The Use of Hyperspectral Sounding Information to Monitor Atmospheric Tendencies Leading to Severe Local Storms

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Earth and Space Science

RESEARCH ARTICLE The use of hyperspectral sounding information 10.1002/2015EA000122-T to monitor atmospheric tendencies leading Key Points: to severe local storms • Hyperspectral sounders add independent information to Elisabeth Weisz1, Nadia Smith1, and William L. Smith Sr.1 existing data sources • Time series of retrievals provides 1Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA valuable details to storm analysis • Forecasters are encouraged to utilize hyperspectral data Abstract Operational space-based hyperspectral sounders like the Atmospheric Infrared Sounder, the Infrared Atmospheric Sounding Interferometer, and the Cross-track Infrared Sounder on polar-orbiting satellites provide radiance measurements from which profiles of atmospheric temperature and moisture can Correspondence to: be retrieved. These retrieval products are provided on a global scale with the spatial and temporal resolution E. Weisz, [email protected] needed to complement traditional profile data sources like radiosondes and model fields. The goal of this paper is to demonstrate how existing efforts in real-time weather and environmental monitoring can benefit from this new generation of satellite hyperspectral data products. We investigate how retrievals from all four Citation: Weisz, E., N. Smith, and W. L. Smith Sr. operational sounders can be used in time series to monitor the preconvective environment leading up to the (2015), The use of hyperspectral sounding outbreak of a severe local storm. Our results suggest thepotentialbenefit of independent, consistent, and information to monitor atmospheric fi tendencies leading to severe local high-quality hyperspectral pro le information to real-time monitoring applications. storms, Earth and Space Science, 2, doi:10.1002/2015EA000122-T. 1. Introduction Received 25 JUN 2015 Accepted 14 AUG 2015 The ability to understand, interpret, and ultimately accurately forecast an event relies on the real-time accu- Accepted article online 19 AUG 2015 mulation of high-quality measurements. Complex weather systems, like severe local storms, are difficult to measure in their entirety and are instead parameterized into basic physical parameters (e.g., temperature, humidity, pressure, and wind) and measured with instruments at a range of scales (e.g., at point source on the Earth’s surface or aloft from radiosondes, aircraft, and satellites). Uncertainty in the measurements and an incomplete knowledge of the physics of weather systems can contribute to the overall uncertainty about weather systems, in particular their prediction. This is especially an issue in the case of severe weather events when reliable monitoring and forecast accuracy is a matter of life and death. We determine here how recent advances in the quality of measurements and products from hyperspectral sounders may contribute to improvements in severe weather monitoring operations. While high spectral resolution (or hyperspectral) satellite sounder data have been available for more than a decade, the information available is not yet entirely understood to adequately be used in data assimilation, numerical prediction models, and in particular in satellite-based monitoring and forecasting applications. Challenges include complex radiative transfer computations and the computational cost due to the large number of channels as well as establishing adequate error characteristics. Furthermore, determining accurate atmospheric profiles, surface temperatures, trace gas concentrations, and cloud parameters from clear as well as cloud-contaminated radiances is still a difficult task, as will be discussed in section 2. Another problem has been the fact that these hyperspectral sounders are on polar-orbiting satellites, each of which gives near- global coverage over the course of a day. At low latitudes, any given location will have an overpass twice per day. Severe weather forecasting relies on timely information and therefore benefits most from measurements made by instruments on geostationary satellites with a regional view and high temporal frequency (e.g., every 15 min). However, the temporal frequency of satellite soundings from polar-orbiting platforms ©2015. The Authors. can be improved by using the four hyperspectral instruments now in operational orbit. The Atmospheric This is an open access article under the Infrared Sounder (AIRS), launched in 2002 on the EOS Aqua platform with an equatorial crossing time of terms of the Creative Commons 1:30 P.M. local time, was the first of this new generation of advanced sounders. The Infrared Atmospheric Attribution-NonCommercial-NoDerivs License, which permits use and distri- Sounding Interferometer (IASI) is flown on MetOp-A and MetOp-B, launched in 2006 and 2012, respectively, bution in any medium, provided the with equatorial crossing times of 9:30 and 10:40 A.M. Another innovative sounder, the Cross-track Infrared original work is properly cited, the use is non-commercial and no modifications Sounder (CrIS), has been launched in 2011 on the Suomi NPP (S-NPP) satellite. Suomi NPP is in the same or adaptations are made. orbital plane as Aqua; i.e., it crosses the equator at 1:30 P.M. but is positioned in slightly higher altitude so that

