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  • View in gallery

    (top) LBLRTM simulated brightness temperature spectra for the U.S. Standard Atmosphere, 1976. (bottom) The difference between the brightness temperature spectra (top) and a simulation without water vapor (blue) and carbon dioxide (green). Note the carbon dioxide absorption feature is near 791 cm−1 and the water vapor absorption feature is near 784 cm−1. (Data: Courtesy of Paolo Antonelli, UW/CIMSS)

  • View in gallery

    (top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for changing lapse rate. The width of the bars represent the offline/online spectral separation used for AFM calculations. (middle) The fast-forward model-simulated brightness temperature spectra for 1700 (red) and 2000 UTC (blue) 8 May 2003 RAMS simulation corresponding to the temperature and moisture profiles. (bottom) The (left) input temperature and (right) water vapor profiles. Both simulations have the same water vapor profile (valid at 1700 UTC 8 May 2003 from the RAMS model), ozone profile, and surface emissivity and are valid at 1700 UTC (red) and 2000 UTC (blue). The skin temperature is always equal to the surface air temperature, which keeps the skin temperature/mean lower-tropospheric temperature difference constant.

  • View in gallery

    Conceptual diagram of how the carbon dioxide weighting function at 791.6 cm−1 is affected by changing lapse rate. (left) The weighting functions for the (right) corresponding temperature profiles. The blue and red weighting functions correspond to a similar color temperature profile. The water vapor profile is the same for both.

  • View in gallery

    (top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for varying amount of column water vapor. The width of the bars represent the offline/online spectral separation used for AFM calculations. (middle) The fast-forward model brightness temperatures and is valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. (bottom) The (left) input temperature and (right) water vapor profiles. Both simulations have the same temperature profile (valid at 1700 UTC 8 May 2003 from the RAMS model), ozone profile, skin temperature, and surface emissivity.

  • View in gallery

    Conceptual diagram of how the water vapor weighting function at 784.7 cm−1 is affected by changing moisture. (left) The weighting functions for the same temperature profile, but differing amounts of water vapor. The blue weighting function corresponds to a moist atmosphere and the red weighting function corresponds to a drier atmosphere. (right) The temperature profile is held constant.

  • View in gallery

    (top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for varying lapse rate and amount of column water vapor. The width of the bars represent the offline–online spectral separation used for AFM calculations. (middle) The fast-forward model brightness temperatures and is valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. (bottom) The (left) temperature and (right) moisture profiles from the 8 May 2003 RAMS simulation, and valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. Both simulations have the same surface emissivity and the skin temperature is set the to surface air temperature.

  • View in gallery

    High spectral resolution brightness temperature reference spectra from LBLRTM for the U.S. Standard Atmosphere, 1976. The thick red bars represent select GOES-12 sounder bands 50% points (spectral response function greater than or equal to 0.50). (Data: Courtesy of Paolo Antonelli, UW/CIMSS)

  • View in gallery

    Many water vapor TDAFMs (blue lines) and GOES-12 sounder split-window time difference (red line) for the RAMS simulation: (top) valid at 1730–1700 UTC, (middle) is valid at 1800–1730 UTC, and (bottom) is valid at 1830–1800 UTC 8 May 2003. Temperature and water vapor were allowed to change between each time step.

  • View in gallery

    (top left) GOES sounder TPW valid at 0000 UTC 16 Mar 2008 with Storm Prediction Center severe weather reports overlaid for the period 1200 UTC 15 Mar 2008–1159 UTC 16 Mar 2008. The gradient in TPW over the southeastern United States and Gulf of Mexico is associated with a synoptic-scale cold front. (top right) IASI 789.0 cm−1 (12.67 μm) brightness temperature image valid at 0222 UTC 16 Mar 2008. The cyan M is a point on the moist side of the synoptic boundary and the black D is a point on the drier side of the synoptic boundary. (bottom left) IASI brightness temperature spectrum for moist point in (top right) [775–855 cm−1 (12.9–11.7 μm)]. (bottom right) IASI brightness temperature spectrum for moist point in (top right) [1190–1258 cm−1 (8.4–7.9 μm)].

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Inferring Convective Weather Characteristics with Geostationary High Spectral Resolution IR Window Measurements: A Look into the Future

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  • 1 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
  • | 2 NOAA/NESDIS Center for Satellite Application and Research, Madison, Wisconsin
  • | 3 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin
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Abstract

A high spectral resolution geostationary sounder can make spectrally detailed measurements of the infrared spectrum at high temporal resolution, which provides unique information about the lower-tropospheric temperature and moisture structure. Within the infrared window region, many spectrally narrow, relatively weak water vapor absorption lines and one carbon dioxide absorption line exist. Frequent measurement of these absorption lines can provide critical information for monitoring the evolution of the lower-tropospheric thermodynamic state. This can improve short-term convective forecasts by monitoring regions of changing atmospheric stability. While providing valuable observations, the current geostationary sounders are spectrally broad and do not resolve the important spectrally narrow absorption lines needed to observe the planetary boundary layer. The usefulness of high spectral resolution measurements from polar-orbiting instruments has been shown in the literature, as has the usefulness of high temporal resolution measurements from geostationary instruments. Little attention has been given to the combination of high temporal along with high spectral resolution measurements. This paper demonstrates the potential utility of high temporal and high spectral resolution infrared radiances.

