The Advanced Technology Microwave Sounder (ATMS) sensor data records (SDRs) product are assimilated for weather forecasts and used to derive environment data records (EDRs) products. Meanwhile, ATMS imagery, derived from SDR product, offers snapshots of weather events, like the warm core of a hurricane. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. Given these challenges, we have proposed a method which combined limb-correction algorithm and AI resolution enhancement to improve ATMS imagery visualization for bands 16, 18 and 19 [1]. This study further optimized the method and aimed to apply it to most of ATMS bands.

Passive microwave radiometer sounders are the backbones for numerical weather prediction under all sky conditions. Modern 5G technology represents a significant advancement in internet and communication technology, but may overlap with some of the passive microwave sounder channels, in particular the 5G’s 24 GHz channel may interfere with the 23.8 GHz total column water vapor channel measurements over land. This paper assesses the importance of the 23.8GHz channel for atmospheric sounding by analyzing the vertical weighting functions at different pressure levels to address the issue of sensitivity of this channel over land, and related issues such as land surface emissivity. Modeling experiments are also performed to assess the impacts of this channel on water vapor retrievals using the Microwave Integrated Retrieval System (MiRS). The role of this channel in detecting planetary boundary layer (PBL) water vapor is also discussed.

Over ten-years, the Integrated Calibration and Validation System (ICVS) Long-Term Monitoring (LTM) System has provided near-real time (NRT) monitoring for Joint Polar Satellite System (JPSS) spacecraft and instruments including their on-orbit status and performance and science data product quality [1] - [4]. The ICVS also harnesses JPSS Sensor Data Record (SDR) data to rapidly (with little latency) visualize radiometric features of severe weather events such as hurricanes and volcanos [5] [6]. This study presents two case studies, one depicting the 3-dimensional (3D) atmospheric warm core structure inside Hurricane Ian from the 2022 North Atlantic Hurricane Season and another showing the 3D temperature structures present during the 2021 Heat Dome event by using JPSS ATMS (and VIIRS for hurricane events) SDR and TDR data. More details and images/animations for hurricane events can be found at https://www.star.nesdis.noaa.gov/smcd/sew/index.php.

We report on the implementation of a U-Net convolutional neural network (CNN) architecture to improve operational satellite retrievals of instantaneous precipitation rate (PR) from the NOAA microwave integrated retrieval system (MiRS). The U-Net architecture was implemented using NOAA-20/advanced technology microwave sounder (ATMS) passive microwave retrievals from the MiRS system. Training data consisted of input features that included operational retrievals of PR, total precipitable water, latitude, and longitude. Training target data (i.e., reference) were hourly PRs from the operational multiradar/multisensor system (MRMS) over the conterminous United States. (CONUS). The U-Net was trained using one year of collocated MiRS and MRMS data over the CONUS during 2021. Independent validation of U-Net was performed using data from 2022. Validation results showed that U-Net predictions were clearly improved relative to the original MiRS retrievals in terms of bias and root–mean-square error (RMSE), as well as categorical scores. The improvement mainly stemmed from a much better depiction of light rainfall distribution. Categorical scores such as the probability of detection (POD) and Heidke skill score were also significantly improved, as were aggregate error statistics. For instance, Heidke skill score and false alarm rate (FAR) improved from 0.42 to 0.50 and 0.057 to 0.014, respectively. Bias improved from 0.033 to −0.006 mm/hr. The spatial distribution correlation coefficient of the accumulated precipitation improved from 0.77 to 0.89. Once trained, the extremely low computational requirements of the U-Net model predictions highlight a potentially attractive means of improving operational retrievals of satellite PRs, where the latency of product dissemination is an important consideration.

