Community Radiative Transfer Model Research Articles

Exploring MERRA-2 global meteorological and aerosol reanalyses for improved SST retrieval

This study explores the use of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) global meteorological and aerosol reanalysis for improving the simulation of satellite sensor infrared brightness temperatures (BTs), and the retrieval of sea surface temperature (SST) in the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) system, with a particular focus on ACSPO long-term reprocessing efforts. Using MERRA-2 upper-air pressure, temperature, and humidity profiles, rather than the currently used NCEP Global Forecast System (GFS) real-time data, as input into the Community Radiative Transfer Model (CRTM), reduces the global clear-sky observation-minus-model (O–M) BT biases in the infrared bands centered at 3.7, 8.6, 11, and 12 μm of the Visible Infrared Imaging Radiometer Suite onboard Suomi-NPP. The improvements are largely due to more accurate water vapor (total amount and/or vertical distribution) at low latitudes in MERRA-2, which brings the modeled BTs closer to observations. Additional stand-alone simulations, performed using RTTOV model and MERRA-2 aerosol profiles, further reduce the ACSPO global O–M BT biases and the dependence of O–M BT biases on the dust aerosol optical depth. The potential skill of MERRA-2 aerosol reanalysis for reducing dust-caused regional biases in the ACSPO global regression SST product is also demonstrated. Preliminary results suggest that MERRA-2 is a viable alternative to NCEP GFS for ACSPO reprocessing efforts.

The Impact of Assimilation of GPM Microwave Imager Clear-Sky Radiance on Numerical Simulations of Hurricanes Joaquin (2015) and Matthew (2016) with the HWRF Model

The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.

Assimilation of MWHS and MWTS radiance data from the FY-3A satellite with the POD-3DEnVar method for forecasting heavy rainfall

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun-3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.

All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study

Abstract Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

Improvements to Cloud-Top Brightness Temperatures Computed from the CRTM at 3.9 μm

ABSTRACT In preparation for all-sky satellite radiance assimilation, the Community Radiative Transfer Model (CRTM), version 2.1.3, was used to produce Geostationary Operational Environmental Satellite-12/13 (GOES-12/13) imagery near 3.9 μm. For the current study, model output simulated from different models, microphysics, and weather events was used by the CRTM to generate imagery over and near the United States. A direct comparison of observed and CRTM GOES-12/13 imagery near 3.9 μm revealed that CRTM brightness temperatures of solid-water cold cloud tops were approximately 30 K less than observed values. Two CRTM errors were identified and resolved: 1) a coding error that was found by the CRTM team and 2) incorrect optical properties of ice, resulting in improved values of brightness temperatures. Further, changes in microphysics also contributed to improvements, save for one case. The coding error solution appeared in the publicly released CRTM, version 2.3.0, on 27 November 2017, while the inclusion of the optical property solution is undetermined. Since the CRTM is the radiative transfer model within the operational data assimilation system at the National Centers for Environmental Prediction (NCEP), improvements to both the CRTM and model microphysics will be beneficial for future all-sky radiance assimilation activities.

Dependence of Simulation Biases at AHI Surface-Sensitive Channels on Land Surface Emissivity over China

AbstractThis study compares the simulation biases of Advanced Himawari Imager (AHI) brightness temperature to observations made at night over China through the use of three land surface emissivity (LSE) datasets. The University of Wisconsin–Madison High Spectral Resolution Emissivity dataset, the Combined Advanced Spaceborne Thermal Emission and Reflection Radiometer and Moderate Resolution Imaging Spectroradiometer Emissivity database over Land High Spectral Resolution Emissivity dataset, and the International Geosphere–Biosphere Programme (IGBP) infrared LSE module, as well as land skin temperature observations from the National Basic Meteorological Observing stations in China are used as inputs to the Community Radiative Transfer Model. The results suggest that the standard deviations of AHI observations minus background simulations (OMBs) are largely consistent for the three LSE datasets. Also, negative biases of the OMBs of brightness temperature uniformly occur for each of the three datasets. There are no significant differences in OMB biases estimated with the three LSE datasets over cropland and forest surface types for all five AHI surface-sensitive channels. Over the grassland surface type, significant differences (~0.8 K) are found at the 10.4-, 11.2-, and 12.4-μm channels if using the IGBP dataset. Over nonvegetated surface types (e.g., sandy land, gobi, and bare rock), the lack of a monthly variation in IGBP LSE introduces large negative biases for the 3.9- and 8.6-μm channels, which are greater than those from the two other LSE datasets. Thus, improvements in simulating AHI infrared surface-sensitive channels can be made when using spatially and temporally varying LSE estimates.

