Moisture Soundings Research Articles

Mesoscale air motion and thermodynamics predict heavy hourly U.S. precipitation

Predicting heavy precipitation remains scientifically challenging. Here we combine Atmospheric Infrared Sounder (AIRS) temperature and moisture soundings and weather forecast winds to predict the formation of thermodynamic conditions favourable for convection in the hours following satellite overpasses. Here we treat AIRS retrievals as air parcels that are moved adiabatically to generate time-varying fields. Over much of the Central-Eastern Continental U.S. during the non-winter months of 2019–2020, our derived convective available potential energy alone predicts intense precipitation. For hourly precipitation above the all-hours 99.9th percentile, performance is marginally lower than forecasts from a convection permitting model, but similar to the ERA5 reanalysis and substantially better than using the original AIRS soundings. Our results illustrate how mesoscale advection is a major contributor to developing heavy precipitation in the region. Enhancing the full AIRS record as described here would provide an alternative approach to quantify multi-decade trends in heavy precipitation risk.

A Fast Piecewise-Defined Neural Network Method to Retrieve Temperature and Humidity Profile for the Vertical Atmospheric Sounding System of FengYun-3E Satellite

A fast piecewise-defined neural network (PDNN) method is presented to produce accurate atmospheric temperature and humidity profiles from satellite hyperspectral infrared and microwave observations in all-sky conditions. The PDNN method relies on a novel classification approach and a principal component-based neural network function to better capture the nonlinear relationship between the spectral radiances and atmospheric state vectors. The algorithm was designed to only rely on satellite measurements. Large datasets were used for network training to make the retrieval robust to random errors in the reference data. For each retrieved profile, an effective quality indicator was obtained by training against the absolute value of the retrieval error. In addition, a reliable rain cloud flag was generated based on the scattering difference of cloud particles between the 50 and 118 GHz channels. Besides the independent reanalysis field data, the algorithm’s performance was also evaluated using radiosonde measurements. Preliminary validation shows that the best temperature retrieval occurred in the mid-troposphere (around 1.0 K). Depending on the reference data, errors in the boundary layer typically ranged from 1.8 K to 2.5 K. For water vapor, the retrieval error was less than 22% in the low-troposphere and less than 35% in the mid-and upper-troposphere when validated against the reanalysis field. The humidity error was approximately 10% lower when compared to radiosondes. The PDNN is used operationally to produce atmospheric temperature and moisture soundings from the Vertical Atmospheric Sounding System of the FengYun-3E satellite, an early-morning-orbit meteorological satellite launched in 2021.

Temperature and Moisture Sounding Performance of Current and Prospective Microwave Instruments under All-Sky Conditions

We provide consistent theoretical and empirical assessments of the major driving factors of the information content and retrieval performance for current and potential future microwave (MW) sounders. For the specific instrument concepts assessed, we find that instrument noise is a major driver, impacting vertical resolution as measured by the degrees of freedom for signal as much as 50%. We also observe diminished performance in the 118 GHz temperature sounding band as compared to the 50–60 GHz band, which is largely due to the increased sensor noise in the assessed 118 GHz sensor for comparable channels—a reduction in the performance gap between 118 GHz and 50 GHz bands can be obtained with a reduction of instrument noise in the 118 GHz temperature sounding channels. As expected, scene-type also significantly impacts the vertical resolution, emphasizing the importance of separating clear, cloudy, rainy, and icy conditions when evaluating instrument performance.

Hyperspectral Microwave Sensors—Advantages and Limitations

Temperature and humidity soundings form the bedrock of modern data assimilation due to their ability to directly constrain the atmospheric state variables. Because of their ability to penetrate clouds and work in all weather conditions, microwave sounders have very large impacts on constraining numerical weather prediction models. Recent advancements in integrated microwave assembly, space-grade high speed analog to digital converters, gigabit-per-second data interconnects, and field programmable gate arrays have enabled a transition from traditional analog detector-based demodulators to digitally channelized systems that allow for hyperspectral, or fine spectral resolution microwave sounders to be viable replacements to the current operational instruments. This article demonstrates that retrievals of temperature and moisture soundings can be improved by as much as 50% when 60–80 appropriately chosen pseudochannels are employed. While the current simulations were limited to cloud free oceans, perhaps even greater benefits can be realized over land and cloud conditions where additional channels can help constrain the surface and clouds. The article also demonstrated the advantages of hyperspectral sensors as a way to detect radio frequency interference in the few Kelvin range, as well as its ability to improve intercalibration efforts due to its ability to match frequency response functions of target sensors.