Passive microwave sounders provide the highest-impact observations ingested by major numerical weather prediction (NWP) forecast models. The Advanced Technology Microwave Sounder (ATMS), built by Northrop Grumman, Azusa, CA, USA, is the latest operational microwave sounder series being launched by the United States to provide both temperature and water vapor soundings of the atmosphere. The first ATMS was launched on the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011. This article focuses on the details of the on-orbit performance characterization of the second ATMS, which launched on November 18, 2017, on the Joint Polar Satellite System-1 (JPSS-1) satellite. After successful commissioning, JPSS-1 was renamed National Oceanic and Atmospheric Administration (NOAA)-20 (N-20). We present performance characterizations from prelaunch and postlaunch tests, including the thermal vacuum (TVAC) campaign, and postlaunch activities that contribute to the radiance data products. Significant improvements were found for reflector emissivity,$1/f$noise performance, antenna beam efficiency, interchannel noise correlation, and scan drive bearing design. New geolocation and pointing algorithms were evaluated. The N-20 ATMS has the same channel set, polarizations, scan geometry, and calibration approach as the SNPP ATMS. The N-20 ATMS meets all performance requirements with margin.

The Advanced Technology Microwave Sounder (ATMS) is a passive microwave radiometer for the current generation of polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA). The first two ATMS instruments are manifested onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Several critical changes have been made to ATMS operational calibration algorithm since March 2017. The calibration processing has been revised from a Rayleigh–Jeans approximated algorithm to a full radiance algorithm in order to reduce the error introduced by the approximation over cold radiances in the higher frequency channels. In addition, based on the lessons learned from S-NPP and NOAA-20 postlaunch calibration/validation tests, some major improvements have been made in the updated operational algorithm. These include reflector emission and antenna pattern corrections. Details of the radiance-based ATMS on-orbit calibration are documented in this report, and results of prelaunch calibration error budget analysis and postlaunch calibration accuracy evaluation are also presented for reference.

Over the years, a fixed window for smoothing radiometer cold-space and warm-load counts and processing brightness temperature in calibration has been used for all microwave sounders at EUMETSAT and NOAA. Although this practice is based on ground tests and legacy satellites, it remains unclear if this empirical parameter is optimal for in-orbit radiometers, as the space environment is different from the ground and radiometers may drift. We found that the fixed window is not optimal and leads to large noise.We have developed an adaptive window that accommodates channel differences and temporal changes in hardware. Our method has reduced noise by as much as 50% for 183 GHz channels of MetOp-C MHS. We observed temporal jumps and shifts in counts, gain and noise of 89 and 190 GHz, and accordingly, the adaptive window can adjust to reduce such an impact. Further analyses reveal that 1/fnoise plays an important role for determining the adaptive window. 1/fnoise is non-stationary and gives rise to the fluctuation of counts and gain. As a result, for channels with large 1/fnoise a short window should be used to mitigate the fluctuation. Our study suggests an adaptive method has advantages over the fixed method for considering channel differences and timevarying noise.

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model was originally trained and tested in global ocean clear-sky domain for nighttime, and were modified and applied for daytime data in this study. CRTM-simulated brightness temperatures (BTs) were defined as model labels and the clear-sky pixels were identified by an FCDN-trained clear-sky mask (FCDN_CSM) model. The preliminary result showed that the FCDN_CRTM prediction minus CRTM simulation (F-C) mean biases are only up to several tens mK for all bands. However, the corresponding standard deviation (STDs) were 4–5 times worse than training and testing data, due to the effect of the daytime solar reflection. The evaluation result suggested that further fine-tune model is needed to improve the daytime prediction accuracies, by improving input data uniformity, adjusting the model architecture, and selecting possible important features.

Reported here are disk-integrated Moon surface microwave brightness temperature ($Tb$) retrievals covering the frequency range of 23–183 GHz. Full Moon observations obtained from the advanced technology microwave sounder (ATMS) onboard the NOAA-20 satellite during a special spacecraft pitch–maneuver operation forms the basis of the retrievals. Instrument nonlinearity, Earth sidelobe contamination, cosmic background radiation, and reflector thermal emission corrections are applied to the observations to obtain accurate values of the Moon’s$Tb$at all frequencies. The measured full Moon$Tb$ranges from ~240 to 293 K with frequency increases from 23 to 183 GHz. A clear frequency trend is detected when the brightness temperature increases.

The Advanced Technology Microwave Sounder (ATMS) is a satellite-based microwave radiometer that provides temperature and humidity sounding observations from low Earth orbit. The instrument utilizes 22 channels that cover a frequency range of 23 to 183 GHz. The first ATMS instrument was launched in 2011 on the Suomi National Polar-orbiting Partnership (S-NPP) satellite and the second ATMS was launched in 2017 on the Joint Polar Satellite System-1 (JPSS-1) satellite (now NOAA-20); both on-orbit ATMS instruments are currently operational. This paper will describe the pre-launch performance of the third ATMS instrument, designated for the JPSS-2 satellite, during ground testing and calibration.

