Correlations of Multispectral Infrared Indicators and Applications in the Analysis of Developing Convective Clouds

Qiong Wu Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Search for other papers by Qiong Wu in
Current site
Google Scholar
PubMed
Close
,
Hong-Qing Wang Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Search for other papers by Hong-Qing Wang in
Current site
Google Scholar
PubMed
Close
,
Yi-Zhou Zhuang Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Search for other papers by Yi-Zhou Zhuang in
Current site
Google Scholar
PubMed
Close
,
Yin-Jing Lin National Meteorological Center, China Meteorological Administration, Beijing, China

Search for other papers by Yin-Jing Lin in
Current site
Google Scholar
PubMed
Close
,
Yan Zhang Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Search for other papers by Yan Zhang in
Current site
Google Scholar
PubMed
Close
, and
Sai-Sai Ding Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

Search for other papers by Sai-Sai Ding in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0 and 10.8 μm (BTD12.0–10.8), and the BT difference between 6.7 and 10.8 μm (BTD6.7–10.8). Correlations among these IR indicators were investigated using MTSAT-1R images for summer 2007 over East Asia. Temporal, spatial, and numerical frequency distributions were used to represent the correlations. The results showed that large BTD12.0–10.8 values can be observed in the growth of cumulus congestus and associated with the boundary of different terrain where convection was more likely to generate and develop. The results also showed that numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, the shapes of the HT2Ds varied with latitude and the type of underlying surface. After carefully analyzing the correlations among the IR indicators, a conceptual model of the convection life cycle was constructed according to these HT2Ds. A new cloud convection index (CCI) was defined with the combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model. The preliminary test results demonstrated that CCI could effectively identify convective clouds. CCI value and its time trend could reflect the growth or decline of convective clouds.

Corresponding author address: Hong-Qing Wang, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China. E-mail: hqwang@pku.edu.cn

Abstract

Three infrared (IR) indicators were included in this study: the 10.8-μm brightness temperature (BT10.8), the BT difference between 12.0 and 10.8 μm (BTD12.0–10.8), and the BT difference between 6.7 and 10.8 μm (BTD6.7–10.8). Correlations among these IR indicators were investigated using MTSAT-1R images for summer 2007 over East Asia. Temporal, spatial, and numerical frequency distributions were used to represent the correlations. The results showed that large BTD12.0–10.8 values can be observed in the growth of cumulus congestus and associated with the boundary of different terrain where convection was more likely to generate and develop. The results also showed that numerical correlation between any two IR indicators could be expressed by two-dimensional histograms (HT2D). Because of differences in the tropopause heights and in the temperature and water vapor fields, the shapes of the HT2Ds varied with latitude and the type of underlying surface. After carefully analyzing the correlations among the IR indicators, a conceptual model of the convection life cycle was constructed according to these HT2Ds. A new cloud convection index (CCI) was defined with the combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model. The preliminary test results demonstrated that CCI could effectively identify convective clouds. CCI value and its time trend could reflect the growth or decline of convective clouds.

Corresponding author address: Hong-Qing Wang, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China. E-mail: hqwang@pku.edu.cn

1. Introduction

The brightness temperatures (BTs) of the 6.7-, 10.8-, and 12.0-μm infrared (IR) channels and their differences (BTDs) have been widely used in monitoring cloud properties. The BT10.8 is used to estimate cloud-top temperature for opaque clouds. Certain thresholds (−32°, −47°, −52°C, and so on), the spatial gradient, and temporal trends (cooling rate) of BT10.8 are usually used to identify convective clouds (Adler and Fenn 1979; Maddox 1980; Adler and Negri 1988; Negri and Adler 1981; Xu et al. 2013, 2014; Adler et al. 1985; Roberts and Rutledge 2003; Sieglaff et al. 2011, 2013, 2014). The BTD6.7–10.8 is used to identify deep convective clouds because it takes large negative values for low-level clouds and its magnitude (absolute value) often decreases with rising cloud-top height, tending to zero near the tropopause (Donovan et al. 2008). Utilizing satellite observations and radiative transfer simulations, many studies have illustrated that BTD6.7–10.8 can be positive when convective clouds penetrate the tropopause (Fritz and Laszlo 1993; Ackerman 1996; Levizzani and Setvák 1996). The physical explanation may involve two aspects: 1) warm water vapor in the lower stratosphere (Schmetz et al. 1997; Tjemkes et al. 1997; Setvák et al. 2008) and 2) “plumes” over the cloud top of severe storms (Wang 2003, 2007; Setvák et al. 2007). Both the water vapor and the plumes can partly absorb radiation from the cold cloud top and emit radiation at higher temperature, which was called the “warm emitter” effect by Lattanzio et al. (2006). For the BTD12.0–10.8, large negative values are typically used to detect thin clouds such as cirrus, anvils, and so on (Inoue 1985; Kurino 1997; Hong et al. 2010) because the absorption of ice particles in the 12.0-μm channel was stronger than that in the 10.8-μm channel. There are many factors contributing to the changes of BTD12.0–10.8, including optical thickness, effective particle size, chemical constitution, water vapor amount, lapse rate, emissivity difference, and so on (Inoue 1987a; Prata 1989; Minnis et al. 2012; Lindsey et al. 2014). The BTD12.0–10.8 of convective clouds is usually negative before the clouds become opaque and approaches 0°C as they are getting thicker. In mature cumulus (BT10.8 < −20°C), BTD12.0–10.8 was also found to be greater than 0°C (Mecikalski and Bedka 2006). Inoue (1987b) pointed out that 1% of BTD12.0–10.8 for clouds (BT10.8 < 0°C) is positive in any of the images they used, and this should be further studied. As compared with the 10.8-μm channel, the 12.0-μm channel is sensitive to moisture, and therefore BTD12.0–10.8 is useful for retrieving low-level water vapor fields and estimating surface temperature in clear-sky area (Chesters et al. 1983; Price 1984; Lindsey et al. 2014). In addition, the BT3.7 of the shortwave IR channel and BTD3.7–10.8 are useful for estimating the effective ice particle radius of tropical deep convective clouds (Hong et al. 2012).

Each IR indicator just partly reflects the cloud properties. In recent years, there have been many studies utilizing combinations of the indicators. Examples include 1) monitoring convective clouds (Feidas and Cartalis 2001; Berendes et al. 2008; Zinner et al. 2008, 2013), 2) nowcasting convective/lightning initiation (CI/LI; Mecikalski and Bedka 2006; Harris et al. 2010; Merk and Zinner 2013; Matthee and Mecikalski 2013), 3) dissecting cloud-top microphysical structure (Melani et al. 2003; Mecikalski et al. 2007, 2010; Setvák et al. 2013), 4) analyzing climatic features of cloud (Wang et al. 2004, 2005), and so on.

In the beginning of our study, by utilizing Multifunctional Transport Satellite-1R (MTSAT-1R) data of a whole summer (June–August) over East Asia, all of these indicators were calculated to analyze convective clouds. As we displayed the results with time animation, one issue emerged when comparing with those results of other satellite data (e.g., MTSAT-2): the indicator BTD12.0–10.8 shows a wider range of variation, and there are more large positive values in developing convective clouds [growing cumulus congestus in the upper level of the troposphere (BT10.8 < −20°C), which are abbreviated as DCC hereinafter]. This problem was not well explained in past studies. We carefully checked all satellite images to see if this effect was caused by error or noise in the data. After confirming that the abnormal results of BTD12.0–10.8 are credible, the study attempts to give possible qualitative explanations from two aspects.

The first aspect is the spectral response function of the 12.0-μm channel. Figure 1a shows the spectral response functions (SRF) of the 12.0-μm channel for many satellites. As we know, the 12.0-μm channel is more sensitive to moisture than is the 10.8-μm channel, especially the >12.0-μm spectral region (Guillory et al. 1993). For the 12.0-μm channel of MTSAT-1R and Fengyun-2E (FY-2E), the SRF are higher at its right half than at its left, which could make the BT12.0 of MTSAT-1R and FY-2E be more influenced by water vapor than those of other satellites. Moreover, the 12.0-μm SRF of FY-2E is more to the right when compared with that of MTSAT-1R. To verify this surmise, we examined the BTD12.0–10.8 of FY-2E. The results show much larger positive values in convective clouds and the range of BTD12.0–10.8 variation is indeed wider than that of MTSAT-1R.

Fig. 1.
Fig. 1.