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the local overpass time differs from that of Aqua by as much as 45 min. Hence, since early 2013, there are at least eight measurements globally per day at low latitudes and up to an order of magnitude more per day at high-latitude locations. Hyperspectral infrared sounders report at a spatial resolution of ~13 km, which is lower than multispectral imagers, like MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), with 1 km or higher spatial resolution. But due to their high spectral resolution, hyperspectral sounders provide more detailed information about the vertical atmospheric structure and composition than the imagers and first-generation broadband multispectral sounders such as the High-resolution Infrared Radiation Sounder. Hence, hyperspectral sounders provide independent measurements that complement existing space-based imagers and have the potential to enhance the performance of information systems focused on weather or environmental monitoring and prediction. Several recent studies demonstrate how hyperspectral measurements improve the assimilation of the atmospheric state for numerical weather prediction [e.g., Le Marshall et al., 2009; Li and Liu, 2009; Guidard et al., 2011; Thepaut et al., 2011; Singh et al., 2012] by adding independent and detailed space-time information about the initial three-dimensional structure of the atmosphere. In this paper we do not focus on data assimilation but instead aim to describe the type and quality of information that hyperspectral sounders add to weather monitoring and short-term forecasting systems, which typically rely on combining measurements from myriad sources to characterize atmospheric and convective conditions. With this, we highlight the consistency and continuity that can be achieved in real-time among sounders on different platforms by applying the same atmospheric profile retrieval algorithm to all radiance measurements. In section 2 we give an overview of the hyperspectral sounder retrieval method, and in section 3 we explain the quality of information from these retrievals with a specific case study of the tornado outbreak that occurred in central Oklahoma on 20 May 2013. A summary is given in section 4.

2. Atmospheric Parameter Retrieval From Hyperspectral Sounders Hyperspectral infrared sounders, such as AIRS (Atmospheric Infrared Sounder) on the EOS Aqua satellite, IASI (Infrared Atmospheric Sounding Interferometer) on the MetOp-A and MetOp-B satellites, and CrIS (Cross-track Infrared Sounder) on the Suomi NPP (S-NPP) satellite, measure the top-of-atmosphere radiance emitted by the Earth system with very high spectral resolution in several thousand channels at À wavenumbers from roughly 650 to 2700 cm 1. The capability of sounders to detect atmospheric structure is determined by the vertical width of the weighting functions, the noise level, and the number of independent spectral channels associated with the radiance measurements. A weighting function, defined as the vertical derivative of the atmospheric transmittance profile, represents that fraction of the Planck radiance, associated with the temperature at each atmospheric level, which contributes to the measured radiance; it therefore determines the height region that is sensed from space by the radiance measurement at each spectral channel. In contrast to first-generation broadband sounders, which have only a few very broad weighting functions, instruments with several thousands of channels have sharper weighting functions because their spectral resolution is smaller than the spectral spacing of atmospheric absorption lines. Given a spectrum of radiance measurements, temperature and humidity profiles and other parameters can be derived using an inverse solution of the radiative transfer equation as part of the so-called inversion problem (i.e., the retrieval of the atmospheric state from atmospheric spectral radiance measurements). Theoretically, a spectrum of infrared radiances at high spectral resolution can be inverted to produce temperature and humidity profiles with a vertical resolution of 1–3 km, dependent upon altitude, and an average accuracy of 1–2Kelvin(K)and10–20%, respectively. Hyperspectral retrieval (or level 2) products are commonly produced on a 101 pressure level numerical quadrature grid (first specified by Strow et al. [2003]), which ranges from 1100 hPa to 0.005 hPa. A number of factors make the design of a good retrieval algorithm challenging. First of all, the inversion problem is underconstrained (or ill-posed) since a near-continuous vertical profile must be retrieved from a finite number of spectral measurements. Also, the weighting functions exhibit certain widths and they overlap with each other. This indicates a vertical correlation in the measurements that makes exact characterization with high vertical resolution challenging. Lastly, the measurements always contain noise, which limits the vertical resolution achievable through the spectral/vertical deconvolution of the radiance measurements. All these