For the on-orbit calibration of passive microwave radiometers, instrument non-linearity is a major error source, causing a scene-temperature-dependent error if not being properly corrected. non-linearity results from the intrinsic feature of the square-law detector and amplifiers used in total-power microwave radiometers, and can only be accurately characterized through the ground-based Thermal Vacuum Test (TVAC). The ground-based non-linearity characterization is then used in the calibration algorithm to attempt to remove this error source. Evaluation results for current operational microwave-sounding instruments show that the magnitude of the non-linearity error varies from channel to channel and from instrument to instrument, with maximum changes of several tenths of kelvins to several kelvins. While the different responses of the detector and amplifier may explain the non-linearity differences in different instruments, errors in TVAC tests could also increase the uncertainty in the non-linearity assessment. Therefore, accurate knowledge of error sources in the TVAC test and their corrections are important for a reliable and accurate non-linearity measurement. In this paper, major error sources in the TVAC test are studied and identified for the NOAA-20 Advanced Technology Microwave Sounder. Correction methods are developed by combining the pre-launch TVAC test and post-launch deep-space-scan test data sets. An on-orbit evaluation method is also proposed to validate the ground-measured instrument non-linearity.

The Metop-C satellite was launched in November 2018, and one year earlier in November 2017, NOAA-20 was launched. Processing of microwave data from these two polar-orbiting satellites was promptly included in the Microwave Integrated Retrieval System (MiRS) proving retrieved variables alongside data already provided by other passive microwave instrument-bearing satellites, e.g. Metop-B and S-NPP. Due to the consistent treatment across sensors of the inversion algorithm used in MiRS, retrieved precipitation from multiple satellites can provide more information than single platforms and is useful for monitoring meteorological events, e.g., floods, tropical cyclones, atmospheric rivers, cold air surges, etc.This paper describes the MiRS extension to Metop-C and NOAA-20 with a focus on rain rate retrievals, along with retrievals from Metop-B, S-NPP, and GPM. Rain rates retrieved from MiRS based on the above five satellites/sensors are validated against National Centers for Environmental Prediction (NCEP) Stage IV precipitation. Performance of monthly, seasonal, annual rain, and extreme flood events over land indicate that multiple satellites extend MiRS capability to reasonably reproduce rainfall observed by ground-based observing systems. Results from the five satellites show comparable and complementary results with each other.

Rain and temperature retrievals from a common algorithm based on microwave sensors onboard five polar orbiting satellites are included to study tropical cyclone (TC) rain rate characteristics and warm core structure. The retrieval algorithm used here is NOAA's operational Microwave Integrated Retrieval System (MiRS). The five satellites/sensors are S-NPP/ATMS, NOAA-20/ATMS, Metop-B/AMSUA-MHS, Metop-C/AMSUA-MHS, and GPM/GMI. Hurricane Florence occurred, which occured in the Atlantic basin in 2018, is chosen as the study case. Hurricane Matthew in 2016 (before NOAA-20 and Metop-C were launched) is also included to illustrate warm core structure retrieved by MiRS.

The second Advanced Technology Microwave Sounder (ATMS) recently launched November 2017 on the Joint Polar Satellite System-l satellite (JPSS-l), now re-named NOAA-20. It joins the first ATMS flight unit aboard the Suomi NPP (S-NPP) satellite, as well as older sounders-the Advanced Microwave Sounding Units A & B (AMSU-A/B) and Microwave Humidity Sounder (MHS)-on polar-orbiting operational weather satellites. Together, these sounders provide critical all-weather temperature and humidity profile information for Numerical Weather Prediction (NWP) models. This paper presents results from a number of special post-launch tests used to characterize the instrument and provide unique calibration information. These special tests-long stares, alternate techniques for lunar intrusion mitigation and geolocation, spacecraft maneuvers, special scan modes, comparisons with NWP models-require nonstandard modes of operation or data analysis, and can only be conducted during commissioning, prior to the start of regular forecast observations.