Corresponding author address: Justin Sieglaff, 1225 West Dayton St., Madison, WI 53706. Email: justins@ssec.wisc.edu

Abstract

A high spectral resolution geostationary sounder can make spectrally detailed measurements of the infrared spectrum at high temporal resolution, which provides unique information about the lower-tropospheric temperature and moisture structure. Within the infrared window region, many spectrally narrow, relatively weak water vapor absorption lines and one carbon dioxide absorption line exist. Frequent measurement of these absorption lines can provide critical information for monitoring the evolution of the lower-tropospheric thermodynamic state. This can improve short-term convective forecasts by monitoring regions of changing atmospheric stability. While providing valuable observations, the current geostationary sounders are spectrally broad and do not resolve the important spectrally narrow absorption lines needed to observe the planetary boundary layer. The usefulness of high spectral resolution measurements from polar-orbiting instruments has been shown in the literature, as has the usefulness of high temporal resolution measurements from geostationary instruments. Little attention has been given to the combination of high temporal along with high spectral resolution measurements. This paper demonstrates the potential utility of high temporal and high spectral resolution infrared radiances.

Corresponding author address: Justin Sieglaff, 1225 West Dayton St., Madison, WI 53706. Email: justins@ssec.wisc.edu

1. Introduction

The National Oceanic and Atmospheric Administration (NOAA) currently has two operational infrared atmospheric sounders in geostationary orbit (GEO). The infrared GEO sounders are part of the Geostationary Operational Environmental Satellites (GOES; Menzel and Purdom 1994). The current GOES sounders measure the infrared portion of the electromagnetic spectrum in 18 broad spectral bands. The GEO sounders scan the entire continental United States (CONUS) every hour. It is the hourly frequency (or finer) over the entire CONUS that make GEO information very valuable to forecasters, especially in monitoring rapidly changing weather conditions, such as severe convective outbreaks (Johns and Doswell 1992; Schmit et al. 2002; Chesters et al. 1983; Petersen et al. 1984). The 18 bands on the current GOES sounders are spectrally broad (10–50 cm−1) compared to high spectral resolution infrared sounders (on the order of 0.5 cm−1).

High spectral resolution infrared sounders currently in low-earth orbit (LEO) include the Atmospheric Infrared Sounder (AIRS; Aumann et al. 2003) on the National Aeronautics and Space Administration (NASA) Aqua satellite and the Infrared Atmospheric Sounder Interferometer (IASI; Cayla and Pascale 1995) on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp satellite. The Crosstrack Infrared Sounder (CrIS; Glumb et al. 2002) is planned for 2011 launch aboard the NASA National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite. These high spectral resolution sounders scan the infrared portion of the electromagnetic spectrum with thousands of spectrally narrow channels. They resolve the fine structure within the infrared window that contains information on the thermodynamic structure of the lower troposphere. High spectral resolution sounder data, such as that from AIRS, improves global numerical weather prediction (NWP) forecasts out to 7 days (Chahine et al. 2006; Le Marshall et al. 2006a,b, 2007).

The LEO satellites also include microwave-sounding instruments. Microwave sounders, such as the Advanced Microwave Sounding Unit (AMSU) and Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E), enable soundings in both clear and nonprecipitating cloudy-sky conditions (albeit at coarser spatial and temporal resolution than geostationary infrared measurements), where infrared soundings are only available in clear-sky regions. Ferraro et al. (2005) show the importance of microwave sounders to weather forecasting and analysis such as low-level moisture plume transport and the relation to precipitation. The information obtained from microwave instruments (when available) within cloudy-sky regions could be combined with information over clear-sky regions from infrared sensors to capture the full picture of the atmospheric state.

Despite the many benefits of LEO instruments, one limitation of LEO instruments is that measurements are made at one location in CONUS only twice daily. This is a significant limitation when trying to observe rapidly changing atmospheric conditions, such as those that produce severe weather (Committee on Earth Science and Applications from Space 2007). A logical conclusion is to combine benefits of high spectral resolution measurements and the high temporal frequency of a GEO instrument. The Committee on Earth Science and Applications from Space (2007) agree and contend that a geostationary high spectral resolution sounder is necessary, and that such an instrument should be made operational by 2018. Other studies (Smith et al. 1990; Schmit et al. 2009) have shown the value of geostationary high spectral resolution sounders for improving temperature and moisture soundings used for mesoscale convective forecasting. This paper demonstrates their value for detecting changes in the lower tropospheric thermodynamic state in clear-sky regions and for improving short-term convective forecasting.

This paper is organized into the following sections. First, a motivation of this work for improving short-term convective forecasting is given in section 2. Section 3 describes the absorption lines of interest and simulates that sensitivity to lower-tropospheric temperature and moisture changes. Section 4 compares the high spectral resolution moisture detection described in section 3 to current broadband GOES sounder instruments. Section 5 presents a technique that tracks temporal changes in lapse rate and moisture using geostationary high spectral resolution sounder data. Section 6 is a summary with recommendations.