(a) Response functions of the 12.0-μm channel. (b) Schematic of the warm-emitter effect for DCC. The gray over the cloud top indicates warm water vapor that has not entrained with the ambient air. Strong (weak) updrafts lead to large (small) BTD12.0–10.8.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

The second aspect is the warm-emitter effect in the troposphere. The positive value of BTD6.7–10.8 with deep convective clouds has been widely explained by the so-called warm-emitter effect in the lower stratosphere. Likewise, we can presume the existence of this warm emitter in the troposphere near/over DCC, which may be the “fresh” warm water vapor brought from lower levels by strong updrafts. The fresh water vapor is usually unstable and has not been mixed with environmental air yet (Wang 2003; Setvák et al. 2007). It can be warmer than the cloud top/surroundings. An intense updraft is able to continuously provide the fresh warm water vapor for DCC. Both the 12.0- and 10.8-μm channels are in an atmospheric window; that is, the absorption by atmospheric gases is relatively small. As compared with the 10.8-μm channel, the 12.0-μm channel is sensitive to water vapor (weak absorption), which is also called the “dirty window.” Unlike the 6.7-μm channel, which is sensitive to water vapor at high level (strong absorption), the influences on the 12.0-μm channel are mainly from water vapor near the cloud top because of the water vapor amount and weak absorption. Meanwhile, given the effect of the 12.0-μm SRF mentioned above, IR absorption and reemission of the warm water vapor near cloud tops could make BTD12.0–10.8 to be positive for DCC. As the updraft weakens, the magnitude of the positive BTD12.0–10.8 gradually decreases. The warm-emitter effect is schematically illustrated in Fig. 1b and preliminarily simulated by the Santa Barbara Discrete Ordinate Radiative Transfer (DISORT) Atmospheric Radiative Transfer (SBDART; Ricchiazzi et al. 1998) in the appendix. In addition, positive BTD12.0–10.8 is also observed in fog/stratus, in clear-sky areas, and in the lower stratosphere with temperature inversion but is rarely observed in semitransparent clouds and dissipating clouds.

In addition to BT10.8, BTD12.0–10.8 and BTD6.7–10.8 are also important IR indicators in characterizing convection processes and monitoring cloud properties. Before using the combinations of them, the correlations (temporal, spatial, numerical, etc.) among them should be explored. Some studies used cases of cloud observations to present these correlations for classifying clouds (Ackerman et al. 1990; Strabala et al. 1994; Saunders and Kriebel 1988). This study used 2134 satellite images to investigate these correlations for analyzing DCC. The correlations are expressed by three types of frequency distributions: 1) temporal and 2) spatial frequency distributions of each indicator and 3) numerical frequency distributions [two-dimensional histogram (HT2D)] between any two indicators.

Section 2 describes the data and methods for frequency distributions. Section 3 shows some results of temporal and spatial frequency distributions and presents analyses of the temporal and spatial relationships among these indicators. Section 4 presents a variety of HT2Ds to reflect numerical correlations among these indicators and discusses some of their important features. Section 5 defines the cloud convection index (CCI) on the basis of the correlation between BTD12.0–10.8 and BTD6.7–10.8, and conducts a preliminary test for identifying DCC. Section 6 provides conclusions and discussion for this study.

2. Data and methods

a. Satellite data

This study used a total of 2134 satellite images (1-h intervals) from MTSAT-1R to investigate correlations among IR indicators. The satellite images were collected from 1 June to 31 August 2007.

The geographical scope of the analysis region (10°–50°N, 105°–145°E) is shown in Fig. 2. The region is divided into 16 small regions with 10° intervals in both latitude and longitude (such as 10°–20°N, 105°–115°E). Several combinations (called subregions) of these small regions are also used in this study. For example, subregion 20°–40°N, 105°–125°E is used in analyzing spatial frequency distributions.

Fig. 2.
Fig. 2.

Analysis region and the 16 small regions. Also shown are subregion 20°–40°N, 105°–125°E (red square) for spatial frequency distributions and two subregions 20°–40°N, 105°–115°E and 10°–30°N, 135°–145°E (dashed rectangles) for comparison of diurnal variations between land and ocean.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

b. Method for temporal and spatial frequency distributions

To observe and compare diurnal variations of the indicators across the convection area, first, some constraints need to be imposed to extract those convective clouds. The thresholds of BT10.8 were set at −52° and −47°C (Maddox 1980; Adler and Fenn 1979). The thresholds of BTD6.7–10.8 were set at −5°, −4°, −3°, −2°, and −1°C (Donovan et al. 2008), and those of BTD12.0–10.8 were set at 1°, 1.5°, 2°, 2.5°, and 3°C (the BTD12.0–10.8 of DCC was usually greater than 0°C in this study; BT10.8 < 0°C was added to exclude clear-sky area where BTD12.0–10.8 might be >0°C in some circumstances). The above multiple thresholds were used to better support our subsequent conclusion. For convenience, the values of indicators satisfying the above constraints were described as cold BT10.8, large BTD6.7–10.8 values, and large BTD12.0–10.8 values in the following. For each indicator in a specific subregion, temporal frequency distributions were calculated as the hourly averages (0–23 h) of the pixel number in each satellite image (2134 in total) satisfying the constraint. To analyze the spatial distributions of these indicators in a convection area, the occurrence frequency that satisfies the constraint was calculated for the whole summer for each geographical grid.

c. Method for numerical frequency distribution

For any two variables V1 and V2, an HT2D could be used to represent/express the numerical correlation between them. In an HT2D, the frequency fV1_V2(x, y) was defined as the number of pixels for which V1 ∈ [x, x + Δx] and V2 ∈ [y, y + Δy], where Δx and Δy were intervals of V1 and V2 for accumulating the number of pixels. The default values in this study were both 1°C.

Three types of HT2Ds were obtained from BT10.8, BTD12.0–10.8, and BTD6.7–10.8. They are recorded as HT2D10.8,12.0–10.8, HT2D10.8,6.7–10.8, and HT2D6.7–10.8,12.0–10.8. HT2D10.8,12.0–10.8 is used to express the relationship between BT10.8 and BT12.0. HT2D10.8,6.7–10.8 is used to express the relationship between BT10.8 and BT6.7. HT2D6.7–10.8,12.0–10.8 is a comprehensive expression of the correlations among BT10.8, BT12.0, and BT6.7. In calculations of these HT2Ds, BT10.8 was in the range [−90°, 5°C], BTD12.0–10.8 was in the range [−10°, 10°C], and BTD6.7–10.8 was in the range [−50°, 10°C].

For each small region, two procedures were involved in calculations of the HT2Ds: 1) constructing the three types of HT2Ds for each cloud image (2134 in total) and 2) calculating the hourly averages (0–23 h, to observe diurnal variation), monthly averages (June–August, to observe monthly variation), and overall average (the whole summer) for each kind of HT2D. The overall average is used in the subsequent sections of this paper, unless otherwise noted.

3. Results of temporal and spatial frequency distributions

a. Temporal relationships

To observe the temporal relationships among the IR indicators, curves of their diurnal variations of frequency with the above constraints are put together in Figs. 3a and 3b for the land and ocean subregions. In the land subregion (20°–40°N, 105°–115°E), the high-frequency period of BTD6.7–10.8 and BT10.8 is ~1200–2000 LST, with a primary peak at ~1700 LST. In addition, a secondary peak exists at ~0400 LST. When convection develops during ~1200–1700 LST, the occurrence frequency of large BTD12.0–10.8 values reached its peak ~2 h before the peak of large BTD6.7–10.8 values.

Fig. 3.
Fig. 3.

Diurnal variations in subregions (a) 20°–40°N, 105°–115°E (land) and (b) 10°–30°N, 135°–145°E (ocean). The blue (red) curves show BTD6.7–10.8 (BTD12.0–10.8). Black dotted curves show BT10.8. Blue (red) dashed polylines show the BTD6.7–10.8 (BTD12.0–10.8) peaks.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

In the ocean subregion (10°–30°N, 135–145°E), the convective activity mainly occurs during the morning and has relatively low frequency of occurrence in the afternoon. In common with the land subregion, the occurrence of large BTD12.0–10.8 values reached its peak before that of large BTD6.7–10.8 values, but the frequency of convective activity is lower, and the strength is generally weaker.

To better illustrate the earlier occurrence of large BTD12.0–10.8 values, Table 1 lists the occurrence time (OT) and the minimum cloud-top temperature (CTTmin) while the average value of BTD6.7–10.8/BTD12.0–10.8 is reaching a maximum for 15 convection cases in different time and space. From Table 1, we can see 1) that the OT of the maximum BTD12.0–10.8 is 1–2 h earlier than that of the maximum BTD6.7–10.8 and 2) that most of the CTTmin for BTD12.0–10.8 are warmer than the tropopause temperature (obtained from 0.5° × 0.5° National Centers for Environmental Prediction Global Forecast System analysis data), meaning that the corresponding cloud tops are still in the upper level of the troposphere. By contrast, most of the CTTmin of BTD6.7–10.8 are already close to (or colder than) the tropopause temperature, and the cloud tops are near (or over) the tropopause. The average values of BTD6.7–10.8 and BTD12.0–10.8 at different stages will be used in section 5.

Table 1.

Temporal relationship and average-value evolution of BTD6.7–10.8 and BTD12.0–10.8 for 15 convection cases. Here, MAV is the maximum average value, OT gives the occurrence time of the MAV of BTD6.7–10.8 or BTD12.0–10.8, and CTTmin is the minimum cloud-top temperature at OT.

Table 1.