Atmospheric soundings from radiosondes are critical for the weather forecasting, particularly for the diagnostic of atmospheric stability conditions that can lead to thunderstorm development. However, radiosonde observations (RAOBs) are temporally and spatially limited throughout the globe, promoting the use of satellite measurements. This paper assesses the applicability to the operational short-term weather forecasting of atmospheric stability indices and parameters (SIPs) derived from thermodynamic profiles retrieved from the National Oceanic and Atmospheric Administration Unique Combined Atmospheric Processing System (NUCAPS) using the Suomi National Polar-orbiting Partnership (SNPP) Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) radiances. For this purpose, we validated NUCAPS SIPs against SIPs derived from conventional and dedicated/reference RAOBs collocated with NUCAPS retrievals within a maximum radius of 50 km and ±1-h time difference, over midlatitudes (60°N-30°N) and tropics (30°N-30°S). Stability parameters evaluated include total precipitable water (TPW), lifted index, K-index, total-totals index, and Galvez-Davison index. NUCAPS TPW exhibited the highest level of statistical agreement with RAOBs, with the remaining NUCAPS SIPs exhibiting favorable results (linear correlations ranging between 0.65 and 0.85). Case studies over the Texas/Oklahoma region and the Northern Coast of Brazil demonstrate NUCAPS capability of generating reliable fields of atmospheric stability, as well as capturing synoptic-scale convective signatures, not feasible with RAOBs. Considering also the benefit of SNPP NUCAPS hyperspectral-infrared (IR) and microwave soundings available for both regions during critical early afternoon periods, our analysis supports the use of NUCAPS CrIS/ATMS SIPs as complementary nowcasting tools for the analysis of preconvective environments.

This paper provides an overview of the validation of the operational atmospheric vertical temperature profile (AVTP) and atmospheric vertical moisture profile (AVMP) environmental data record (EDR) products retrieved from the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS), two passive sounding systems onboard the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The CrIS/ATMS suite serves as the U.S. low earth orbit (LEO) satellite sounding system and will span the future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm capable of retrieving atmospheric profile EDR products with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The SNPP NUCAPS AVTP and AVMP EDR products are validated using extensive global in situ baseline data sets, namely, radiosonde observations launched from ground-based networks and ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon statistical analyses using these data sets, the SNPP AVTP and AVMP EDRs are determined to meet the JPSS Level 1 global performance requirements.

This paper continues an overview of the validation of operational profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP), with focus here given to the infrared (IR) ozone profile environmental data record (EDR) product. The SNPP IR ozone profile EDR is retrieved using the cross-track IR sounder (CrIS), a Fourier transform spectrometer that measures high-resolution IR earth radiance spectra containing atmospheric state information, namely, vertical profiles of temperature, moisture, and trace gas constituents. The SNPP CrIS serves as the U.S. low earth orbit (LEO) satellite IR sounding system and will be featured on future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm that retrieves atmospheric profile EDR products, including ozone and carbon trace gases, with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The NUCAPS ozone profile product is assessed in this paper using extensive global in situ truth data sets, namely, ozonesonde observations launched from ground-based networks and from ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon rigorous statistical analyses using these data sets, the NUCAPS ozone profile EDRs are determined to meet the JPSS Level 1 global performance requirements.

Accurate global observations from space are critical for global climate change study. However, atmospheric temperature trend derived from spaceborne microwave instruments remains a subject of debate, due mainly to the uncertainty in characterizing the long-term drift of instrument calibration. Thus, a highly stable target with a well-known microwave radiation is required to evaluate the long-term calibration stability. This paper develops a new model to simulate the lunar emission at microwave frequencies, and the model is then used for monitoring the stability of the Advanced Technology Microwave Sounder (ATMS) onboard Suomi NPP satellite. It is shown that the ATMS cold space view of lunar radiation agrees well with the model simulation during the past five years and this instrument is capable of serving the reference instrument for atmospheric temperature trending studies, and connecting the previous generation of microwave sounders from NOAA-15 to the future Joint Polar Satellite System Microwave Sounder onboard NOAA-20 satellite.