2. Motivation of convective forecasting application of geostationary high spectral resolution infrared sounders

The improvement of short-term convective forecasting is a very important socioeconomic goal. Severe convective storm events have killed and injured many people and have resulted in billions of dollars of economic damage (Moller 2001). The Committee on Earth Science and Applications from Space (2007) states “the need for improved forecasts and warning systems are critical for the assets worth $2–3 trillion that are directly or indirectly sensitive to weather and climate.” With such an impact on lives and the economy, better forecasts and warning systems represent a significant societal benefit. The measurements of lower-tropospheric temperature (lapse rate) and moisture available from a geostationary high spectral resolution infrared sounder could help to achieve these goals. A brief insight into the convective forecasting process provides an appreciation of how high spectral resolution infrared sounders can improve short-term convective forecasts.

For deep convection to occur, three ingredients must be in place. There must be sufficient moisture in the lower troposphere, the lapse rate must be steep enough so that lifted parcels become positively buoyant, and there must be sufficient lift of parcels from the low moist layer to the parcel’s level of free convection (LFC; Johns and Doswell 1992). To assess the potential for deep convection, Johns and Doswell (1992) state that “a forecaster must be able to diagnose the current thermodynamic structure of the troposphere and to forecast changes resulting from thermal advection, moisture advection, and vertical motion fields.” The predominately land-based radiosonde network across the world currently measures the thermodynamic structure of the atmosphere twice daily (at 0000 and 1200 UTC). This sparse temporal and spatial nature of the radiosonde network leaves large gaps for those monitoring phenomena that occur on small spatial scales and small time scales, like severe convection. Johns and Doswell continue, “The challenge [of the forecaster] is to deduce the structure between observations in space and time, utilizing the limited sounding data, and to project temporal changes in this structure for the forecast period in question.” Moller (2001) agrees “… since radiosondes are released only twice a day, accurately deducing the stability structure between sounding times (0000 and 1200 UTC) is a formidable task [for the forecaster].” Wagner (2006) suggests that new convective indices that utilize high temporal information may help improve convective forecasting. The gaps between radiosonde stations and observation times are a significant obstacle and demonstrate the need for more frequent and accurate observations of the vertical thermodynamic structure of the atmosphere.

The lower-tropospheric thermodynamic information available from geostationary high spectral resolution infrared sounders encompasses two of the three elements needed for monitoring deep convection outlined by Johns and Doswell (1992). The geostationary platform can provide hourly measurement frequency or better over the entire CONUS, enabling soundings in clear-sky conditions. Accurate observations of thermodynamic structure of the atmosphere will undeniably have positive impacts on short-term convective forecasting. Doswell (2001) states that “… the importance of thermodynamic structure … for DMC [deep moist convection] processes cannot be underestimated. … Satellite-borne remote sensors continue to improve and … the high temporal resolution capability afforded by the current and future generations of meteorological satellites, offer much promise.” In fact, the next-generation imager on GOES-R will scan the CONUS area at least every 5 min (Schmit et al. 2005), but only with spectrally broad bands, similar to those on the current GOES sounders.

Other studies have shown that high temporal frequency measurements of the thermodynamic structure of the lower troposphere are useful for short-term convective forecasts. Kurimski and Schumacher (2005), Mamrosh et al. (2005a), and Fischer (2005) show that high vertical and temporal resolution aircraft profile data acquired during the Great Lakes Fleet Experiment (Mamrosh et al. 2005b) were very useful in convective forecasting. A survey of National Weather Service (NWS) forecasters (Schmit et al. 2002) showed that over 75% of forecasters agree that hourly GOES sounder products increase short-term convective forecast confidence. The survey also showed 70%–80% of NWS forecasters thought hourly GOES sounder derived products provided either significant or slightly positive impact on short-term convective forecasts. Yet, the same survey highlighted the challenges presented by the slow coverage rate and coarse vertical resolution of the current broadband sounders.

The literature shows NWP output struggles to accurately depict the evolution of preconvective boundary layers. Kain et al. (2005) show in the 2005 Storm Prediction Center/National Severe Storms Laboratory (SPC/NSSL) Spring Experiment that NWP output is often unreliable in forecasting preconvective boundary layers. Kain et al. (2005) attribute NWP problems with preconvective boundary layers to numerous physical parameterizations, which often fail because interactions within the boundary layer and between the boundary layer and free atmosphere are highly nonlinear and complex. Kurimski and Schumacher (2005), Mamrosh et al. (2005a), and Fischer (2005) also demonstrate through case studies that North American Model (NAM) and Rapid Update Cycle (RUC) forecast profiles often inaccurately forecast the evolution of preconvective boundary layers (i.e., low-level temperature inversions and fluxes of low-level moisture) compared to the in situ aircraft temperature and moisture profiles.

It is necessary to have accurate representation of the thermodynamic structure of the lower troposphere at appropriate spatial and temporal intervals to properly forecast convection. Geostationary high spectral resolution infrared sounders will improve the ability to detect changes in lower-tropospheric temperature and moisture in clear-sky regions over current GOES sounders and still provide high temporal resolution CONUS coverage.

3. High spectral resolution sounder measurements

High spectral resolution sounders measure the infrared portion of the spectra with spectral resolution of approximately 0.5 cm−1. Figure 1 shows a portion of the high spectral resolution brightness temperature spectrum for the infrared window calculated from Line-By-Line Radiative Transfer Model (LBLRTM) version 7.04 (Clough et al. 1992) for the U.S. Standard Atmosphere, 1976. Within the infrared window (approximately 770–1300 cm−1, aside from ozone absorption between 1000 and 1075 cm−1) the atmosphere is relatively transparent to radiation. In clear skies, an instrument viewing the earth will primarily measure the effective radiation of the earth’s surface. However, there are a series of absorption lines, due to water vapor and one due to carbon dioxide, where the radiative signal comes from the lower troposphere. The absorption lines are evident as the cooler regions of the brightness temperature spectra (Fig. 1). To resolve these absorption lines the spectral resolution must be better than one wavenumber (Wang et al. 2007).

