RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER

Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study

The Advanced Technology Microwave Sounder (ATMS): A New Operational Sensor Series

ATMS is a new satellite microwave sounding sensor designed to provide operational weather agencies with atmospheric temperature and moisture profile information for global weather forecasting and climate applications. ATMS will continue the microwave sounding capabilities first provided by its predecessors, the Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU). The first ATMS was launched October 28, 2011 on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite. Microwave soundings by themselves are the highest-impact input data used by Numerical Weather Prediction (NWP) models; and ATMS, when combined with the Cross-track Infrared Sounder (CrIS), forms the Cross-track Infrared and Microwave Sounding Suite (CrIMSS). The microwave soundings help meet NWP sounding requirements under cloudy sky conditions and provide key profile information near the surfac

On-Orbit Special Testing of NOAA-20/JPSS-1 ATMS

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 non-standard modes of operation or data analysis, and can only be conducted during commissioning, prior to the start of regular forecast observations

The Advanced Technology Microwave Sounder (ATMS): The First 10 Months On-Orbit

The Advanced Technology Microwave Sounder (ATMS) is a new satellite microwave sounding sensor designed to provide operational weather agencies with atmospheric temperature and moisture profile information for global weather forecasting and climate applications. A TMS will continue the microwave sounding capabilities first provided by its predecessors, the Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU). The first ATMS was launched October 28, 2011 on board the NPOESS Preparatory Project (NPP) satellite. Microwave soundings by themselves are the highest-impact input data used by Numerical Weather Prediction (NWP) models, especially under cloudy sky conditions. ATMS has 22 channels spanning 23-183 GHz, closely following the channel set of the MSU, AMSU-A1/2, AMSU-B, Microwave Humidity Sounder (MHS), and Humidity Sounder for Brazil (HSB). All this is accomplished with approximately 1/4 the volume, 1/2 the mass, and 1/2 the power of the three AMSUs. A description of ATMS cal/val activities will be presented followed by examples of its performance after its first 10 months on orbit

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

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

Improving ATMS Imagery Visualization Using Limb Correction and AI Resolution Enhancement

The advanced technology microwave sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting and climate research, and helps us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This article proposes a method to enhance the imagery visualization for ATMS, combining limb correction (LC) with artificial intelligence (AI) resolution enhancement (RE). Measurement data from the ATMS onboard NOAA-20 were utilized to train the LC method, which were then validated using newly acquired NOAA-21 ATMS data. The AI RE was performed using enhanced super-resolution generative adversarial networks, which increased the pixel resolution by a factor of four. The high-resolution (HR) Advanced Microwave Scanning Radiometer 2 data served as a reference to initially and quantitatively evaluate the RE method. The combined method of LC and AI RE produced an angular-dependence-free and HR ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal HR imagery visualization system—JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release