This work presents an analysis of the vertical resolution of the temperature and water vapor retrieved by the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) using averaging kernels as a diagnostic tool. One of the goals of an atmospheric profile retrieval system is to estimate the state of the atmosphere using an optimal set of observations containing independent information content in order to simultaneously minimize the retrieval error and maximize the vertical resolution. The averaging kernels are also used to compute the number of degrees of freedom of retrieved temperature and water vapor in order to estimate the number of useful independent observations contained in the infrared and microwave observations. Our work uses data obtained from the Suomi National Polar-orbiting Partnership (S-NPP) Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS) to diagnose the vertical resolution of NUCAPS retrieved sounding products over different latitudes and seasons, and to support comparisons of retrievals against correlative radiosonde measurements. This work is expected to be extended to other NUCAPS products, since the generation of the averaging kernels is a functionality embedded as part of NUCAPS.

This paper applies a physical inversion approach to retrieve geophysical properties from the single instrumental field-of-view (FOV) spectral radiances measured by the Infrared Atmospheric Sounding Interferometer (IASI) under all-sky conditions. We demonstrate the use of a principal-component-based radiative transfer model (PCRTM) and a physical inversion methodology to simultaneously retrieve cloud radiative and microphysical properties along with atmospheric thermodynamic parameters. By using a fast parameterization scheme, the PCRTM can include the cloud scattering properties simulation in radiative transfer calculations without incurring much more computational cost. The computational speed achieved for a single FOV forward simulation under cloudy skies is similar to that normally achieved for clear skies. The retrieval algorithm introduced herein adopts a novel cloud phase determination scheme, to stabilize and/or constrain retrieval iterations, based on characteristics of the reflectance and transmittance of ice and water clouds. A modified Gaussian-Newton minimization technique is employed in the iterative inversion process in order to overcome a highly nonlinear cost function introduced by the cloud parameters. We carry out a rigorous error analysis for the retrieval of temperature, moisture, ozone (O3), and carbon monoxide (CO) from IASI measurements under cloudy-sky conditions. Our algorithm is applied to real IASI observations. Retrieval results are validated using European Center for Medium-Range Weather Forecasting data and collocated Lidar/Radar measurements, and the dependence of retrieval accuracy on cloud optical depth is illustrated.

In this study, stability indices and parameters computed from vertical profiles of temperature and moisture generated by (a) the Microwave Integrated Retrieval System (MIRS) v9.2 derived from the Advanced Technology Microwave Sounder (ATMS) and (b) the NOAA Unique Cross-track Infrared Sounder (CrIS)/ATMS Processing System (NUCAPS) v1.5 are compared against radiosonde observations (RAOBs) to verify the overall quality of satellite-derived stability indices, and potential applicability to the operational meteorological routine. Results of the statistical assessment show better performance of NUCAPS in relation to MIRS. Comparison results over a real weather case demonstrate that NUCAPS is a useful tool in assessing the static stability of the atmosphere, providing an additional information source for the weather forecasting.

The Microwave Integrated Retrieval System (MIRS) is an operational system at National Oceanic and Atmospheric Administration (NOAA) providing satellite derived information since 2007. The system has been extended to retrieve variables based on the Global Precipitation Measurement Microwave Imager (GPM/GMI) instrument observed radiances. The inversion algorithm is a One-Dimensional Variational (1DVAR) scheme and is consistent across sensors and uses an iterative approach in which a solution is sought that best fits the observed satellite radiances, subject to other constraints. The Community Radiative Transfer Model (CRTM) is used as the forward and Jacobian operator to simulate the radiances at each iteration prior to fitting the measurements to within the noise level. This paper describes the MiRS retrieval algorithm, its extension to GPM/GMI, and validation against several reference data sets, including National Centers for Environmental Prediction (NCEP) Stage IV precipitation analyses, as well as with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results indicate that MiRS can retrieve details of the atmospheric water vapor, hydrometeors and surface precipitation in an accurate and self-consistent manner.

NUCAPS (NOAA Unique CrIS/ATMS Processing System) uses the same scientific foundation as AIRS team science algorithm. The NUCAPS is being run operationally at NOAA for Suomi-NPP CrIS/ATMS, Metop-A/B IASI/AMSUA/MHS and provides atmospheric profiles of temperature, moisture, ozone, carbon oxide, carbon monoxide, and Methane. The sounding team at NASA developed a retrieval system using principal component radiative transfer model (PCRTM). The NASA retrieval system can be applied under all weather conditions at single field of view (FOV). In this presentation, we will present the trace gaseous environmental data record from hyperspectral sounders.