GEO sensors can monitor the state of the atmosphere with high temporal resolution, which is useful for short-term convective forecasting. These absorption lines in the IR window can be used to monitor changes in lower-tropospheric temperature (lapse rate) and moisture.

To demonstrate this, we simulated such measurements using output from a numerical weather prediction (NWP) model processed through a radiative transfer model to calculate top-of-atmosphere (TOA) radiances. Temperature and moisture profiles used as input to the fast-forward model were obtained from the Colorado State University/Regional Atmospheric Modeling System (CSU/RAMS) at the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University (Pielke et al. 1992; Dostalek et al. 2004). The temperature and moisture profiles are valid for central Kansas on 8 May 2003. The model profiles were generated every 5 min, from 1700 to 2000 UTC. The output profiles have 25-hPa vertical resolution between 1000 and 100 hPa. Above 100 hPa, midlatitude summer climatology was used. A severe convective outbreak occurred over the central plains and middle Mississippi valley during that day. There were numerous reports of tornadoes, severe hail, and severe wind gusts across the region (more information is available online at http://www.spc.noaa.gov/climo/reports/030508_rpts.html).

The atmospheric profiles are interpolated to the AIRS radiative transfer model 101 pressure levels (Strow et al. 2003) before the profiles are used to simulate TOA radiances. The simulated radiances do not have noise added and thus can be used to determine the signal-to-noise ratios required on future instruments to apply the methods presented here. For more information about the data and processing details see Sieglaff (2007).

Equation (1) is a simplified version of the radiative transfer equation for upwelling radiation in the absence of scattering:
i1520-0426-26-8-1527-e1
where ɛλ,sfc is the surface emissivity for a given wavelength, Bλ(Tsfc) is the Planck predicted radiance for the surface temperature for a given wavelength, and tλ* is the total atmospheric transmittance for a given wavelength. Here is the vertical integration of emitted radiance between the surface and top of atmosphere for the given wavelength. Here W(z) represents the upwelling weighting function for a given wavelength, and W(z) represents the downwelling weighting function for a given wavelength. The simplified radiative transfer equation can be broken into two components. The first term includes the transmitted surface emission as well as the downward-transmitted atmospheric emission reflected from the surface and the second term is the upward-transmitted atmospheric emission. Within absorbing regions of the spectrum the first term is smaller and the second term larger than in window regions (relatively flat portions of the infrared brightness temperature spectrum).
The magnitude of the brightness temperature (TBB) difference between the peak of an absorption line (online) and an adjacent portion of the spectra (offline) will be referred to as the absorption feature magnitude (AFM) and is defined by
i1520-0426-26-8-1527-e2
AFM changes with time can be used to infer changes in lower-tropospheric lapse rate, moisture, and surface skin temperature. The time difference of the AFM will be referred to as the time difference absorption feature magnitude (TDAFM), which is given by
i1520-0426-26-8-1527-e3

The surface emission [first term of Eq. (1)] is a significant portion of the signal within the infrared window; the offline brightness temperature value in the calculation of AFM is largely a measure of the surface skin temperature. In these simulations we set the skin temperature equal to the bottom layer air temperature; this is often not the case in observations, as the skin temperature can vary considerably from the bottom layer air temperature. This is further explained in Sieglaff 2007. Changes in the surface skin temperature must be accounted for with data where skin temperature is allowed to vary naturally, but by removing this variable in the simulations it is possible to explicitly examine the effect of changing atmospheric thermodynamics with time.

To demonstrate the sensitivity of different absorption lines to temporal changes of lower-tropospheric thermodynamics, three simulations were performed for the period 1700–2000 UTC 8 May 2003 with 5-min temporal resolution. For brevity, only 1700 and 2000 UTC are presented. The first simulation examines the effect of changing the temperature profile with time (lapse rate); the water vapor, surface emissivity, and skin temperature/bottom layer air temperature difference were held constant. The second simulation examines the effect of changing the moisture with time; the temperature profile, surface emissivity, and surface skin temperature were held constant. The third simulation combines the first two, allowing both temperature (lapse rate) and moisture to change simultaneously, while fixing the surface emissivity and surface skin temperature/bottom layer air temperature difference.

Using these simulated radiances for 1700–2000 UTC 8 May 2003, the TDAFM was calculated for the carbon dioxide absorption feature near 12.6 μm or 791 cm−1 and water vapor absorption feature near 12.7 μm or 784 cm−1 (shown in Fig. 1). The carbon dioxide absorption feature near 791 cm−1 was chosen because it has sensitivity to lapse rate change of the lower troposphere but little sensitivity to water vapor change. The water vapor absorption line near 784 cm−1 was chosen because its weighting function peaks at nearly the same height as the carbon dioxide weighting function. Since the weighting functions peak at the same height, this water vapor absorption line has the same sensitivity to lapse rate change as the carbon dioxide line. In general, two absorption features within the infrared window with the same AFM have weighting functions peak at the same height. For the first simulation, the temperature profile changed between 1700 and 2000 UTC, while the moisture profile was held constant,1 as was the skin temperature. Figure 2 shows the carbon dioxide and water vapor TDAFM for the changing lapse rate.