In the above analysis, large BTD12.0–10.8 values reach their highest frequency of occurrence before large BTD6.7–10.8 values and cold BT10.8 do. Considering that large BTD6.7−10.8 values and cold BT10.8 are usually used to indicate mature cumulonimbus, large BTD12.0–10.8 values were related to DCC (strong updrafts).

b. Spatial relationships

The spatial frequency distributions were used to analyze the spatial relationships among the IR indicators. Figures 4a and 4b show the spatial frequency distributions of BTD12.0–10.8 ≥ 1.5°C and BTD6.7–10.8 ≥ −2°C in the 20°–40°N, 105°–125°E subregion.

Fig. 4.
Fig. 4.

Spatial frequency distributions for (a) BTD12.0–10.8 ≥ 1.5°C (and BT10.8 < 0°C) and (b) BTD6.7–10.8 ≥ −2°C in June–August 2007. The blue line indicates the coastline.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

From Fig. 4, it can be seen that high-frequency areas of large BTD12.0–10.8 values and large BTD6.7–10.8 values are mainly distributed south of 32.5°N but that the concentration areas for them are different. In Fig. 4a, the high-frequency areas of large BTD12.0–10.8 values are closely related to the terrain, which is considered to be an important feature of BTD12.0–10.8. Most of these areas are located near the sea–land boundary (such as China’s southeast coastal areas) and the mountain–plain boundary. In Fig. 4b, the linkage between large BTD6.7–10.8 values and the terrain is weaker. Time animations of satellite images and comparison of Figs. 4a and 4b indicate that convection is more likely to be generated and develop in high-frequency areas of large BTD12.0–10.8 values and then move to the areas of large BTD6.7–10.8 values. This result reveals that BTD12.0–10.8 is a significant indicator for DCC and that large BTD12.0–10.8 values appear earlier than large BTD6.7–10.8 values.

c. Case analysis

Cross sections of IR indicators at different times were used to show the value changes of these indicators within the evolution period of a convective system: “onset” stage (strong local development of convective clouds in the low/middle level and BT10.8 ≥ −20°C), “developing” stage (rapid growth and expansion of convective clouds in the upper level, related to DCC), “mature” stage (the size of the convective body reaches a maximum and the cirrus anvil appears, corresponding to mature cumulonimbus), and “dissipating” stage (the area of the cirrus anvil is increasing and the main body of convection is decreasing). The definitions of the four stages in deep convection were referenced to Zinner et al. (2008).

The convective system that occurred over Hainan Island on 1 June 2007 lasted approximately 9 h. Figure 5 only shows part of these cross sections. 1) In the onset stage (figure not shown), there are many disturbances at low level, which could not be reflected in BT6.7, and BT12.0 is colder than BT10.8 (negative BTD12.0–10.8). 2) In the developing stage (Figs. 5a–c), many large BTD12.0–10.8 values are observed in the storm cells. Note that in Fig. 5a both BTD12.0–10.8 and BTD6.7–10.8 are greater than 0°C at cell B but that BT10.8 is only ~−50°C (the tropopause temperature is ~−79°C at B). This situation implies that BT6.7 is rarely affected by water vapor in the higher troposphere (the air above B is dry), and the warm water vapor near the cloud top causes BTD6.7–10.8 to be positive. In addition, the BTD12.0–10.8 of cell C has become positive but BT10.8 and BTD6.7–10.8 are only approximately −25° and −18°C, respectively. For cumulus humilis (cells D and E in Fig. 5a), the BTD12.0–10.8 values are negative because of the semitransparent clouds and the influence of surface radiation. This period (BT10.8 ≥ −20°C) will not be involved in this study. From satellite images at the following hour (Fig. 5b), it can be seen that cells C, D, and E developed very fast and almost merged together. 3) In the mature stage (Fig. 5d), BT10.8 and BTD6.7–10.8 of the main body are approximately −70° and 0°C, respectively. Relative to the developing stage, the magnitude of BTD12.0–10.8 becomes smaller but the sign is still positive. Thick anvil clouds (−3° ≤ BTD12.0–10.8 ≤ −1°C and −60° ≤ BT10.8 ≤ −40°C) have been observed around the convective system. 4) In the dissipating stage (figure not shown), the main body of convection becomes smaller along with widespread thin anvil clouds (BTD12.0–10.8 < −5°C).

Fig. 5.
Fig. 5.

(left) Cross sections and (right) indicator images of a convective system during the (a)–(c) developing and (d) mature stages. The cells labeled A–E are discussed in section 3c.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

In summary, from the temporal and spatial relationships among IR indicators, we can see that BTD12.0–10.8 can be used to identify DCC. In addition to the impact of cloud microphysical parameters [optical thickness, effective particle radius (Rosenfeld et al. 2008), etc.], the value of BTD12.0–10.8 is also related to the convective updraft (Mecikalski et al. 2010): the stronger the updraft is, the greater is the magnitude of positive BTD12.0–10.8.

4. Results of numerical frequency distributions

Section 3 presented results that demonstrated the temporal and spatial relationships among the IR indicators. In this section, the numerical correlations were investigated with three kinds of HT2Ds: HT2D10.8,12.0–10.8, HT2D10.8,6.7–10.8, and HT2D6.7–10.8,12.0–10.8. These HT2Ds were constructed from 2134 satellite images covering the entire analysis region and all small regions (see section 2). The general characteristics of the HT2Ds and shape variations in different regions were also analyzed.

For HT2Ds, the shape variations are described with two aspects: the outline and the high-frequency axis line (hereinafter the axis line). The outline is defined as a contour line for a low frequency (e.g., 0.25). The axis line is a polyline of high-frequency points along the horizontal axis.

a. General characteristics

Figures 6a–c show the HT2D10.8,12.0–10.8, HT2D10.8,6.7–10.8, and HT2D6.7–10.8,12.0–10.8 for a small region (20°–30°N, 105°–115°E). Regardless of shape variations, the following five general characteristics are obtained from detailed comparison of original cloud images and the HT2Ds.

  1. In Figs. 6a–c, most of the optically thick clouds are distributed in or above the area of high frequency, whereas optically thin clouds (such as cirrus) are distributed below that area. The thinner the clouds are, the closer they are to the lower boundary.

  2. In Fig. 6a, the lower boundary of HT2D10.8,6.7–10.8 approximates a straight line (the thick, purple solid line) in the range −55° ≤ BT10.8 ≤ 0°C (varies with different latitudes). For a given BT10.8, the cloud pixels with minimum BTD6.7–10.8 (BT6.7) are on/near the straight line. By extracting these cloud pixels from satellite images, we can see that most of them correspond to thinner cirrus/anvils related to cumulonimbus. The BT10.8 of the intersection point of two lines (the lower boundary and BTD6.7–10.8 = 0°C) is associated with the actual cloud-top temperature of cirrus (Szejwach 1982), which approximates the tropopause temperature (to be confirmed in future research). This BT10.8 is abbreviated as TTBT and will be used later.

  3. In Fig. 6b, the axis line is an upward line from warmer to colder BT10.8. It crosses the line of BTD12.0–10.8 = 0°C near −40°C (varies with different underlying surfaces). In warmer BT10.8 areas (e.g., BT10.8 ≥ −20°C), there are also many pixels with BTD12.0–10.8 greater than 0°C. When these pixels are extracted from satellite images, it is found that they usually appear on clear nights over the land surface or in areas of fog/stratus. This phenomenon may be related to temperature inversion.

  4. When comparing HT2D10.8,6.7–10.8 and HT2D10.8,12.0–10.8 in Figs. 6a and 6b, it can be seen that, after clouds reach the tropopause (BTD6.7–10.8 = ~0°C), the magnitude of positive BTD6.7–10.8 continues to increase while that of positive BTD12.0–10.8 continues to decrease with the cooling of BT10.8. This situation implies that BTD12.0–10.8 are not at a maximum for the cloud pixels with the coldest BT10.8. In addition, there are several large fluctuations of frequency contours (marked by black circles in Figs. 6a and 6b). The corresponding BT10.8 values are approximately −5°, −15°, −30°, and −40°C. They are close to the temperatures of cloud-phase transitions (Jin and Nasiri 2014), and the potential linkage needs to be further studied.

  5. HT2D6.7–10.8,12.0–10.8 (Fig. 6c) is used to express the numerical correlation between BTD6.7–10.8 and BTD12.0–10.8, which will be analyzed with changing convection in section 5 (applications).

Fig. 6.
Fig. 6.