Recently, the Optimal Spectral Sampling (OSS) method was implemented in a development version of the Community Radiative Transfer Model (CRTM) at JCSDA. This presentation describes the way that the OSS is implemented in CRTM, and some preliminary evaluation of the performance of the CRTM-OSS in comparison with CRTM-ODPS method. One of the important benefits of the OSS method is its capability to simulate unapodized radiance, which could lead to better sensitivity in retrievals and data assimilation for numerical weather prediction (NWP). The accuracy and speed of the OSS simulations are two trade-off factors, depending on how many spectral nodes are used during training and simulation. In general, the OSS simulations of hyper spectral IR sensors, such as IASI and CrIS, can archive similar accuracy and double speed as compared to the CRTM-ODPS simulations.

This paper presents a comparison of methane (CH4), one important greenhouse gas in terrestrial atmosphere, observed from two hyperspectral infrared sensors on satellites, one is the Atmospheric Infrared Sounder (AIRS) onboard NASA/AQUA and the other is the Infrared Atmospheric Sounding Interferometer (IASI) onboard METEOP-A and -B. Comparison using about 500 cases shows the mean DOFs from AIRS is slightly smaller than IASI, with the difference of -0.049±0.152. Overall the retrieved CH4from AIRS is larger than IASI, and their difference is 10.2±23.8 ppb below 650 hpa and 27.4±26.9 ppb above 350 hPa. In the most sensitive layer of infrared sounder between 350 and 650 hPa, their difference is as small as 2.8±17.2 ppb. Compared to aircraft measurements, AIRS retrievals tend to be overestimated while IASI retrievals tend to be underestimated in most cases, suggesting the necessarity to further improve the algorithms and call for more dedicated aircraft measurements for validation.

VIIRS thermal emissive bands (TEB) have been analyzed. The analysis results indicate the VIIRS TEB is stable and exceeds the specification. Comparisons between the VIIRS and other sensors such as AVHRR, MODIS and CrIS demonstrated that VIIRS TEB agrees well with those sensors. Using Community Radiative Transfer Model (CRTM), developed at U.S. Joint Center for Satellite Data Assimilation (JCSDA), we found that M12 band image striping at day time over a uniform ocean was caused by the difference of sensor azimuthal angles among detectors.

The Community Radiative Transfer Model (CRTM), developed at U.S. Joint Center for Satellite Data Assimilation (JCSDA), has been used for infrared and microwave satellite radiance simulations and their derivatives to the surface/atmospheric parameters in data assimilation, physical retrieval, and many others. The CRTM has also been applied visible sensors and visible channel radiance/reflectance is simulated for studying aerosol and cloud effect. Together with the CRTM aerosol optical depth (AOD) module, the MODIS AOD satellite products can be assimilated and enhance air quality forecasting. This paper gives an overview of the CRTM developments and functionalities as well as its applications.

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

Satellite measurements from microwave instruments have made a significant contribution to the skill of numerical weather forecasting, on both global and regional scales. A FAST microwave Emissivity Model (FASTEM), which was developed by the Met Office, U.K., has been widely utilized to compute the surface emitted radiation in forward calculations. However, the FASTEM model was developed for frequencies in the range of 20-60 GHz, and it is biased at higher and lower frequencies. Several critical components such as variable sea surface salinity and full Stokes vector have not been generally taken into account. In this paper, the effects of the permittivity models are investigated, and a new permittivity model is generated by using the measurements for fresh and salt water at frequencies between 1.4 and 410 GHz. A modified sea surface roughness model from Durden and Vesecky is applied to the detailed two-scale surface emissivity calculations. This ocean emissivity model at microwave is now being used in the Community Radiative Transfer Model, and it has resulted in some major improvements in microwave radiance simulations. This paper is a joint effort of the Met Office, U.K., and the Joint Center for the Satellite Data Assimilation, U.S. The model is called as FASTEM-4 in the Radiative Transfer for TIROS Operational Vertical Sounder model.