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factors combine to make the inversion of a spectrum of measurements into an exact atmospheric profile state impossible. These factors need to be addressed in the retrieval method, which is typically designed to maximize the signal-to-noise ratio and retrieve the best possible estimate (e.g., statistically the most probable solution) of the true atmospheric state. In general, retrieval methods are based on either linear regression or on an optimal estimation approach. The use of linear regression as an inversion technique of satellite radiance measurements has a long and proven success rate [e.g., Smith et al., 1970; Huang and Antonelli, 2001; Zhou et al., 2007]. A linear regression algorithm uses sets of simulated radiances regressed against a set of atmospheric profiles to obtain the coefficients that will serve to map any radiance spectrum into a retrieval estimate. For computational speed and efficiency, the radiances are often compressed by eigenvectors to reduce the matrix dimension. One drawback of a simple linear retrieval method is that it does not adequately account for the nonlinear relationship between the radiance measurements and the atmospheric state, in particular moisture and clouds. Another type of inversion technique is the optimal estimation method [Rodgers, 2000], which incorporates location-specific a priori information as well as radiative transfer and weighting function calculations for every single field of view (FOV). Whereas optimal estimation retrieval techniques are iterated to account for the nonlinearity of the retrieval problem, they are often too computa- tionally expensive for use in global or real-time forecasts. In this paper we make use of the satellite multi-instrument dual-regression (DR) retrieval system [Weisz et al., 2011, 2013; Smith et al., 2012, 2014], which was originally developed during the early 1990s for deriving atmospheric profiles from the first hyperspectral radiance data obtained from aircraft [Smith et al., 1994, 2005; Zhou et al., 2002]. However, it needs to be mentioned that any other retrieval method, which is capable of processing hyperspectral radiance data, can be used to achieve similar results, i.e., obtain the same kind of information about the atmospheric state when analyzing weather events. The dual-regression retrieval technique differs from traditional retrieval algorithms by combining the advantages of regression (i.e., speed and efficiency) and optimal estimation inversion (i.e., accounting for the nonlinearity of the retrieval problem) to produce atmospheric profile, surface, and cloud parameters at single FOV resolution. Since this technique is based on linear regression, it ensures the processing speed necessary for real-time applications. Between 25 and 75 FOVs (depending on the computational resources on hand) can be processed per second, with the entire suite of retrieval parameters provided for each FOV. Altogether, the data received via direct-broadcast antennas can be processed (i.e., from raw data to calibrated radiances and then to retrieval products) and made available to the weather research, forecasting, and data assimilation community within a few minutes of each satellite overpass. The DR method includes geophysical classification based on window region brightness temperatures, scan- ning angle, and most importantly on cloud heights to account for the nonlinear dependence of IR radiances on the atmospheric state, in particular moisture and clouds. The term “dual-regression” refers to the fact that two sets of regression coefficients are computed from training sets (of profiles including surface and cloud parameters and of their corresponding simulated radiances), one for clear and one for cloudy atmospheric states, respectively. These regression coefficients are precomputed once for each instrument. It is important that the training sets are globally representative with respect to each parameter. The regression coefficients are then used to invert radiance measurements with an accuracy that is consistent across the globe. Inversion is achieved by applying each set of coefficients separately to a radiance measurement (i.e., one radiance spectrum) to obtain a clear- and a cloud-trained solution. The DR method derives cloud top pressure by applying the logic that under clear conditions (and above clouds) the clear- and cloudy-trained retrieved temperature profiles will be very close, whereas under cloudy conditions the clear-trained solution will be colder than the cloud-trained solution below the cloud. The cloud top pressure is then assigned to that pressure level where the profiles start to deviate from each other. Once the cloud top pressure is determined, or the FOV is identified to be clear based on a number of rigorous threshold tests, the clear and cloudy profiles are combined to create the final sounding product. Since clouds hinder retrieval of geophysical information from space-based infrared measurements below clouds (irrespective of the instrument and algorithm), profiles from below optically thick clouds are rejected. On the other hand, thin and/or broken clouds allow enough information to reach the instrument and retrievals are often provided below the cloud top (given that they pass quality control). The DR method uses the full information of high spectral resolution measurements (i.e., all the spectrally accurate channels are used). And unlike optimal estimation (Bayesian) inversion techniques, the DR method