(a) HT2D10.8,6.7–10.8 (contour interval is 5; outline is 0.25), (b) HT2D10.8,12.0–10.8 (contour interval is 10; outline is 0.25), and (c) HT2D6.7–10.8,12.0–10.8 (contour interval is 10; outline is 5) in small region 20°–30°N, 105°–115°E. The thick, purple solid line in (a) shows the low boundary of HT2D10.8,6.7–10.8. The thin, red solid line shows the BT10.8 of the intersection point. The thick white lines in (c) show BT10.8 contours with 5°C intervals. Black circles indicate large fluctuations in the frequency contours. Also shown are the variations of (d) HT2D10.8,6.7–10.8, (e) HT2D10.8,12.0–10.8, and (f) HT2D6.7–10.8,12.0–10.8 in four small regions: 10°–20°N (red), 20°–30°N (black), 30°–40°N (green), and 40°–50°N (blue), all with the same longitude range 105°–115°E.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

b. Shape variations of HT2Ds

The shape of HT2D reflects the IR radiative characteristics of clouds. Because the shape variations of HT2Ds with longitude (same underlying surface) and time are not obvious, we mainly analyzed the variations with different latitudes and underlying surfaces.

A careful comparison of HT2Ds for all small regions shows that 1) the outlines change with latitude, which is mainly influenced by the tropopause height, and 2) the axis lines change with the underlying surface, which is mainly influenced by convection intensity, mid- and low-level temperature, and moisture fields.

Figures 6d–f show outlines (thin solid line) and axis lines (thick dotted line) of three types of HT2Ds in four small regions. These small regions are in the same longitude range (105°–115°E) and different latitude ranges: 10°–20°N (red), 20°–30°N (black), 30°–40°N (green), and 40°–50°N (blue). Note that the underlying surface of the small region for 10°–20°N is mostly ocean, and other regions are land.

Overall, the low boundaries of HT2D10.8,6.7–10.8 (Fig. 6d) and HT2D10.8,12.0–10.8 (Fig. 6e) move upward from low to high latitudes, and in particular there is a big jump near 30°N, which is consistent with the significant tropopause break (Pan and Munchak 2011). As we said before, cloud pixels at the low boundary of HT2D10.8,6.7–10.8 correspond to thin anvils of cumulonimbus clouds, and TTBT is related to the tropopause height. From south to north, TTBT of the four small regions is −70°, −68°, −60°, and −57°C, respectively.

From Figs. 6d and 6e we can see that, despite the different latitude ranges, all three axis lines of land surface (black, green, and blue) change little. Relative to them, the axis line of the ocean surface (red) is much lower in the range of BT10.8 ≥ −30°C. The reason for this phenomenon is that the values of BTD6.7–10.8 and BTD12.0–10.8 are affected by the mid- and low-level temperature and moisture fields (Chesters et al. 1983; Petersen et al. 1984). In addition, for Fig. 6e, the axis line of the ocean surface is also lower than those of land surface in the range −70° ≤ BT10.8 < −30°C, because the convection over land is generally more intense than that over ocean.

In summary, HT2Ds are a statistical/visual expression of the relationships among IR indicators for different geographical areas, underlying surfaces, and cloud zones. Our work is only a preliminary analysis of them. Many important features warrant further investigation, such as low-level cloud properties and tropopause climatological behavior.

5. Applications

BTD6.7−10.8 can be used to represent the cloud-top height relative to the tropopause or dry air aloft (Schmetz et al. 1997), and large BTD12.0–10.8 values are related to updrafts or a change in cloud-top height (Mecikalski and Bedka 2006; Mecikalski et al. 2013). On the basis of the temporal, spatial, and numerical relationships among IR indicators, we try to combine them for the purpose of analyzing DCC. Statistical results in this study have shown that BTD12.0–10.8 can be used to identify DCC and distinguish them from dissipating clouds (e.g., thick cirrus and anvil clouds) in a single image.

HT2D6.7–10.8,12.0–10.8 is a comprehensive expression of correlations among BT10.8, BT12.0, and BT6.7. In this section, we first attempt to generate a conceptual model of the convection life cycle (Fig. 7a) on the basis of HT2D6.7–10.8,12.0–10.8 and then we combine BTD12.0–10.8 and BTD6.7–10.8 to define a new CCI. Last, we undertake a preliminary test to check the validity of the CCI for identifying convective clouds, especially DCC.

Fig. 7.
Fig. 7.

(a) Conceptual model of the convection life cycle. The purple loop shows the convective clouds, the green polyline shows the trajectory of changing convection, the green arrow’s distance shows time intervals, and plus signs show the 15 convection cases and their average in Table 1. (b) CCI distribution. The thick white lines show BT10.8 contours with 5°C intervals, the red and black lines show CCI contours, and the colored polygons show regions for a CI forecast (purple) and a cumulonimbus mask (yellow).

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

a. Cloud convection index

The conceptual model of the convection life cycle in Fig. 7a was constructed through a detailed comparison of HT2D6.7–10.8,12.0–10.8 and original cloud images, which included the following steps: 1) choosing ranges of BTD6.7–10.8 and BTD12.0–10.8 (constraints) in HT2D6.7–10.8,12.0–10.8 and extracting pixels that meet the constraints in the original cloud images and 2) selecting different types of cloud pixels in the original cloud images and observing their distributions in HT2D6.7–10.8,12.0–10.8. The top-left area of Fig. 7a (BTD6.7–10.8 ≤ −35°C and BTD12.0–10.8 ≥ −5°C) corresponds to clear-sky regions (most of the land has greater BTD12.0–10.8 than does the ocean in summer). The lower area of Fig. 7a (BTD12.0–10.8 ≤ −5°C) corresponds to thin clouds, such as cirrus and anvil clouds. Convective clouds in the upper level are on the right side of HT2D6.7–10.8,12.0–10.8 (BTD6.7–10.8 ≥ −15°C): developing clouds are in the upper area (BTD12.0–10.8 > 0°C), and dissipating clouds are in the lower area (BTD12.0–10.8 ≤ 0°C) of Fig. 7a. Two special areas (Mecikalski and Bedka 2006) are also marked in Fig. 7b: 1) the area (−35° ≤ BTD6.7–10.8 ≤ −15°C, −3° ≤ BTD12.0–10.8 ≤ 0°C, and −20° ≤ BT10.8 ≤ 0°C; purple polygon) for a CI forecast and 2) the area (BTD6.7–10.8 ≥ −10°C and BTD12.0–10.8 ≥ 0°C; yellow polygon) for a cumulonimbus mask.

A typical trajectory of changing convection is represented by the green polyline with arrows in Fig. 7a, which was made by calculating the average values of BTD6.7–10.8 and BTD12.0–10.8 during the life cycle of convection using the 15 cases in Table 1. Starting from the onset stage, after the BTD12.0–10.8 of convection changes to positive, its magnitude increases quickly and then gradually decreases while BTD6.7–10.8 stays negative but its magnitude continues to decrease until the convection matures. In the developing stage, the magnitude of BTD12.0–10.8 (positive) increases and that of BTD6.7–10.8 (negative) decreases, and most of the cloud pixels are distributed on the upper right of HT2D6.7–10.8,12.0–10.8. In the mature stage, the main body of convection is located in the area of high frequency on the right side (BTD12.0–10.8 and BTD6.7–10.8 are all near 0°C). Thick (thin) anvil is near the lower-right (lower left) boundary in the dissipating stage.

According to the conceptual model of the convection life cycle, the CCI is defined by a combination of BTD6.7–10.8 and BTD12.0–10.8:
e1
where k12.0–10.8 and k6.7–10.8 are the weighting coefficients of BTD12.0–10.8 and BTD6.7–10.8, respectively. The c is a constant that represents the index level at BTD6.7–10.8 = BTD12.0–10.8 = 0°C (i.e., BT10.8 = BT12.0 = BT6.7). For a given c (such as 5 in this study), calculations of k12.0–10.8 and k6.7–10.8 only require two control points. For example, if CCI is assigned to 0 at point (−20°C, 0°C), which is approximately the beginning of cumulus congestus, and to 10 at point (0°C, 5°C), which is close to the maximum value of BTD12.0–10.8 at BTD6.0–10.8 = 0°C (corresponding to very strong cloud pixels; few of the cloud pixels can reach this point), then k12.0–10.8 = 1 and k6.7–10.8 = ¼ are obtained. That is,
e2
For any BTD12.0–10.8 and BTD6.0–10.8, the CCI value can be calculated according to Eq. (2). Figure 7b shows the numerical distribution of CCI with contour lines (a 2.5 interval, ranging from 0 to 10). The BT10.8 distribution (thick white contour lines) is also shown, which is used to illustrate the relationship between BT10.8 and CCI.

b. Identification of convective clouds

In this preliminary test for identifying convective clouds, we calculate the CCI for each pixel in the satellite images. CCI images only retain pixels with CCI > 0 and BTD12.0–10.8 > 0°C. The condition BTD12.0–10.8 > 0°C is used to exclude thick cirrus/anvil clouds. Figures 8a–d show BT10.8 images (left panels), CCI images (center panels), and multithreshold images (right panels) of China’s southeast coastal areas from 1433 to 2133 LST at 2-h intervals on 13 July 2007. From qualitative comparisons of BT10.8, CCI, and multithreshold images in Fig. 8, we can see that 1) large CCI (e.g., ≥ 6) can effectively identify DCC with strong updraft, 2) CCI is negative for most nonconvective clouds (such as thin cirrus/anvil clouds), 3) CCI variation within the convective clouds can reflect the difference in the convective strength, and 4) CCI ≥ 8 usually corresponds to very strong convection and therefore special attention should be paid when monitoring severe weather.