In this simulation, the carbon dioxide and water vapor TDAFMs have roughly the same magnitude, approximately 1.5 K (Fig. 2). The water vapor distribution of this scene resulted in the 784 cm−1 weighting function peaking at the same height as the 791 cm−1 carbon dioxide weighting function. This is not necessarily the case with other scenes; rather the peak of water vapor lines is a function of the water vapor content and distribution. Therefore, a water vapor absorption line within the infrared window with the same AFM as the carbon dioxide AFM should be chosen for comparison. (This will be illustrated further in section 5 with an IASI overpass over the Gulf of Mexico.)

In this first simulation, only the temperature profile (lapse rate) of the atmosphere changed between the two time steps (no change in distribution or concentration of carbon dioxide or water vapor and hence very little change in weighting functions), both absorption lines will measure at nearly the same vertical layer at 1700 and 2000 UTC. Thus, only differential change in the temperature at that level and/or a change in the surface temperature will affect in the TDAFM.

A conceptual model of the weighting functions is shown in Fig. 3 for the carbon dioxide absorption line near 791.6 cm−1 (the water vapor absorption line at 784.7 cm−1 weighting function has very similar shape to the carbon dioxide weighting function, not shown). The red temperature profile occurs at time 1 and the blue temperature profile occurs at time 2. The carbon dioxide AFM is dictated by the difference between the surface temperature and temperature at the weighting function peak. If the AFM at times 1 and 2 are identical, the corresponding TDAFM is zero. If the AFM at time 2 is greater (less) than the AFM at time 1, the TDAFM is greater (less) than 0. This conceptual model can be applied to the simulation shown in Fig. 2 to show why the carbon dioxide and water vapor TDAFM are similar to each other and nonzero.

Applying the conceptual model to the first simulation shown in Fig. 2, since both the carbon dioxide and water vapor absorption line weighting functions have a similar shape and similar sensitivity to changes in lapse rate, the AFMs behave similarly to changes in temperature profiles (lapse rate). Since the weighting function peak changes very little for either absorbing species and both absorbing species weighting functions have similar shape, +1.5-K carbon dioxide and water vapor TDAFMs infers the lapse rate increased from 1700 and 2000 UTC.

The second simulation changed the atmospheric water vapor profile and held the temperature profile, ozone profile, surface emissivity, and surface skin temperature constant. Figure 4 shows the carbon dioxide and water vapor TDAFM for changing water vapor. The differences between the two simulated spectra are generally less than 2.0 K. The 1700 UTC brightness temperature spectrum is cooler than the spectrum at 2000 UTC. The differences between the two spectra are due to different amounts of water vapor, the total precipitable water (TPW; or integrated column water vapor) was 33.0 mm for 1700 UTC and 18.9 mm for 2000 UTC. The larger amount of water vapor in the 1700 UTC profile resulted in more absorption of surface emitted radiation and thus the brightness temperature spectrum is cooler. The water vapor TDAFM near 784 cm−1 has a value of −0.52 K (Fig. 4), which implies that the water vapor between 1700 and 2000 UTC decreased.

A conceptual model (Fig. 5) shows why a negative water vapor TDAFM implies decreasing moisture. The atmosphere is moist at time 1 (blue weighting function) and at time 2 the atmosphere has dried (red weighting function). For a drying atmosphere the water vapor absorption line weighting function peaks lower in the atmosphere. The difference between the surface temperature and weighting function peak temperature is much larger for the moist atmosphere (time 1) than the drier atmosphere (time 2; Fig. 5), resulting in a negative water vapor TDAFM.

From Fig. 4, the carbon dioxide TDAFM has a positive value. In the region of the carbon dioxide TDAFM (within the absorption line and in spectrally adjacent offline regions) there is a small, uniform sensitivity to water vapor due to water vapor continuum absorption. The carbon dioxide absorption line weighting function does not change because the only parameter changing is water vapor. One may infer that uniform water vapor continuum absorption across the carbon dioxide TDAFM and no change lapse rate would result in a carbon dioxide TDAFM of exactly zero. However, this is not the case; rather the carbon dioxide TDAFM is +0.48 K.

The carbon dioxide TDAFM in this case is not zero because there is different effective water vapor continuum absorption in the associated offline and online regions. First, within the carbon dioxide absorption feature (online region) the carbon dioxide absorption is greater than the water vapor continuum absorption. Second, the carbon dioxide absorption primarily occurs above the water vapor continuum absorption (due to well-mixed nature of carbon dioxide and the concentration of water vapor in the lower troposphere). The results of the simulations demonstrate offline regions of the spectra have an increased brightness temperature due to decreased moisture, but within the carbon dioxide absorption line, the carbon dioxide absorption masks the effect of water vapor continuum absorption, and there is little or no brightness temperature difference (Fig. 4). The different effective water vapor continuum absorption results in a nonzero carbon dioxide TDAFM. This complicates the use of the carbon dioxide TDAFM as a measure of lapse rate change with time. The offline region has a greater sensitivity to water vapor. This must be taken in account to accurately assess lapse rate change with the carbon dioxide TDAFM.