How Can Microwave Observations at 23.8 GHz Help in Acquiring Water Vapor in the Atmosphere over Land?

Due to concerns about radio frequency interference from emerging telecommunications technology, there have been intensive discussions on the changes to the international Radio Regulations around 24 GHz at the recent World Radiocommunication Conference in 2019. Although the sensitivity to total precipitable water (TPW) at 23.8 GHz over land is small, and in some cases close to zero, state-of-the-art retrieval systems with no dependence on real-time ancillary data (e.g., numerical weather prediction (NWP) model forecasts), such as the National Oceanic and Atmospheric Administration (NOAA) operational Microwave Integrated Retrieval System (MiRS), have been producing reliable TPW products over both ocean and land, which implies that the microwave channel at 23.8 GHz is providing valuable information on TPW over land as well as over ocean. The contradiction between the zero or near-zero sensitivity and practical performance over land raises questions for the remote sensing community and public users of such data. In this study, we examine the underlying physics and include mathematical explanations, which address and clarify the apparent contradiction. The channel at 23.8 GHz is a direct measurement and indispensable for its combined use with microwave temperature and moisture sounding channels.

The Reprocessed Suomi NPP Satellite Observations

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.

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.

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.

Near-Real-Time Surface-Based CAPE from Merged Hyperspectral IR Satellite Sounder and Surface Meteorological Station Data

AbstractNear-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate.

Tropospheric Moisture Sounding Using Microwave Imaging Channels: Application to GCOM-W1/AMSR2

Traditionally, space-based microwave imagers have been used exclusively for providing total atmospheric water vapor along with hydrometeor and surface parameters, using a combination of window channels. These channels operate away from the strong water vapor absorption lines used for atmospheric moisture sounding. Nevertheless, they are sensitive to the vertical distribution of moisture in the atmosphere. This is due to the fact that the water vapor absorption continuum varies monotonically, and the wing effect of the strong absorption lines has a nonconstant effect, across the microwave spectrum. Therefore, the transmittances of window channels are different, allowing a varying level of penetration into the atmospheric column. To demonstrate the capability of imagers to operate as sounders, we introduce penetration depth, which defines the vertical sensitivity of window channels to moisture. In addition, we evaluate the degree to which data from spaceborne microwave imagers, specifically the Global Change Observation Mission-Water 1 (GCOM-W1) Advanced Microwave Scanning Radiometer-2 (AMSR2) sensor, can be used to perform sounding of tropospheric moisture. The capability is assessed using numerical weather prediction (NWP) analysis as the reference. In simulations using a regression approach, the performance of AMSR2 moisture sounding matches that of Suomi-NPP ATMS up to 700 hPa, with standard deviations of 10%–20%. AMSR2 outperforms other pure moisture sounders (Microwave Humidity Sounder and Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie) up to 500 hPa, before degrading rapidly. The performances using real observations and a physical algorithm are consistent with those found in simulations. These results highlight new applications for microwave imagers like GCOM-W1 AMSR2, including in Nowcasting and NWP, which are heavily reliant on lower tropospheric moisture information.

Substrate‐sensitive relationships of dielectric permittivity and water content: implications for moisture sounding

ABSTRACTDielectric permittivity is a fundamental parameter for all investigations based on electromagnetic waves, particularly for moisture sounding. However, the physical principles of dielectric permittivity in natural materials and its influence on propagation velocity, the reflection coefficient, and the decay of the electromagnetic waves have been rarely addressed for variable substrate compositions or frequencies. Hence, this paper aims to fill this gap by systematically investigating quantitative relationships between dielectric permittivity and soil water content for a continuum of the three most common soil components in moderately temperate humid latitudes (i.e., illite, carbonate, and quartz‐rich sand). We carried out frequency‐dependent permittivity measurements for a large variety of artificial and natural substrates with an impedance analyser at frequencies ranging from 10 MHz to 1 GHz. Based on these measurements, we selected 200 MHz as the most commonly used frequency value for geological, ground‐penetrating radar applications and developed a set of empirical equations from third‐degree and ternary factorial diagrams to quantify the relationship between dielectric permittivity and volumetric moisture content of pure sand, clay, carbonate, and mixtures. The results show systematic trends, which can be explained by competing electrophysical properties of minerals and their wetting behaviour. This investigation shows that different wetting affinities of 1:1 and 2:1 clay minerals strongly control permittivity. Thresholds and non‐linearity effects were identified, where specific mineral properties and/or wetting processes became dominant in complex mixtures of soil materials. Bulk densities and porosities, respectively, were shown to have only minor influence compared with the impact of water. Consequently, the presented method is applicable to both undisturbed and disordered samples and, hence, to mobile devices or permanent installations in the field after compositional laboratory or onsite analyses. The permittivity of natural soils was measured in two field studies. The results show a good correlation between calculated and measured moisture contents. Our study confirms that the widely used Topp equation is only valid for sandy soil materials, i.e., the soil types it was originally designed for. For soils with a clay content >30% and/or a moisture level >20%, the new equations reduce the error in the level of permittivity measurements from up to 20% (using the Topp equation) to 5%.