Fig. 8.
Fig. 8.

Images for (left) BT10.8, (center) CCI, and (right) multithresholds at (a) 1433, (b) 1633, (c) 1833, and (d) 2033 LST 13 Jul 2007. The white circle shows convection feature A, and the green ellipse shows convection feature B. (e) Evolution track (purple solid polyline) of convection feature A with 1-h intervals on HT2D6.7–10.8,12.0−10.8 (20°–30°N, 105°–145°E).

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

In Fig. 8, white circles mark local convection (labeled A) and green ellipses mark frontal convection (labeled B). Figure 8e shows the evolution track of the convection associated with label A at 1-h intervals. CCI and its time trend can clearly depict the onset, developing, mature, and dissipating stages of convection at A.

6. Conclusions and discussion

The BTD12.0–10.8 of convection peaks at the developing stage and is an important IR indicator for characterizing DCC. As compared with MTSAT-2, there are many large positive values observed in DCC for MTSAT-1R. We try to qualitatively explain this issue with the distinctive shape of 12.0-μm SRF and the warm emitter brought from lower levels by strong updrafts, and a simple simulation is given. This surmise should be verified with higher-resolution observations and more comprehensive simulations.

In this study, correlations among IR indicators were represented by three kinds of frequency distributions: temporal, spatial, and numerical. They were investigated with 2134 satellite images from the summer. There are three main conclusions from this study. 1) The diurnal variation and spatial distribution of BTD12.0–10.8 are different from those of BTD6.7–10.8 and BT10.8. The peak of occurrence of large BTD12.0–10.8 values is in the developing stage of convection, 1–2 h earlier than that of large BTD6.7–10.8 values. Over land areas, large BTD12.0–10.8 values usually appear in the afternoon (~1200–1800 LST), and most of them are associated with the boundaries of different terrain. In oceanic areas, large BTD12.0–10.8 values usually appear in the morning (~0000–0600 LST). Also, its frequency is significantly lower, and its strength is generally weaker. 2) HT2Ds were used to represent the numerical correlation between IR indicators and were constructed from 2134 satellite images in this study. They are essentially statistical/visual expressions of spectral responses of clouds in different wavelength bands. They contain many characteristics of clouds: type, phase, height, and so on. The shapes of the HT2Ds varied with different latitudes and types of underlying surface. 3) After carefully analyzing the correlations of IR indicators, a conceptual model of the convection life cycle was constructed with HT2D6.7–10.8,12.0–10.8. A new CCI was defined with a simple combination of BTD12.0–10.8 and BTD6.7–10.8 on the basis of the conceptual model, which can be used in identifying convective clouds, especially those that are developing.

Identifying DCC is helpful for monitoring the convective weather as well as warning of hazards related to thunderstorms. The thick cirrus/anvil clouds, which belong to dissipating clouds, are not easily discriminated from DCC (growing cumulus congestus) with BT10.8 and BTD6.7–10.8 in a single image. HT2D10.8,12.0–10.8/HT2D6.7–10.8,12.0–10.8 clearly show that the ranges of BTD12.0–10.8 of dissipating clouds and DCC are different, however. Positive BTD12.0–10.8 is observed in DCC, and negative values are seen in dissipating clouds. When identifying DCC, the dissipating clouds should be excluded. This is the basic idea for CCI definition. In fact, CCI definition is flexible, because the weighting coefficients of BTD12.0–10.8 and BTD6.7–10.8 can be adjusted. Other constraints (e.g., BTD12.0–10.8 > 0°C in section 5) also can be added in practical applications.

This is a preliminary study with regard to the analyses and applications of correlations among IR indicators. In the future, from a better understanding of the statistical results, additional studies should include the following: 1) verification of the correspondence between large BTD12.0–10.8 values and the developing stage of convective clouds with other measurements/approaches, 2) quantitative assessment of CCI in identifying DCC, 3) examination of the characteristics of low clouds in HT2Ds, 4) classification of cloud types and cloud phases with HT2Ds, 5) estimation of the tropopause temperature and the cloud-top height of semitransparent clouds (Szejwach 1982), and 6) applications of CCI in forecasting CI and deep convection.

Only MTSAT-1R data from East Asia within the summer were used in this study. Because of the relatively wide range of BTD12.0–10.8 variations for MTSAT-1R as well as the differences in moisture and temperature fields, the results of frequency statistics may vary with different satellites, seasons, and field of views. Many parameters should be adjusted accordingly. In addition, this study did not include a cloud autotracking algorithm or quantitative calculations of the CCI time trend. All should be considered in the future.

Acknowledgments

MTSAT-1R data were downloaded from the National Satellite Meteorological Center of the China Meteorological Agency (NSMC/CMA; http://satellite.cma.gov.cn/PortalSite/Data/Satellite.aspx). SRF data from GOES-11 and Meteosat-9 were downloaded from the Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin (http://cimss.ssec.wisc.edu/goes/calibration/), SRF data from NOAA-18 were downloaded from the Global Space-based Inter-Calibration System Coordination Center (http://www.star.nesdis.noaa.gov/smcd/GCC/instrInfo-srf.php), SRF data from MTSAT-1R and MTSAT-2 were downloaded from Meteorological Satellite Center of Japan Meteorological Agency (http://www.data.jma.go.jp/mscweb/en/operation/index.html), and SRF data from FY-2E were obtained from NSMC/CMA (http://satellite.cma.gov.cn/PortalSite). NCEP Global Forecast System analysis data were obtained online (http://rda.ucar.edu/datasets/ds335.0; accessed 28 November 2013). This research work is supported by China Special Fund for Meteorological Research in the Public Interest (GYHY201306047 and GYHY201206003) and National Science Funding of China (41275112). The authors thank Professor Zuyu Tao and Wanbiao Li (Peking University) for their helpful advice. We also thank all of the anonymous reviewers for their constructive comments that significantly improved the quality of this paper.

APPENDIX

Simulation of the Warm-Emitter Effect with SBDART

SBDART (Ricchiazzi et al. 1998) is a 1D radiative transfer model used to compute plane-parallel radiative transfer in clear and cloudy conditions. Six standard atmospheric profiles (e.g., tropical, midlatitude summer, or subarctic winter) and five basic surface types (e.g., ocean water, snow, or sand) are available. This model was used to simulate the warm-emitter effect to the BTs of 10.8-, 12.0-, and 6.7-μm channels in the following sensitivity tests. The conditions adopted in the simulations include a standard tropical profile (solid line in Fig. A1), a black surface (unit emissivity), 50 layers, a view angle of 0°, and spectral resolution of 2 nm. In addition, the SRFs of the 10.8-, 12.0-, and 6.7-μm channels in MTSAT-1R were used in the experiments.

Fig. A1.
Fig. A1.

Profiles used in SBDART simulations (the solid line shows the standard tropical profile, the dashed line with circles shows the control-test profile, and the dashed line with plus signs shows the sensitivity-test profile; red indicates temperature, and blue is for humidity) and schematic of the simulation for warm water vapor over convective clouds.

Citation: Journal of Applied Meteorology and Climatology 55, 4; 10.1175/JAMC-D-15-0081.1

A warm emitter near/over cloud top in deep convection may be water vapor or small ice crystals brought from subcloud layers by strong updraft. The warm outflow has been observed by soundings that penetrated rapidly expanding cloud tops (Bosart and Nielsen 1993; Dickinson et al. 1997; Davies-Jones 1974). In a control test, an ice cloud [base (top) height of 4 (9) km] is added to the standard tropical profile (the control-test profile is represented by a dashed line with circles in Fig. A1), and the BT10.8, BT12.0, and BT6.7 of cloud top are calculated. To eliminate the influence of surface radiation, the cloud optical thickness τ0.6 and ice particle effective diameter De are set to 100 and 50 μm, respectively. In sensitivity tests, the influence on the BTs from warm water vapor in the height range [9.1, 9.4 km] overlying the opaque ice cloud is simulated with variable water vapor temperature (WVT) and water vapor amount/density (WVD). The rule of changing water vapor conditions is that the stronger the updraft is, the warmer (larger) the WVT (WVD) will be. One of the sensitivity-test profiles (WVT = 250 K and WVD = 1.66 × 10−1 g cm−2 km−1; dashed line with crosses) and a schematic of the simulation are illustrated in Fig. A1.

In the first sensitivity test (S1), the WVT is fixed at 250 K (~6 K warmer than cloud top) and the WVD is changed from 1.66 (units: 10−1 g cm−2 km−1) to 8.26 with an interval of 1.65. In the second sensitivity test (S2), the WVD is constant (8.25) and the WVT is changed from 250 to 254 K with an interval of 1 K. The results (warm-emitter effects to BTs and BTDs) from these tests are listed in Table A1. From the simulation results, we can see that 1) a warm emitter over the cloud top can cause the BTD12.0–10.8 to be positive while the BTD6.7–10.8 is still negative in the troposphere and 2) the warmer (larger) the WVT (WVD) is (i.e., the stronger the updraft), the larger is the magnitude of positive BTD12.0–10.8.