The Community Radiative Transfer Model (CRTM) has been implemented in the Gridpoint Statistical Interpolation (GSI) system at the National Center for Environmental Prediction (NCEP) to simulate microwave and infrared radiances. We investigate impacts on analysis from a recent sensor, Special Sensor Microwave Imager Sounder (SSMIS). Both retrievals and radiance assimilation using the SSMIS show that SSMIS is unique in precisely determining stratospheric temperatures. We also utilize the SSMIS radiance for studying Hurricane Katrina. A "control-run" using current NCEP analysis field and a "test-run" using additional SSMIS data are compared. It shows again that the SSMIS radiance assimilation is helpful for hurricane forecasting.

The community radiative transfer model (CRTM), developed at U. S. Joint center for satellite data assimilation (JCSDA), has been used for infrared and microwave satellite radiance simulations and their derivatives to the surface/atmospheric parameters in the physical retrieval, data assimilation, and many others. CRTM has been become a key component in U. S. data assimilation for weather forecasting at the national center for environmental prediction (NCEP). This paper presents a new extension of the CRTM to ultraviolet (UV) and visible radiation for ozone and aerosol applications. This paper also demonstrates scattering effects on microwave radiative transfer.

The ability to provide temperature and water-vapor soundings under extreme weather conditions, such as hurricanes, could extend the coverage of space-based measurements to critical areas and provide information that could enhance outcomes of numerical weather prediction (NWP) models and other storm-track forecasting models, which, in turn, could have vital societal benefits. An NWP-independent 1D-VAR system has been developed to carry out the simultaneous restitutions of atmospheric constituents and surface parameters in all weather conditions. This consistent treatment of all components that have an impact on the measurements allows an optimal information-content extraction. This study focuses on the data from the NOAA-18 satellite (AMSUA and MHS sounders). The retrieval of the precipitating and nonprecipitating cloud parameters is done in a profile form, taking advantage of the natural correlations that do exist between the different parameters and across the vertical layers. Stability and the problem's ill-posed nature are the two classical issues facing this type of retrieval. The use of empirically orthogonal-function decomposition leads to a dramatic stabilization of the problem. The main goal of this inversion system is to be able to retrieve independently, with a high-enough accuracy and under all conditions, the temperature and water-vapor profiles, which are still the two main prognostic variables in numerical weather forecast models. Validation of these parameters in different conditions is undertaken in this paper by comparing the case-by-case retrievals with GPS-dropsondes data and NWP analyses in and around a hurricane. High temporal and spatial variabilities of the atmosphere are shown to present a challenge to any attempt to validate the microwave remote-sensing retrievals in meteorologically active areas.

A new methodology is developed to monitor and calibrate the Windsat 3rd and 4th Stokes parameters through tropical rainforest measurements over Amazon and the central Africa. It is found that the Windsat 4th Stokes parameter at 18 GHz has biases on an order of 0.5 Kelvin, which could severely impact the wind vector retrievals.

The measurements from satellite microwave imaging and sounding channels are simultaneously utilized through a one-dimensional (1-D) variation method (1D-var) to retrieve the profiles of atmospheric temperature, water vapor and cloud water. Since the radiative transfer model in this 1D-var procedure includes scattering and emission from the earth's atmosphere, the retrieval can perform well under all weather conditions. The iterative procedure is optimized to minimize computational demands and to achieve better accuracy. At first, the profiles of temperature, water vapor, and cloud liquid water are derived using only the AMSU-A measurements at frequencies less than 60 GHz. The second step is to retrieve rain and ice water using the AMSU-B measurements at 89 and 150 GHz. Finally, all AMSU-A/B sounding channels at 50-60 and 183 GHz are utilized to further refine the profiles of temperature and water vapor while the profiles of cloud, rain, and ice water contents are constrained to those previously derived. It is shown that the radiative transfer model including multiple scattering from clouds and precipitation can significantly improve the accuracy for retrieving temperature, moisture and cloud water. In hurricane conditions, an emission-based radiative transfer model tends to produce unrealistic temperature anomalies throughout the atmosphere. With a scattering-based radiative transfer model, the derived temperature profiles agree well with those observed from aircraft dropsondes.