FY-3A Microwave Data Assimilation Based on the POD-4DEnVar Method

A four-dimensional ensemble variational assimilation system for FY-3A satellite data is constructed using the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar Satellite Assimilation System). Using the community radiative transfer model (CRTM) as the observation operator for satellite data, ensemble samples are mapped to the observation space and observation perturbations are generated. The observation perturbations matrix of satellite data is then decomposed to obtain the orthogonal eigenvectors and the eigenvalues for the observation perturbations matrix. The observation perturbations matrix and model perturbations matrix are transformed using orthogonal eigenvectors as basis functions and an explicit expression for the analysis increment is obtained. The expression includes the flow-dependent background error covariance and avoids the difficulty of solving the adjoint model for four-dimensional variational assimilation. In order to evaluate the capability of POD-4DEnVar Satellite Assimilation System, single observation experiments and observation system simulation experiments (OSSEs) for FY-3A MWHS and MWTS sensor data were designed to simulate a large-scale precipitation event occurring over the middle and lower reaches of the Yangtze River. The results of single observation experiments show that POD-4DEnVar Satellite Assimilation System can assimilate satellite data correctly, and the background error covariance of POD-4DEnVar Satellite Assimilation System has obvious flow-dependent characteristics. The results of the OSSEs show that the root-mean-square errors (RMSEs) of the assimilation analysis field with respect to the “true” field are lower than those of the background field, which indicates that the POD-4DEnVar Satellite Assimilation System can assimilate satellite data effectively. The sensitivity of the POD-4DEnVar Satellite Assimilation System to the percentage of truncated eigenvalues, the number of ensemble members, assimilation time window length, and the horizontal localization scale (which are key parameters for POD-4DEnVar Satellite Assimilation System) was tested in sensitivity experiments. These experiments show that if the percentage of truncated eigenvalues for POD decomposition is more than 80%, POD-4DEnVar Satellite Assimilation System has strong assimilation skill. Increasing the number of initial ensemble members has little influence on the assimilation ability of POD-4DEnVar Satellite Assimilation System. But, increasing the number of the physical ensemble members can clearly increase the assimilation ability. The assimilation skill of POD-4DEnVar Satellite Assimilation System is optimal when the length of the assimilation time window is 5 h or 3 h and the horizontal localization scale is 500 km or above. The assimilation ability of POD-4DEnVar Satellite Assimilation System is preliminarily tested by single observation experiments and OSSEs. The results show that it is feasible to assimilate satellite data using the POD-4DEnVar method. In the future, a variety of real satellite data and a variety of mesoscale weather cases will be used to further verify the stability of POD-4DEnVar Satellite Assimilation System.

Radiance‐Based Evaluation of WRF Cloud Properties Over East Asia: Direct Comparison With FY‐2E Observations

AbstractRadiance‐based comparison between model‐simulated and satellite‐observed atmosphere shows its unique advantage for model evaluation. This study couples a fast radiative transfer model, namely, the community radiative transfer model, and the Weather Research and Forecasting (WRF) to generate synthetic satellite observed infrared (IR) brightness temperatures (BTs) over East Asia. Simulated IR BTs are used to evaluate WRF cloud properties by direct comparison with observations from the Visible and Infrared Spin‐Scan Radiometer (onboard the FengYun‐2E geostationary satellite). A deep cyclone over North China is investigated as our case study. The simulated and observed BTs in the 6.8‐μm channel demonstrate agreement with an averaged bias of <3 K, whereas the performances in two IR window channels are relatively poor with averaged BT differences almost 10 K. For typical precipitation regions, BT differences between the 6.8‐ and 10.8‐μm channels indicate that the WRF simulation underestimates low‐ to midlevel clouds but reproduces high‐level clouds better. Overall, the WRF has solid capabilities to capture the cloud characteristics broadly, whereas some limitations are shown in the cloud temporal and variation cloud cover, especially 3 days after the WRF initialization.

Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 μm) Radiances into a Warn-on-Forecast System

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.

Representing Precipitation Ice Species With Both Spherical and Nonspherical Particles for Radiative Transfer Modeling of Microphysics‐Consistent Cloud Microwave Scattering Properties

AbstractCloud microwave scattering properties for the Community Radiative Transfer Model (CRTM) have previously been created to be consistent with the particle size distributions specified by the WSM6 single‐moment microphysics scheme. Here substitution of soft sphere scattering properties with nonspherical particle scattering properties is explored in studies of Hurricane Karl (2010). A nonsphere replaces a sphere of the same maximum dimension, and the number of particles of a given size is scaled by the ratio of the sphere to nonsphere mass to keep the total mass of a given particle size unchanged. The replacement of homogeneous soft sphere snow particles is necessary to resolve a highly evident issue in CRTM simulations: precipitation‐affected brightness temperatures are generally warmer at 183 GHz than at 91.7 GHz, whereas the reverse is seen in observations. Using sector snowflakes resolve this issue better than using columns/plates, bullet rosettes, or dendrites. With sector snowflakes, both of these high frequencies have low simulated brightness temperatures compared to observations, providing a clear and consistent suggestion that snow is being overproduced in the examined simulation using WSM6 microphysics. Graupel causes cold biases at lower frequencies which can be reduced by either reducing graupel water contents or replacing the microphysics‐consistent spherical graupel particles with sector snowflakes. However, soft spheres are likely the better physical representation of graupel particles. The hypotheses that snow and graupel are overproduced in simulations using WSM6 microphysics shall be examined more systematically in future studies through additional cases and ensemble data assimilation of all‐sky microwave radiance observations.

A stochastic model for density-dependent microwave Snow- and Graupel scattering coefficients of the NOAA JCSDA community radiative transfer model

A structural model is developed for the single-scattering properties of snow and graupel particles with a strongly heterogeneous morphology and an arbitrary variable mass density. This effort is aimed to provide a mechanism to consider particle mass density variation in the microwave scattering coefficients implemented in the Community Radiative Transfer Model (CRTM). The stochastic model applies a bicontinuous random medium algorithm to a simple base shape and uses the Finite-Difference-Time-Domain (FDTD) method to compute the single-scattering properties of the resulting complex morphology.

An efficient radiative transfer model for hyperspectral IR radiance simulation and applications under cloudy‐sky conditions

AbstractAn efficient radiative transfer model has been developed for hyperspectral infrared radiance simulation under both clear‐ and cloudy‐sky conditions. The hyperspectral IR cloudy radiative transfer model (HIRTM) combines atmospheric transmittances due to molecular absorption and cloud absorption and scattering from cloud hydrometeors. An efficient analytical Jacobian methodology is also developed under both clear‐ and cloudy‐sky conditions, which is needed both to assimilate cloudy radiances directly into numerical weather prediction models and to retrieve atmospheric soundings and cloud properties simultaneously from cloudy radiance measurements. In comparing HIRTM and its analytical Jacobian with the community radiative transfer model (CRTM), our research has shown that HIRTM's Jacobian calculations are similar to those of CRTM. HIRTM and CRTM synthetic observations derived from model output are compared with corresponding real observations from Geostationary Operational Environmental Satellite 13 Imager observations, and both perform similarly under water clouds, while CRTM is colder than HIRTM for thick ice clouds.