Table A1.

Simulation results of the warm-emitter effect to BTs (K) and BTDs (K) from MTSAT-1R data with SBDART. Here, C indicates the control test and S1 and S2 are sensitivity tests.

Table A1.

REFERENCES

  • Ackerman, S. A., 1996: Global satellite observations of negative brightness temperature differences between 11 and 6.7 μm. J. Atmos. Sci., 53, 28032812, doi:10.1175/1520-0469(1996)053<2803:GSOONB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ackerman, S. A., W. L. Smith, H. E. Revercomb, and J. D. Spinhirne, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8–12 μm window. Mon. Wea. Rev., 118, 23772388, doi:10.1175/1520-0493(1990)118<2377:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and D. D. Fenn, 1979: Thunderstorm intensity as determined from satellite data. J. Appl. Meteor., 18, 502517, doi:10.1175/1520-0450(1979)018<0502:TIADFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and A. J. Negri, 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 3051, doi:10.1175/1520-0450(1988)027<0030:ASITTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., M. J. Markus, and D. D. Fenn, 1985: Detection of severe Midwest thunderstorms using geosynchronous satellite data. Mon. Wea. Rev., 113, 769781, doi:10.1175/1520-0493(1985)113<0769:DOSMTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berendes, T. A., J. R. Mecikalski, W. M. MacKenzie, K. M. Bedka, and U. S. Nair, 2008: Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering. J. Geophys. Res., 113, D20207, doi:10.1029/2008JD010287.

    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., and J. W. Nielsen, 1993: Radiosonde penetration of an undilute cumulonimbus anvil. Mon. Wea. Rev., 121, 16881702, doi:10.1175/1520-0493(1993)121<1688:RPOAUC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chesters, D., L. W. Uccellini, and W. D. Robinson, 1983: Low-level water vapor fields from the VISSR Atmospheric Sounder (VAS) “split window” channels. J. Climate Appl. Meteor., 22, 725743, doi:10.1175/1520-0450(1983)022<0725:LLWVFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Davies-Jones, R. P., 1974: Discussion of measurements inside high-speed thunderstorm updrafts. J. Appl. Meteor., 13, 710717, doi:10.1175/1520-0450(1974)013<0710:DOMIHS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dickinson, M. J., L. F. Bosart, W. E. Bracken, G. J. Hakim, D. M. Schultz, M. A. Bedrick, and K. R. Tyle, 1997: The March 1993 superstorm cyclogenesis: Incipient phase synoptic- and convective-scale flow interaction and model performance. Mon. Wea. Rev., 125, 30413072, doi:10.1175/1520-0493(1997)125<3041:TMSCIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Donovan, M. F., E. R. Williams, C. Kessinger, G. Blackburn, P. H. Herzegh, R. L. Bankert, S. Miller, and F. R. Mosher, 2008: The identification and verification of hazardous convective cells over oceans using visible and infrared satellite observations. J. Appl. Meteor. Climatol., 47, 164184, doi:10.1175/2007JAMC1471.1.

    • Search Google Scholar
    • Export Citation
  • Feidas, H., and C. Cartalis, 2001: Monitoring mesoscale convective cloud systems associated with heavy storms using Meteosat imagery. J. Appl. Meteor., 40, 491512, doi:10.1175/1520-0450(2001)040<0491:MMCCSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fritz, S., and I. Laszlo, 1993: Detection of water vapor in the stratosphere over very high clouds in the tropics. J. Geophys. Res., 98, 22 95922 967, doi:10.1029/93JD01617.

    • Search Google Scholar
    • Export Citation
  • Guillory, A. R., G. J. Jedlovec, and H. E. Fuelberg, 1993: A technique for deriving column-integrated water content using VAS split-window data. J. Appl. Meteor., 32, 12261241, doi:10.1175/1520-0450(1993)032<1226:ATFDCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Harris, R. J., J. R. Mecikalski, W. M. MacKenzie Jr., P. A. Durkee, and K. E. Nielsen, 2010: The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteor. Climatol., 49, 25272543, doi:10.1175/2010JAMC2575.1.

    • Search Google Scholar
    • Export Citation
  • Hong, G., P. Yang, A. K. Heidinger, M. J. Pavolonis, B. A. Baum, and S. E. Platnick, 2010: Detecting opaque and nonopaque tropical upper tropospheric ice clouds: A trispectral technique based on the MODIS 8–12 μm window bands. J. Geophys. Res., 115, D20214, doi:10.1029/2010JD014004.

    • Search Google Scholar
    • Export Citation
  • Hong, G., P. Minnis, D. Doelling, J. K. Ayers, and S. Sun-Mack, 2012: Estimating effective particle size of tropical deep convective clouds with a look-up table method using satellite measurements of brightness temperature differences. J. Geophys. Res., 117, D06207, doi:10.1029/2011JD016652.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region. J. Meteor. Soc. Japan, 63, 8899.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1987a: A cloud type classification with NOAA 7 split-window measurements. J. Geophys. Res., 92, 39914000, doi:10.1029/JD092iD04p03991.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1987b: An instantaneous delineation of convective rainfall areas using split window data of NOAA-7 AVHRR. J. Meteor. Soc. Japan, 65, 469481.

    • Search Google Scholar
    • Export Citation
  • Jin, H., and S. L. Nasiri, 2014: Evaluation of AIRS cloud-thermodynamic-phase determination with CALIPSO. J. Appl. Meteor. Climatol., 53, 10121027, doi:10.1175/JAMC-D-13-0137.1.

    • Search Google Scholar
    • Export Citation
  • Kurino, T., 1997: A satellite infrared technique for estimating “deep/shallow” precipitation. Adv. Space Res., 19, 511514, doi:10.1016/S0273-1177(97)00063-X.

    • Search Google Scholar
    • Export Citation
  • Lattanzio, A., P. D. Watts, and Y. Govaerts, 2006: Activity report on physical interpretation of warm water vapour pixels. EUMETSAT Tech. Memo. 14, 43 pp. [Available online at http://www.eumetsat.int/website/home/search/index.html?pState=1&search=Activity+report+on+physical+interpretation+of+warm+water+vapour+pixels&pState=1&sg=T1BT.]

  • Levizzani, V., and M. Setvák, 1996: Multispectral, high-resolution satellite observations of plumes on top of convective storms. J. Atmos. Sci., 53, 361369, doi:10.1175/1520-0469(1996)053<0361:MHRSOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lindsey, D. T., L. Grasso, J. F. Dostalek, and J. Kerkmann, 2014: Use of the GOES-R split-window difference to diagnose deepening low-level water vapor. J. Appl. Meteor. Climatol., 53, 20052016, doi:10.1175/JAMC-D-14-0010.1.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 13741387, doi:10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Matthee, R., and J. R. Mecikalski, 2013: Geostationary infrared methods for detecting lightning-producing cumulonimbus clouds. J. Geophys. Res. Atmos., 118, 65806592, doi:10.1002/jgrd.50485.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978, doi:10.1175/MWR3062.1.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and Coauthors, 2007: Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash. Bull. Amer. Meteor. Soc., 88, 15891607, doi:10.1175/BAMS-88-10-1589.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., W. M. MacKenzie Jr., M. Koenig, and S. Muller, 2010: Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part I: Infrared fields. J. Appl. Meteor. Climatol., 49, 521534, doi:10.1175/2009JAMC2344.1.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., X. Li, L. D. Carey, E. W. McCaul Jr., and T. A. Coleman, 2013: Regional comparison of GOES cloud-top properties and radar characteristics in advance of first-flash lightning initiation. Mon. Wea. Rev., 141, 5574, doi:10.1175/MWR-D-12-00120.1.

    • Search Google Scholar
    • Export Citation
  • Melani, S., E. Cattani, F. Torricella, and V. Levizzani, 2003: Characterization of plumes on top of deep convective storm using AVHRR imagery and radiative transfer simulations. Atmos. Res., 67–68, 485499, doi:10.1016/S0169-8095(03)00061-9.

    • Search Google Scholar
    • Export Citation
  • Merk, D., and T. Zinner, 2013: Detection of convective initiation using Meteosat SEVIRI: Implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM. Atmos. Meas. Tech., 6, 19031918, doi:10.5194/amt-6-1903-2013.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2012: Simulations of infrared radiances over a deep convective cloud system observed during TC4: Potential for enhancing nocturnal ice cloud retrievals. Remote Sens., 4, 30223054, doi:10.3390/rs4103022.

    • Search Google Scholar
    • Export Citation
  • Negri, A. J., and R. F. Adler, 1981: Relation of satellite-based thunderstorm intensity to radar-estimated rainfall. J. Appl. Meteor., 20, 288300, doi:10.1175/1520-0450(1981)020<0288:ROSBTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pan, L. L., and L. A. Munchak, 2011: Relationship of cloud top to the tropopause and jet structure from CALIPSO data. J. Geophys. Res., 116, D12201, doi:10.1029/2010JD015462.