Nonetheless, the fact that the water vapor and carbon dioxide TDAFMs in this simulation have different signs is significant (from Fig. 4, water vapor TDAFM is −0.52 K and carbon dioxide TDAFM is +0.48 K). The carbon dioxide and water vapor TDAFMs behave differently with changes in the atmospheric water vapor profile, while the TDAFMs behave similarly to changes in the temperature profile (Fig. 2). This distinction allows for the separation of temperature and moisture changes in a time sequence of observed high spectral resolution radiances; this is shown more clearly with the third simulation.

The third simulation simultaneously changed the temperature and moisture profiles. The simulated brightness temperature spectra for 1700 and 2000 UTC and the corresponding water vapor and carbon dioxide TDAFM are shown in Fig. 6. The carbon dioxide TDAFM is 1.98 K and the water vapor TDAFM is 1.07 K. The total signal seen here is roughly the sum of the component simulations shown previously. Table 1 shows the component simulations and total simulation TDAFMs. Both TDAFMs were shown to be equally sensitive to changes in lapse rate (temperature profile), so an unequal change in the carbon dioxide and water vapor TDAFMs implies that the lower-tropospheric water vapor must have changed.

4. High spectral resolution versus current broadband sounder measurements

The current GOES sounder series measures the same portions of infrared spectra, but in spectrally wide bands compared to the absorption features of interest (Menzel and Purdom 1994; Schmit et al. 2002). A simulated LBLRTM U.S. Standard Atmosphere, 1976, high spectral resolution spectrum with the current GOES sounder channels overlaid is shown in Fig. 7. A single band on the GOES series sounders can span as many as 50–100 cm−1, in contrast with the high spectral resolution sounders that have a resolution of 0.5–1.0 cm−1. It is clear the absorption features of interest are not resolvable with current sensors; however, it has been shown low-level moisture can be detected using current broadband GEO sounders by utilizing the 11- and 12-μm split-window difference (Chesters et al. 1983; Petersen et al. 1984). The sensitivity of the split-window difference to lower-tropospheric moisture is compared to the sensitivity to various water vapor absorption lines in similar portions of the IR spectrum. To illustrate broadband capabilities for our case study, the GOES-12 sounder bands 7 and 8 (12 and 11 μm, respectively) were simulated by convolving the GOES-12 sounder spectral response functions with the RAMS simulated high spectral resolution brightness temperature spectra. Various high spectral resolution water vapor TDAFMs and simulated GOES-12 sounder split-window time-difference are shown for three time differences (Fig. 8). The TPW for the 4 times in Fig. 8 are 33.0 mm at 1700 UTC, 32.0 mm at 1730 UTC, 29.4 mm at 1800 UTC, and 25.9 mm 1830 UTC.

The GOES-12 sounder split-window time-difference values are presented along with several TDAFMs for online/offline combinations in the window region. The high spectral resolution water vapor TDAFMs have a range of 0.4–0.6 K over the three time differences, while the GOES sounder split-window time-difference only had a range of 0.1 K. The water vapor TDAFMs show an increased signal (4–6 times the signal) over the GOES sounder split-window time-difference.

Not all water vapor TDAFMs shown in Fig. 8 behave identically. The various water vapor lines have different sensitivities to the vertical distribution of water vapor, depending upon the absorption line strength. Some water vapor absorption lines are stronger, and have more sensitivity to water vapor changes at higher levels than comparatively weaker absorption lines, which have greater sensitivity to water vapor changes at lower levels in the troposphere. The various TDAFMs can be used to infer the vertical extent of water vapor. The presence of multiple high spectral resolution water vapor absorption lines (and hence multiple TDAFMs) can also be used to increase signal-to-noise ratio compared to the signal of the GOES sounder split-window time-difference by averaging many TDAFM measurements.

5. Tracking changes in lapse rate and moisture

It remains necessary to develop a technique that mitigates the effects of changing surface skin temperature [e.g., isolate the atmospheric term in Eq. (1) from the surface emission term, which these studies did via simulation]. It has been shown (Knuteson and Revercomb 2004; Knuteson et al. 2004; Li et al. 2007; Li and Li 2008) that it is possible to retrieve surface emissivity and surface skin temperature independently from high spectral resolution IR measurements.

In the framework of short-term convective forecasting there are two tasks planned. One is to utilize the carbon dioxide and water vapor TDAFMs to aid in short-term convective forecasting. The carbon dioxide absorption feature is sensitive to changes in the lower-tropospheric lapse rate. It has been shown through the simulations that sufficiently increased lower-tropospheric lapse rate with time will result in a positive carbon dioxide TDAFM, and vice versa. A simple lapse rate product
i1520-0426-26-8-1527-e4
should yield information about the time rate of change of the lower-tropospheric lapse rate:
  • If , then lapse rate increased;
  • If , then lapse rate did not change;
  • If , then lapse rate decreased.
These relationships hold true for cases with sufficiently large changes in lapse rate; however, when values are near zero, the possibility of water vapor contamination may cause the relationship to fail.