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does not depend on in situ measurements, or a priori estimates of the atmosphere, to initialize inversion. This has the beneﬁt that the retrieved solution is independent of any other data source and has a consistent quality across the globe. It is suitable for the study of mesoscale to synoptic-scale (i.e., 10 km to 1000 km) variation of atmospheric temperature, moisture, cloud height, cloud optical thickness, as well as atmospheric stability changes that typically occur in rapid fashion before severe weather events. The DR retrieval algorithm is available as a stand-alone tool in the CIMSS (Cooperative Institute for Meteorological Satellite Studies) Community Satellite Processing Package to process AIRS, IASI, and CrIS radiance data. Open access to both the hyperspectral sounder radiance data on polar-orbiting satellites (which can be received via direct-broadcast antennas) and the retrieval software allows real-time monitoring of the three-dimensional atmosphere anywhere on the globe. This paper discusses a speciﬁc case, namely, the Moore, Oklahoma, tornadic event of 2013, but the reader should bear in mind that the retrieval algorithm can be applied to any severe weather event anywhere in the world with the same accuracy and precision.

3. Severe Weather Application Example: Moore, Oklahoma, Tornado of 20 May 2013 The 18–20 May 2013 event included a signiﬁcant tornado outbreak that affected parts of the Central United States and lower Great Plains. This event also produced the most deadly and devastating tornado of 2013 in the United States: an EF-5 tornado struck the city of Moore (latitude/longitude: 35.3°, À97.5°) in Oklahoma (OK) and adjacent areas in the afternoon of 20 May 2013, claiming many lives and causing extensive damage. Current understanding of tornado genesis on convective storm scale is still not complete, but science of severe storms advanced considerably over recent decades and shows that tornadoes in the Central United States cannot simply be explained by a clashing of adjacent air masses as it is often reported by the media [Schultz et al., 2014]. For a tornado to develop, several conditions must be present in the atmosphere, namely, low-level moisture, atmospheric instability resulting from warm moist air beneath cold dry air, and a lifting mechanism. That is, warm moist air converges in an area where there is advection of cooler dry air aloft. The latter can be associated with a cold front or a dry line. Such dry lines are common in the Central Plains region of the United States where dry air from the north or west meets warm moist air moving northward from the Gulf of Mexico. Typically, the convection is initially suppressed by a low-level temperature inversion produced by nocturnal cooling and associated subsidence. Once the nocturnal “moisture capping” inversion erodes due to daytime surface heating, the warm air rises rapidly as an updraft and produces the thunderstorm cloud. Some of the most violent tornadoes develop from supercell thunderstorms, which are characterized by a persistent rotating updraft (mesocyclone) due to a strong vertical change in wind speed and direction. This wind shear is produced in part by the jet stream and the development of lee mountain cyclones, which can also lead to the development of strong low-level moisture advection, adding further energy to an evolving system. On 20 May the weather conditions of warm moist air convergence under cold dry air along a frontal boundary combined over central Oklahoma with strong wind shear due to the presence of a jet stream to produce a violent tornado outbreak.

3.1. A Timeline of the Tornado Event Several days before the tornado outbreak, the National Weather Service (NWS) office at Norman, Oklahoma, in coordination with the Storm Prediction Center correctly predicted severe thunderstorms for 20 May 2013 over the Oklahoma region as detailed in the NOAA NWS Service Assessment Report [2013] and the NOAA NWS Forecast Office Norman Presentation [2013]. On the morning of 20 May high instability indices of the convective available potential energy, or CAPE, were detected at Norman along with high air and dew point temperatures and high wind sheer speeds. These conditions together indicate extreme atmospheric instability. Furthermore, a frontal boundary and dry line intersected near Norman. A tornado watch including Moore and Newcastle, located about 10 km southwest of Moore, was issued at 1810 UTC (1310 central standard time). A severe thunderstorm warning was issued at 1912 UTC. The supercell thunderstorm quickly intensified into a tornadic supercell as observed on radar images and reported by storm spotters. The NWS at Norman subsequently issued a tornado warning at 1940 UTC. The Newcastle-Moore tornado first touched down about 6 km west of Newcastle at 1956 UTC and moved toward the southern Oklahoma City metropolitan area. At 2001 UTC, NWS-Norman issued a tornado emergency for both southern Oklahoma City and Moore. At 2014 UTC, the EF-5 tornado touched

