well simulate the radiance at the HIRAS spectral ranges under liquid water cloud condition as compared with the HIRAS observation with mean absolute error (MAE) of brightness temperature of less than 1 K. However, for ice cloud conditions, ARMS model using assumed spherical ice properties exhibits large biases between simulation and observation. CRTM with nonspherical ice properties using 16-stream approximation shows MAE less than 1 K and MAE of about 1 K using 2-stream approximation.

A Precipitation Detection Method for MWTS-II Radiance Assimilation in Typhoon Simulation

FY-3C Microwave Temperature Sounder Ⅱ (MWTS-Ⅱ) lacks observations at 23.8 GHz, 31 GHz and 89 GHz, making it difficult to remove the data contaminated by precipitation in assimilation. In this paper, a fast forward operator based on the Community Radiative Transfer Model (CRTM) was used to analyze the relationship between the observation minus background simulation (O-B) and the cloud fractions in different MWTS-Ⅱ channels. In addition, based on the community Gridpoint Statistical Interpolation (GSI) system, the radiation brightness temperature of the MWTS-Ⅱ was assimilated in the regional Numerical Weather Prediction (NWP) model. In the process of assimilation, Visible and Infrared Radiometer (VIRR) cloud detection products were matched to MWTS-Ⅱ pixels for precipitation detection. For typhoon No. 18 in 2014, impact tests of MWTS-Ⅱ data assimilation was carried out. The results show that, though the bias observation minus analysis (O-A) of assimilated data can be reduced by quality control only with | O-B | &lt; 3K; however, the O-A becomes much smaller while the precipitation detection is performed with Fvirr &lt; 0.9 (VIRR cloud fraction threshold of 0.9). Besides, the change of the environmental field around the typhoon is more conducive to make the simulated track closer to the observation. The 72-hour typhoon track simulation error also shows that, after the precipitation detection, the error of simulated typhoon track is significantly reduced, which reflects the validity of a precipitation detection method based on a double criterion of | O-B | &lt; 3K and Fvirr &lt; 0.9.

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.

Toward Ensemble Assimilation of Hyperspectral Satellite Observations with Data Compression and Dimension Reduction Using Principal Component Analysis

AbstractSatellite-based hyperspectral radiometers usually have thousands of infrared channels that contain atmospheric state information with higher vertical resolution compared to observations from traditional sensors. However, the large numbers of channels can lead to computational burden in satellite data retrieval and assimilation. Furthermore, most of the channels are highly correlated and the pieces of independent information contained in the hyperspectral observations are usually much smaller than the number of channels. Principal component analysis (PCA) was used in this research to compress the observational information content contained in the Atmospheric Infrared Sounder (AIRS) channels to a few leading principal components (PCs). The corresponding PC scores were then assimilated into a PCA-based ensemble Kalman filter (EnKF) system. In this proof-of-concept study based on simulated observations, hyperspectral brightness temperatures were simulated using the atmospheric state vectors from convection-permitting ensemble simulations of Hurricane Harvey (2017) as input to the Community Radiative Transfer Model (CRTM). The PCs were derived from a preexisting training dataset of brightness temperatures calculated from convection-permitting simulation over a large domain in the Indian Ocean representing generic atmospheric conditions over tropical oceans. The EnKF increments from assimilating many individual measurements in the brightness temperature space were compared to the EnKF increments from assimilating significantly fewer numbers of leading PCs. Results showed that assimilating about 10–20 leading PCs could yield increments that were nearly indistinguishable to that from assimilating hyperspectral measurements from orders of magnitude larger number of hyperspectral channels.

Global analysis and forecast impact assessment of CubeSat MicroMAS-2 on numerical weather prediction

The Micro-Sized Microwave Atmospheric Satellite (MicroMAS-2) is a 12-channel passive microwave radiometer on a CubeSat developed by Massachusetts Institute of Technology Lincoln Laboratory. MicroMAS-2 observations (1) enhance the current observing system capabilities and (2) mitigate the potential loss of some existing observations. Observing System Simulation Experiments (OSSEs) quantitatively estimate the expected impact of adding the MicroMAS-2 to the current global operational analysis and forecast systems or to a data gap scenario. First, MicroMAS-2 data on one low Earth-orbiting platform were simulated by the Community Radiative Transfer Model from the NASA GEOS-5 Nature Run, which serves as the truth in the OSSE. The assimilation of the MicroMAS-2 observations into the research version of the January 2015 NOAA Global Data Assimilation System in addition to the current operational observation system and in addition to a data gap scenario shows improvement on both analysis and forecast performance skills.

Verification of Fengyun-3D MWTS and MWHS Calibration Accuracy Using GPS Radio Occultation Data

The newly launched Fengyun-3D (FY-3D) satellite carries microwave temperature sounder (MWTS) and microwave humidity sounder (MWHS), providing the global atmospheric temperature and humidity measurements. It is important to assess the in orbit performance of MWTS and MWHS and understand their calibration accuracy before using them in numerical weather prediction and many other applications such as hurricane monitoring. This study aims at quantifying the biases of MWTS and MWHS observations relative to the simulations from the collocated Global Positioning System (GPS) radio occultation (RO) data. Using the collocated FY-3C Global Navigation Satellite System Occultation Sounder (GNOS) RO data under clear-sky conditions as inputs to Community Radiative Transfer Model (CRTM), brightness temperatures and viewing angles are simulated for the upper level sounding channels of MWTS and MWHS. In order to obtain O − B statistics under clear sky conditions, a cloud detection algorithm is developed by using the two MWTS channels with frequencies at 50.3 and 51.76 GHz and the two MWHS channels with frequencies centered at 89 and 150 GHz. The analysis shows that for the upper air sounding channels, the mean biases of the MWTS observations relative to the GPS RO simulations are negative for channels 5–9, with absolute values < 1 K, and positive for channels 4 and 10, with values < 0.5 K. For the MWHS observations, the mean biases in brightness temperature are negative for channels 2–6, with absolute values < 2.6 K and relatively small standard deviations. The mean biases are also negative for channels 11–13, with absolute values < 1.3 K, but with relatively large standard deviations. The biases of both MWTS and MWHS show scan-angle dependence and are asymmetrical across the scan line. The biases for the upper air MWTS and MWHS sounding channels are larger than those previously derived for the Advanced Technology Microwave Sounder.

Assessment of the CubeSat Infrared Atmospheric Sounder impact on global numerical weather prediction using observational system simulation experiments

The CubeSat Infrared Atmospheric Sounder (CIRAS) is a proposed small satellite IR sounder developed in response to the challenges of high cost and possible data gaps for currently operational hyperspectral IR sounders, such as Atmospheric Infrared Sounder, Infrared Atmospheric Sounding Interferometer, and Crosstrack Infrared Sounder. A key objective of CIRAS is to demonstrate the technologies required for a low cost-to-orbit, operational IR sounder. CIRAS was designed by National Aeronautics and Space Administration’s Jet Propulsion Laboratory, with 625 channels measuring upwelling IR radiation of the Earth in the mid-wavelength IR spectrum region. Through observational system simulation experiments, the impact of assimilating CIRAS on global numerical weather prediction was assessed. CIRAS was simulated by applying the Community Radiative Transfer Model to profiles extracted from the Goddard Earth Observing System Model, Version 5 nature run for an afternoon polar orbiting sensor, and then assimilated by the National Centers for Environmental Prediction Global Data Assimilation System. Assimilating CIRAS improved global analysis and forecasts, when added to the currently operational observing configuration and to a data gap scenario.

Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO3− are the most significant. The correlation coefficient between PM2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m−3, which respectively compared to those without DA.