Similarly, a simple lower-tropospheric water vapor product remains to be developed. The carbon dioxide and water vapor absorption features (near 791.6 and 784.7 cm−1, respectively) were chosen for this case study because of the similar sensitivity to changes in the temperature profile (lapse rate) but different sensitivity to changes in the water vapor profile. Separation of changes in lower-tropospheric temperature from changes in lower-tropospheric water vapor suggests a simple lower-tropospheric water vapor product:

  • If ; TDAFMH2O > TDAFMCO2, then moisture increased;
  • If ; TDAFMH2O = TDAFMCO2, then no moisture change;
  • If ; TDAFMH2O < TDAFMCO2, then moisture decreased.

A demonstration of the above techniques is given using an IASI overpass at 0222 UTC 16 March 2008 (Fig. 9). This case was chosen because a significant severe weather outbreak occurred along a synoptic boundary over the southeastern United States. Across this boundary, there was a large gradient in water vapor as seen in the GOES sounder TPW image valid at 0000 UTC 16 March 2008 (Fig. 9). The low earth orbit of IASI data does not provide frequent refresh over CONUS locations. As a proxy for atmospheric changes with time, we demonstrate the above techniques by using two locations: one on the warm, moist side of a synoptic cold front and another toward the cooler, drier side of the front (Fig. 9). The locations chosen for comparison are over the Gulf of Mexico because the air temperature/skin temperature difference is small, which may not necessarily be true over land surfaces. As stated earlier, the skin temperature/lowest layer air temperature difference affects AFMs and TDAFMs; choosing points over water mitigates this effect.

Taking the moist point (cyan “M” in Fig. 9) as “time 1” and the drier point (black “D” in Fig. 9) as “time 2,” then the demonstration simulates a cold front passing through a location with time. The drier point chosen in Fig. 9 is the best point available within the IASI overpass. A better point (greater moisture contrast) would have been off the coast of southwestern Louisiana, but there was no IASI coverage at that location at that time. The point chosen does demonstrate an even more subtle change in moisture. The carbon dioxide TDAFM for these two points is −2.8 K (the carbon dioxide TDAFM calculation used the same online/offline points described previously). The water vapor absorption line chosen is at 1244 cm−1 and the corresponding offline wavenumber is 1234 cm−1. This water vapor absorption line was chosen because it has similar AFM as the carbon dioxide AFM, which infers weighting functions peaking at the same height and hence a similar sensitivity to changes in lapse rate. (The water vapor absorption line chosen in this case is different than that in section 3, since we chose the water vapor AFM most similar to the carbon dioxide AFM at time 1 for comparison.) In practice a quick method for determining which AFM to use is to compute the differences between all water vapor AFMs and the carbon dioxide AFM at time 1, then select the water vapor AFM with the difference closest to 0.

The water vapor TDAFM for these two points is −5.5 K. The carbon dioxide TDAFM less than 0 suggests the lower tropospheric lapse rate decreased going across the synoptic boundary, from the cyan M to the black D. The water vapor TDAFM less than the carbon dioxide TDAFM suggests the lower-tropospheric water vapor content decreased going across the synoptic boundary, from the cyan M to the black D. These results qualitatively agree with the derived GOES TPW from 0000 UTC 16 March 2008 (Fig. 9).

The methods described in this section are not intended to replace the derived products generated (e.g., CAPE, LI, TPW, etc.) from full physical retrievals of temperature and moisture profiles. Online/offline indices can be generated more simply and quickly than full physical retrievals. The timeliness of online/offline products allows information to be distributed in near–real time, which is especially important for rapidly changing environments, such as those found with convective outbreaks. One future item is to develop a more quantitative analysis using TDAFMs. For example, severity thresholds for TDAFM values can be developed to aid forecasters whether the detected TDAFM signal is minor, moderate, or severe. A severity system threshold may be something similar to current severe weather indices such as the convective available potential energy (CAPE), the lifted index (LI), the severe weather threat index (SWEAT), etc.

6. Conclusions

High spectral resolution IR measurements have the ability to spectrally resolve narrow water vapor and carbon dioxide absorption lines that current broadband instruments cannot resolve. The absorption lines within the infrared window have been shown to be sensitive to changes in lower-tropospheric temperature (lapse rate) and moisture. The brightness temperature difference between the absorption line peak and adjacent, more transparent, portion of the spectrum, is defined as the absorption feature magnitude (AFM), and the time change of the absorption feature magnitude (TDAFM). The TDAFMs are an indication of how the lower-tropospheric temperature and moisture has changed over time. The calculation of TDAFMs is simple and fast, which is important for short-term convective forecasting.

Surface emission complicates the use of the absorption lines and associated TDAFMs. By using simulated datasets it was possible to mitigate the surface emission changes between time steps, allowing for the atmospheric source to be isolated. Future work must develop a method for mitigating the change of surface skin temperature (surface emission) with time, which will enable separation of the atmospheric signal.

As much of the literature on convection has shown, the largest obstacle toward accurate short-term convective forecasts is filling in the gaps (spatial and temporal) in the radiosonde network. The presence of geostationary sounders have helped fill these data gaps; however, the measurements obtained by current instruments is spectrally broad and has little accurate information in the lower troposphere. A high spectral resolution sounder that resolves absorption lines within the infrared window would provide more information about the lower-tropospheric temperature and moisture. Combined with high spatial and temporal coverage, such an instrument would be very capable of monitoring rapidly changing environments associated with convection and such measurements should prove useful for improving short-term convective forecasting.