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À Figure 1. (a) GOES-East visible channel image (top), CrIS brightness temperatures at 910 cm 1 (middle), and retrieved CrIS cloud top pressures (bottom). (b) Cloud top temperature (CTT) retrieved from Metop-A IASI (top left), Metop-B IASI (top right), CrIS (middle left), and AIRS (middle right). White areas refer to clear regions. Visible channel images from VIIRS and MODIS are shown in Figure 1b (bottom left and bottom right). All results from 20 May 2013.

down in Moore. The Oklahoma Department of Emergency Management reported that 24 people were killed and approximately 1200 homes in Oklahoma City and Moore were destroyed.

3.2. The Tornado Forecast and Response The current level of forecasting technology led in this case to a very valuable forecast; i.e., the forecasting offices were well prepared, and a major threat was communicated to the public. Nonetheless, the warning time was insufficient for many. Often the lack of detailed information about the initial state of the environment prevents a more accurate and timely forecast of the path, location, and strength of any severe weather event. More detailed and timely observations in temperature, moisture, and stability changes can potentially increase the warning time [Stensrud et al., 2009]. For tornado events there is the eminent need of additional diagnostic information, such as three-dimensional thermal and moisture fields, to aid the forecasting and decision-making process. For example, the NOAA NWS Service Assessment Report [2013] states that while the traditional suite of forecast model data are useful, such as the Global Forecast System, which is displayed and analyzed in Advanced Weather Interactive Processing System, nontraditional models proved extremely useful in making more specific time references to the approaching threats. Hence, it is important that all available data (e.g., from radar, satellites, and models) are readily available to the forecasters such that more timely warnings can be communicated to the public.

3.3. Augmenting the Forecast With Satellite Soundings In this section we show that satellite soundings add mesoscale detail to the horizontal and vertical analysis of the storm system. These could provide the necessary data to improve the timeliness of severe storm predictions. On 20 May 2013, overpasses by Metop-A, Metop-B, Suomi NPP, and Aqua satellites occurred

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À Figure 2. (a) Lifted index retrieved from Metop-B IASI (top) and S-NPP CrIS (bottom). Underlying gray areas refer to brightness temperatures at 910 cm 1. (b) Cross section of equivalent potential temperature retrieved from Metop-B IASI (top) and S-NPP CrIS (bottom) along the red line shown in the map on the right. White areas describe the presence of optically thick clouds. All results from 20 May 2013.

approximately 4 h, 3 h, 1 h, and 30 min, respectively, before the tornado touchdown at Moore. This time series of atmospheric profiles allows a consistent analysis of the preconvective and convective environment across the entire region of the storm system. This is not possible with radiosonde profile data that can only offer dispersed and often fixed atmospheric profile measurements. Moreover, such a time series offers an independent investigation of the 3-D atmosphere that supplements other sources of data describing the atmospheric state. The dual-regression (DR) retrieval method was applied to radiance measurements from the AIRS, IASI, and CrIS instruments to retrieve atmospheric moisture and temperature profiles, surface parameters, and cloud parameters in near real-time. In addition, stability indices were derived and distributed along with the retrieved parameters at single FOV resolution. Figure 1a shows visible imagery from GOES-East at 19 UTC, as well as CrIS brightness temperature for a À “window” spectral channel (at wavenumber 910 cm 1) and CrIS cloud top pressure retrievals. The frontal line is readily visible in these images as are the convective cells along that line. The strongest cells are observed over southern and central Oklahoma, but severe storms with strong winds, hail, and tornadoes occurred as far north as the Great Lakes region. CrIS cloud top pressure retrievals, although at lower spatial resolution than the GOES imager, depict similar cloud features but with the added advantage of contributing quantitative information about the cloud altitudes to the visible imagery. Figure 1b shows a closer view of the line of rapidly developing thunderstorms over southern and central Oklahoma, where the northernmost cell produced the Moore tornado. Figure 1b (top left, top right, middle left, and middle right) shows temperatures associated with the cloud heights retrieved from Metop-A IASI (at ~1600 UTC), Metop-B IASI (~1650 UTC), CrIS (at ~1905 UTC), and AIRS (at ~1935 UTC), respectively. High cloud altitudes with very low temperatures (lower than À70°C) confirm the presence of strong updrafts, an indicator of storm severity. Shadows of overshooting cloud tops are also noticeable in the visible channel images of VIIRS and MODIS in Figure 1b (bottom left and bottom right). It can be seen from Figure 1b that both Metop overpasses occurred before any convective development in that region. About 2 h later, at the time of the S-NPP overpass (i.e., approximately 1 h before the Moore tornado), convective cells