Improved AIRS Temperature and Moisture Soundings with Local A Priori Information for the 1DVAR Method

AbstractA moving-window regression technique was developed for obtaining better a priori information for one-dimensional variational (1DVAR) physical retrievals. Using this technique regression coefficients were obtained for a specific geographical 10° × 10° window and for a given season. Then, regionally obtained regression retrievals over East Asia were used as a priori information for physical retrievals. To assess the effect of improved a priori information on the accuracy of the physical retrievals, error statistics of the physical retrievals from clear-sky Atmospheric Infrared Sounder (AIRS) measurements during 4 months of observation (March, June, September, and December of 2010) were compared; the results obtained using new a priori information were compared with those using a priori information from a global set of training data classified into six classes of infrared (IR) window channel brightness temperature. This comparison demonstrated that the moving-window regression method can successfully improve the accuracy of physical retrieval. For temperature, root-mean-square error (RMSE) improvements of 0.1–0.2 and 0.25–0.5 K were achieved over the 150–300- and 900–1000-hPa layers, respectively. For water vapor given as relative humidity, the RMSE was reduced by 1.5%–3.5% above the 300-hPa level and by 0.5%–1% within the 700–950-hPa layer.

Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

Abstract. To simulate passive microwave radiances in all-sky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30–50 GHz) and too little scattering at high frequencies (e.g. 150–183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60%. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

Analysis of ATMS striping noise from its Earth scene observations

AbstractThe differences between brightness temperature observations and simulated observations based on numerical weather predictions, i.e., O‐B, for Advanced Technology Microwave Sounder (ATMS) temperature sounding channels exhibit a clear striping pattern [Bormann et al., 2013]. We propose to first use the principal component analysis to isolate scan‐dependent features such as the cross‐track striping from the atmospheric signal and then to use an Ensemble Empirical Mode Decomposition (EEMD) to extract the striping noise in ATMS Earth scene brightness temperature observations for both temperature and water vapor sounding channels. It is shown that the Principal Component (PC) coefficient of the first PC mode, which mainly describes a scan‐dependent feature of cross‐track radiometer measurements, captures the striping noise. The EEMD is then applied to the PC coefficient to extract the first three high‐frequency intrinsic mode functions (IMFs), which are denoted as the PC1/IMF3 noise. When the PC1/IMF3 noise is removed from the data, the striping noise is imperceptible in the global distribution of O‐B for ATMS temperature sounding channels 1–16. Using the same method, it is demonstrated that the striping noise is also present in ATMS water vapor sounding channels 17–22. The magnitude of the ATMS striping noise is about ±0.3 K for the temperature sounding channels and ±1.0 K for the moisture sounding channels. The same technique is also applied to Advanced Microwave Sounding Unit‐A (AMSU‐A), AMSU‐B, and Microwave Humidity Sounder (MHS). The striping noise is undetectable for AMSU‐A but present in AMSU‐B and MHS data.

Improvements on global meteorological observations from the current Fengyun 3 satellites and beyond

The Fengyun 3 (FY-3) series is the second generation of Chinese sun-synchronous meteorological satellites. The first two, FY-3A and FY-3B, were launched successfully on 27 May 2008 and 5 November 2010, respectively. FY-3A and FY-3B share the same design, equipped with 11 payloads to observe the Earth system, but FY-3A is on a monitoring-orbit and FY-3B is on an afternoon-orbit. As a satellite constellation, FY-3A and FY-3B comprehensively improved meteorological observations in spectral wavelength, spatial coverage, and temporal frequency. This paper summarizes the improvements of the FY-3A and FY-3B satellites. New features, including optical imaging capacity from kilometer to hundred-meter resolution, passive microwave imaging, atmospheric temperature and moisture sounding, atmospheric chemistry remote sensing, and Earth radiation budget measurement, are presented with demonstrations of their use. Instruments scheduled for the FY-3C and beyond are introduced as well.

Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

AbstractAn accurate land surface emissivity (LSE) is critical for the retrieval of atmospheric temperature and moisture profiles along with land surface temperature from hyperspectral infrared (IR) sounder radiances; it is also critical to assimilating IR radiances in numerical weather prediction models over land. To investigate the impact of different LSE datasets on Atmospheric Infrared Sounder (AIRS) sounding retrievals, experiments are conducted by using a one-dimensional variational (1DVAR) retrieval algorithm. Sounding retrievals using constant LSE, the LSE dataset from the Infrared Atmospheric Sounding Interferometer (IASI), and the baseline fit dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) are performed. AIRS observations over northern Africa on 1–7 January and 1–7 July 2007 are used in the experiments. From the limited regional comparisons presented here, it is revealed that the LSE from the IASI obtained the best agreement between the retrieval results and the ECMWF reanalysis, whereas the constant LSE gets the worst results when the emissivities are fixed in the retrieval process. The results also confirm that the simultaneous retrieval of atmospheric profile and surface parameters could reduce the dependence of soundings on the LSE choice and finally improve sounding accuracy when the emissivities are adjusted in the iterative retrieval. In addition, emissivity angle dependence is investigated with AIRS radiance measurements. The retrieved emissivity spectra from AIRS over the ocean reveal weak angle dependence, which is consistent with that from an ocean emissivity model. This result demonstrates the reliability of the 1DVAR simultaneous algorithm for emissivity retrieval from hyperspectral IR radiance measurements.

An Improvement in Forecasting Rapid Intensification of Typhoon Sinlaku (2008) Using Clear-Sky Full Spatial Resolution Advanced IR Soundings

Abstract Hyperspectral infrared (IR) sounders, such as the Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI), provide unprecedented global atmospheric temperature and moisture soundings with high vertical resolution and accuracy. In this paper, the authors investigate whether advanced IR soundings of water vapor and temperature observations can improve the analysis of a tropical cyclone vortex and the forecast of rapid intensification of a tropical cyclone. Both the IR water vapor and temperature soundings significantly improve the typhoon vortex in the analysis and the forecast of the rapid intensification of Typhoon Sinlaku (2008). The typhoon track forecast is also substantially improved when the full spatial resolution AIRS soundings are assimilated. This study demonstrates the potential important application of high spatial and hyperspectral IR soundings in forecasting tropical cyclones.

Improved hurricane track and intensity forecast using single field‐of‐view advanced IR sounding measurements

Hyperspectral infrared sounders such as the AIRS and IASI provide unprecedented global atmospheric temperature and moisture soundings with high vertical resolution and accuracy. The AIRS and IASI data have been used in global numerical weather prediction models with positive impact on weather forecasts. The high spatial resolution single field‐of‐view soundings retrieved from AIRS have been applied to hurricane track and intensity assimilations and forecasts. The newly developed NCAR WRF/DART ensemble data assimilation is performed at 36 km resolution. Studies show that the track error for Hurricane Ike (2008) is reduced greatly when AIRS soundings are used compared with the control run which includes other observations such as radiosonde, satellite cloud winds, aircraft data, ship data, and land surface data, etc. The hurricane intensity forecast is also substantially improved when AIRS data are assimilated. This study demonstrates the importance of high spatial and hyperspectral IR sounding data in forecasting hurricanes.

Ground-based measurements with the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) engineering demonstration unit - experiment description and first results

The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) Engineering Demonstration Unit (EDU) is an imaging infrared spectrometer designed for atmospheric soundings. It measures the infrared spectrum in two spectral bands (14.6 to 8.8 um, 6.0 to 4.4 um) using two 128x128 detector arrays with an unapodized spectral resolution of 0.57 cm-1 (i.e., with a maximum optical path difference of ~0.88 cm) and a full spectral resolution scan duration of ~11 seconds. From a geosynchronous orbit, the instrument will have the capability of taking successive measurements of different geographical areas to scan any desired region of the globe, from which atmospheric temperature and moisture soundings, cloud and surface parameters, wind profiles, as well as other derived products can be retrieved. The GIFTS EDU provides a flexible and accurate testbed for the new challenges of the emerging hyperspectral era. The EDU ground-based measurement experiment, held in Logan, Utah during September 2006, demonstrated extensive capabilities and the potential for geosynchronous satellite and other platform Earth observing environmental measurements. This paper addresses the ground-based experiment objectives and provides first-look results illustrating the overall performance of the sensor system with a focus on the GIFTS EDU imaging capability and proof of the GIFTS measurement concept.

NOAA operational hydrological products derived from the advanced microwave sounding unit

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.