Acknowledgments

The authors thank Jim Gurka of NOAA for funding this project, Jun Li of CIMSS for useful discussions and support, Louie Grasso of CIRA for RAMS temperature and moisture data, Bob Rabin of NSSL/CIMSS for hourly RUC data, and Paolo Antonelli of SSEC for providing LBLRTM data. This study was partially supported by NOAA GOES-R Grant NA06NES4400002. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. government position, policy, or decision.

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Fig. 1.
Fig. 1.

(top) LBLRTM simulated brightness temperature spectra for the U.S. Standard Atmosphere, 1976. (bottom) The difference between the brightness temperature spectra (top) and a simulation without water vapor (blue) and carbon dioxide (green). Note the carbon dioxide absorption feature is near 791 cm−1 and the water vapor absorption feature is near 784 cm−1. (Data: Courtesy of Paolo Antonelli, UW/CIMSS)

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 2.
Fig. 2.

(top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for changing lapse rate. The width of the bars represent the offline/online spectral separation used for AFM calculations. (middle) The fast-forward model-simulated brightness temperature spectra for 1700 (red) and 2000 UTC (blue) 8 May 2003 RAMS simulation corresponding to the temperature and moisture profiles. (bottom) The (left) input temperature and (right) water vapor profiles. Both simulations have the same water vapor profile (valid at 1700 UTC 8 May 2003 from the RAMS model), ozone profile, and surface emissivity and are valid at 1700 UTC (red) and 2000 UTC (blue). The skin temperature is always equal to the surface air temperature, which keeps the skin temperature/mean lower-tropospheric temperature difference constant.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 3.
Fig. 3.

Conceptual diagram of how the carbon dioxide weighting function at 791.6 cm−1 is affected by changing lapse rate. (left) The weighting functions for the (right) corresponding temperature profiles. The blue and red weighting functions correspond to a similar color temperature profile. The water vapor profile is the same for both.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 4.
Fig. 4.

(top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for varying amount of column water vapor. The width of the bars represent the offline/online spectral separation used for AFM calculations. (middle) The fast-forward model brightness temperatures and is valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. (bottom) The (left) input temperature and (right) water vapor profiles. Both simulations have the same temperature profile (valid at 1700 UTC 8 May 2003 from the RAMS model), ozone profile, skin temperature, and surface emissivity.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 5.
Fig. 5.

Conceptual diagram of how the water vapor weighting function at 784.7 cm−1 is affected by changing moisture. (left) The weighting functions for the same temperature profile, but differing amounts of water vapor. The blue weighting function corresponds to a moist atmosphere and the red weighting function corresponds to a drier atmosphere. (right) The temperature profile is held constant.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 6.
Fig. 6.

(top) The carbon dioxide (near 791 cm−1) and water vapor (near 784 cm−1) TDAFMs for varying lapse rate and amount of column water vapor. The width of the bars represent the offline–online spectral separation used for AFM calculations. (middle) The fast-forward model brightness temperatures and is valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. (bottom) The (left) temperature and (right) moisture profiles from the 8 May 2003 RAMS simulation, and valid at 1700 (red) and 2000 UTC (blue) 8 May 2003. Both simulations have the same surface emissivity and the skin temperature is set the to surface air temperature.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 7.
Fig. 7.

High spectral resolution brightness temperature reference spectra from LBLRTM for the U.S. Standard Atmosphere, 1976. The thick red bars represent select GOES-12 sounder bands 50% points (spectral response function greater than or equal to 0.50). (Data: Courtesy of Paolo Antonelli, UW/CIMSS)

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 8.
Fig. 8.

Many water vapor TDAFMs (blue lines) and GOES-12 sounder split-window time difference (red line) for the RAMS simulation: (top) valid at 1730–1700 UTC, (middle) is valid at 1800–1730 UTC, and (bottom) is valid at 1830–1800 UTC 8 May 2003. Temperature and water vapor were allowed to change between each time step.

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Fig. 9.
Fig. 9.

(top left) GOES sounder TPW valid at 0000 UTC 16 Mar 2008 with Storm Prediction Center severe weather reports overlaid for the period 1200 UTC 15 Mar 2008–1159 UTC 16 Mar 2008. The gradient in TPW over the southeastern United States and Gulf of Mexico is associated with a synoptic-scale cold front. (top right) IASI 789.0 cm−1 (12.67 μm) brightness temperature image valid at 0222 UTC 16 Mar 2008. The cyan M is a point on the moist side of the synoptic boundary and the black D is a point on the drier side of the synoptic boundary. (bottom left) IASI brightness temperature spectrum for moist point in (top right) [775–855 cm−1 (12.9–11.7 μm)]. (bottom right) IASI brightness temperature spectrum for moist point in (top right) [1190–1258 cm−1 (8.4–7.9 μm)].

Citation: Journal of Atmospheric and Oceanic Technology 26, 8; 10.1175/2009JTECHA1210.1

Table 1.

Shows the TDAFMs for the carbon dioxide absorption feature near 791.6 cm−1 and water vapor absorption feature near 784.7 cm−1 for varied temperature profile/fixed water vapor profile, varied water vapor profile/fixed temperature profile, simultaneous varied temperature and water vapor.

Table 1.

1

Because of interpolation to different pressure levels there is actually a 0.3-mm difference between the two water vapor amounts in the 1700 and 2000 UTC simulations, but the vertical structure is nearly identical and the difference of 0.3 mm is taken to be negligible.

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