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Figure 3. Temperature (solid black), dew point temperature (dashed black), and equivalent potential temperature (green), as well as relative humidity proﬁles retrieved from (a) CrIS and (b) AIRS at 33.8° latitude and À97.2° longitude. All results from 20 May 2013.

are noticeable, and these cells explosively intensified as shown in the Aqua overpass 30 min later. About 30 min after the Aqua overpass, an EF-5 tornado hit Moore. This illustrates the potential importance of using all available satellite data including hyperspectral sounder data in the study/analysis of the preconvective environment associated with real-time severe weather events. The rapid destabilization that occurred throughout the day is illustrated in Figure 2. The lifted index (LI) in degrees Celsius derived from Metop-B IASI and CrIS measurements is shown in Figure 2a. A negative LI value indicates an unstable atmosphere, and values below À4 describe large to extreme instabilities favorable to intense convection and severe thunderstorms. IASI LI values indicate a slightly unstable atmosphere, where thunderstorms are a possibility. About 2 h later highly negative values below À10 are identified from the CrIS retrievals, in particular southeast of Moore. The atmosphere is very unstable, and strong updrafts with developing thunderstorms are likely. Other measurements of atmospheric instability, like CAPE, potential temperature, and equivalent potential temperature, are also derived from hyperspectral retrieval profiles and can be easily and routinely incorpo- rated in convective forecasting activities. Figure 2b shows cross sections of equivalent potential temperature

(theta-e, θe) in degrees Kelvin retrieved from both Metop-B IASI and CRIS measurements. θe compares temperature and moisture content at different pressure levels and indicates unstable air if θe decreases with altitude. The IASI retrievals show a slight decrease in θe south of Moore (i.e., south of 35° latitude), whereas the CrIS retrievals detect a significantly stronger decrease in θe from the surface up to 700 hPa indicating unstable stratification when low-level warm moist converges under cold dry air aloft. The same dynamics is illustrated in Figure 3, where temperature, dew point temperature, theta-e, and relative humidity profiles derived from CrIS and AIRS measurements at one specific location (south of Moore, near the Texas border) are shown. From the CrIS (Figure 3a) to the AIRS overpass (Figure 3b) dew point temperature increases to equal the air

temperature at levels below approximately 880 hPa resulting in an abrupt increase in θe and relative humidity; therefore, instability grows, which is conﬁrmed by increased CAPE and decreased LI values. The AIRS relative humidity proﬁle shows the impact of the advection above 880 hPa, where the air is much drier than during the time of the CrIS overpass. So far, we have shown that a time sequence of hyperspectral retrievals obtained from multiple sounders is capable of describing a severe weather event in a quantitative consistent manner. These retrieval products, when produced in near real-time provide a useful complement to analysis and forecast products from weather models like the North American Mesoscale Forecast System, Rapid Refresh (RAP), and the hourly updated High-Resolution Rapid Refresh model. Figure 4 shows hyperspectral retrievals compared to products from a local and a global model. Relative humidity at the 500 hPa pressure level from the NCEP

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Figure 4. (a) Relative humidity at 500 hPa from the NCEP RAP model analysis at 1700 UTC (top left), retrieved from Metop-B IASI (top right), from the NCEP RAP model analysis at 1900 UTC (bottom left) and retrieved from S-NPP CrIS (bottom right). White areas describe the presence of optically thick clouds. (b) Overall (surface to tropopause) level mean root-mean-square difference (RMSD) derived from differences between retrieved and NCEP GDAS temperature proﬁles. All results from 20 May 2013.