    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., L. W. Uccellini, A. Mostek, and D. A. Keyser, 1984: Delineating mid-and low-level water vapor patterns in pre-convective environments using VAS moisture channels. Mon. Wea. Rev., 112, 21782198, doi:10.1175/1520-0493(1984)112<2178:DMALLW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Prata, A. J., 1989: Observations of volcanic ash clouds in the 10–12 μm window using AVHRR/2 data. Int. J. Remote Sens., 10, 751761, doi:10.1080/01431168908903916.

    • Search Google Scholar
    • Export Citation
  • Price, J. C., 1984: Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. J. Geophys. Res., 89, 72317237, doi:10.1029/JD089iD05p07231.

    • Search Google Scholar
    • Export Citation
  • Ricchiazzi, P., S. Yang, C. Gautier, and D. Sowle, 1998: SBDART: A research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere. Bull. Amer. Meteor. Soc., 79, 21012114, doi:10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584, doi:10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., W. L. Woodley, A. Lerner, G. Kelman, and D. T. Lindsey, 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res., 113, D04208, doi:10.1029/2007JD008600.

    • Search Google Scholar
    • Export Citation
  • Saunders, R. W., and K. T. Kriebel, 1988: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123150, doi:10.1080/01431168808954841.

    • Search Google Scholar
    • Export Citation
  • Schmetz, J., S. A. Tjemkes, M. Gube, and L. Van de Berg, 1997: Monitoring deep convection and convective overshooting with Meteosat. Adv. Space Res., 19, 433441, doi:10.1016/S0273-1177(97)00051-3.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., R. M. Rabin, and P. K. Wang, 2007: Contribution of the MODIS instrument to observations of deep convective storms and stratospheric moisture detection in GOES and MSG imagery. Atmos. Res., 83, 505518, doi:10.1016/j.atmosres.2005.09.015.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., D. T. Lindsey, R. M. Rabin, P. K. Wang, and A. Demeterová, 2008: Indication of water vapor transport into the lower stratosphere above midlatitude convective storms: Meteosat Second Generation satellite observations and radiative transfer model simulations. Atmos. Res., 89, 170180, doi:10.1016/j.atmosres.2007.11.031.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., K. Bedka, D. T. Lindsey, A. Sokol, Z. Charvát, J. Šťástka, and P. K. Wang, 2013: A-Train observations of deep convective storm tops. Atmos. Res., 123, 229248, doi:10.1016/j.atmosres.2012.06.020.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., L. M. Cronce, W. F. Feltz, K. M. Bedka, M. J. Pavolonis, and A. K. Heidinger, 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126, doi:10.1175/2010JAMC2496.1.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., D. C. Hartung, W. F. Feltz, L. M. Cronce, and V. Lakshmanan, 2013: A satellite-based convective cloud object tracking and multipurpose data fusion tool with application to developing convection. J. Atmos. Oceanic Technol., 30, 510525, doi:10.1175/JTECH-D-12-00114.1.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., L. M. Cronce, and W. F. Feltz, 2014: Improving satellite-based convective cloud growth monitoring with visible optical depth retrievals. J. Appl. Meteor. Climatol., 53, 506520, doi:10.1175/JAMC-D-13-0139.1.

    • Search Google Scholar
    • Export Citation
  • Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 1994: Cloud properties inferred from 8–12-μm data. J. Appl. Meteor., 33, 212229, doi:10.1175/1520-0450(1994)033<0212:CPIFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Szejwach, G., 1982: Determination of semi-transparent cirrus cloud temperature from infrared radiances: Application to Meteosat. J. Appl. Meteor., 21, 384393, doi:10.1175/1520-0450(1982)021<0384:DOSTCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tjemkes, S. A., L. Van de Berg, and J. Schmetz, 1997: Warm water vapour pixels over high clouds as observed by Meteosat. Contrib. Atmos. Phys., 70, 1521.

    • Search Google Scholar
    • Export Citation
  • Wang, C. C., G. T. J. Chen, and R. E. Carbone, 2004: A climatology of warm-season cloud patterns over East Asia based on GMS infrared brightness temperature observations. Mon. Wea. Rev., 132, 16061629, doi:10.1175/1520-0493(2004)132<1606:ACOWCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, C. C., G. T. J. Chen, and R. E. Carbone, 2005: Variability of warm-season cloud episodes over East Asia based on GMS infrared brightness temperature observations. Mon. Wea. Rev., 133, 14781500, doi:10.1175/MWR2928.1.

    • Search Google Scholar
    • Export Citation
  • Wang, P. K., 2003: Moisture plumes above thunderstorm anvils and their contributions to cross-tropopause transport of water vapor in midlatitudes. J. Geophys. Res., 108, 4194, doi:10.1029/2002JD002581.

    • Search Google Scholar
    • Export Citation
  • Wang, P. K., 2007: The thermodynamic structure atop a penetrating convective thunderstorm. Atmos. Res., 83, 254262, doi:10.1016/j.atmosres.2005.08.010.

    • Search Google Scholar
    • Export Citation
  • Xu, W., R. F. Adler, and N.-Y. Wang, 2013: Improving geostationary satellite rainfall estimates using lightning observations: Underlying lightning–rainfall–cloud relationships. J. Appl. Meteor. Climatol., 52, 213229, doi:10.1175/JAMC-D-12-040.1.

    • Search Google Scholar
    • Export Citation
  • Xu, W., R. F. Adler, and N.-Y. Wang, 2014: Combining satellite infrared and lightning information to estimate warm-season convective and stratiform rainfall. J. Appl. Meteor. Climatol., 53, 180199, doi:10.1175/JAMC-D-13-069.1.

    • Search Google Scholar
    • Export Citation
  • Zinner, T., H. Mannstein, and A. Tafferner, 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteor. Atmos. Phys., 101, 191210, doi:10.1007/s00703-008-0290-y.

    • Search Google Scholar
    • Export Citation
  • Zinner, T., C. Forster, E. D. Coning, and H. D. Betz, 2013: Validation of the Meteosat storm detection and nowcasting system Cb-TRAM with lightning network data–Europe and South Africa. Atmos. Meas. Tech., 6, 15671583, doi:10.5194/amt-6-1567-2013.

    • Search Google Scholar
    • Export Citation
Save
  • Ackerman, S. A., 1996: Global satellite observations of negative brightness temperature differences between 11 and 6.7 μm. J. Atmos. Sci., 53, 28032812, doi:10.1175/1520-0469(1996)053<2803:GSOONB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ackerman, S. A., W. L. Smith, H. E. Revercomb, and J. D. Spinhirne, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8–12 μm window. Mon. Wea. Rev., 118, 23772388, doi:10.1175/1520-0493(1990)118<2377:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and D. D. Fenn, 1979: Thunderstorm intensity as determined from satellite data. J. Appl. Meteor., 18, 502517, doi:10.1175/1520-0450(1979)018<0502:TIADFS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and A. J. Negri, 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 3051, doi:10.1175/1520-0450(1988)027<0030:ASITTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., M. J. Markus, and D. D. Fenn, 1985: Detection of severe Midwest thunderstorms using geosynchronous satellite data. Mon. Wea. Rev., 113, 769781, doi:10.1175/1520-0493(1985)113<0769:DOSMTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Berendes, T. A., J. R. Mecikalski, W. M. MacKenzie, K. M. Bedka, and U. S. Nair, 2008: Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering. J. Geophys. Res., 113, D20207, doi:10.1029/2008JD010287.

    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., and J. W. Nielsen, 1993: Radiosonde penetration of an undilute cumulonimbus anvil. Mon. Wea. Rev., 121, 16881702, doi:10.1175/1520-0493(1993)121<1688:RPOAUC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chesters, D., L. W. Uccellini, and W. D. Robinson, 1983: Low-level water vapor fields from the VISSR Atmospheric Sounder (VAS) “split window” channels. J. Climate Appl. Meteor., 22, 725743, doi:10.1175/1520-0450(1983)022<0725:LLWVFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Davies-Jones, R. P., 1974: Discussion of measurements inside high-speed thunderstorm updrafts. J. Appl. Meteor., 13, 710717, doi:10.1175/1520-0450(1974)013<0710:DOMIHS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dickinson, M. J., L. F. Bosart, W. E. Bracken, G. J. Hakim, D. M. Schultz, M. A. Bedrick, and K. R. Tyle, 1997: The March 1993 superstorm cyclogenesis: Incipient phase synoptic- and convective-scale flow interaction and model performance. Mon. Wea. Rev., 125, 30413072, doi:10.1175/1520-0493(1997)125<3041:TMSCIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Donovan, M. F., E. R. Williams, C. Kessinger, G. Blackburn, P. H. Herzegh, R. L. Bankert, S. Miller, and F. R. Mosher, 2008: The identification and verification of hazardous convective cells over oceans using visible and infrared satellite observations. J. Appl. Meteor. Climatol., 47, 164184, doi:10.1175/2007JAMC1471.1.