Assimilating moderate resolution imaging spectroradiometer radiance with the weather research and forecasting data assimilation system

The performance of the weather research and forecasting (WRF) model relies on the accuracy of the initial field provided by data assimilation. The initial field usually contains large uncertainties, especially for regions where observations are sparse or lacking. Assimilating additional observations is an efficient way to reduce this uncertainty. The moderate resolution imaging spectroradiometer (MODIS) is one of the most critical data sources of various indirect assimilating applications due to its wide swath and remarkable quality. Therefore, assimilating MODIS data into WRF data assimilation (WRFDA) system is meaningful to improve the accuracy of weather forecasts. This study developed a module to directly assimilate the MODIS radiances into the WRFDA based on the 3-D variational data assimilation method and community radiative transfer model. An assimilation experiment was carried out on August 2014, from which the background field has been relatively improved. Specifically, the improvement of the temperature, humidity, and wind speed at the near surface layer is about 0.2°C, 1.2%, and 0.2 m / s , respectively. Additional capabilities and increased potential of MODIS data assimilation based on WRFDA need to be further investigated and tested under various conditions and applications.

Comparison of using distribution‐specific versus effective radius methods for hydrometeor single‐scattering properties for all‐sky microwave satellite radiance simulations with different microphysics parameterization schemes

AbstractThe Community Radiative Transfer Model (CRTM) presently uses one look‐up table (LUT) of cloud and precipitation single‐scattering properties at microwave frequencies, with which any particle size distribution may interface via effective radius. This may produce scattering properties insufficiently representative of the model output if the microphysics parameterization scheme particle size distribution mismatches that assumed in constructing the LUT, such as one being exponential and the other monodisperse, or assuming different particle bulk densities. The CRTM also assigns a 5 μm effective radius to all nonprecipitating clouds, an additional inconsistency. Brightness temperatures are calculated from 3 h convection‐permitting simulations of Hurricane Karl (2010) by the Weather Research and Forecasting model; each simulation uses one of three different microphysics schemes. For each microphysics scheme, a consistent cloud scattering LUT is constructed; the use of these LUTs produces differences in brightness temperature fields that would be better for analyzing and constraining microphysics schemes than using the CRTM LUT as‐released. Other LUTs are constructed which contain one of the known microphysics inconsistencies with the CRTM LUT as‐released, such as the bulk density of graupel, but are otherwise microphysics‐consistent; differences in brightness temperature to using an entirely microphysics‐consistent LUT further indicate the significance of that inconsistency. The CRTM LUT as‐released produces higher brightness temperature than using microphysics‐consistent LUTs. None of the LUTs can produce brightness temperatures that can match well to observations at all frequencies, which is likely due in part to the use of spherical particle scattering.

Potential Applications of Small Satellite Microwave Observations for Monitoring and Predicting Global Fast-Evolving Weathers

Two new constellations comprising 14 small satellites with microwave instruments onboard are proposed in this study. Properly arranged, the first constellation is capable of covering the entire globe at an hourly interval and the second constellation is more favorable for the tropical area. Compared to the current JPSS and MetOp satellite constellation, which has passive microwave sounding instruments ATMS or AMSU, a small satellite constellation is more cost effective, requires a shorter development cycle, and has a smaller launch-failure impact. For a designated microwave small satellite constellation, the brightness temperature distribution in space and time is simulated using the operational forecast fields as inputs to the Community Radiative Transfer Model (CRTM). It is demonstrated that the structural change of fast-evolving weather systems such as a middle-latitude cyclone can be well captured from small satellite brightness temperatures.

A comparison between three surface type data sets adopted by the community radiative transfer model

ABSTRACTThe accuracy of simulated radiances for surface-sensitive visible and infrared channels is affected by the specification of surface type. The current version of Community Radiative Transfer Model (CRTM) employs three surface type data sets, which are referred to as the National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U. S. Geological Survey (USGS), and the International Geosphere-Biosphere Programme (IGBP) data sets, respectively. In this study, the consistencies and discrepancies among these three surface type data sets are examined. The surface type is specified consistently among the three data sets in equatorial and polar regions, but not in other regions especially around 60° N. The desert and snow types have the highest consistency among the three surface type data sets. The tundra in the IGBP data set, the needle-leaf forecast and mixed forest in the NPOESS data set, and the mixed forest in the USGS data set have the greatest discrepancy from the other two data sets. Finally, a canonical case is used to demonstrate that the impacts of the three surface type datasets on CRTM simulations are significant, pointing to a need for a rigorously defined surface type data set.