(National Centers for Environmental Prediction) RAP (Rapid Refresh) model at 1700 UTC is shown together with IASI retrievals, 1900 UTC with CrIS retrievals in Figure 4a. Both the retrievals and the model products have a horizontal resolution of ~14 km. Note that the model products are derived by combining different data sources, whereas the retrievals are derived directly from hyperspectral radiance measurements with no ancillary data needed. A primary benefit of the hyperspectral soundings is that they can identify small-scale details of changing weather conditions. A similar comparison of the retrieved temperature with NCEP GDAS (Global Data Assimilation System) temperature is shown in Figure 4b as the overall tropospheric level mean root-mean-square difference (RMSD). Again, the two products are in reasonable agreement (within 3 K). But the GDAS assimilation process tends to smooth out small-scale variations, while the hyperspectral soundings do not. It should be emphasized that these hyperspectral retrieval products are especially important in geographical regions where radiosonde observations and other in situ measurements are scarce, where accurate local models may not be available or where global models are less reliable. The dual-regression retrieval products have been evaluated under various weather conditions by compari- sons with in situ, satellite, and aircraft observations as well as data derived using other retrieval methods as shown in previous publications. For example, AIRS error statistics with respect to radiosondes, model fields, and operational retrieval products are shown by Weisz et al. [2011] and Smith et al. [2012]; the latter publica- tion also describes Hurricane Isabel (September 2003) and the Joplin, Missouri, tornadic events of May 2011 using AIRS and IASI retrievals. And cloud heights retrieved from AIRS and CrIS have been compared with products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation satellite as part of an analysis of Superstorm Sandy (October 2012) by Weisz et al. [2013].

4. Summary Measured radiances from satellite-based hyperspectral instruments contain high information content on the full atmospheric state, but the full exploitation and optimal use of thousands of spectral channels within a

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regional weather monitoring and forecasting operation is still a challenging task. The dual-regression (DR) retrieval method has a demonstrated capability to infer properties of the vertical atmospheric column as well as surface and cloud parameters with consistent accuracy in near real-time at sensor FOV resolution to sup- port mesoscale data assimilation and forecasting. Through examination of the May 2013 tornado outbreak, which affected parts of the Central United States, we have shown that satellite soundings provide valuable 3-D information on cloud conditions and stability changes as well as mesoscale details of the vertical atmospheric structure. These atmospheric details can clearly contribute to forecasting operations using high spatial resolution numerical weather prediction models capable of assimilating such observations. With this case study we substantiated that hyperspectral retrieval products add reliable, consistent, and independent information to supplement the traditional suite of information sources in environmental monitoring systems. These retrievals have the capability to enhance the quality of weather information services to the beneﬁt of weather forecasting and disaster monitoring alike.