    • Search Google Scholar
    • Export Citation
  • Feidas, H., and C. Cartalis, 2001: Monitoring mesoscale convective cloud systems associated with heavy storms using Meteosat imagery. J. Appl. Meteor., 40, 491512, doi:10.1175/1520-0450(2001)040<0491:MMCCSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fritz, S., and I. Laszlo, 1993: Detection of water vapor in the stratosphere over very high clouds in the tropics. J. Geophys. Res., 98, 22 95922 967, doi:10.1029/93JD01617.

    • Search Google Scholar
    • Export Citation
  • Guillory, A. R., G. J. Jedlovec, and H. E. Fuelberg, 1993: A technique for deriving column-integrated water content using VAS split-window data. J. Appl. Meteor., 32, 12261241, doi:10.1175/1520-0450(1993)032<1226:ATFDCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Harris, R. J., J. R. Mecikalski, W. M. MacKenzie Jr., P. A. Durkee, and K. E. Nielsen, 2010: The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteor. Climatol., 49, 25272543, doi:10.1175/2010JAMC2575.1.

    • Search Google Scholar
    • Export Citation
  • Hong, G., P. Yang, A. K. Heidinger, M. J. Pavolonis, B. A. Baum, and S. E. Platnick, 2010: Detecting opaque and nonopaque tropical upper tropospheric ice clouds: A trispectral technique based on the MODIS 8–12 μm window bands. J. Geophys. Res., 115, D20214, doi:10.1029/2010JD014004.

    • Search Google Scholar
    • Export Citation
  • Hong, G., P. Minnis, D. Doelling, J. K. Ayers, and S. Sun-Mack, 2012: Estimating effective particle size of tropical deep convective clouds with a look-up table method using satellite measurements of brightness temperature differences. J. Geophys. Res., 117, D06207, doi:10.1029/2011JD016652.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region. J. Meteor. Soc. Japan, 63, 8899.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1987a: A cloud type classification with NOAA 7 split-window measurements. J. Geophys. Res., 92, 39914000, doi:10.1029/JD092iD04p03991.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1987b: An instantaneous delineation of convective rainfall areas using split window data of NOAA-7 AVHRR. J. Meteor. Soc. Japan, 65, 469481.

    • Search Google Scholar
    • Export Citation
  • Jin, H., and S. L. Nasiri, 2014: Evaluation of AIRS cloud-thermodynamic-phase determination with CALIPSO. J. Appl. Meteor. Climatol., 53, 10121027, doi:10.1175/JAMC-D-13-0137.1.

    • Search Google Scholar
    • Export Citation
  • Kurino, T., 1997: A satellite infrared technique for estimating “deep/shallow” precipitation. Adv. Space Res., 19, 511514, doi:10.1016/S0273-1177(97)00063-X.

    • Search Google Scholar
    • Export Citation
  • Lattanzio, A., P. D. Watts, and Y. Govaerts, 2006: Activity report on physical interpretation of warm water vapour pixels. EUMETSAT Tech. Memo. 14, 43 pp. [Available online at http://www.eumetsat.int/website/home/search/index.html?pState=1&search=Activity+report+on+physical+interpretation+of+warm+water+vapour+pixels&pState=1&sg=T1BT.]

  • Levizzani, V., and M. Setvák, 1996: Multispectral, high-resolution satellite observations of plumes on top of convective storms. J. Atmos. Sci., 53, 361369, doi:10.1175/1520-0469(1996)053<0361:MHRSOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lindsey, D. T., L. Grasso, J. F. Dostalek, and J. Kerkmann, 2014: Use of the GOES-R split-window difference to diagnose deepening low-level water vapor. J. Appl. Meteor. Climatol., 53, 20052016, doi:10.1175/JAMC-D-14-0010.1.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1980: Mesoscale convective complexes. Bull. Amer. Meteor. Soc., 61, 13741387, doi:10.1175/1520-0477(1980)061<1374:MCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Matthee, R., and J. R. Mecikalski, 2013: Geostationary infrared methods for detecting lightning-producing cumulonimbus clouds. J. Geophys. Res. Atmos., 118, 65806592, doi:10.1002/jgrd.50485.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978, doi:10.1175/MWR3062.1.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and Coauthors, 2007: Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash. Bull. Amer. Meteor. Soc., 88, 15891607, doi:10.1175/BAMS-88-10-1589.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., W. M. MacKenzie Jr., M. Koenig, and S. Muller, 2010: Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part I: Infrared fields. J. Appl. Meteor. Climatol., 49, 521534, doi:10.1175/2009JAMC2344.1.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., X. Li, L. D. Carey, E. W. McCaul Jr., and T. A. Coleman, 2013: Regional comparison of GOES cloud-top properties and radar characteristics in advance of first-flash lightning initiation. Mon. Wea. Rev., 141, 5574, doi:10.1175/MWR-D-12-00120.1.

    • Search Google Scholar
    • Export Citation
  • Melani, S., E. Cattani, F. Torricella, and V. Levizzani, 2003: Characterization of plumes on top of deep convective storm using AVHRR imagery and radiative transfer simulations. Atmos. Res., 67–68, 485499, doi:10.1016/S0169-8095(03)00061-9.

    • Search Google Scholar
    • Export Citation
  • Merk, D., and T. Zinner, 2013: Detection of convective initiation using Meteosat SEVIRI: Implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM. Atmos. Meas. Tech., 6, 19031918, doi:10.5194/amt-6-1903-2013.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2012: Simulations of infrared radiances over a deep convective cloud system observed during TC4: Potential for enhancing nocturnal ice cloud retrievals. Remote Sens., 4, 30223054, doi:10.3390/rs4103022.

    • Search Google Scholar
    • Export Citation
  • Negri, A. J., and R. F. Adler, 1981: Relation of satellite-based thunderstorm intensity to radar-estimated rainfall. J. Appl. Meteor., 20, 288300, doi:10.1175/1520-0450(1981)020<0288:ROSBTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pan, L. L., and L. A. Munchak, 2011: Relationship of cloud top to the tropopause and jet structure from CALIPSO data. J. Geophys. Res., 116, D12201, doi:10.1029/2010JD015462.

    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., L. W. Uccellini, A. Mostek, and D. A. Keyser, 1984: Delineating mid-and low-level water vapor patterns in pre-convective environments using VAS moisture channels. Mon. Wea. Rev., 112, 21782198, doi:10.1175/1520-0493(1984)112<2178:DMALLW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Prata, A. J., 1989: Observations of volcanic ash clouds in the 10–12 μm window using AVHRR/2 data. Int. J. Remote Sens., 10, 751761, doi:10.1080/01431168908903916.

    • Search Google Scholar
    • Export Citation
  • Price, J. C., 1984: Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer. J. Geophys. Res., 89, 72317237, doi:10.1029/JD089iD05p07231.

    • Search Google Scholar
    • Export Citation
  • Ricchiazzi, P., S. Yang, C. Gautier, and D. Sowle, 1998: SBDART: A research and teaching software tool for plane-parallel radiative transfer in the earth’s atmosphere. Bull. Amer. Meteor. Soc., 79, 21012114, doi:10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584, doi:10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., W. L. Woodley, A. Lerner, G. Kelman, and D. T. Lindsey, 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res., 113, D04208, doi:10.1029/2007JD008600.

    • Search Google Scholar
    • Export Citation
  • Saunders, R. W., and K. T. Kriebel, 1988: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123150, doi:10.1080/01431168808954841.

    • Search Google Scholar
    • Export Citation
  • Schmetz, J., S. A. Tjemkes, M. Gube, and L. Van de Berg, 1997: Monitoring deep convection and convective overshooting with Meteosat. Adv. Space Res., 19, 433441, doi:10.1016/S0273-1177(97)00051-3.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., R. M. Rabin, and P. K. Wang, 2007: Contribution of the MODIS instrument to observations of deep convective storms and stratospheric moisture detection in GOES and MSG imagery. Atmos. Res., 83, 505518, doi:10.1016/j.atmosres.2005.09.015.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., D. T. Lindsey, R. M. Rabin, P. K. Wang, and A. Demeterová, 2008: Indication of water vapor transport into the lower stratosphere above midlatitude convective storms: Meteosat Second Generation satellite observations and radiative transfer model simulations. Atmos. Res., 89, 170180, doi:10.1016/j.atmosres.2007.11.031.

    • Search Google Scholar
    • Export Citation
  • Setvák, M., K. Bedka, D. T. Lindsey, A. Sokol, Z. Charvát, J. Šťástka, and P. K. Wang, 2013: A-Train observations of deep convective storm tops. Atmos. Res., 123, 229248, doi:10.1016/j.atmosres.2012.06.020.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., L. M. Cronce, W. F. Feltz, K. M. Bedka, M. J. Pavolonis, and A. K. Heidinger, 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126, doi:10.1175/2010JAMC2496.1.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., D. C. Hartung, W. F. Feltz