A New Variational Assimilation Method Based on Gradient Information from Satellite Data

With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. A gradient information assimilation method is proposed in this paper to eliminate systematic bias. This method uses a gradient operator for gradient transformation between the model variable and observation variable and reaches the objective of eliminating systematic bias. An ideal experiment of variational data assimilation is conducted using the Community Radiative Transfer Model (CRTM) and Advanced Microwave Sounding Unit-A (AMSU-A) data, indicating that only assimilating gradient information can eliminate the smooth systematic bias in observation data. Then, a numerical simulation of tropical cyclone (TC) Megi and data assimilation experiment are conducted using the Weather Research Forecast (WRF) and WRF Data Assimilation (WRFDA) model as well as the Atmospheric Infrared Sounder (AIRS) data. The results show that the method of gradient information assimilation can improve the accuracy of TC tracks forecast and is also applicable for dealing with unreliable satellite data.

Sea‐surface emissivity parametrization from microwaves to millimetre waves

In preparation for new observations on board the next generation of the European operational meteorological satellite (EUMETSAT Polar System—Second Generation (EPS‐SG)), a parametrization of the sea‐surface emissivity is proposed from 10–700 GHz: Tool to Estimate Sea‐Surface Emissivity from Microwaves to sub‐Millimeter waves (TESSEM2). It is based on the community model FAST microwave Emissivity Model (FASTEM) at frequencies up to 200 GHz, where FASTEM has been calibrated and validated operationally. It follows a physical emissivity model at higher frequencies. A preliminary evaluation of TESSEM2 has been conducted with success, by comparison with airborne International Sub‐Millimetre Airborne Radiometer (ISMAR) observations from 118–325 GHz, under low wind speed (3–5 m s−1) off the coast of Scotland. TESSEM2 is a fast parametrization that can easily be implemented in a community radiative transfer model. The code is available to the community and will be distributed with the next version of Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV).

Uncertainty in Fengyun-3C Microwave Humidity Sounder Measurements at 118 GHz With Respect to Simulations From GPS RO Data

Microwave Humidity Sounder (MWHS) onboard the Chinese FengYun-3C satellite has a total of 12 channels. Eight of these channels are located near the 118-GHz oxygen absorption band for probing atmospheric temperature and humidity fields from space. While the water vapor sounding channels near 183 GHz have been extensively studied in the past, we report the first satellite observations at 118 GHz. In this paper, the MWHS calibration accuracy is assessed by comparing the satellite observations with simulations. Using the collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data in clear-sky conditions as inputs to Community Radiative Transfer Model, MWHS brightness temperatures are simulated for the five upper level sounding channels 2–9 located near the 118-GHz oxygen absorption band. For quality control of clear-sky radiance, a new cloud index is first developed based on the two MWHS window channels 1 and 10. Monthly mean biases of the antenna brightness temperature observations for the 118-GHz sounding channels are quantified by using more than 2000–5000 collocated COSMIC and MWHS data from August 2014 to February 2015. It is found that the bias of MWHS data relative to COSMIC RO simulation is dependent on channel and ranges within ±1.5 K. The standard deviation from the bias is the largest at MWHS channel 2, indicating a larger variability of the measurements of channel 2 than the other channels.

Improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model

AbstractNoticeable improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model (CRTM) are reported, which are based on the most recent advances in understanding ice cloud microphysical (particularly, ice particle habit/shape characteristics) and optical properties. The new CRTM ice cloud model is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 ice cloud habit model, which represents ice particles as severely roughened hexagonal ice column aggregates with a gamma size distribution. The single‐scattering properties of the new ice particle model are derived from a state‐of‐the‐art ice optical property library and are constructed as look‐up tables for rapid CRTM computations. Various sensitivity studies concerning instrument‐specific applications and simulations are performed to validate CRTM against satellite observations. In particular, radiances in a spectral region covering the infrared wavelengths are simulated. Comparisons of brightness temperatures between CRTM simulations and observations (from MODIS, the Atmospheric Infrared Sounder, and the Advanced Microwave Sounding Unit) show that the new ice cloud optical property look‐up table substantially enhances the performance of the CRTM under ice cloud conditions.