Acknowledgments References This work is supported by the NOAA/NESDIS JPSS (Joint Polar Satellite Guidard, V., N. Fourrié, P. Brousseau, and F. Rabier (2011), Impact of IASI assimilation at global and convective scales and challenges for the – System) PGGR (Proving Ground Risk assimilation of cloudy scenes, Q. J. R. Meteorol. Soc., 137, 1975 1987, doi:10.1002/qj.928. Reduction) Program. We gratefully Huang, H.-L., and P. Antonelli (2001), Application of principal component analysis to high-resolution infrared measurement compression and – acknowledge the use of GOES imagery retrieval, J. Appl. Meteorol., 40, 365 388. – from the NASA/NOAA GOES Project and Le Marshall, J., et al. (2009), Improving global analysis and forecasting with AIRS, Bull. Am. Meteorol. Soc., 87, 891 894, doi:10.1175/BAMS-87-7-891. fi RAP model data from nomads.ncdc. Li, J., and H. Liu (2009), Improved hurricane track and intensity forecast using single eld-of-view advanced IR sounding measurements, noaa.gov/. VIIRS and MODIS imagery are Geophys. Res. Lett., 36, L11813, doi:10.1029/2009GL038285. fi provided courtesy of University of NOAA NWS Forecast Of ce Norman Presentation (2013), May 20, 2013, Newcastle-Moore Tornado. [Available at http://www.srh.noaa.gov/ Wisconsin-Madison Space Science and images/oun/wxevents/20130520/products_presentation.pdf.] fl fl Engineering Center. The authors would NOAA NWS Service Assessment Report (2013), May 2013 tornadoes and ash ooding. [Available at http://www.nws.noaa.gov/om/ also like to thank Scott Bachmeyer, assessments/pdfs/13oklahoma_tornadoes.pdf.] Jordan Gerth, and Kathy Strabala Rodgers, C. (2000), Inverse Methods for Atmospheric Sounding: Theory and Practice, World Sci. Co., Singapore, River Edge, N. J. “ (all CIMSS) for their insightful discussions Schultz, D. M., Y. P. Richardson, P. M. Markowski, and C. A. Doswell III (2014), Tornadoes in the Central United States and the Clash of Air ” – on severe weather development and Masses , Bull. Am. Meteorol. Soc., 95, 1704 1712, doi:10.1175/BAMS-D-13-00252.1. prediction. We also wish to thank the two Singh, R., C. M. Kishtawal, S. P. Ojha, and P. K. Pal (2012), Impact of assimilation of Atmospheric InfraRed Sounder (AIRS) radiances and anonymous reviewers for their valuable retrievals in the WRF 3D-Var assimilation system, J. Geophys. Res., 117, D11107, doi:10.1029/2011JD017367. comments and suggestions that have Smith, N., E. Weisz, W. L. Smith, and H.-L. Huang (2014), An overview of the UW hyperspectral retrieval system for AIRS, IASI and CrIS, paper helped to improve this manuscript. (8.03) presented at the 19th International TOVS Study Conference, ITSC-XIX, Jeju Island, South Korea. Smith, W. L., H. M. Woolf, and W. J. Jacob (1970), A regression method for obtaining real time temperature and geopotential height profiles from satellite spectrometer measurements and its application to Nimbus-III “SIRS” observations, Mon. Weather Rev., 98, 582–603. Smith, W. L., H. E. 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(2009), Convective-scale warn-on forecast: A vision for 2020, Bull. Am. Meteorol. Soc., 90, 1487–1499, doi:10.1175/ 2009BAMS2795.1. Strow, L., S. E. Hannon, S. De Souza-Machado, H. E. Motteler, and D. Tobin (2003), An overview of the AIRS radiative transfer model, IEEE Trans. Geosci. Remote Sens., 41(2), 303–313, doi:10.1109/TGRS.2002.808244. Thepaut, J.-N., S. English, A. P. McNally, and P. Bauer (2011), Use of satellite data at ECMWF, Document C24_Thepaut_T217B.pdf, ECMWF, Reading, U. K. Weisz, E., W. L. Smith, J. Li, W. P. Menzel, and N. Smith (2011), Improved profile and cloud top height retrieval by using dual-regression on high-spectral resolution measurements, paper (HWA4) presented at the OSA topical meeting: Hyperspectral Imaging and Sounding of the Environment (HISE), Toronto, Canada. Weisz, E., W. L. Smith Sr., and N. Smith (2013), Advances in simultaneous atmospheric profile and cloud parameter regression based retrieval from high-spectral resolution radiance measurements, J. Geophys. Res. Atmos., 118, 6433–6443, doi:10.1002/jgrd.50521. Zhou, D. K., W. L. Smith, J. Li, H. B. Howell, G. W. Cantwell, A. M. Larar, R. O. Knuteson, D. C. Tobin, H. E. Revercomb, and S. A. Mango (2002), Thermodynamic product retrieval methodology and validation for NAST-I, Appl. Opt., 41(33), 6957–6967, doi:10.1364/AO.41.006957. Zhou, D. K., W. L. Smith Sr., X. Liu, A. M. Larar, S. A. Mango, and H.-L. Huang (2007), Physically retrieving cloud and thermodynamic parameters from ultraspectral IR measurements, J. Atmos. Sci., 64, 969–982, doi:10.1175/JAS3877.1.

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